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Featured
7 min read
Chris Butler
Organizations often assume pushing workforce data directly into a general-purpose warehouse like Snowflake before connecting to an analytics platform is an optimal strategy. While this could be appealing for enterprise consistency, this warehouse-first approach overlooks critical workforce-specific requirements. Starting with One Model's comprehensive data extraction and modeling capabilities before moving to an enterprise warehouse ensures greater success, efficiency, and analytical accuracy. The Hidden Complexity of Workforce Data Workforce data is dynamic, constantly evolving, and deeply intertwined with organizational operations. Generic data warehouse systems are effective for storage but lack specialized capabilities for extracting and modeling HR-specific complexities such as transaction-based employee data, hierarchical structures, and frequent retroactive updates. Where a Warehouse-First Approach Falls Short Organizations considering a warehouse-first strategy should recognize several limitations: 1. Delayed Implementation and Compromised Data Integrity HR data extraction requires specialized and time-sensitive transaction log-based incremental updates, not repetitive snapshots, to avoid redundancy, inflated costs, and reduced query performance. Without dedicated subject matter expertise or experience extracting HR data into a warehouse, this complexity can delay HR time-to-value significantly and introduces unintended engineering decisions that compromise data quality. 2. Complex Workforce Data Modeling Generic data warehouse solutions lack pre-built schemas necessary to handle nuances of sensitive workforce data like performance, terminations, rehires, and salary adjustments accurately. Starting from scratch on these builds can lead to downstream analytical inaccuracies and inefficiencies when making use of data that isn't modeled properly. 3. Manual Maintenance and Increased Security Risk Warehouse-first strategies require continuous manual oversight and updates of both pipelines and stored data, increasing the risk of errors, compliance issues, and security vulnerabilities. Managing and maintaining these challenges manually in-house can lead to time delays which in turn diminishes organizational trust in analytics. 4. Unprepared for Advanced Analytics and AI Data warehouses typically optimize data storage and architecture for basic querying and lite reporting rather than deeper analytical processes like predictive modeling or preparing data for use in AI initiatives. Without specialized modeling, organizations face severe limitations in extracting strategic value from their workforce data. The One Model Advantage One Model is a comprehensive, full-stack solution designed explicitly for People Analytics, emphasizing openness, flexibility, and transparency. One Model provides expertise and support while also allowing your team full visibility and control over data transformations through accessible SQL code, facilitating seamless integration and customization. Specialized Data Extraction One Model's managed connectors efficiently handle complex data extraction at scale from industry leading HRIS systems like Workday, Oracle, and SAP, as well as hundreds of systems across the HR tech and Work Tech stack. Unlike brute-force methods taking monthly snapshots, One Model intelligently captures meaningful data changes nightly, significantly enhancing scalability, accuracy, and speed. Self-Healing Data Models One Model utilizes self-healing data models powered by advanced algorithms and AI-driven error correction. One Model tools continuously monitor and automatically correct data anomalies, ensuring ongoing accuracy, especially critical for managing common HR challenges like retroactive updates. Built-in Compliance and Security Sensitive employee information demands stringent security. One Model strictly adheres to industry-leading compliance standards such as SOC 2 and ISO 27001. The platform incorporates robust role-based access controls, ensuring secure, compliant data management, and protection. Flexibility and Control in Data Flow One Model supports flexible data flows, whether directly from source systems like Oracle or Workday into One Model, or subsequently pushing refined data to warehouses like Snowflake. One Model can also ingest directly from warehouses like Snowflake for ad hoc data loads or full system loads of historical data. Organizations retain complete control, choosing precisely how and when data moves, ensuring no compromise to data integrity, governance, or analytical capability. The Right Data in the Right Place Even with the challenges outlined above, there is clear strategic value in centralizing workforce data within enterprise warehouses like Snowflake, Redshift, or BigQuery. The issue isn't the warehouse itself but rather how the data arrives there. A warehouse-first approach mistakenly treats workforce data like just another standard dataset, missing the complexity and specialized structure needed for effective analytics. To bridge this gap, One Model created the Data Destinations toolkit. Rather than forcing a choice between HR-specific modeling and an enterprise-wide warehouse strategy, Data Destinations ensures that once the data is modeled in our platform, organizations can easily push carefully structured, analytics-ready, and secure workforce data directly from One Model into their warehouse of choice. This maintains the integrity, accuracy, and privacy of the workforce data, making your warehouse strategy not only scalable but strategically valuable. Conclusion Starting your People Analytics journey directly with One Model addresses the foundational concerns raised at the outset: managing complexity, ensuring data accuracy, and maintaining compliance and security. Unlike the generic warehouse-first approach, One Model prioritizes capturing and modeling workforce data in its raw, uncompromised form, specifically tailored for the intricate demands of HR analytics. By opting for direct integration, your organization gains not only superior analytical capabilities and efficiency but also robust security and compliance that are built directly into the data pipeline. Rather than forcing HR data into a one-size-fits-all solution, One Model’s specialized approach empowers your analytics team to produce precise, actionable insights. Ultimately, investing first in One Model ensures your HR analytics infrastructure is both strategically aligned with business objectives and resilient enough to adapt and evolve with organizational needs, transforming your workforce data into a reliable driver of informed, strategic decision-making. Want to learn more?
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Featured
6 min read
Taylor Clark
Imagine it’s Monday morning. Your HR team needs critical insights on employee turnover, but you’re still waiting for IT to validate new data sources. As the hours pass, the data arrives late, and you miss the opportunity to proactively address retention concerns. With Agentic AI, this scenario becomes a thing of the past. Agentic AI can automatically connect and validate new data sources, accelerating the process and delivering actionable insights in real-time. At One Model, we’re revolutionizing HR by automating complex tasks, allowing your team to focus on high-impact decisions. Our solution empowers you to implement your data sources 50% faster through a self service approach, enhancing operational efficiency and delivering real business value when it matters most. What is Agentic AI? Think of Agentic AI as a highly skilled, reliable assistant. It’s someone who anticipates what you need next and handles routine complexities effortlessly, allowing you to focus on strategic decisions. Unlike simple automation tools or chatbots, Agentic AI can make decisions, reason through processes, and carry out workflows with little to no human involvement. The AI agents are capable of interacting with multiple tools, gathering insights, and executing tasks independently. As they work with dynamic data and learn from their actions, Agentic AI adapts and adjusts workflows to better meet your organization’s goals. This autonomy delivers more flexible and powerful solutions to complex problems, ensuring that tasks are completed efficiently, and business outcomes are optimized. Key Insights on Leveraging Agentic AI for HR Transformation To fully harness the potential of Agentic AI in HR, organizations must address three critical areas: Why You Need a Good Data Foundation The power of AI, including Agentic AI, lies in its access to quality data. Without a well-structured data foundation, AI cannot provide valuable insights. One of the primary hurdles organizations face when implementing AI is ensuring that their data is clean, organized, and accessible. AI models, including generative AI, can only answer questions effectively when the right data is available. For instance, without structured event data, it’s impossible for AI to generate meaningful business insights. At One Model, we focus on creating the right data models that can be leveraged by AI agents to drive value and solve complex business problems. How Agentic AI Will Generate Value for Organizations Agentic AI can transform organizations by automating routine, mundane tasks and allowing human workers to focus on more strategic activities. By automating repetitive processes, agents free up HR teams to focus on higher-value work, like improving employee engagement, identifying drivers of performance and retention,and making data-driven decisions. One Model's approach leverages agentic workflows to accelerate tasks like insights generation, enabling faster, more accurate decision-making. This results in significant time savings and improved business outcomes across HR functions. Democratized Data Engineering One of the key advantages of Agentic AI is how it empowers HR teams to achieve more with less. Traditionally, HR teams relied on large data engineering teams, IT departments, or external vendors to handle data transformation and validation. With Agentic AI, One Model's platform brings data expertise directly into HR systems, enabling HR professionals to manage and analyze their data seamlessly without needing constant support from technical teams. As AI agents handle the complex technical work, HR teams can focus on what truly matters, strategic decision-making. This democratization of data engineering means HR leaders can now achieve the level of insight and impact typically reserved for large teams of analysts, boosting efficiency and enabling smarter business outcomes at a fraction of the effort. One Model’s Role in Agentic AI One Model is leading the integration of Agentic AI into HR and People Analytics. By automating tasks like data analysis, we make these processes faster and more impactful. While Agentic AI is still evolving, we’re dedicated to refining its capabilities to ensure effectiveness, security, and ethical integrity. Our focus includes expanding the scope of agentic workflows to give HR teams more autonomy and flexibility. One Model is focused on: Creating real value with AI agents Enhancing AI agents' ability to manage diverse HR data Strengthening data security and privacy Ensuring collaboration between AI agents and HR teams while maintaining transparency and data integrity As our AI agents mature, One Model will continue to enhance their capabilities, helping clients fully leverage Agentic AI while retaining control over their processes. One Model and the Future of Agentic AI in HR As Agentic AI continues to evolve, One Model is positioning itself at the forefront of this revolution in HR technology. By embedding AI agents into our platform, we are helping HR teams move faster, make smarter decisions, and ultimately generate more value for their organizations. As we continue to refine and expand the capabilities of Agentic AI, we remain committed to empowering HR professionals with the tools they need to unlock the full potential of their data. With a solid data foundation, a focus on value-driven automation, and the ability to reduce reliance on external data engineers, One Model is ready to lead the way in the next phase of People Analytics. The future of HR is here, and it’s powered by Agentic AI. Ready to learn more about One Model's Agentic AI? Request a demo today.
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Featured
11 min read
The One Model Team
As the world emerges from years of working from home, some organizations are facing the complex challenge of crafting an effective Return to Office (RTO) strategy. This task is not as simple as flicking a switch; it involves a labyrinth of data-driven decisions that must factor in various aspects such as employee engagement, facilities management, and overall workplace safety. For leaders in finance, facilities, and human resources, the stakes are remarkably high. The pitfalls of a poorly executed RTO strategy can lead to disgruntled employees, high turnover rates, overcrowded facilities, and potential public relations nightmares. Wherever You are in Your RTO Journey, One Model has You Covered Many companies have already implemented RTO, while others are just starting to transition (and some are staying home). Either way, we’re here to help. Whether you’re refining a fully operational office-based schedule or still weighing the pros and cons of remote vs. hybrid approaches, One Model’s platform delivers the insights needed to make informed decisions at every stage. In this intricate puzzle, One Model emerges as a crucial partner, adept at unifying and streamlining the disparate data sources that are essential for tracking time, attendance, badge data, leave of absence, operations data, and more. Our platform is uniquely positioned to tackle the massive data integration challenge, providing you with the actionable insights needed to navigate this complex journey. Want to learn more specifics for your RTO strategy? Join our March 18 webinar: Return to Office - Creating Positive Impact. Why RTO Planning is a Herculean Task The years spent working from home globally have permanently altered the landscape of work, making the transition back to physical offices anything but straightforward. Many reports and research studies indicate that a significant portion of employees prefer the flexibility of remote or hybrid work arrangements. However, many organizations still believe in the benefits of having employees in the office for at least part of the week, citing productivity, company culture, and team cohesion as key considerations. Let’s set that debate aside for a minute. If you are looking to head back, here are some ideas and tips to help you accomplish that goal easily, efficiently, and from a data-informed perspective. With multiple stakeholders involved—finance, facilities, and HR—each bringing their own set of needs and expectations, coordination can be immense. The data needed to effectively manage this transition is often scattered across various systems, making integration a colossal task. Here’s why RTO planning presents such a challenge: Diverse Data Requirements: A successful RTO strategy requires a holistic view of numerous data points, including time and attendance records, badge access logs, facilities usage, and even employee preferences regarding remote work. Cross-Functional Coordination: Aligning different organizational units like HR, finance, and facilities management toward a cohesive plan is inherently complicated without the right tools and data insights. Risk of Misalignment: Ineffective planning can lead to underutilized or overcrowded spaces, lapses in safety protocols, reduced employee satisfaction, and damaging headlines. Complex Data Integration: Consolidating data from various HRIS, time management systems, and facility usage trackers into a unified, actionable format is a monumental task. Real-World Consequences of Poor RTO Execution Countless organizations have stumbled in their attempt to bring back their workforce, suffering severe repercussions. For instance, Apple faced employee backlash when it insisted on returning to a predominantly office-based work structure, resulting in public resignations and bad press. According to an article by The Verge, the company had to deal with resignations from several high-profile employees who cited the rigid RTO policy as a critical factor in their departure. Similarly, companies like JPMorgan Chase encountered hurdles when their initial RTO policies were met with discontent, leading to a re-evaluation of strategies to better accommodate employee demands. Multiple news outlets reported on the tensions this friction created between management and employees, highlighting the delicate balance organizations must strike to avoid such controversies. These examples underscore the potential pitfalls of an inadequate RTO strategy: They not only disrupt operations internally but can also tarnish an organization’s external reputation. The Risks of RTO Missteps The pressure to “get it right” is immense because the downside of errors is particularly stark: Employee Dissatisfaction and Turnover: A mismatched RTO strategy can fuel dissatisfaction, prompting a mass exodus of talent—a risk that’s especially heightened in today’s competitive job market. Operational Inefficiencies: Inaccurate planning can lead to poor space utilization, thereby increasing operational costs and diminishing productivity. Public Relations Challenges: In the age of social media and 24-hour news cycles, a poorly managed return to office can quickly turn into a PR crisis. Health and Safety Concerns: Failing to consider updated health guidelines or employee comfort levels can pose real safety risks. How One Model Powers Data-Driven RTO Success The challenges of RTO planning demand a strategic, analytics-driven approach rather than guesswork. One Model acts as your essential command center for HR, finance, facilities, and operations teams, simplifying how you manage and leverage scattered data, ensuring that decisions are grounded in reality, not assumptions. Integrating Disparate Data Sources for a Unified View One Model excels at consolidating a wide range of data from multiple functions—bringing the data you have, whether it be time-tracking systems, attendance logs, badge usage data, or employee sentiment, into a single cohesive view accessible by all of the teams, but with role-based security to ensure data privacy. This central hub facilitates smooth, informed decision-making processes for finance, facilities, and HR departments, ensuring alignment across all stakeholders. Actionable Insights to Craft an Effective RTO Plan Through advanced analytics and intuitive dashboards, One Model can provide a detailed, accurate picture of your workforce’s dynamics. With customizable dashboards tailored to track key metrics such as attendance trends and space utilization rates, you gain the clarity needed to design policies that meet both business objectives and employee needs. AI-Powered Predictive and Proactive Management Our predictive analytics capabilities can allow you to forecast trends. By understanding potential future issues—whether it's employee turnover risks, facility overcrowding, or engagement declines—you can make proactive adjustments to your RTO strategy, ensuring minimal disruptions and fostering higher levels of employee satisfaction. Comprehensive Support for Successful Execution Beyond just providing a platform, One Model offers expert guidance to help integrate and interpret your workforce data effectively. From automating reporting processes to identifying trends that shape long-term policies, our tools empower you to build a sustainable RTO strategy that adapts to evolving needs. By leveraging One Model’s analytics capabilities, organizations gain the ability to create structured, data-driven RTO plans that align with operational goals while prioritizing employee well-being and retention. The Ripple Effect of a Successful RTO Strategy By effectively leveraging One Model’s capabilities when implementing RTO, you stand to gain numerous organizational benefits: Reduced Risk: Don’t get caught without data when it comes to reporting on progress or making decisions related to RTO. Sensitive topics require careful attention to details. Improved Employee Engagement: A well-crafted RTO strategy acknowledges and respects employee preferences, which can enhance engagement and retention. Optimized Space Usage: Strategic data insights allow for better use of facilities, reducing overhead costs while improving the workplace experience. Bolstered Corporate Image: Successfully managing the return to office showcases your organization as thoughtful and proactive, boosting its reputation internally and externally. The Path Forward with One Model Navigating the complexities of a Return to Office strategy might seem daunting, but with One Model’s comprehensive data integration and analytics platform, you’re equipped to overcome challenges with confidence. Our tools and expertise help you build a strategy that supports business goals, employee satisfaction, and efficient facility usage all at once—no matter where you stand in your RTO journey. Are you ready to take the next step in your RTO journey? Reach out to schedule a demo, and discover how our dashboards and data engineering solutions can illuminate your path forward and ensure a smooth transition back to the office. Together, we can translate complex data into actionable strategies that foster a resilient and adaptable organizational environment. This comprehensive approach not only positions you to avoid common pitfalls but empowers you to make informed, strategic decisions that bolster both your workforce and facilities management. Trust One Model to guide your organization toward a successful RTO strategy that meets the demands of the present and sets the foundation for future operational agility. See how One Model can support your unique approach to RTO. Request a demo of One Model.
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Featured
4 min read
Steve Hall
In our previous blog on problem-solving with One Model, we explored how analytics platforms must go beyond standard reports to uncover deeper insights. Building on that foundation, this post examines why adaptability is crucial for organizations facing complex, evolving workforce challenges. Many organizations rely on pre-configured People Analytics solutions, but these often fall short when unique challenges arise. Every business has its own workforce dynamics, and rigid tools can’t always provide the insights needed for complex, real-world decisions. That’s why adaptability is the game-changer in People Analytics. Why Adaptability Matters in People Analytics Workforce data isn’t one-size-fits-all. Organizations face evolving questions that demand flexibility in how data is collected, structured, and analyzed. A standardized analytics platform might answer common HR questions, but when deeper exploration is needed—such as uncovering hidden turnover patterns or understanding engagement shifts over time—rigid systems become a barrier. The Problem with Pre-Built Analytics Many platforms impose a fixed structure on users, restricting how they ingest, model, and visualize data. While this simplifies reporting for standard KPIs, it becomes a roadblock when leaders need to analyze specific, nuanced issues. For example, a company may want to examine how compensation changes impact voluntary turnover in specific regions over the past three years. If their platform only provides a broad turnover metric, they’re left with surface-level insights that don’t capture the full picture. This lack of flexibility can lead to oversimplified conclusions, which is a risk businesses can’t afford. Breaking Free with Granular Data Control One Model takes a different approach. Instead of forcing users into pre-built reports, it provides the building blocks of workforce data—think of them like Lego bricks that users can combine in any way necessary. This means companies can analyze workforce trends on their own terms. Instead of being limited to pre-set dashboards, they can explore trends like: How internal mobility connects to performance and engagement The impact of hybrid work on long-term retention Whether specific hiring channels lead to higher-quality hires By enabling teams to create custom models and visualizations, One Model helps businesses get to the real insights behind their data and not just the ones predefined by software limitations. The Freedom to Solve Real-World Problems Having full control over data isn’t just about customization. It’s about ensuring analytics can evolve alongside shifting business needs. The ability to refine and explore data dynamically allows leaders to move beyond predefined assumptions and uncover insights that drive more precise decision-making. This adaptability is essential because: Every business problem is unique: Organizations need tools that can handle their challenges, not just generic ones. New questions emerge constantly: Analytics should evolve as business needs change, without waiting for software updates. Decisions require precision: When making decisions about people, generalized reports aren’t enough. The Future of People Analytics: More Control, More Impact The best People Analytics platforms don’t dictate how organizations should think about their workforce—they empower them to explore, analyze, and act with confidence. One Model ensures that businesses aren’t just consuming analytics. Instead, they’re shaping their insights to fit their specific needs. True adaptability in data modeling and visualization gives organizations the power to own their workforce strategy, rather than relying on out-of-the-box assumptions. Analytics should work for you, not the other way around. And that’s what One Model delivers. Want to learn more?
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Featured
4 min read
Steve Hall
When it comes to People Analytics, the most valuable tool is one that lets you to ask the right questions and explore solutions. Canned insights can't answer the real questions you need to answer. Recently, during a demo with a prospective client, a question came up that perfectly illustrates how One Model is a platform built for problem-solving rather than just offering irrelevant canned insights. The Situation: A Forecasting Challenge The scenario began with a focus on Female Representation metrics, specifically forecasting whether the organization was on track to meet its diversity targets for women. The forecast feature showed trends for different job levels, and while representation looked promising for some levels, there was a noticeable downward trend for the executive level. Naturally, the prospect wanted to know: Why is this happening? This was not a question with an easy, pre-packaged answer. Instead, it required a deeper dive into the data—an approach that highlights One Model's value as a tool for discovery and insight generation. Digging Deeper: How We Tackled the Problem To address the question, we demonstrated how to use filters and visualizations to isolate and explore the data. Here's how it unfolded: Applying Filters: We filtered the data by job level and gender to focus specifically on female executives. From there, we looked at key metrics like net hiring trends and termination rates. Identifying Patterns: The data revealed a significant drop in representation between 2023 and 2024, which appeared dramatic due to the auto-scaling of the graph. Exploring Causes: By clicking through different visualizations, we identified that termination rates, particularly "other" terminations, were higher than expected. Using One Model's hotspot maps, we further pinpointed the specific business unit and region where the issue was most acute. Forming Hypotheses: Using this information, we leveraged One Model's built-in predictive AI capabilities to identify potential turnover drivers and develop actionable insights. Flexibility Matters This scenario underscores something critical about One Model: We don’t solve all your problems; we give you the tools to solve them. Other platforms that rely on rigid, canned use cases might struggle in this situation; no solution can offer pre-built analyses for all possible scenarios. Without a pre-built guide addressing their specific issue in this specific organization, the user will hit a wall. One Model, by contrast, enables users to dynamically filter, explore, and analyze data to uncover answers. Why This is Critical for People Analytics This scenario demonstrates the real-world challenges of People Analytics. Insights are rarely handed to you on a silver platter. Instead, they require a combination of curiosity, exploration, and judgment —qualities not even AI will bring to the table. While some HRBP-level professionals might not engage in this level of analysis, advanced People Analytics practitioners understand that solving complex, niche problems—like representation trends at a specific level—requires more than surface-level data. The One Model Advantage Here’s why One Model is different: Speed: Because One Model creates a unified single source of truth for your organization, you can explore complex interactions without having to manually manipulate data, saving you time. Flexibility: You’re not limited to prebuilt Storyboards or canned content. You can adapt and dig into unique questions in real-time, even in situations where you need to create new metrics to explore an issue. Depth of Insights: By enabling dynamic exploration, One Model allows for nuanced and complete answers that out-of-the-box solutions can’t deliver. The takeaway from this use case is simple: Good insights require effort. Platforms that promise quick, prebuilt solutions often oversimplify problems or deliver incomplete answers. One Model’s strength lies in empowering users to dig deeper and uncover real insights—even when the questions are complex. With One Model, you’re not just using a People Analytics platform—you’re solving real problems.
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Featured
3 min read
The One Model Team
The Only Constant in HR is Change Every year, HR leaders face a new workforce crisis. The Great Resignation, hybrid work shifts, talent shortages, return-to-office debates—the list goes on. Just when you think you've got a handle on things, a new challenge emerges, demanding fresh insights and real-time action. The problem? Too many People Analytics platforms look good on the surface but fall apart when real-world complexity hits. The Limitations of Traditional People Analytics Many platforms promise fast answers, but they often come with hidden constraints: Inflexible Data Models: Predefined frameworks make it difficult to align with your organization’s unique needs. Slow to Adapt: When a new workforce issue arises, you’re stuck waiting for vendor updates instead of getting the insights you need. Opaque Processes: If you can’t see how the numbers are built, how can you trust them? These limitations force HR teams to operate reactively, leaving them struggling to provide leadership with clear, accurate workforce insights when they need them most. Why One Model is Different One Model is designed for organizations that need flexibility, transparency, and control over their workforce data. Here’s how: Your Data, Your Way: No black-box models—One Model integrates with your unique data sources, definitions, and business logic. Full Transparency: You can see the underlying data sources and logic used to generate insights, ensuring accuracy and confidence. Flexibility Built-In: When the next workforce crisis hits, you won’t be stuck with rigid, prebuilt reports. One Model’s adaptable framework lets you build the reports you need, when you need them—with our team ready to support custom reporting as required. The Strategic Advantage of a Flexible Partner Organizations using flexible People Analytics solutions gain several advantages: Better Decision-Making: Real-time insights empower HR to take proactive action. Improved Employee Experience: Data-driven strategies help HR teams identify factors that drive engagement, satisfaction, and retention. Optimized Workforce Planning: With visibility into trends and risks, organizations can allocate resources more effectively. By leveraging these benefits, HR leaders can move from reacting to crises to staying ahead of workforce trends. Change is inevitable. Your analytics should be ready for it.
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Featured
48 min read
Richard Rosenow
Insights from Practitioners: Where Are We Heading This Year? The field of People Analytics continues to grow and so does the conference circuit! To help practitioners and HR leaders navigate the crowded landscape, we put together our second annual People Analytics Conference survey for professionals to find out which events they plan to attend in 2025 and which ones they would attend and prioritize if budgets were limitless (one can dream!). Responses from ~120 professionals reaffirm the importance of conferences for advancing knowledge, networking, and shaping the future of People Analytics. Alert! Live Webinar February 18th Join Richard Rosenow from One Model and Cole Napper from Lightcast as they discuss the most popular People Analytics events this year. Register for Live Webinar Quicklinks: 2025 Events PA Meetup Groups Find One Model in Person Top Conferences Practitioners Are Attending in 2025 The first question we asked respondents was "Which conferences are you planning to attend in 2025". The responses were varied as the conference circuit is diverse, but there were a few standouts! We've rank ordered the top five responses below: 1. Local People Analytics Meetups Attending: 42% of respondents (up from ~33% in 2024) Why It’s Popular: You can’t beat a meetup. This year’s survey proved that meetups have taken hold in the People Analytics space, springing up across the US from their start in NYC to the Bay Area to many other cities like Columbus and Salt Lake City. Globally there’s also been an explosion of new events from Sweden to Brisbane and Nigeria to Latin America. These grassroots gatherings are the heart of community-driven professional development. With minimal costs and maximum networking opportunities, meetups are perfect for both early career professionals looking to enter the field and seasoned practitioners looking to share experiences and ideas. About This Conference: While each meetup is run locally by a different volunteer team, People Analytics meetups generally offer a community feel and collaborative space for People Analytics professionals to exchange practical insights. They’re especially beneficial for those who may not have the budget or time to attend larger, national conferences. Communities in NYC and the Bay Area lead the pack in maturity and tenure (10+ years running for some!), but many other cities have run with the format and meetups are spreading rapidly to other cities worldwide. Repeat or New: Local meetups saw a surge in attendance on our 2024 survey and continued that growth in 2025. More info: Make sure to visit SPA’s meetup list to see if there’s a meetup in a city near you. And if you can’t find one, reach out to me (Richard) and I’d be happy to connect you with some friends in the area. People Analytics people are everywhere now. 2. SIOP 2025 - Denver Attending: ~30% of respondents Why It’s Popular: In terms of “official” conferences, the Society for Industrial and Organizational Psychology (SIOP) annual conference continues to draw the attention of respondents to this survey from People Analytics professionals with its robust mix of academic research and practical insights. Known for fostering connections between IO Psychology and People Analytics, this conference is a must-attend for leaders seeking evidence-based approaches to workforce challenges. About This Conference: Scheduled this year from 4/2 to 4/5 in Denver, Colorado, SIOP is a massive gathering of over 5,000 attendees. Sessions span diverse topics, from AI ethics to leadership assessments. The format includes as many as 10 concurrent sessions per hour, along with posters to see, masterclasses to attend, and people to meet – all of which creates an atmosphere of exciting FOMO. For People Analytics professionals, SIOP bridges academic rigor and workplace application, making it an unparalleled learning experience. Important note:Everyone can attend SIOP! There’s a rumor that it is only for IO Psychology PhDs and that’s just not the case. I’ve got an MBA background and have attended for two years running now (and they haven’t thrown me out yet). I can’t recommend this conference enough to anyone interested in deep conversations about applications of analytics to the workplace. Repeat or New: SIOP was the most attended conference in 2024, and its popularity shows no signs of waning. More info: SIOP Annual Conference Bonus info: My 2023 SIOP travelogue 3. People Analytics World NYC & People Analytics World London Attending: ~26% of respondents Why It’s Popular: People Analytics World (PAW) has been the premier stage in Europe for People Analytics for ten years running and, with groundfall in NYC in 2024, the event has officially gone global. This event is the true home for practitioners in People Analytics and has the feel of a family reunion and a team brainstorm all mixed together. It’s the perfect blend of networking, learning, and practical applications for People Analytics. About This Conference: The events are scheduled for London (4/29 to 4/30) and New York City (10/15 to 10/16). The PAW conference is run by the Tucana team (who additionally run some fantastic SWP events globally). People Analytics World NYC and London both combine global thought leadership with practitioner-led insights. Known for their deep dives into storytelling with data, scaling analytics, and cross-functional collaboration, this event caters to mature analytics teams who want to stay in touch with what’s going on across the industry. Repeat or New: PAW London had a great showing in last year’s event survey and the NYC conference debuted in late 2024, building on Tucana’s strong legacy with its London event. More info: People Analytics World 4. TALREOS Chicago Attending: 21% of respondents Why It’s Popular: Cited as a “hidden gem” in 2024, TALREOS Chicago (Talent Analytics Leadership Roundtable & Economic Opportunity Summit) had a breakout year with People Analytics professionals. Praised for its round-table “Chatham House Rule” approach to dialogue and strong controls over vendor involvement, this conference has quietly become a must-attend destination for People Analytics leaders of advanced teams who want to meet peers and dive deep into analytics and the future of work. About This Conference: Scheduled for June 4-6 in Chicago, TALREOS has been running for over 10 years out of Northwestern as part of the Workforce Science Project. It offers a balance of practical workshops and thought-provoking keynotes. The smaller size and invite-only nature of the event fosters meaningful networking and provides attendees with actionable frameworks they can implement immediately. Repeat or New: TALREOS appeared on the radar in last year's survey and has quickly gained momentum. With more practitioners prioritizing it in 2025, it’s transitioning from a “hidden gem” to a recognized pillar of the People Analytics community. More info: TALREOS 5. Wharton People Analytics Conference Attending: 17% of respondents Why It’s Popular: Led by Matthew Bidwell and a team of renowned academics, the Wharton People Analytics Conference offers a unique blend of strategic insights for HR leaders and groundbreaking academic contributions. Consistently ranked as a favorite among People Analytics professionals, the conference is known for its rigorous content and engaging sessions. A standout feature is the annual People Analytics Case Competition, which continues to be a highlight for participants and attendees alike. About This Conference: Scheduled for April 10-11, 2025 at the University of Pennsylvania, this two-day event features a diverse lineup of speakers, including experts from academia and industry. The conference offers sessions on the latest advances in People Analytics, complemented by networking opportunities with students, academics, and industry professionals. Highlights include keynote addresses, panel discussions, and competitions that showcase innovative research and applications in the field. Repeat or New: Wharton was a top wish-list event in 2024, and this year’s data shows its appeal has only grown. I hope to see you there this year! More info: Wharton People Analytics What’s on the Wish List? Even when budgets are tight, practitioners still dream big. Here are the events topping their aspirational lists (where they wish they could attend if budget wasn’t an issue): 1. People Analytics World NYC & People Analytics World London Wish List Interest: 31% of respondents Why It’s Desired: As covered above, People Analytics World (PAW) is a tremendous event series. After a sold-out and breakout year in New York City, PAW has swept up the People Analytics space with excitement. I was delighted to see this one top the charts this year and I hope all of you who wanted to attend are able to get your budget approved! About This Conference: The London event is scheduled for 4/29 to 4/30 and New York City from 10/15 to 10/16. The PAW conference is run by the Tucana team (who additionally run some fantastic SWP events globally). People Analytics World NYC and London both combine global thought leadership with practitioner-led insights. Known for their deep dives into storytelling with data, scaling analytics, and cross-functional collaboration, this event caters to mature analytics teams who want to stay in touch with what’s going on across the industry. Repeat or New?: This conference was a top wish-list item in 2024 and has proven to be highly sought-after in 2025. More info: People Analytics World 2. Wharton People Analytics Conference Wish List Interest: 27% of respondents Why It’s Desired: Covered above as well! For the second year running, Wharton appears prominently on the wish list for People Analytics leaders. The history and long-running presence of this conference has firmly established it in the People Analytics world as a pillar of the conference circuit. About This Conference: Scheduled for April 10-11, 2025 at the University of Pennsylvania, this two-day event features a diverse lineup of speakers, including experts from academia and industry. The conference offers sessions on the latest advances in people analytics, complemented by networking opportunities with students, academics, and industry professionals. Highlights include keynote addresses, panel discussions, and competitions that showcase innovative research and applications in the field. Repeat or New?: A repeat favorite from 2024, Wharton remains one of the most highly sought after conferences in the field. More info: Wharton People Analytics 3. HR Technology Conference Wish List Interest: 19% of respondents Why It’s Desired: The HR Technology Conference (HR Tech) stands out as a pinnacle event for those looking to stay ahead in the rapidly evolving realm of HR tech. From AI-driven talent management solutions to cutting-edge analytics platforms, the showcase of emerging trends draws both tech-savvy HR leaders and People Analytics professionals alike. It’s a one-stop-shop for learning everything you need to know to stay on top of HR technology advances or, if you’re feeling bold, to purchase your entire HR Tech stack. About This Conference: Scheduled for September 16-18, 2025 at Mandalay Bay in Las Vegas, the HR Technology Conference offers hands-on access to the latest technologies from over 500 leading and emerging providers. With over 200 sessions, attendees can discover industry trends and gain actionable strategies to leverage technology for success in various HR functions. The conference also provides numerous networking opportunities with peers and industry experts. Repeat or New?: HR Tech featured as a must-attend conference in the 2024 survey and repeats here in 2025 More info: HR Technology Conference 4. HR Analytics Summit London Wish List Interest: 17% of respondents. Why It’s Desired: The HR Analytics Summit London has become a pivotal event for professionals eager to harness the power of data in HR. With a focus on practical applications of People Analytics, the summit addresses critical areas such as employee engagement, HR operations, and the future of work. Attendees are drawn to its comprehensive agenda, featuring inspiring keynotes, interactive panels, and deep-dive workshops led by industry thought leaders. About This Conference: Scheduled for September 4, 2025 in London, the HR Analytics Summit offers a turbo-charged day of learning and networking. The conference brings together over 300 HR and workforce leaders from a variety of industries with 20+ expert speakers. Sessions delve into innovative approaches to workforce analytics, empowering strategic decision-making processes to tackle pressing issues in human capital management. The event also emphasizes the ethical use of AI in HR, balancing data-driven insights with human empathy. Notably, 5% of all ticket sales are donated to the charity Mind, reflecting the summit's commitment to mental well-being. Repeat or New?: A newcomer to the list for 2025 and an exciting one to watch going forward! Website: HR Analytics Summit London 5. Insight222 Global Executive Retreat Wish List Interest: 15% of respondents Why It’s Desired: The Insight222 Global Executive Retreat is highly coveted among HR executives and People Analytics leaders for its exclusive, invite-only format. Offering meticulously curated sessions, the retreat offers deep dives into strategic topics, fostering an environment where executives can "Think, Reflect, and Plan" their future initiatives. Participants value the opportunity to engage with top-tier business speakers and peers in a distraction-free setting, enhancing their leadership journey. About This Conference: The retreat is held annually at spectacular venues, such as the historic Duin & Kruidberg estate near Amsterdam. It features a select number of in-depth discussions and workshop-style activities led by world-class speakers. The 2024 theme, "The Changing Role of the People Analytics Executive," focused on the evolving influence of People Analytics leaders within organizations (2025 theme TBD). Attendees gain strategic insights, engage in peer learning, and develop actionable plans to drive value in their roles. Repeat or New: A consistent favorite on the wish list for many People Analytics leaders, the Insight222 Global Executive Retreat continues to attract senior leaders seeking a premier, immersive experience in the People Analytics domain. More info: Insight222 Global Executive Retreat And these are just the top 5 from each category! The survey was close and there are MANY more incredible events in our space. Please jump to the end of the blog to see the full list of conferences included in the study. Each one represents incredible community, sessions, and exciting ideas and experiences. New (and Noteworthy) Conferences for 2025 As the People Analytics conference landscape continues to expand, 2025 introduces some exciting developments, from fresh additions to reimagined formats. These events stand out as either brand-new opportunities or evolving platforms that are reshaping the way HR and People Analytics professionals engage with the community. RedThread Research's ELEVATE Conference New for 2025: This year marks the debut of ELEVATE, a highly anticipated conference led by industry thought leaders Stacia Garr, Dani Johnson, and the team at RedThread Research. Known for their influential insights and industry research on talent, learning, and People Analytics, this event is going to be incredible. Why Attend: ELEVATE aims to deliver an intimate, invite-only, high-value experience by bringing together industry thought leaders, actionable insights, and forward-thinking strategies with an emphasis on a Director+ audience. About the Conference: Scheduled for June 17–19, 2025 at Snowbird, Utah, Elevate promises a mix of interactive sessions, collaborative problem-solving, and exclusive research findings. The conference is deliberately designed to spark innovation and build stronger bridges between data, decision-making, and people. Learn More: Find details and join the waitlist HERE! SIOP Leading Edge Consortium (LEC): People Analytics New for 2025: Chaired by Cole Napper and Stephanie Murphy, this year’s SIOP LEC focuses on People Analytics, providing a deep dive into the strategic and operational challenges facing today’s analytics teams. Why Attend: The LEC’s smaller, specialized format encourages targeted conversations and emphasizes practical applications. Attendees can expect to engage directly with experts, participate in robust discussions, and leave with actionable strategies tailored to their unique challenges. About the Conference: Scheduled for Oct 23-24th in Atlanta, Georgia the LEC combines SIOP’s academic rigor with emerging trends in People Analytics. The event brings together researchers and practitioners for a collaborative exchange of ideas, making it an essential gathering for teams looking to refine their approaches and elevate their impact. Learn More: Keep an eye on SIOP LEC Cole Napper shares, "The SIOP Leading Edge Consortium is focused on People Analytics this year. It has a stellar lineup of speakers (soon to be revealed), and is welcome to I/O psychologists and non-I/O psychologists alike. It should be one of the most practical, scientific yet fun conferences to date - chaired by myself and Stephanie Murphy." UT Austin Voice Conference 2025 New for 2025: Making its debut this year, the Employee Voice Conference at UT Austin is an exclusive, invite-only gathering led by Ethan Burris. Hosted at the McCombs School of Business, this inaugural event brings together leading academics, VP Talent/CHROs, and People Analytics leaders to explore cutting-edge employee voice research and practice. Why Attend: Compared to other conferences, there is significant focus on building bridges across boundaries. Attendees will participate in intimate roundtables, thought-provoking discussions, and carefully curated sessions designed to foster cross-disciplinary collaboration. With a highly targeted attendee list, participants gain unparalleled access to peers and thought leaders who are driving innovation in employee voice strategies. About the Conference: Scheduled for April 17–18, 2025 in Austin, Texas, the event focuses on understanding methods and advances in employee feedback and innovations around employee listening. A joint-effort founding team of academics and leaders from across the space have come together to innovate within this conference ensuring actionable insights and meaningful relationship-building opportunities. Learn More: Follow announcements from UT Austin’s McCombs School of Business or reach out to Ethan Burris for updates. A few seats are remaining, so if you have a passion for employee listening, be sure to reach out! These events offer unique opportunities to gain fresh insights, connect with leading thinkers, and stay ahead in the ever-evolving world of People Analytics. Whether you’re attending for the second iteration of a rising star or diving into a brand-new experience, these conferences are set to make a lasting impression in 2025. Insights from Practitioners: Enhancing the Conference Experience This year, we also included two new questions in our survey to uncover deeper insights into why professionals attend conferences and how organizers can improve the overall experience. 1. Why do professionals attend conferences? 2. What do you wish conference organizers knew (from practitioners) Here’s what we learned from the responses: Why Do Professionals Attend Conferences? Networking emerged as the dominant reason for attending conferences, with respondents emphasizing the value of connecting with peers, exchanging ideas, and learning from others in the People Analytics community. Beyond that, learning and staying on top of industry trends were also top priorities. Here’s a breakdown of the common themes: Networking and Collaboration: Many professionals highlighted the importance of meeting others in the field to exchange ideas, build relationships, and discover potential collaborators. Conferences provide unique opportunities to engage with peers facing similar challenges and working on similar initiatives. Learning and Staying Current: Respondents consistently mentioned the need to stay informed about the latest trends, research, and technologies in People Analytics. Many seek practical solutions, detailed use cases, and innovative ideas to bring back to their organizations. Sharing Knowledge and Giving Back: Several practitioners also view conferences as a platform to share their expertise, present their work, and contribute to the growth of the field. Professional Growth and Inspiration: The excitement of gaining new perspectives and sparking fresh ideas was another frequently cited reason. Attendees look for moments of inspiration that push their thinking and help them grow professionally. Discovering Emerging Tech and Best Practices: Keeping an eye on emerging technologies, methodologies, and strategies remains a key goal for many attendees. Key Takeaway: Conferences are not just about presentations—they are critical hubs for community building, knowledge sharing, and inspiration. Make sure to build in time for networking sessions or conference organized networking events. What Practitioners Wish Conference Organizers Knew When asked how conferences could improve, attendees provided thoughtful and candid feedback. These insights highlight areas where organizers can refine the experience to better serve the needs of the People Analytics Community Here’s a breakdown of the common themes: Networking is Key Similar to above, respondents want more intentional, well-designed networking opportunities. Suggestions included planned 1:1 matchups, structured group discussions, and color-coded badges to help identify peers with similar roles or goals. Longer lunch breaks, dedicated networking periods, and informal social activities were also suggested to facilitate meaningful connections. Balance Content with Connection Many participants expressed a desire for fewer sessions and more opportunities to connect with others. "Less content and more connection" was a recurring theme. Hands-on interactive sessions and workshops were highly valued over traditional panels or theory-heavy presentations. Accessibility and Inclusivity Virtual attendance options were a popular request, with many noting the value of hybrid formats for professionals with limited budgets or travel constraints. Suggestions included offering livestreams, post-conference breakout access, or on-demand recordings at a reduced cost. Respondents also emphasized the need for conferences to cater to neurodivergent attendees, introverts, and individuals from underrepresented groups through thoughtful design, diverse speakers, and accessible content. Thoughtful Vendor Participation A common frustration was the prevalence of vendor-led presentations that felt like sales pitches. Attendees vastly prefer sessions, even from vendors, that focus on sharing insights, research findings, and practical applications rather than direct product promotion. Demonstrations that show rather than tell, along with panels featuring practitioner voices, were seen as more effective. Content Design and Variety Respondents want more practical case studies, detailed use cases, and real-world examples, especially from industries like manufacturing and low-margin businesses. There was also a desire for broader representation in speakers, both in terms of backgrounds and company sizes, to better reflect the diversity of the field. Pre-Conference Resources Pre-shared attendee lists, session itineraries, and other preparatory materials were highlighted as valuable tools for more intentional networking and better conference planning. Acknowledge Real-World Constraints Budgeting challenges were frequently mentioned, with many participants noting that their organizations approve conference budgets the year prior. Providing earlier information on dates, costs, and speakers would help attendees secure funding. Some respondents also mentioned a sense of "conference fatigue," suggesting that organizers consider consolidating events or ensuring differentiation in their offerings. Key Takeaway: Conference organizers have an opportunity to create more inclusive, impactful, and engaging experiences by prioritizing networking, balancing content, and addressing accessibility and budget challenges. Conclusion: Building a Better Conference Experience The feedback from this year’s survey offers a roadmap for conference organizers looking to elevate their events. By focusing on community building, providing diverse and practical content, and addressing accessibility concerns, conferences can better serve the evolving needs of People Analytics professionals. For practitioners, these insights reinforce the importance of carefully selecting events that align with their goals—whether it’s connecting with peers, learning about the latest innovations, or gaining inspiration for new challenges. Takeaways for 2025 The People Analytics conference circuit is more dynamic than ever. Whether you’re a seasoned leader or a practitioner just starting your analytics journey, there’s a conference tailored to your needs. From the academic rigor of Wharton to the accessibility of local meetups, these events offer a mix of inspiration, networking, and actionable insights. If your budget is tight, prioritize meetups and virtual sessions; if you’re looking for deeper insights, conferences like SIOP and People Analytics World are worth the investment. What’s Next? If you’re attending any of these conferences, we’d love to connect and hear about your experiences. And if you’re still deciding which events to prioritize, I hope this guide can be your roadmap for 2025. And if you’re on the fence, reach out to me (Richard) and let me know what you’re thinking and hoping to achieve! I’d be happy to weigh in with experiences from the field. See you out there! Connect with us in person in 2025? Tell us which events you plan to attend and let's meet up! What to stay in the loop? Follow One Model on LinkedIn Follow Richard on LinkedIn Events list from Survey (non-vendor specific events that were included in the survey or mentioned in survey results) People Analytics Summit Toronto - February 26-27, 2025 - Toronto, Canada - Link HR Data Analytics and AI Summit - March 4, 2025 - Atlanta - Link HR West 2025 - March 11-12, 2025 - Oakland Marriott City Center, Oakland, CA, USA - Link Transform US - Las Vegas - March 17-19 - Link SHRM Talent Conference & Expo 2025 - March 24-26, 2025 - Music City Center, Nashville, TN, USA - Link SIOP 2025 Annual Conference - April 2-5, 2025 - Denver, CO, USA - Link Wharton People Analytics Conference - April 10-11, 2025 - Philadelphia, PA, USA - Link UT Austin Voice Conference - April 17–18 - Austin, TX, USA - Link TBD People Analytics World - London (Tucana) - April 29-30, 2025 - London, UK - Link Unleash America - May 6-8, 2025 - Las Vegas, NV, USA - Link TALREOS Chicago - June 4-6 - Chicago, IL, USA - Link RedThread Research: ELEVATE - June 17-19 - Snowbird, Utah, USA - Link People Analytics Exchange (Minneapolis) - June 24-25, 2025 - Minneapolis, MN, USA - Link SHRM Annual Conference & Expo 2025 - June 22-25, 2025 - San Diego - Link AHRI National Conference - August 19-21, 2025 - Sydney, Australia - Link HR Analytics Summit London - September 4, 2025 - London, UK - Link HR Technology Conference (Las Vegas) - September 16-18, 2025 - Mandalay Bay, Las Vegas, NV, USA - Link HR L&D Tech Fest - September 22-23, 2025 - Sydney, Australia - Link Gartner ReimagineHR Conference (London) - October 7-9, 2025 - London, UK - Link People Analytics World - NYC (Tucana) - October 15-16, 2025 - New York City, NY, USA - Link Unleash World (Paris) - October 21-22, 2025 - Paris, France - Link SIOP Leading Edge Consortium: People Analytics - October 23-24, 2025 - Atlanta, GA - Link Gartner ReimagineHR Conference (Orlando) - October 27-29, 2025 - Orlando, FL, USA - Link Nordic People Analytics Summit - November 2025 (Exact dates TBA) - Stockholm, Sweden - Link Gartner ReimagineHR Conference (Sydney) - November 17-18, 2025 - Sydney, Australia - Link Dates TBD: CIPD People Analytics - UK - TBD - London, UK - Link Insight222 Global Executive Retreat - TBD - TBD - Link Learning Forum People Analytics Council - TBD - TBD - Link Which events did we miss? Send Richard an email at Richard.Rosenow@onemodel.co (Note: we do not include single vendor (hosted by one vendor) or tech sales events in this review of conferences) 2025 People Analytics Meetup Groups And now time for the meetups! These meetups happen frequently throughout the year, so the best way to be involved and stay involved is to connect with their local site / meetup / LinkedIn group. Where we can, we’ve included some details about how to connect and when there was not a site yet available, we’ve added in local organizers. Definitive list from the Society for People Analytics: https://societyforpeopleanalytics.org/meet-ups Brisbane (AU): (Link to event) New York: https://lnkd.in/gbfu_Mjc (Jeremy Shapiro / Stela Lupushor) Bay Area: https://lnkd.in/gnrgRBnH (Annika Schultz / Mariah Norell) Chicago: https://lnkd.in/ghgc3EDb - (Chris Broderick) Philadelphia: https://lnkd.in/g-bWmX5y - (Fiona Jamison, Ph.D.) Pittsburgh: https://lnkd.in/eCdP7KFC (Ken Clar / Richard Rosenow) Minneapolis: https://lnkd.in/eS2aUH3W (Stephanie Murphy, Ph.D. / Mark H. Hanson) Seattle: Bennet Voorhees / Marcus Baker / Philip Arkcoll Denver: Kelsie L. Colley, M.S. ABD / Zach Williams / Gabriela Mauch Boston: Hallie Bregman, PhD / Noel Perez, PMP Dallas: Jordan Hartley, MS-HRM / Cole Napper Austin: Ethan Burris / Roxanne Laczo, PhD Houston: Amy Frost Stevenson, PhD / Jugnu Sharma, SHRM-CP Atlanta: Sue Lam Nashville: Dan George Orlando: James Gallman / Danielle Rumble, MBA Omaha: Justin Arends Salt Lake City: Willis Jensen Toronto: Danielle Bushen / Konstantin Tskhay, PhD Washington DC: Rewina Bedemariam Portland: Rosanna Van Horn
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When it comes to leveraging workforce data for strategic decision-making, organizations need tools that go beyond simple reporting. People Analytics Platforms collect, clean, and analyze data, deliver valuable insights, use predictive analytics, and integrate with other business systems. While major Human Capital Management platforms like SuccessFactors, Workday, and Oracle offer analytics capabilities, One Model stands out as a critical addition to these built-in solutions. Here’s why: Achieve More Accurate Insights with Effortless Data Integration The ability to integrate diverse data sources effortlessly is critical for gaining accurate insights. By pulling real-time data from multiple sources, organizations can make more informed decisions that directly impact their overall strategy. Workday Prism: Workday Prism aims to provide a unified analytics layer but has challenges with integrating data from third-party sources. The platform requires users to publish individual data sets, and the data preparation logic is often inconsistent. This can lead to confusion and inefficiencies, specifically for organizations that need to bring together diverse data sources. SuccessFactors Workforce Analytics: One of the major drawbacks of SAP SuccessFactors WFA is its limited data integration capabilities. While it offers workforce analytics, it primarily only works within the confines of specific SuccessFactors modules. Integrating external data from other systems or non-HR business data is either not possible or costly and cumbersome. Oracle Fusion: Similarly, Oracle’s analytics solution is tightly coupled with its HCM platform. While it provides robust tools for managing HR data, the system lacks flexibility with integrating with non-Oracle systems. This creates data silos. Additionally, Oracle’s analytics solution comes with hidden costs, such as fees for custom analytics, data storage, and specialized expertise. One Model: One Model excels at integrating all of your HR data and external business data into a cohesive platform. With pre-built connectors to popular systems like Workday, SuccessFactors, and Oracle, as well as the ability to integrate non-HR data, One Model creates a unified data warehouse that provides deeper insights across all business functions. This flexibility ensures businesses have a complete, real-time view of their workforce and business performance. Quick Time to Value for Faster Decision-Making When speed is critical for decision-making, long implementation timelines can hinder an organization’s ability to take action. One Model’s quick deployment ensures that companies don’t have to wait months or years to start seeing value from their analytics investments. Workday Prism: Workday Prism involves an in-depth implementation and the need for specialized BI developers to maintain the system after implementation. SuccessFactors Workforce Analytics: The time to value with SuccessFactors Workforce Analytics can range from 12 to 24 months, as the system requires significant customization, data preparation, and integration efforts. Oracle Fusion: Similarly, Oracle Fusion's analytics solution requires extensive customization, and companies often face long implementation times before seeing value. One Model: One Model has the ability to deliver actionable insights in as little as 6-12 weeks. With a user-friendly platform and expert support, One Model allows organizations to experience value faster, making it the ideal choice for businesses that need rapid insights to drive strategic decision-making. With robust role-based security and pre-built, customizable HR metrics, users can access the insights they need without waiting for IT or BI teams. Empower HR Teams with a Tailored Solution to Align with Evolving Goals HR teams need to be agile and adapt their analytics strategies to meet shifting organizational priorities. Solutions that allow for easy customization can empower HR professionals to act quickly and align their insights with business needs. Workday Prism: Customizing reports and data models in Prism requires the involvement of skilled BI developers, leading to delays in decision-making. SuccessFactors Workforce Analytics: While SuccessFactors offers basic reporting capabilities, customizing reports and metrics is time-consuming and requires significant technical expertise, slowing down the ability to generate actionable insights. Additionally, the platform's rigid data architecture can make it challenging to create highly customized solutions that meet unique organizational requirements. Oracle Fusion: Oracle Fusion’s self-service capabilities are limited, and users need specialized skills to customize reports and analytics models. In contrast, One Model stands out for its ease of customization. One Model is designed to allow users to quickly create and customize dashboards and metrics in minutes. By offering a platform that simplifies customization, One Model enables HR teams to take full ownership of their analytics and ensure they are always aligned with evolving organizational goals. Understand Workforce Behaviors and Drive Efficiency with Predictive Analytics and Machine Learning As organizations continue to rely on data to shape their strategies, predictive analytics and machine learning are becoming increasingly vital for understanding and forecasting workforce behaviors. Solutions with built-in AI capabilities can provide deep insights into areas like turnover, performance, and employee satisfaction. Workday Prism: Prism offers basic reporting and visualization, but predictive capabilities are limited, and the platform is not optimized for machine learning applications. SuccessFactors Workforce Analytics: SuccessFactors lacks built-in machine learning and predictive analytics, leaving HR leaders with limited capabilities to forecast trends like attrition and performance. Oracle Fusion: Oracle’s analytics tools fall short in providing integrated predictive insights. Oracle offers a visualization layer, but predictive modelling requires significant customization. One Model: One Model sets itself apart with its powerful machine learning engine, One AI, which enables predictive insights across key HR metrics like attrition and performance. One Model offers complete transparency into its AI models, ensuring that businesses can maintain control over algorithms and stay compliant with legal and ethical standards. Why One Model is the Perfect Partner for Enhancing Your Existing HCM Solution When compared to Workday, SuccessFactors, and Oracle Fusion’s built-in analytics modules, One Model stands out as the most flexible, cost effective, and customizable solution for People Analytics. With faster time to value, data integration across multiple HR and business systems, and advanced predictive analytics, One Model helps businesses use their people data as a strategic asset. For organizations seeking fast, effective data-informed decision-making, One Model is the clear choice for enhancing your existing HCM solution.
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The integration of artificial intelligence into HR practices has begun to transform how companies engage, support, and optimize their workforce. By adopting a holistic, employee-centric approach to AI deployment, organizations and People Analytics teams can foster a culture of innovation, boost productivity, and ultimately create a workplace where employees are empowered to thrive. A Strategic and Employee-Focused Approach to AI Integration Implementing AI within an HR organization demands a strategic and well-orchestrated approach. A key insight from companies successfully embracing AI is to prioritize change management for AI and employee engagement. Rolling out AI solutions shouldn’t feel like a top-down directive imposed on People Analytics teams but rather a collaborative, bottoms-up process that centers on empowering employees. When People Analytics leaders take a gradual, people-first approach, they can ease anxieties associated with AI adoption. The fear that AI will replace jobs has been pervasive, and addressing this concern upfront is essential for cultivating trust. Leaders need to clearly communicate that AI is a tool to enhance, not replace, human effort. This message must resonate throughout the HR department and company, helping employees view AI as a career-enabling partner rather than a threat. Celebrating AI Adoption as a Driver of Change Measuring initial AI adoption within the HR department is a critical first step. Successful teams have shown that fostering a culture where AI usage is celebrated can accelerate adoption. One effective strategy is creating department-wide channels where People Analytics teams can share their experiences using AI and the benefits it has brought to their workflows. Highlighting these success stories not only reinforces positive engagement but also builds momentum as teams see real examples of AI delivering tangible value. Recognition programs that reward early adopters can further stimulate interest and promote active participation. This step aligns with AI change management best practices, which emphasize that for organization transformation with AI to take hold, the individuals most affected must feel supported and valued. Key Areas of AI Implementation in HR Leading organizations have deployed AI across several HR functions to drive efficiency and enhance decision-making. Below are examples of impactful implementations of AI in HR departments: 1. Recruitment and Interview Processes Integrating AI in recruitment has revolutionized how interviews are conducted and evaluated. AI-powered tools can assist hiring managers by recording interviews, generating time-stamped notes, and linking key interview moments to questions asked. This capability alone can save managers substantial time—up to 30–40 minutes per interview—by automating note-taking and providing instant access to video highlights. 2. HR Chatbots for Employee Services Advanced, AI-powered HR chatbots are streamlining routine tasks by handling knowledge base inquiries and processing transactional requests. Integrated within platforms like Slack, these bots can facilitate actions such as submitting time-off requests or accessing benefits information, freeing up HR teams to focus on more strategic work. This integration also simplifies data access and enhances the overall employee experience. 3. AI-Enhanced Learning and Development AI’s application in L&D involves deploying intelligent tools that help curate learning content, suggest development paths, and assist employees with feedback and coaching tailored to organizational competencies and values. The introduction of AI coaches for career growth discussions or navigating difficult conversations empowers employees with customized guidance that aligns with company culture and goals. 4. Democratize People Data Across Management Teams Team leaders at any level of the organization are better leaders when they better understand the workforce under their care. Business and People Analytics teams are instrumental in that goal through building dashboards and answering big complex questions. But what happens when there are too many ad hoc manager questions across the organization for the people analytics teams to answer? Originally, that meant either questions went unanswered or people analytics teams were pulled from larger, more impactful projects to help. With solutions, like One Model, that is no longer an issue. One AI Assistant is helping companies today by giving managers the ability to ask questions on the people data they can access. HR analytics teams can have confidence in the results of One AI Assistant because it provides clear explainability and transparency of outputs. Laying the Foundation for Long-Term Success Sustainable AI integration in HR and People Analytics is not just about deploying new technologies but about embedding AI into the company culture. From day one, leaders need to ensure that the tools are intuitive, accessible, and aligned with the company’s core values. Building trust in AI begins with demonstrating how these tools support employees' roles, making HR tasks less burdensome and enabling teams to tackle more strategic initiatives. HR leaders should collaborate with engineering and data teams to customize AI solutions that fit specific organizational needs. This might involve developing unique AI assistants or prompts that streamline operations and ensure consistency across processes. For instance, internal tools that summarize interview notes or assist with coding can enhance productivity without fundamentally altering job responsibilities. Creating a Legacy of Innovation and Engagement The ultimate goal of integrating AI into HR is not just to boost efficiency but to foster an environment where employees feel excited about leveraging cutting-edge tools. Organizations that prioritize a culture of continuous learning and innovation will find that their employees are more engaged, adaptive, and capable of driving the company forward. By recognizing the transformative potential of AI and implementing it thoughtfully, HR and People Analytics leaders can elevate their people strategy with AI and position their organization as a leader in the future of work. Learn more about One AI Assistant If you would like to learn more about One AI Assistant and how other One AI tools can help your team empower your entire company with business-driving insights into their workforce, reach out. Your Data. Real Answers.
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The data doesn’t lie—these are the One Model resources your peers keep coming back to. We’ve rounded up our top three most-downloaded whitepapers, plus a bonus newcomer that’s already making waves. Whether you’re searching for fresh strategies or sharpening your existing skills, these resources have proven to be invaluable to your People Analytics professionals like yourself. If you haven’t explored them yet, now’s the time to join the conversation. 1. Why Data-Informed Storytelling is the Future of HR As the field of People Analytics becomes increasingly data-savvy, this whitepaper has resonated with readers across industries, earning its place as our most downloaded resource. If you’ve struggled to tell a meaningful narrative around your data and its objective but sometimes hidden insights, data storytelling is the missing link. Stories, human anecdotes, and yes - even emotion - can help bring your data to life. It’s a powerful combo that can truly drive action for organizations. But how do you tell a meaningful data story? And why is it such a valuable skill for today’s HR teams? Download this eBook today to learn: The evolution of storytelling in HR How to craft data-informed HR stories Examples of impactful data-informed HR stories How to tell better data stories with One Model Learn how to turn raw data into compelling narratives that engage stakeholders and drive better decisions. 2. People Analytics 101 Coming in a close second in popularity, this fundamental guide is the perfect entry point for getting started in People Analytics. But even seasoned HR professionals sometimes wonder what to prioritize when establishing a strong People Analytics foundation. This content is meant for everyone, from CHROs to HR leaders looking to upskill, providing foundational knowledge that aligns your people data with your organization’s goals. Download this eBook today to learn: What People Analytics is, and why it's important How to prepare your organization for People Analytics Why employee attrition is a good starting point Steps for completing your own People Analytics projects Discover how to tailor People Analytics to your organization’s unique needs. 3. Measuring the Value of People Analytics Prove the ROI of your efforts with this comprehensive, tactical guide to measuring the tangible impact of People Analytics. A must-read for leaders seeking to align HR initiatives with business outcomes or make a business case for People Analytics. Download this whitepaper today to learn: How to redefine and measure the value of People Analytics beyond traditional ROI metrics. The three levels of People Analytics impact—direct, indirect, and induced—and how they drive better talent decisions. A practical formula for assessing the value of analytics deliverables and prioritizing resources effectively. Strategies for scaling People Analytics impact through self-service solutions and fostering a data-driven decision-making culture. Confidently calculate and articulate the impact of your HR analytics on organizational performance. Bonus: From Data to Strategy: The New Workforce Systems Leaders Transforming HR Our newest whitepaper, authored by our VP of People Analytics Strategy Richard Rosenow, recently launched to an enthusiastic reception. Clearly, it struck a chord. Focused on the emergence of a new People Analytics role that aligns the flow of data through an organization (which Richard dubbed the people data supply chain), this highly anticipated resource provides insight into the typically uncharted path of People Analytics leaders. Download this eBook today to learn: Key challenges in People Analytics (it’s not just you!) Actionable strategies for mastering the People Data Supply Chain, including an real-world example for managing attrition Who are Workforce Systems Leaders and what do they do? Get prepared to lead the next evolution of workforce management. Next steps? Contact us with your questions or to schedule a One Model Demo.
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15 min read
The One Model Team
Transcript: Hi, everyone. Let’s dive right in. Today, we’re going to talk about unlocking the power of people data platforms—what that means, how to access your data, and how to connect with it in meaningful ways to drive insights across the workforce. Introduction I’m Richard, and for those I haven’t met, it’s great to meet you. A bit about me: I started my career in nonprofits—shout out to others here with a nonprofit background! From there, I moved into HR, focusing on workforce planning at GE Capital, followed by roles at Citibank. Eventually, I discovered my passion for People Analytics, which shaped the trajectory of my career. I’ve had the privilege of working on People Analytics teams at companies like Facebook, Uber, Nike, and Argo AI. Each experience taught me something new about building scalable teams and leveraging technology to solve big challenges. For example, at Facebook (when it was still Facebook), our People Analytics team grew from 15 to 150 people. It was an exhilarating time, but not every organization can afford that kind of scale. So, when I moved to Uber, the focus was on how to scale smarter—how to build products and platforms instead of large teams. At Nike, I also helped build data foundations and worked closely with data engineering teams to develop a more robust HR data hub. When I moved to Argo AI, I worked across HR tech, People Analytics, and project management. I was heavily involved with Workday and began exploring One Model, which shaped my approach to building scalable analytics solutions. Fast forward to today: I’m now VP of People Analytics Strategy at One Model, where I get to connect with hundreds of People Analytics teams annually. This has given me a unique perspective on what’s working, what’s not, and where we’re all heading. Why is People Analytics So Hard? Let’s start with a key question I ask every time I talk about People Analytics: Why is this so hard? People Analytics as we know it is still relatively new. The modern function emerged maybe 15 years ago, and while it’s evolved a lot since then, we’re still figuring it out. There are so many names, frameworks, and definitions out there—it’s confusing for everyone. If you’re struggling to make sense of this within your organization, know that you’re not alone. From Facebook to smaller companies, everyone finds this hard. Defining People Analytics When I talk about People Analytics, I use three definitions: Community People Analytics is a community of practitioners working to create better workplaces through data. If there’s one thing you take away today, it’s this: there is a thriving People Analytics community out there. It’s full of nerdy, passionate people who love this topic. If you’re curious about data or looking to use it more effectively in your organization, find these people—they’re everywhere and eager to connect. The Act The act of People Analytics is simply using data to support workforce decisions. This isn’t just an HR responsibility—everyone in the organization, from managers to the CEO, makes workforce decisions. They should all be using data to do it. The Function The People Analytics function is the team within HR that supports this work. They build systems, provide guardrails, and help the organization use data effectively. The Invisible Work of People Analytics Leaders One challenge for People Analytics leaders is that they’re often hired for one job but end up doing another. Their job descriptions focus on descriptive, predictive, and prescriptive analytics. But once they start, they realize IT and HR haven’t spoken in years. They’re stuck cleaning data, navigating politics, and trying to get access to systems—none of which were in the job description. This invisible work is critical but goes unrecognized. If you have a People Analytics leader, send them a note and let them know you see and appreciate their effort. The "Skipped Step" in HR: People Data Here’s where the real problem lies: HR skipped a step. We went from strategy to operations to technology without fully addressing people data—the process of extracting, cleaning, and organizing data into a comprehensive HR data hub. Analytics teams are left backtracking to fix foundational issues before they can deliver insights. This skipped step creates pain for everyone. And it’s becoming even more critical now that HR data systems are feeding not just analytics but also Generative AI. The Little Red Hen Moment This brings us to what I like to call the "Little Red Hen Moment." You might remember the story: the Little Red Hen finds some corn and asks the other farm animals, "Who will help me plant the corn?" They all say no. So she plants it herself. Later, she asks, "Who will help me harvest the corn?" Again, no one helps. She does it herself. Then she bakes the bread and asks, "Who will help me eat the bread?" And, of course, suddenly everyone wants in. This is exactly what happens with People Data in many organizations. HR leaders ask, “Who will help us build the business case for People Analytics?” The data engineering team says, "Not I," because they’re busy maintaining data pipelines for sales. The IT team says, "Not I," because they’re focused on streamlining the vendor landscape. The enterprise analytics team says, "Not I," because they’re prioritizing metrics for finance. So HR is left to plant the corn, harvest it, and bake the bread on its own. We pull together data manually, build foundational systems, and lay the groundwork for analytics and insights—all while trying to establish a sustainable workforce data supply chain. But once those insights are ready—once the bread is out of the oven—everyone shows up to eat. The same teams that didn’t prioritize People Data suddenly want the insights it produces. They’re eager to see workforce metrics, predictive models, or generative AI results, but they don’t recognize the effort it took to get there. This isn’t just an HR problem; it’s a structural issue. HR has been underinvested in and systemically held back. Other business functions—like finance, marketing, and operations—have robust platforms and strong executive support. HR deserves the same level of investment to drive business outcomes. The message here is simple: it’s time for HR to demand a seat at the table and take HR data ownership People Data and build a robust HR data hub to succeed.. We need to make the case for why this work matters—not just for HR, but for the entire organization. The Framework for People Data Platforms Let’s talk about People Data platforms—which are essentially the foundation for a workforce data supply chain. A platform has two key components: The Data Foundation This is where data is extracted, modeled, and organized. It’s the backbone of everything, including generative AI. The Application Layer This is where data is visualized, analyzed, and put to use. At One Model, we’ve developed a framework that covers every stage: extract, model, store, analyze, and deliver. Each step has detailed requirements, and we provide tools to help organizations navigate them effectively. Dive deeper into the 5 Steps to Get Data Extraction Right. Conclusion People Analytics is hard, but the opportunities are enormous. By investing in People Data platforms and supporting our teams, we can create better workplaces and drive smarter decisions. Q&A Q1: During the modeling phase, are you prioritizing data? Is all of it being stored, or are you storing it in multiple places? Are you saying, “This is the most useful for dashboards,” and keeping other data as a backup in case it’s needed for KPIs later? What does that look like? A: That’s a really good question. Here’s how it typically works: You have data that sits in your core HR tools, the data you extract from those tools, and the modeled data you use for analytics. Along the way, you need to maintain copies—raw files, for example—for audits. But it’s the modeled data that should be driving your business decisions. What often happens in HR is that we’re told, “Just pick what you need,” because we aren’t given the resources to extract and store everything. This is one of the things One Model addresses—we create a single, unified data model where all your data is combined and accessible in one spot. This approach is becoming the norm for mature People Analytics teams. They no longer accept being limited to a single report from Workday or any other system. Instead, they demand full access and make sure their data is modeled and ready for any use case. And this is important because features in your data can play into your models in surprising ways. For instance, data from internal communications platforms like FirstUp can be remarkably effective for attrition prediction, but it’s often difficult to get access to that data. Q2: So, you’re doing predictive modeling as well. Can you use the same scripts or frameworks and apply them to different datasets? A: That’s a great question. Another key point to understand here is the difference between data extracts for reporting and data extracts for data science. For example, Workday provides daily snapshots of data. That works fine for reporting, but for predictive modeling, you need data over time. HR data is especially time-sensitive—more so than in many other functions—because of how events like transfers, exits, and tenure affect workforce insights. You can’t have someone transferring after they’ve already quit. The sequence of events really matters. This is where taking raw file snapshots and turning them into analytical feeds becomes critical. The ability to extract data for machine learning and predictive modeling is fundamentally different from extracting data for reporting. It’s something HR teams need to be aware of and push their IT teams to support because I’ve seen too many teams pressured into settling for reporting-level extracts, and it’s just not enough. Q3: When working with highly customized platforms like Workday or your ATS system, you often can’t—or don’t—make changes. For example, adding regrettable versus non-regrettable turnover as a data point can require defining those terms and assigning someone to audit that information. What advice do you have for making the business case for these changes? A: That’s an excellent question. Two things come to mind. Bring the stakeholder’s pain with you Let’s say you have a stakeholder downstream who’s really feeling the pain from a lack of data, like not knowing whether turnover is regrettable or not. Often, HR tries to solve this issue internally, on behalf of the stakeholder, by negotiating changes with upstream teams like HR tech. The problem is, the tech team doesn’t feel that pain firsthand, so they don’t prioritize the change. Instead, bring the stakeholder along with you to these discussions. Let them articulate their challenge directly to the HR tech team. When the tech team sees how their choices—or lack of action—are impacting the business, they’re more likely to respond. Create a new umbrella function The other solution I’ve seen gaining traction is hiring a leader to oversee People Analytics, HR technology, HR strategy, and operations as a single function. We call this the “workforce systems leader.” About 40 Fortune 50 companies have already started building roles like this. This umbrella leader can help navigate the politics and make tough decisions more efficiently. For instance, they can prevent unnecessary internal friction, like the head of People Analytics clashing with the head of HR tech. Instead, this leader would coordinate those efforts to drive progress forward. Q4: How do you recommend building a relationship with IT so they understand HR’s needs without seeing it as interference with data governance? A: That’s a fantastic question. I’ll give you two approaches—one "nice" and one a little more assertive. The nice way A lot of times, IT leaders (and finance leaders too) don’t fully understand HR’s technical needs. But they do understand their role as people leaders. So, start by framing the conversation in terms they’ll relate to. For example, you might say, “You’re leading a 400-person organization. Do you have visibility into what’s happening in your own team? Do you know where your pain points are?” This can help them see how better data access benefits not just HR but also their own leadership. The assertive way Here’s the reality: Other functions, like IT or finance, have no problem saying "no" to HR. But when they need something—like hiring 50 new project managers—they come to us, and we almost always say "yes." HR is often the ultimate team player, taking on more than its share of the load. While that’s great in theory, it can sometimes weaken our bargaining position. To build a stronger relationship with IT, we need to be more assertive about our needs. For example, we might say, “If you want to continue working the way you are, we’re going to need support from you. Let’s come to the table and figure this out together.” In short, it’s about clear communication, mutual accountability, and, sometimes, standing our ground to get what we need. Thanks, everyone! Ready for a deeper dive? Download Achieving People Analytics Maturity with a People Data Platform today for more insights on maturing your workforce data for actionable insights.
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3 min read
Kelley Kirkpatrick
Workplace gender equality is a critical focus for Australian employers, supported by initiatives like the Workplace Gender Equality Agency (WGEA). Established to promote and improve gender equality in workplaces, WGEA regulations require organisations with 100 or more employees to report gender data across six gender equality indicators. These indicators include key aspects such as pay equity, workforce composition, and representation in management roles. The reporting process is detailed, requiring a combination of point-in-time employee data and aggregate metrics spanning the reporting year. While this mandate enables organisations to reflect on and address gender equality, the process itself is challenging. Teams often spend weeks—or even months—sourcing data from disparate systems, aligning it with WGEA’s strict criteria, and meticulously validating it. However, with the right tools, this complex task can be streamlined with One Model. Tackling the Challenges of WGEA Reporting For most organisations, WGEA reporting is not just about compliance—it’s about leveraging the reported data to foster deeper insights into workforce gender equality. Yet, the process is notoriously cumbersome. Compiling detailed employee information, annualised salary data, and metrics such as leave and movement patterns often requires manual intervention and significant cross-team collaboration. This can lead to inefficiencies, inaccuracies, and limited time to analyse the results. But what if the data collection and validation process could be automated? This is where One Model comes in. Automating WGEA Reporting: How One Model Makes it Simple The One Model People Analytics platform transforms the arduous WGEA reporting process into a streamlined, automated operation. By centralising workforce data, aligning it with WGEA’s submission templates, and enabling robust validation, One Model allows organisations to achieve compliance efficiently while focusing on what matters most: understanding and acting on the insights derived from the data. Customer Spotlight An Australian customer recognised the platform’s potential to simplify their WGEA reporting. Some of the required workforce data was already housed in One Model, but integrating the full dataset—including annualised salary details and movement metrics—was the next step. By ingesting and modelling all the necessary data, One Model provided the customer with: A single source of truth: Data was centralised, validated, and securely accessible to analysts across various teams. Streamlined workflows: Automation reduced the need for manual data manipulation and cross-referencing. Tailored insights: The customer leveraged One Model’s analytics capabilities to create Storyboards and executive reports, turning raw data into actionable insights. The outcome? The customer not only completed their WGEA submission in one day instead of five, but also unearthed valuable insights for their leadership team, who were impressed by the quality and depth of the analysis. Beyond Compliance: The True Benefits of Automating WGEA Reporting Automating WGEA reporting increases efficiency and confidence while shining a light on what’s going well and areas for improvement. Efficiency: Teams save weeks of effort through automation, ensuring submissions are accurate and on time. Data Integrity: By centralising data and applying consistent validation, organisations can trust their numbers. Insights-Driven Culture: Once the reporting is complete, the data can be repurposed to drive conversations around workforce planning, pay equity, and diversity initiatives. With One Model, you can transform a complex compliance process into a streamlined, automated workflow. This not only saves time but also provides valuable insights that drive meaningful progress in workplace gender equality across the country. Ready to simplify your WGEA Reporting?
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4 min read
The One Model Team
Having the right vendor partnership can make a huge difference. And the wrong one can lead to huge headaches. One Model understands this, and we strive to be more than just another software provider. We seek to be a trusted partner for both HR and IT teams, deeply entrenched in the success of both departments. By partnering with One Model, tech teams get: Expert resources to field HR’s requests A common challenge many businesses face is the reliance of HR teams on their internal IT for business intelligence (BI) support. This not only strains IT resources but also may not always result in optimal solutions tailored for HR needs. With One Model, HR gets access to expert People Analytics resources. This isn't just about having an extra set of hands; it's about having a skilled set of hands, well-versed in BI, ready to converse, collaborate, and create. More time to focus on IT initiatives With One Model, tech teams can channel their energies and expertise towards initiatives directly tied to their KPIs. Our proposition is simple: let us empower HR with solutions that meet their BI needs while IT reallocates their time towards other tech initiatives. This isn't about pitting departments against each other; it's about recognising and optimising strengths of both groups. Increased transparency and accessibility If there's ever a need for IT to get involved, no problem. One Model's platform is built on transparency. Developers can literally inspect the SQL, ensuring a seamless integration of our platform into your ecosystem. This creates a harmonious interplay between HR and IT, with both departments benefitting. A cost-effective approach to People Analytics The cost of hiring and maintaining a single data engineer is substantial, and it’s not easy to find IT candidates with People Analytics experience. Data engineers often earn an annual salary of over $110,000 each year. And this doesn’t even include additional expenses your organisation will need for data architects, project managers, and other resources — especially as you scale. Partnering with One Model's team is much more cost-efficient, allowing you to allocate your resources more strategically. “From the tech leader’s perspective, there’s a significant cost to having HR rely on your internal IT team for BI support. So as you consider building your own solution from scratch or buying a People Analytics tool, One Model’s flexible platform is ideal because we’ll partner with your HR team and deliver the best of both worlds. We specialise in supporting HR’s needs, so tech teams can focus on their own KPIs. And, if developers ever have questions, One Model is open enough for them to jump in and literally look at the SQL. It’s a win-win for HR and IT.” — Taylor Clark, Chief Data Scientist, One Model Navigating the complexity of people data While many development teams are adept builders, navigating the labyrinth of people data is a different beast altogether. A common misconception is that IT teams can effortlessly manage data extractions, transformations, and integrations from HR systems. The reality? People data is complex, intricate, and often disorganised. “Many IT teams are already handling data extractions, transformation, and integrations across HR systems. With that experience, the justifiable assumption is that People Analytics will be a straightforward project. But the challenges of People Analytics are unique. For example, creating historically accurate, effective dated data models across multiple systems. One Model is the only vendor that confronts these challenges head on.” — John Carter, Senior Sales Engineer, One Model With One Model, you're not just getting a People Analytics platform, you're gaining a partner skilled in deciphering, managing, and optimising people data. Where many falter, we excel. The challenges that often stymie others, like managing Workday's unique constraints, are where our expertise comes to the forefront. We do the heavy lifting, ensuring that HR's data needs are met so tech teams can avoid the typical complexities. Our approach isn't just about providing a platform. It's about building a valuable, long-term partnership and commitment to ensure the success of HR, IT, and the overall company. Ready to learn more Download our whitepaper Why Tech Leaders Prefer One Model’s People Analytics Platform to learn 4 key reasons IT teams choose our platform over others on the market.
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6 min read
The One Model Team
As we look ahead to the coming year, we’re proud to reflect on the groundwork we’ve laid. This year has been about creating and improving the tools and partnerships you need to transform your workforce analytics and HR technology into a strategic advantage. At One Model, we know the best insights come from seamless HR data integration. That’s why we’ve doubled down on enhancing our connections with industry-leading platforms like Workday People Analytics, SAP SuccessFactors HR solutions, and Oracle PeopleSoft HCM. These integrations don’t just connect systems — they unlock the full potential of your workforce data, empowering you to make decisions that drive real impact. 1. Our Workday Integration: Driving Innovation Through Partnership This year was a big one for us: One Model became a proud Workday Innovation Partner! This milestone reflects our commitment to delivering cutting-edge solutions for Workday customers. Our enhanced Workday Connector is designed to ensure you can move beyond the basics and get actionable insights that truly make a difference. Why Workday + One Model stands out Direct access to Workday’s core data sets without disrupting your workflows. Advanced analytics that make Workday data even more impactful. Innovation recognized by Workday itself through our official partner status. Curious about what this means for you? Take a deeper dive into the possibilities of liberating your data with our updated Workday People Analytics Playbook. 2. Our SuccessFactors Integration: Unlocking Your HR Powerhouse SuccessFactors is a go-to platform for HR leaders, known for its robust workforce data. But having great data is only half the battle — making it actionable is where One Model comes in. With our SuccessFactors integration, you get clean, consistent data flows and a foundation for high-impact analytics. What makes this integration special? Streamlined extraction of key people data like workforce demographics, performance, and learning metrics. Flexible data models that adapt to your business needs. Real-time insights that empower smarter decision-making. Explore how to make the most of your HR AND non-HR data. Download our guide to Unlocking SuccessFactors People Data. 3. Our Oracle Integration: Turn Complex Data Into Actionable Insights Oracle’s platforms are known for their depth and versatility, offering rich data opportunities. But managing that complexity can sometimes feel overwhelming. With One Model’s Oracle integration, you can cut through the noise, unifying data streams and simplifying access and empowering your team to focus on insights that drive results. Why this integration matters: Brings together diverse Oracle data sources for a unified view of HR metrics. Scales with your organization’s needs, no matter how complex your data ecosystem gets. Provides decision-ready insights so you can spend less time wrangling data and more time driving strategy.. Get the full story on maximizing Oracle’s potential and bringing your data to life. Download our Oracle People Analytics Playbook. Ready to Unlock Your People Data? Integrations are the backbone of effective analytics, and with One Model’s solutions for Workday, SuccessFactors, and Oracle, (and many other data platforms) you can empower your team with the insights they need to drive success. Whether you’re looking to simplify data extraction, enhance workforce planning, or align your HR strategies, our integration whitepapers are a great way to get started. Explore them now and see what more your data could be doing for you.
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3 min read
Pria Shah
What Exactly is a Golden Ticket Query? Is it the data science counterpart to the elusive Golden Ticket from Charlie and the Chocolate Factory—a one-way pass to an all-encompassing insight? At first glance, these queries promise ease and instant answers, but do they truly deliver the nuanced understanding your organization needs? Let's dive deeper into what makes these pre-built queries so appealing yet limited, and why embracing a more adaptable approach might be the key to smarter, data-driven decision-making. Golden Ticket Queries, also known as Hard-Coded Queries, are pre-built, standardized queries designed to pull data in the same way every time. Think of them like preset questions that a system will always answer in the same way, without flexibility or context. These queries are often very basic, structured to address common use cases, but they don’t adapt to the unique needs of different users or businesses. Why Are Golden Ticket Queries Often Criticized by Practitioners? Golden Ticket Queries are frequently called out by analytics and AI professionals for a few key reasons: Lack of Flexibility: These queries are static and don’t adapt to new business priorities or shifting data. For example, a query tracking headcount changes might miss important nuances like department-specific trends or seasonal fluctuations. Surface-Level Insights: They often provide basic answers without digging deeper. For example, simply knowing how many employees have high performance ratings doesn’t help you understand what factors contribute to their success or how you can foster high performance across the organization. Missed Opportunities: By sticking with preset queries you miss out on the chance to ask more nuanced specific questions that could reveal new opportunities or solutions tailored to your organization's goals. For instance, turnover metrics might look fine on the surface but miss patterns could emerge when broken down by tenure, engagement, or location. At first glance, Golden Ticket Queries might seem like a quick win, but they rarely provide the depth needed for effective, data-driven decision-making. Our Approach at One Model At One Model, we take a different approach to People Analytics and AI, one that embraces flexibility, customization, and the ability to ask the right questions, tailored to your unique needs. Context Matters: People Analytics isn’t one-size-fits-all. The questions you ask and the insights you seek should be shaped by the specific challenges and goals of your organization. One Model’s One AI Assistant lets you ask dynamic questions that reflect your context, providing answers that are more relevant and actionable. Deeper Insights, Smarter Decisions: By moving away from rigid, canned queries, you open the door to deeper, more thoughtful exploration of your data. Custom queries allow you to uncover insights that are directly tied to your business objectives. Scalable and Adaptable: As your business evolves, so should your analytics. The flexibility built into One Model ensures that as your organization grows, your data exploration and insights grow with it. This adaptability means that your analytics can stay ahead of trends, adjust to new business strategies, and continuously inform smarter decisions. Golden Ticket Queries may seem convenient, but their limitations can hold your organization back from achieving its full potential. Their rigidity and surface-level approach to analytics make them an imperfect fit for today’s complex business environments. One Model believes that AI should help you ask the right questions, not just the easiest ones. With tools designed for flexibility, customization, and actionable insights, we help you uncover the deeper patterns that lead to smarter decisions and better outcomes.
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10 min read
Richard Rosenow
In taking on a new role in People Analytics, unsuspecting leaders often find themselves navigating much more than a new work environment. Though they were recruited to deliver workforce insights and instill a data-driven mindset into HR, they quickly encounter difficulties upstream in what we call the people data supply chain, revealing unexpected obstacles in their path to access the tools and data they need that reach across HR functions. Within weeks new people analytics leaders almost always find themselves working closer than they expected with data engineering, technology, HR operations, and senior leaders to achieve a singular goal: clean, strategic, and impactful workforce data that can be used to generate insights that drive business results. For those embarking on a People Analytics career, this path may seem overwhelming, but it is the foundation of high-impact analytics work. The People Analytics Leader’s Journey to Actionable Data It can help to know that this is the gig; you're not alone. It comes with the territory. To that end, the image above depicts the 30,000-foot view of this uncharted path. Dive deeper into the experience below or listen to the author’s keynote speech of the journey of people analytics leaders at People Analytics World in London. We’re going to be following a fictional People Analytics leader who just joined a large tech company. The company has heard about People Analytics for some time and finally decided to dip their toes into the water. This new leader is tasked with building out their People Analytics function and capabilities for the first time. It usually goes something like this: People Analytics Leader as Data Engineer The new People Analytics leader begins by taking inventory of available data, identifying extraction points, convincing stakeholders of the need for access, and understanding the company's unique measures and metrics. These steps are crucial because People Analytics requires more than just raw data; it requires architected analytical models to perform meaningful analysis. Unfortunately, like many companies before the “analytics revolution,” the organization hadn’t prioritized their data and is now unprepared, lacking readily available information for People Analytics. The leader quickly realizes the necessity of being scrappy and working with what can be begged, borrowed, and improvised. This is nothing new for People Analytics leaders, as they have shown they can produce significant value with very little access to data. But soon there will be questions about data acquisition and quality. People Analytics Leader as Technologist Our People Analytics leader soon hits a wall with the available data and realizes that the issue isn’t with the data itself, but with system configurations and report generation. Despite the team's investment in advanced HR systems like Workday, SuccessFactors, and Greenhouse, obtaining reliable, actionable insights continues to be a struggle. This drives the leader to delve into HR data analyst roles and responsibilities, such as troubleshooting system issues, reconfiguring setups, and working closely with IT, diverting even more focus from the primary role. This is challenging enough for a People Analytics leader but, surprisingly, HR technologists and IT teams can also be unprepared for these issues. They’re used to focusing on implementing scalable HR systems and enhancing the workforce experience, not on ensuring data is ready for advanced analytics. Once the technology goes live, their role typically ends. This leaves gaps in addressing downstream data challenges that end up on our leader’s plate. To be fair, People Analytics is relatively new to many technologists. But more recently, the unfortunate reality of significant, multiple reworkings of technology has helped this role move into partnership with People Analytics leaders early on. It’s becoming more common for People Analytics teams to be involved in HRIS or new HR technology implementations. So our People Analytics leader eventually realizes their technologist role is not over. It turns out these modern HR technologies are incredibly configurable and rarely – if ever – set up only one way (at the enterprise level). Enterprise-grade HR tools are built to customize to the unique and varied needs of large companies. This configurability leads to massive variation in how a technology system can be implemented and most HR tech teams don’t get the final say in configuration. Upstream partners dictate what the technology needs to accomplish in order to align with the business process. Since our downstream People Analytics leader is still having data challenges, it’s necessary once again to reach upstream, this time to HR Operations. People Analytics Leader as HR Operations Leader Now this leader encounters a fundamental rule of tech implementations: Without standardized processes, documented operational methods, and established guardrails for repeatable processes, this comprehensive undertaking doesn’t stand a chance. The data flowing from random operations will be of poor quality, and even with analysis, it won’t be able to connect to operational needs and goals. Take, for example, an Applicant Tracking System (ATS) that relies heavily on standardized processes. If a recruiter, anxious to close a candidate, works around standard process flows, interaction paths, or outreach cadences, the ATS can’t accurately reflect activities or produce clean data for People Analytics. Even the best recruiting tools require subject matter expert process maps, such as “which stage comes first” or “how to handle evergreen requisitions.” Solutions that promise to revolutionize and streamline HR or automate and simplify HR functions don’t address the fact that they still have to do requirements gathering and process standardization. This critical link between operations and technology implementations is often overlooked but is essential for success. New tools can’t fix operational flaws; they cannot replace the need for strong operational documentation, change management, and implementation support. Armed with this knowledge, the leader now steps into the role of operations leader to address these challenges they never expected as part of the People Analytics job description. Extensive collaboration with HR operations teams to standardize processes, understand business logic, and create checklists for consistent data entry begins. These efforts lead to configuration and data architecture work for the People Analytics team downstream, but it’s worth it to get clean and usable data for People Analytics. Despite these improvements, a new issue surfaces: the lack of a clear workforce strategy. The organization can't standardize its way out of a problem or build a path, program, or process if they don’t know where they’re going. They are at a crossroads. Without a strategic framework to guide these processes, the improvements made in operations are likely to be short-lived and disconnected from broader business objectives. People Analytics Leader as Strategist By this stage, the journey of our People Analytics leader has revealed that without a workforce strategy, data standardization alone is insufficient. A documented strategy is needed to provide a structured framework for how HR resources its programs, processes, and technology to achieve business goals. Strategy is a guiding light for People Analytics, enabling the leader and team to assess the effectiveness of their work across HR. The most mature People Analytics teams influence, support, and direct workforce strategy. While the CHRO maintains ownership of setting the strategy, our leader collaborates closely to orchestrate business needs, assess current HR capabilities, and prioritize requests across the function. Leaders skilled in strategic execution and project management are essential for HR success and bring significant value to their people analytics career. This alignment allows them to automate, scale, and accelerate operations through excellent technology implementations and finally, with the right operations and technology in place, they finally gain access to clean data that is crucially tied to the business strategy. With this clean and aligned data in hand, our leader can return to the core aspects of their people analytics job description. This journey has revealed more than the need for clean data. It has surfaced the people data supply chain. We shall not cease from exploration. And the end of all our exploring Will be to arrive where we started And know the place for the first time. - T.S. Eliot A Guide for Charting the Path Ahead The journey of a People Analytics leader is a winding one, passing through multiple functions in the quest for reliable, strategic data. By collaborating with roles that span data engineering, technology, operations, and strategy, these leaders are not just data analysts; they are strategic partners transforming the HR landscape. In fact, we’ve identified an emerging new role in this function that is transforming HR. To learn more about optimizing the people data supply chain and recognizing the critical role of Workforce Systems Leaders, download Richard Rosenow’s From Data to Strategy: The New Role of Workforce Systems Leaders in Transforming HR. Download Whitepaper Now
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3 min read
Chelsea Schott
The holiday season is here! And with it comes family, food, and perhaps a little friendly debate. This year, let's set aside the usual topics that lead to eye rolls and heated discussions with Uncle Bob. Here are 3 people analytics topics to elevate the dinner table banter and avoid an argument over the turkey. 1. Overworking in a post-pandemic world Did you know that employees are clocking in three extra hours each day compared to pre-pandemic times? It's a surprising revelation that demands our attention, especially when coupled with the fact that workplace burnout is affecting a significant 41% of workers. The extended work hours may be a consequence of the blurred lines between professional and personal life in the remote work era. As you gather around the holiday table, consider discussing how businesses can strike a balance between productivity and well-being, fostering a work environment that prioritizes both. 2. Lacking engagement leads to less profits Here's a captivating statistic: highly engaged teams exhibit a remarkable 21% greater profitability. Yet, only 20% of employees report being engaged at work. So how can organizations cultivate a culture that not only satisfies employees but truly engages them? Discussing the impact of workplace culture on employee engagement can open up avenues for exploring innovative approaches to create environments where employees feel valued and motivated. 3. Staying remote has increased productivity With over 50% of employers citing increased productivity as the primary benefit of remote work, the landscape of work is undergoing a significant transformation. As you dive into that pumpkin pie, consider exploring the potential of remote work in the long run. How can organizations harness this newfound productivity without compromising collaboration and team dynamics? Delve into the dynamics of remote work and share insights on striking the right balance for sustained success. Incorporating these people analytics topics into your holiday conversations can provide a fresh perspective on the evolving nature of work. As we reflect on the challenges and opportunities of the past year, let's toast to a future where work not only sustains our livelihoods but also enriches our lives. Cheers to a Thanksgiving feast filled with engaging conversations, and happy Thanksgiving from One Model! Did Uncle Bob ask, "What in the world is people analytics"? Download this eBook for an enjoyable read that covers everything from understanding the basic foundation of people analytics to advanced HR strategy. Guaranteed to start a productive family conversation on how data can drive meaningful change across any organization.
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14 min read
Phil Schrader
As a people analytics leader, you’re going to be confronted with some not-so-simple, horribly open-ended questions: “Hey, so what do you want to measure?” Where should we start?” or… “What HR dashboards should we build?” Perhaps these words have been uttered by a well-intentioned business analyst from IT, peering at you from behind a laptop, eager to get your items added into an upcoming sprint before all their resources get tied up with something else. What do you say? Something really gnarly and fancy that shows your analytic savvy? Something that focuses on a key issue confronting the organization? Something basic? Fear not. In this blog post, we’ll walk you through eight essential people analytics dashboards. You should be able to get the HR data for all of these from your core HRIS or HCM solution, even if they’re in different modules and you have to combine it into one dataset. The key performance indicators (KPIs) in these views will give you the highest impact: Headcount Metrics Dashboard Span of Control Dashboard Employee Turnover Dashboard or Attrition Dashboard Talent Flow Dashboard Career Growth / Promotions Dashboard Diversity (DE&I) Dashboard Employee Tenure Dashboard …see below… 1: Headcount Metrics Dashboard Headcount metrics are the foundation of people analytics. Headcount speaks volumes. Trend it over time, break it out by key groupings, and you are well on your way to doing great people analytics. Here’s an initial view that captures the basics. Here’s what’s included in this dashboard so you can get a handle on headcount. In the upper right, you’ve got what I call the “walking around the number”. It’s not anything that will help you make an informed decision on anything. But this is the stat that you would feel embarrassed if someone asked you and you didn’t know off the top of your head. Here it’s the total number of employees as of the current point in time. (EOP is shorthand for End of Period. Be precise in how you define things. More on this at the end.) Next, you’ll want to see the headcount trended over time. Here we have a monthly trend paired with the same period last year. Boom. Now you can see how things are changing and how they compare with the previous year. Also, these two visuals are a great test run for your existing reporting and analytics capabilities. In the bottom right, here you have headcount broken out by org unit (or business unit, or supervisory org for you Workday types). Here you want not only the total counts but ideally a stacked column view so you can see the proportion of contractors, part-time, co-op, or other employment types. Different orgs might get their work done in different ways. You should know the differences. Finally, a map view of headcount by geography. It’s not a basic visual, but it has certainly become essential. Things happen in the world. You need to know where your workforce is so you can quickly estimate the impact and plan support. In just the past two years, employees have been impacted by wildfires, heat domes, political unrest, blizzards, cold snaps, flooding, and, of course, COVID. Geo maps have officially gone from fancy visual to essential view. 2: Span of Control Dashboard I’m going to change things up a bit by elevating the span of control to the second slot on this list. Don’t worry. We dive into attrition and representation later in the article. As a people leader, you’ve got to maintain some perspective on how efficiently your workforce gets work done. There are many ways to do this. You could calculate the total cost of your workforce. You could align those costs against revenue over time. By all means, do that. But this list is also there to help you get started. With just the data from your core HCM / HRIS system, your team should be able to show you the span of control and organizational layers. These metrics always remind me of stepping on a scale. If your span of control is ticking down, you’re getting less lean. If you’re adding more layers, your internal coordination costs are going up. There could be good reasons for this– but there sure as heck can be bad reasons for this. Here you’ll find your key Span of Control Metrics, your trend over time, and your layers and org units visualized. The real killer metric – if you’ve got the stomach for it – is a simple list of the number of managers in your organization that have only one or two direct reports. Use these views to keep your talent management processes grounded in business reality. If your existing team/technology can’t produce these views then shift them back. 3: Employee Turnover Dashboard or Attrition Dashboard Ok, we can’t go any further without employee turnover. Attrition if you’re feeling fancy. Turnover is the strongest signal you get from your workforce. Someone worked here and– for one reason or another– it didn’t work out. Changing jobs and firing an employee are both major events. Your workforce is telling you something and you need to listen to help you with employee retention. Here’s a basic view to get you started. Again, get your rolling 12-month termination rate up at the top and trend it out with the previous year for context. Below that, you see a breakout of voluntary and involuntary termination rates. Then, you can see breakouts by business unit, location, and org tenure groupings. Now with a glance, you can see how turnover rates are changing, where they are high, and whether it’s you or the employee forcing the change. Learn more how to calculate the cost of turnover. 4: Talent Flow Dashboard Once you’ve got a turnover view squared away, you can move into broader views of talent movement within your organization. Here’s a high-level talent flow view to get started. It leads off with a consolidated view of hires, terms, and net hires trend over time. I love this view because it lends itself to discussions of churn and the cost of turnover. The top area (green) shows external hires. The bottom (red) shows exits/terminations. The dark bars show the difference: net hires. The big question. How much of that time and money that you put into recruiting is just to replace the people who leave the company? A great variation on this view is to limit it to women or underrepresented groups. Are you working hard to attract these demographics, only to have them leave because they don’t find the organization to be a fit for them? We’ll get to more workforce representation views below. Next to the Net Hire Trend, you can mix in a growth metric and a helpful breakout by “business unit, so you can keep an eye on what segments of the organization are growing/shrinking. Are they the ones you expect? Later when you bring in data from other systems like learning, this view can be a place to collaborate with the learning team to answer questions like: Are you adding more employees, when you could be upskilling? Finally, get a solid crosstab view of promotions or movements. This will help you optimize talent development and answer questions like: Do people move from function to function? If so, what are the common paths? What paths don’t exist? Should they? 5: Career Growth / Promotions Dashboard After you get the big picture on movements, dig into promotions. In my mind, the movement and span of control views are about what the organization is experiencing. Promotions put you more in the mind of your employees and what career opportunities look like in your organization. I’ve added two of our key metrics to the top of this one. What’s the rate at which people get promoted and how long is the typical wait for promotion? Once you know the typical (average or median is fine) wait time, keep your ears out for high potential / high performers who have run past that mark. They’re probably keeping a rough estimate of that metric in their minds as well. Below that are two breakout views. The first one - “Manager Hires vs. Promotions to Manager” - is meant to look at a key milestone in career growth. I’ve used promotion to manager, but you might have unique ones. Then for each business unit, I’ve compared the number of promotions into that key group with the number of outside hires in that group. Are you growing your own leaders (or another key group)? If not, why? Filling out the bottom row is the “Termination Rate and Headcount by Time since Last Promotion” view. Look for two things here: 1) Do people leave if they don’t get promoted? 2) Do people leave right after they get promoted? 6: Diversity (DE&I) Dashboard It’s past time we brought in views of the diversity, equity and inclusion (DE&I) in your workforce. Many of the views in the dashboard below are split out versions of the metrics introduced above. Above is a sample diversity dashboard using male / female breakouts. Use this as a template for other representation breakouts including ethnicity, gender identity, age, etc. Any of these views could be modified to incorporate multiple, rather than just two, groupings. The top bar shows activity differentials over time. Hires are done simply as counts. Do you hire more men than women? Are promotions and terminations handed as rates to monitor for disproportionate outcomes?, i.e. are men promoted more often than women? The second row shows representation by key grouping in stacked horizontal bars. I like organizational layer and salary band to show if high career outcomes are disproportionate. I’d recommend the inclusion of tenure as well, however. If your organization had a history of disproportionate staffing, you will get a clue in this view. That could explain why today’s initiatives have not yet balanced out outcomes in level or pay. Or differences in tenure might be explained by differences in termination rates, depicted directly above in this view. This is a multifaceted issue. 7: Employee Tenure Dashboard Confession. I love tenure. I’ve come of age in my career amid data telling me that I’ll work for something like 11 companies before I retire. And, to be honest, I’ve done my share of career hopping. But it turns out that when you stick around somewhere, you learn things. You make connections with your co-workers. Employee tenure represents the accumulation of invaluable knowledge and connections that help you measure the value of your human capital. Next to average tenure, this dashboard shows the total accumulated workforce tenure in years. While not exactly a “walking around number,” you can use this to impress your fellow leaders into thinking about your workforce like the treasured asset it is. “Hey, our team has x millennia of accumulated experience!” Rounding out this view is a sorted view of positions or job titles with lots of accumulated experience as well as a stacked trend over time to see how tenure groupings are changing. 8: Dashboard Definitions and Details This final section is not a specific dashboard suggestion. Rather, it’s intended as a sobering reminder that none of the dashboards above will make an impact in your organization if you can’t explain your logic and build trust in your data. I like to build little glossary style views right into the dashboards I create. For example, at the bottom of our standard attrition storyboards, I’ve added breakouts showing which termination reason codes are included as voluntary and which are involuntary. Next to my glossary, I’ve created a table that breaks out the subcomponents of turnover rate, such as total headcount and days in period. I like to include at least one leap year for a bit of showmanship. “Look, I’ve even accounted for the fact that 2020 had 366 days, so back off.” Ready To Learn More? Get All Your Questions Answered One-on-one. Finally, if your security models and technology support it, drill to detail. This is the number one, all-time champion feature of people analytics. Click on headcount, terminations, whatever and see the actual people included in the data. Bonus points for adding the definition and “bread crumb trail” for metrics that build off of other metrics. Below is a view of how we do that in One Model. If you’d like to see these people analytics dashboards in action or learn more about people analytics software for your organization, reach out to us!
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7 min read
The One Model Team
As you know, People Analytics has evolved far beyond basic HR metrics like turnover rates or headcount tracking. As organizations seek to make smarter, proactive decisions about their workforce, they’re turning to more sophisticated People Analytics techniques. Moving beyond foundational metrics, advanced analytics — like predictive modeling, sentiment analysis, employee journey mapping, and ethical AI considerations — offer HR professionals the opportunity to play a powerful, proactive role in shaping their organization’s future. With these tools, HR leaders can anticipate challenges, influence key decisions, and drive meaningful, strategic change. This blog explores four advanced analytics techniques that help People Analytics leaders move from basic reporting to making decisions that resonate throughout the organization. 1. Predictive Analytics: Looking Ahead with Confidence What It Is: Predictive analytics leverages historical data to forecast future workforce trends, such as turnover risks, performance outcomes, or employee engagement levels. By identifying patterns within existing data, organizations can make proactive decisions, positioning them to address issues before they arise. Applications: Predictive analytics is highly valuable for HR teams aiming to prevent turnover in high-risk teams, pinpoint factors that impact employee engagement, or understand potential productivity trends. For instance, if a team shows signs of elevated turnover risk, leaders can intervene early — offering targeted support or resources to improve retention. Example: Consider a company that uses predictive analytics to identify teams with high burnout risk based on previous data trends, like prolonged overtime hours or low engagement scores. With this foresight, HR can intervene with support initiatives, helping employees recharge and boosting retention. Learn more about our One AI and One AI Assistant predictive analytics. 2. Sentiment Analysis: Understanding Employee Emotion at Scale What It Is: Sentiment analysis uses natural language processing (NLP) to interpret the emotional tone behind employee feedback, open-ended survey responses, and internal communication channels. By analyzing this data, companies gain a real-time understanding of employee morale and can detect early signs of dissatisfaction. Applications: Sentiment analysis can track morale trends across the organization, identify engagement dips, and help HR better understand employee needs. This technique allows for “pulse” insights, where sentiment can be monitored continuously, alerting leaders to shifts in morale. Example: A company might use sentiment analysis to monitor feedback on a recent policy change. If negative sentiment spikes, leadership can quickly address concerns, maintaining trust and morale by responding with empathy and transparency. 3 Keys to Effective Listening at Scale 3. Employee Journey Mapping: Visualizing the Employee Experience What It Is: Employee journey mapping visualizes each stage of an employee’s experience, from recruitment to exit, identifying critical touchpoints that affect engagement, satisfaction, and retention. By mapping these interactions, HR can see where employees thrive or struggle, allowing for targeted interventions. Applications: Journey mapping is valuable for tracking specific experiences such as onboarding effectiveness, career development paths, and retention at pivotal moments. It provides insights into the employee lifecycle, helping HR design initiatives that enhance satisfaction and reduce turnover. Example: Using Sankey diagrams, a company could visualize the journey from onboarding through various career milestones — revealing, for instance, that many employees exit after two years in certain roles. This insight enables HR implement targeted engagement or development programs during critical points in an employee’s journey. 4. Ethical Considerations in Advanced Workforce Analytics Why It Matters: As People Analytics methods become more advanced, ethical considerations grow in importance, especially around data privacy and employee consent. Ensuring responsible data use is essential for maintaining employee trust and aligning with broader organizational values. Best Practices: To conduct People Analytics ethically, companies should anonymize data wherever possible, obtain clear employee consent, and maintain transparency about data collection and usage. A commitment to ethical guidelines isn’t just about compliance — it strengthens trust and encourages openness to analytics-driven initiatives. Example: Organizations risk overstepping by monitoring too closely, which can lead to feelings of surveillance among employees. Ethical People Analytics is about balance: using data to benefit the organization while respecting employees’ privacy and autonomy. Conclusion: Moving from Insight to Impact The field of People Analytics has grown into a powerful strategic tool, and advanced HR analytics techniques like these (and others) enable HR leaders to anticipate, understand, and enhance the employee experience in proactive, strategic ways. Ready to take your People Analytics impact to the next level? Measuring the Value of People Analytics dives even further into implementing these advanced analytics strategies and gaining a sustainable advantage. Complete the form below to download it today and empower your People Analytics team with the tools needed for meaningful, data-driven change.
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5 min read
The One Model Team
The Cost-Time-Quality triangle can be a helpful tool when comparing various technology options. This framework is commonly called the “Project Triangle,” but we have modified it here to break out Flexibility as a critical fourth element — creating a diamond shape. The rationale for extending the triangle to include Flexibility is that analytics in all domains is full of unknowns. A rigid design could never anticipate all the future use cases and content demands of an ever-changing world. Doing a meaningful comparison of solutions requires an understanding of the relative flexibility of the various options being considered. And so our suggested framework for comparing people analytics technology options is the Project Diamond. Comparing the three options Below you’ll find a generalised comparison of each option using the Project Diamond framework. These findings are based on our direct experiences and discussions with customers and other people analytics experts, so consider it illustrative. Cost To build a solution from scratch, you’ll still need to buy a bunch of technology (eg, BI tools, data warehouses, and hardware). And there are often many hidden costs associated with that approach. In a buy option, the cost is typically the technology license/service fee, which has a two or three year commitment, as well as any initial implementation services. In a build option, the cost represents the IT and PA resources that are needed to create the data model and metrics definitions, the warehouse and visualisation tool costs, and any required maintenance and change order costs. Opting for a platform that offers the best of both buy and build generally has the same initial entry cost as the buy option, but is less expensive overall since access to the skilled data engineers and an experienced customer success team augments your resources, supports additional requests, and reduces rework throughout your people analytics journey. Depending on the vendor you select, that type of support will cost you extra red tape, extra dollars, and potentially even extra resources towards manual work. Time Internal build projects nearly always run slower than planned, and they often fail altogether. There are countless stories and statistics on failed business intelligence projects. Buy options leverage pre-built assets to deliver a “turnkey” people analytics experience that can get you up and running relatively quickly. But for some vendors, implementation can be a long and drawn-out process. Instead, you could opt for the best of both buy and build. So you get a fast implementation experience with a proven and pre-built starting point, and you also get the ability to enhance and build upon that starting point over time — either on your own or alongside the vendor’s knowledgeable customer success team and skilled data engineers. Quality This element has more potential for overlap. There is wide variation in what may be built internally since internal IT teams have considerably less experience working with data from HR systems in an analytics context. While high quality builds can exist, they require superb internal IT resources and incredible amounts of time and money. A common downfall here is that the initial implementation team declares success and rolls off to another project, leaving a knowledge and capability gap. Buy could be better quality than a build since you get a pre-built starting point, but that depends on the vendor. Choosing a PA platform that can deliver the best of both buy and build will ensure the highest quality solution. This option allows teams to get the full value out of all their HR data — by centralising it into a single source of truth, transforming it into an integrated dataset purpose-built for people analytics, and configuring the platform and analyses to their organisation’s exact requirements. And if the vendor has a highly-skilled team of data engineers available to support, PA teams gain a partnership with talented individuals who can ensure the quality of the data assets they create. Flexibility The most significant gap is in flexibility. Internal build solutions usually involve multiple teams, and the data and analytics needs of HR must compete for resources with the business’ core product and customer data needs. Meaning the HR function often needs to wait in line for basic changes to their data warehouse and visualisations, and their simple request could be very challenging to execute. In a buy scenario, there is ongoing innovation from the provider as they need to remain relevant and competitive in the marketplace. But that vendor may not make the enhancements your team needs or allow for configuration within the tool they’re selling. If you’re looking for the best of both worlds, you’ll want to purchase a flexible PA platform that allows teams to either build their own data assets within the purchased solution, or partner with the vendor to support that build. The ideal vendor will focus on transparency, flexibility, and customisation — enabling people analytics teams to access the backend of the platform to configure their instance to fit their exact needs. Ready to learn more? As you use Project Diamond to assess your people analytics technology options, you may want to download our whitepaper The Evolution of the Buy vs. Build Conversation in People Analytics, which can help you use Project Diamond to determine if buying an out-of-the-box solution, building an in-house solution, or choosing a path that delivers the best of both worlds is right for you.
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10 min read
Chelsea Schott
As Halloween approaches, it's time to gather 'round the virtual campfire and hear some spine-tingling tales of people analytics gone awry. Here are six not-so-fictional stories sure to scare even the bravest of HR teams. But never fear ... One Model can help you avoid these spooky situations. 1. The Deceptive AI As the clock struck midnight on the first night of budget season, Melinda, a weary HR director at a tech startup, huddled over her laptop. Shadows deepened under her eyes as she scanned the AI-generated analytics dashboard, watching as trusted employees were flagged as “at risk” or “low achievers.” With time running out, she pushed forward, blaming the strange anomalies on minor glitches. But as the night wore on, her nerves frayed. It felt as though the AI had taken on a sinister life of its own, turning top performers into liabilities and boosting unremarkable staff. Every adjustment she made only deepened the web of confusion, as the data twisted into more surreal distortions. Relentlessly, the algorithm spun its tale, indifferent to the human impact of its errors. By dawn, her team arrived, horrified to see that the AI’s deceptions had led them down a dangerous path. Layoff plans were made based on false conclusions, with a list targeting their best employees. In the aftermath, Melinda realized the cost of trusting an unchecked AI and the need for a human touch—an eerie reminder that digital solutions aren’t always what they seem. The One Model Solution: With One Model's reliable One AI Assistant, Melinda's team could have seen clearly through the data fog. Built specifically to circumvent and control for hallucinations that other AI tools suffer from, One Model dispels ghostly errors, ensuring critical decisions stay firmly rooted in truth. 2. The Phantom Data Breach Nightmare Tom, an HR director at a major retail chain, awoke to a chilling alert: thousands of employee records had been compromised. Private details—salaries, addresses, even social security numbers—were scattered in the digital winds, accessible to unknown eyes. As word spread, a sense of dread crept through the office. Employees whispered, wondering which of their secrets might surface next. Tom and his team scrambled to contain the breach, yet every solution seemed futile. Like a ghost haunting their systems, the source of the leak remained elusive, lurking just beyond their reach. As panic intensified, trust eroded, and Tom knew that restoring confidence would be no easy feat. Days blurred into nights as Tom sought answers, each failed attempt weighing heavily on him. The breach had shaken their foundation, revealing the frightening truth: Any system that is improperly secured risks being haunted by unseen vulnerabilities, putting both data and trust in jeopardy. The One Model Solution: With the most recent ISO certification, One Model offers a secure platform, prioritizing IT security and limiting security risks to safeguard your data. Your sensitive information is locked away, protected from malicious entities. 3. The Mysterious Analytics Abyss It was a tense meeting with finance leadership, and John’s people analytics team was on the spot. When asked a straightforward question about headcount, John confidently turned to their analytics platform. But as he searched, an unsettling realization struck—key insights lay hidden beneath a murky interface, unreachable and obscured in a vast sea of metrics. Frustration mounted as John sifted through shallow reports, aware that the answers were somewhere in the depths of the system, but the platform seemed intent on keeping them out of reach. Then the frightful question came from the CHRO, "Can we break these numbers down by cohorts or the recent satisfaction survey to see if there are any correlations?" No! John began to sweat. By the end of the meeting, John was left with his head hanging and nothing but vague numbers and a growing sense of unease. The analytics abyss had swallowed his insights whole, and he feared they’d invested in a tool that could never deliver the in-depth insights they needed. The One Model Solution: One Model's platform is transparent and flexible, empowering you to get urgent answers HR metrics answers fast for actionable insights to solve your business problems. It can also empower HR teams and Finance teams to have two different, yet both accurate, views of headcount. 4. The Haunted (In-)House Build At her tech startup that had grown into a robust company, Lydia had pinned her hopes on a custom-built analytics system that would satisfy her every desire. The project began with fervour, but even after two grueling years, her dream of a seamless solution had turned into a nightmare. Reports froze mid-load, she could not get important data out of her systems and into her tool, back-dated updates made recent insight vanish, and each new fix only seemed to unearth another glitch lurking in the code. As the cycle of troubleshooting dragged on, Lydia felt trapped, her team ensnared in a haunted system that consumed resources without mercy. The "tailored" solution she’d envisioned had become a never-ending horror show, draining time and morale. Each day she feared the day IT would say, "No More!" With each passing day, Lydia faced a harsh truth: the in-house build that was supposed to empower her team had instead bound them to endless repairs, a ghostly presence sapping their productivity from the shadows. The One Model Solution: One Model offers the only people analytics platform that truly combines the benefits of both buying and building, delivering a best-of-both worlds scenario. Teams get the flexibility of leveraging pre-built elements as is, or they can opt to build within the platform and configure it to fit their organization’s unique needs, on their own, or with the help of our highly-skilled team of data engineers and customer success professionals. With this choose-your-own-adventure approach, enterprises can enjoy both the simplicity of buying and the optional customisation of building. Learn more in this whitepaper. 5. The Regulatory Non-Compliance Graveyard Ben, the CHRO of a healthcare startup, trusted his new AI tool to ensure compliance. But during a surprise audit, a skeleton of missed requirements surfaced, revealing that non-compliance could cost the company up to €20M—or worse, a 4% hit to annual revenue. The AI, bought in haste to stay competitive, had failed to meet regulatory standards, leaving them exposed. The findings felt like a graveyard of compliance oversights, each one a potential pitfall that threatened the company’s reputation and future. Ben’s stomach churned as he realized the scale of the error—his trust in the AI had led them down a costly, dangerous path. Facing penalties and a potential PR crisis, Ben knew they couldn’t afford another misstep. The allure of an “untested” AI had come with a hefty price, haunting both the company and its once-trusted compliance team. The One Model Solution: One Model's robust HR data governance protocols keep you up to date on AI regulations and ensure your platform remains compliant through the approaching wave of regulatory scrutiny. No need to fear the consequences of non-compliance when you have One Model as your trusted people analytics partner. 6. The Legend of the Unguarded Data Vault Mia, the People Analytics director at a biotech firm, was horrified to discover employees were building reports and using generative AI that was giving them incorrect data. She learned that Bill had accidently gained access to salary data in their analytics system. Linda had produced a report that said there was 124K headcount and Susan a report that said there was 54K. As word spread, trust in Mia’s people analytics tool unraveled, and employees grew anxious, fearing that that no place was a safe harbor knowledge. The teams reverted back to changes based solely on "guy instinct". Dashboards with sensitive information were deleted. Mia felt the weight and mourned that she ever believed the fantastic pitch made by the people analytics software company, knowing that the lapse could haunt her team for months, if not years. Determined to repair the damage, Mia began implementing stronger safeguards. But the incident left a lasting reminder that without mechanisms to assign customized security clearance, even the most secure vaults can hold hidden backdoors, ready to unleash chaos at the worst possible moment. The One Model Solution: One Model's role-based security guarantees that only authorized personnel access sensitive data, maintaining the confidentiality and integrity of your information. One Model does what is promised and is the most trusted people analytics software. Illuminating the Path Forward No matter the pitfalls your people analytics team may face, rest assured that One Model's people analytics platform is your guiding light in the darkness. We offer a secure, transparent, flexible, and customizable platform, backed by dedicated support to navigate the most challenging HR scenarios. Don't let these spine-chilling tales haunt your dreams. Reach out for a demo and discover how we can lead you to data-driven HR success. This Halloween season, ensure your HR analytics journey is a treat, not a terrifying trick!
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5 min read
The One Model Team
In his talk, "Under the Hood: AI-Driven Engineering Workflows for Future of Work," Chris Butler, CEO of One Model, addressed what's coming and how it will impact everything. The key takeaway? AI is about to change the game for productivity by enabling what Chris calls "agentic workflows." Here’s a peek at what that means and why it’s a big deal for your workplace. The AI Ecosystem is Opening Up The enterprise AI ecosystem is evolving quickly. Imagine an AI at work that doesn’t just answer questions but can also take action—accessing tools, managing processes, and optimizing workflows. According to Chris, the likes of Microsoft’s Copilot, Apple’s assistant, and other major players like Salesforce are poised to become the AI linchpins of the workplace. Soon, AI won’t just assist; it will be seamlessly integrated into every facet of your digital workspace. Enter Agentic Workflows One Model is currently building agentic workflows to turbocharge workplace efficiency. Think of a group of specialized AI agents, each with its own job description, working collaboratively—just like a project team. From gathering data, analyzing it, and critiquing results to creating dashboards, these agents mimic the roles of a traditional data team. The result? Faster, smarter outputs that scale without needing more people. Chris gave real-world examples of ai agents in action: An AI project manager, data engineer, and analyst worked together to gather compensation data, clean it, and create insightful reports—tasks that normally take days were completed in hours. The AI agents interact with each other in natural language, refine each other’s work, and iterate until the job is done right. From Four Agents to a Swarm What started as four distinct agents evolved into a swarm—a scalable network of specialized agents able to handle increasingly complex projects. By shifting to a directed graph model, One Model made it possible for multiple agents to work in parallel, dramatically reducing project time. Chris shared an impressive example: A task that two senior data engineers estimated would take twenty days was completed by AI in just 45 minutes. Another key takeaway is that the more specialized the agents work, the higher quality the output. Therefore, having more specialized agents is better than a few multi-purpose ones. What Does This Mean for Productivity? The implications are huge. AI-driven workflows mean fewer manual tasks, faster data processing, and a deeper focus on insights that matter. Companies can double down on their core missions while relying on AI to handle tedious, data-intensive work. Chris predicts that enterprise AI will become the interface we use to ask questions and get work done—a one-stop assistant that pulls insights from different tools and presents them in a digestible way. Dashboards Are Dead—Almost In the future dashboards as we know them may become secondary. Instead of static reports, enterprise AI will generate dynamic, on-demand insights and even make recommendations. Dashboards will still exist, but they’ll be an interface controlled by the AI—just one of many tools in the box. The first point of interaction will be the AI itself, which will decide what tools to use to provide you with answers. Securing the AI Frontier Chris also highlighted a critical concern: securing enterprise AI. As these AIs gain more access to tools and data, the risk of improper usage grows. HR and People Analytics teams need to partner closely with IT to ensure that the right security measures are in place—because once access is lost, it’s hard to regain control. Welcome agentic workflows to the team. Agentic workflows are reshaping the future of work. The enterprise AI of tomorrow won’t just assist employees; it will be an active participant in getting work done—faster, smarter, and more securely. Are you ready to work with your new AI teammates? Are you thinking about using AI? You'll need a solid data platform. Learn why that is so critical and see how you can achieve success by reading our whitepaper.
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8 min read
The One Model Team
Imagine you’re preparing your team for a project involving cutting-edge AI tools that didn’t even exist five years ago. You’ve assembled the best people you can find, but you quickly realize there’s a significant skills gap. Or perhaps you’re ready to expand into new markets, but the local talent is scarce, and your remote work policies are outdated. You need the right people, with the right skills, at the right time—yet it’s no small feat to make that happen. This is the reality for organizations today. Rapid technological advancements, especially in artificial intelligence, mean that the skills needed to stay competitive are constantly evolving. And while it’s tempting to address these challenges with reactive hiring and quick fixes, that approach only goes so far. What companies need is a sustainable, proactive approach to workforce planning—one that ensures your team isn’t just equipped for today’s challenges, but positioned for future growth as well. Here’s a quick overview of a modern workforce planning methodology for doing exactly that. STRATEGY: Start with the Vision Before diving into the details of talent acquisition strategy, take a step back and ask: What’s our long-term vision, and how should our workforce evolve to support it? As AI transforms job roles across industries, workforce strategy must adapt by aligning every decision about hiring for skills gaps, development, and retention with the company’s future needs. For example, a tech startup embracing AI-driven innovation might prioritize flexibility and tech-savviness in its talent acquisition strategy, looking for individuals who can work alongside AI tools and understand how to leverage them for greater efficiency. OPERATIONS: The Infrastructure that Keeps Things Moving With AI tools entering the workplace, operations play a critical role in ensuring that systems and processes keep pace. Imagine operations as the logistical backbone of workforce planning—it encompasses the workflows that handle headcount requests, onboarding protocols, and ongoing workforce management, but also integrates AI where it can streamline processes and enhance efficiency. A manufacturing company, for instance, might utilize AI-driven scheduling tools to manage production ramp-ups more effectively. Strong operations allow organizations to react to immediate needs—such as ramping up production or hiring for skills gaps—without compromising on strategic goals. ANALYTICS: Gaining Insight into the Workforce AI-driven analytics now enable organizations to gather workforce planning insights with unprecedented speed and precision. Leveraging analytics allows companies to track workforce trends, assess AI’s impact on skill requirements, and even forecast future needs based on anticipated AI developments. For instance, a healthcare organization might use workforce analytics powered by AI to predict staffing needs, identify high-turnover roles, and uncover insights that guide decision-making. By using AI-enhanced analytics, leaders can transition from intuition-based decisions to data-driven strategies that keep the workforce planning process aligned with evolving business needs. PLANNING: Mapping the Path from Today to Tomorrow Planning is where strategy and analytics converge to form a clear, actionable roadmap, especially crucial in an AI-powered world. With AI transforming industries at breakneck speed, organizations need planning that not only fills immediate gaps but also anticipates long-term shifts. Consider a retail company that uses AI to predict customer demand for the holiday season. By using this data to create a workforce planning strategy, they can assess the skills needed, optimize staffing levels, and allocate resources efficiently. A well-defined plan helps organizations stay a step ahead, allowing them to allocate talent where it’s needed most—both today and in an AI-driven future. INTELLIGENCE: Looking Beyond the Company Walls A strong workforce planning methodology also demands a focus on external intelligence. This means staying attuned to shifts in the talent market, industry developments, and the competitive landscape—especially as AI reshapes the types of skills that are in demand. By gathering insights on AI-related trends, organizations can make better-informed decisions about where and when to invest in talent. For instance, a company might discover that its competitors are investing heavily in AI training programs for employees. This intelligence can drive proactive decisions, like launching an internal AI upskilling initiative to stay competitive and attract tech-forward talent. Putting It All Together By taking a holistic approach to workforce planning, companies can move from being reactive to AI-driven change to proactively leveraging AI’s potential. Through the pillars of Strategy, Operations, Analytics, Planning, and Intelligence (what we call the SOAPI framework), leaders can create a workforce that’s equipped to not only meet today’s demands but thrive in the AI age. In a world where technology is reshaping work at every level, those organizations that take a proactive, integrated approach to workforce planning will be best positioned to lead. Whether you’re preparing for an AI-driven project, expanding into new markets, or future-proofing your team, it’s time to move beyond quick fixes and build a workforce that’s truly ready for what comes next. The One Model Difference Effective workforce planning is powered by data and AI, and One Model offers the tools to make it seamless. With One AI and the One AI Assistant integrated into the People Data Cloud™, One Model provides a powerful people analytics platform that consolidates, cleanses, and models workforce data. This AI-enhanced solution equips HR teams with real-time insights, enabling smarter, faster decisions across every stage of workforce planning. Whether forecasting talent needs or optimizing current roles, One Model ensures organizations can proactively build a workforce that’s aligned with AI-driven change while upholding high standards of data security and privacy. Ready to dive into the full SOAPI framework structure and set a foundation for a thriving workforce planning strategy? Download now!
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5 min read
The One Model Team
Workforce planning and forecasting have become paramount for finance leaders to navigate market uncertainties and stay ahead of the competition. One Model's advanced People Analytics platform enables finance leaders to make smarter data-driven decisions, propelling their business toward sustainable growth and increased profitability. Centralise HR and Finance data for accurate predictions. The foundation of effective workforce planning lies in the ability to consolidate data from various sources into a single, reliable location. One Model achieves this by seamlessly integrating HR data with finance data, creating a centralized hub of valuable insights. By breaking down silos and allowing for data collaboration, finance leaders can gain a comprehensive understanding of their workforce, leading to more accurate predictions and tactical strategies. Slice and dice data more efficiently. Traditional ERP systems often struggle to handle the sheer volume and complexity of workforce data, leading to sluggish reporting and analysis. One Model, on the other hand, offers the ability to slice and dice data with ease, providing real-time insights and a granular, employee-level detail. Finance leaders can effortlessly examine the cost and productivity drivers at a departmental or individual level, empowering them to implement strategic initiatives with surgical precision. Identify high performers and which roles deliver the most value. Understanding the contribution of each role within an organization is crucial for effective workforce planning. One Model's advanced analytics capabilities offer improved visibility into productivity, revealing which roles deliver the most value to the organization. By identifying top-performing roles and focusing on their development, companies can reduce costly turnover, unleash the full potential of their workforce, and bolster overall performance. Better prepare for mergers, acquisitions, and divestitures. The financial services sector often witnesses mergers, acquisitions, and divestitures, which can lead to complex organizational changes and talent restructuring. With One Model, finance leaders can confidently embark on these transformations by leveraging the platform's capabilities. One Model can provide quick insight into topics such as your spans and layers that would traditionally involve high-cost and time-consuming consulting projects. From developing clear organizational structures to conducting talent audits to retain key personnel, One Model ensures a smooth transition and alignment of talent with strategic goals. Make more data-informed business decisions. Quick and informed decisions are critical for CFOs. With One Model, you can build your own metrics and definitions for headcount, FTE (full-time equivalents) updated daily, and other performance indicators to assess the return on investment from talent programs. And if Finance and HR can’t agree on how a certain metric (e.g., headcount) is calculated, One Model can support both variations. With clear insight into headcount and FTEs, you can better measure performance and plan labor needs. One Model delivers a holistic view of talent distribution and performance so Finance leaders can optimize headcount for the company’s needs, maintain cost-efficiency, and strike the perfect balance between talent and resources. Facilitate deeper conversations between HR and Finance. HR and Finance teams can have more meaningful and pointed conversations using One Model — where all the workforce data is captured, data quality is managed, and all related dimensions (e.g., hierarchies, employee attributes) are available for analysis. Bringing HR and Finance teams together can help your company accelerate your People Analytics journey and more easily identify opportunities to turn a profit. With One Model you can gain insight into more advanced metrics like Return on Human Investment Ratio (the ratio of operating profit, adding back total compensation expense, returned for every dollar invested in employee compensation and benefits) and hundreds of others to level up your HR and Finance decision making. Two examples of content specifically designed to align HR and Finance teams and empower them to make smarter data-driven decisions are: Headcount Storyboard — Setting up a storyboard which shows headcount represented in multiple ways: FTEs vs. employee counts, variations of which statuses are included/excluded, etc. This information becomes readily comparable with the metric definitions only a click away. Even better, the storyboard can be shared with the finance and HR partners in the discussion to explore on their own after the session. One Model is the best tool for counting headcount over time because it can support multiple variations. Hierarchy Storyboard — Providing views of the headcount as seen using the supervisor and cost hierarchies side-by-side will help to emphasize that both are simultaneously correct (i.e., the grand total is exactly the same). This can also provide an opportunity to investigate some of the situations where the cost and organizational hierarchy are not aligned. In many cases, these situations can be understood. Still, occasionally there are errors from previous reorganizations/transfers which resulted in costing information not being updated for a given employee (or group of employees). One Model is your partner for profitable growth One Model stands out as the ideal People Analytics partner for companies seeking to drive profitability through data-driven decision-making. If you’re ready to learn more, download our eBook 4 Ways CFOs Can Increase Profitability with One Model’s People Analytics Platform to discover even more ways our platform can enhance your profits.
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5 min read
Pria Shah
One AI Assistant is a generative AI tool designed to revolutionize the way you work. Let's take a closer look at how One AI Assistant saves the day for enhancing the daily routines of two key personas in the HR world: an HR Data Analyst and an HR Manager. Meet the Team Mark, the HR Manager Role: Mark blends strong leadership with a keen sense of employee needs to lead HR initiatives that build a positive workplace culture. As an HR Manager, he oversees all aspects of human resources, from recruitment and onboarding to employee development and retention. Mark is known for his empathetic approach and his ability to navigate complex situations with fairness and tact, making him a trusted advisor to both employees and senior management Pain Points: Mark finds it challenging to keep up with administrative tasks while maintaining a strategic focus. He needs real-time insights on workforce analytics, performance metrics, and compensation to make informed decisions. Maria, the HR Data Analyst Role: Maria uses her HR data expertise to uncover insights that help guide key decisions and shape the organization’s direction. As an HR Data Analyst, she’s the bridge between raw data and strategic HR initiatives, expertly translating complex datasets into clear, meaningful reports and dashboards. Maria is known for her keen attention to detail and her ability to align data analysis with the company's goals, making her an invaluable partner to the HR team. Pain Points: Maria frequently gets interrupted throughout the day to handle quick analysis tasks on top of the larger projects she’s involved in. Her workload is heavy, so efficiency is key. Mark and Maria’s Day with One AI Assistant 8:00 AM - Starting His Day Mark starts his day by reviewing his to-do list. He sees an urgent request from senior leadership for a 12 PM meeting to discuss impacts of performance ratings on turnover. He reviews some of his existing Storyboards in One Model and finds a few charts that will be useful. He pins them to a new Storyboard and reaches out to Maria for assistance in completing the analysis. 8:30 AM - Starting Her Day As Maria logs in for the day, she immediately sees three messages from Mark on Slack. One of them is an urgent request: Help prepare a detailed One Model Storyboard on employee turnover rates for a leadership meeting scheduled for 12 PM today. 10:00 AM - Generating Insights in Minutes Using natural language, Maria asks One AI Assistant to display her organization's turnover rate broken out by performance rating and department. One AI Assistant’s intuitive interface ensures Maria can create charts and graphs quickly and easily without training. The visualization is generated immediately and is easy to interpret. With the selections clearly displayed, she’s able to drill through to the employee level data available. Maria confirms that the selections are correct, pins the chart to the Storyboard Mark shared with her, and repeats the process for a number of other breakouts. She also prompts One AI Assistant for areas of the company with the highest turnover and the lowest turnover. She adds these insights to the Storyboard along with a few notes as she preps for the approaching deadline. 11:00 AM - Quick Chat Mark and Maria meet to review the Storyboard they collaborated on. As usual, Mark is impressed with the quality of Maria’s work, especially considering the quick turnaround. He realizes though that they did not include any trend data. Maria reminds him that they can do even better. She asks One AI Assistant for a turnover trend for high performers for one of the key breakouts including a forecast. The chart is generated and includes forecasts years into the future. She pins it to the Storyboard and Mark is ready for the upcoming meeting. 12:00 PM - Strategic Planning Meeting Mark meets with senior management to discuss turnover at their organization. Using the Storyboard he created with Maria, he presents data-driven recommendations on talent acquisition and retention strategies. Senior leadership has more questions about retention in California. Mark isn’t fazed as he opens his One Model instance to leverage One AI Assistant. He's able to find his answer within seconds. He confidently shares results, knowing the insights come solely from his organization's data, ensuring accuracy without hallucinations. 2:00 PM - Crisis Averted As the meeting wraps up, Mark and Maria exchange relieved glances. Thanks to their quick thinking and the support of One AI Assistant, they’ve once again turned a tight deadline into an opportunity to shine. Senior leadership leaves the room with actionable insights, and the company’s HR strategy is stronger than ever. Mark and Maria walk out as the heroes of the day, proving that with the right tools and teamwork, no challenge is too great to overcome. -------------------------------- Request a demo to see for yourself how One AI Assistant can help you work faster, smarter, and empower your entire organization. Experience the future of people analytics today - One AI Assistant is included with all enterprise licenses.
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8 min read
Gina Calvert
Are you as intentional about measuring the value of your data infrastructure and models as you are about building them? In the video below, our Solutions Architect Phil Schrader recently revealed at People Analytics Summit in Toronto the importance of (and some strategies for) using analytics to evaluate the impact of your analytics investments. From leveraging machine learning to track improvements to thinking creatively about integrating predictive models into everyday workflows, you'll gain insights on how to apply analytics to your own analytics. Short on time? We’ve summarized his presentation for you below. The Core Problem: Evaluating Data Investments When we talk about people analytics, we often focus on the tools, processes, and models that drive better decisions. But what happens when we turn that lens inward—when we use analytics to assess the very work of analytics itself? The idea is simple: if we’re investing in building data infrastructure and models, we should be just as intentional about measuring the value of those investments. Anyone leading a people analytics team knows the balancing act. On one side, there’s the pressure to deliver quick insights, the kind that keeps operations running smoothly. On the other side, there’s the longer-term need to build out robust data systems that support advanced analytics. Yet, as essential as these data initiatives are, we often struggle to quantify their value. How do we measure the ROI of building a data lake? How do we ensure that the data we’re collecting today will pay off down the road? Solution: Analytics About Analytics Here’s where we can take a different approach—by applying analytics to our own analytics. The falling cost of technical work in machine learning (ML) has opened up new possibilities, allowing us to embed these tools within our day-to-day operations. Instead of just using ML models for predictions, we can use them as a means to measure how good our data is and how effective our processes are. Essentially, we can start to think analytically about how we do analytics, especially when it comes to creating a predictive model that measures improvements over time. A Concrete Metric: Precision, Recall, and the F1 Score The foundation of this approach lies in the well-known metrics used to evaluate machine learning models: precision, recall, and the F1 score. In brief: Precision asks: When the model makes a prediction, how often is it correct? Recall asks: Out of all the events that should have been predicted, how many did the model actually identify? The F1 score strikes a balance between these two metrics, offering a single number that reflects how well your model performs overall. By tracking this metric, we can gauge the quality of our data and see how incremental improvements—like adding new data sources—translate into better predictive power. This kind of measurement becomes crucial as we think about the future of machine learning and how it integrates into everyday operations. Building Analytics for Growth This method doesn’t just give us a way to measure progress; it gives us a framework to demonstrate that progress in tangible terms. Start with the basics—core HR data like job titles, tenure, and compensation. As you layer in additional data points—learning metrics, performance reviews, engagement scores—you can observe how each new addition boosts your model’s F1 score. It’s a practical way to quantify the value of your data and justify continued investment. The Changing Landscape: Embedding Predictive Models Predictive modeling no longer needs to be a separate, resource-intensive project. As the tools become more accessible, we can embed this capability directly into our workflows. Think of it as using predictive models the way we use pivot tables—regularly, as a quick check to see how well our data is performing. This kind of embedded analytics allows us to experiment, iterate, and find creative ways to leverage machine learning without overcommitting resources. With AI continually reshaping business practices, this shift will allow teams to use predictive models in increasingly versatile ways, driving more efficient decision-making. Beyond Traditional Metrics: Rethinking the Value of Data By adopting this approach, we’re able to ask—and answer—a critical question: How valuable is our data, really? If we can demonstrate that our data is increasingly effective at predicting key outcomes like employee turnover or high performance, we’re no longer just talking about data quality in abstract terms. We’re providing a concrete metric that resonates with stakeholders and gives us a way to collaborate more effectively, whether it’s across HR functions or with external vendors whose data feeds into our models. Looking Ahead: Embracing Innovation as Costs Fall The future of AI and the workplace is advancing quickly, blurring the line between strategic and routine applications. What was once a complex, time-consuming effort will soon be something we do without a second thought. This shift requires a mindset change—being open to ideas that may seem wasteful or unconventional today but could become standard practice tomorrow. The key is to embrace this shift and look for new, innovative ways to use predictive analytics. In summary, by taking an “analytics for analytics” approach, we gain more than just better models—we gain clarity on the value of our data investments. The ability to measure progress in predictive power isn’t just a technical exercise; it’s a strategic advantage that drives smarter decision-making across the board. Not sure where to start? Download Key Questions to Ask When Selecting an AI-Powered HR Tool to get the answers you need. Download Your Buying Guide Now
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3 min read
The One Model Team
One Model was founded around a goal of helping teams tell data-informed stories that lead to brilliant, data-driven talent decisions. By leveraging data and story, we can help teams communicate a deeper understanding of the tangible benefits of diversity, equity, and inclusion initiatives, and how they contribute to the success of a business. Data-informed stories can be a powerful tool for uncovering how the work environment is impacting our employees. Through data, we can demonstrate the positive impact of treating people well, and how this can drive business success. Let’s walk through one of the “classic stories” we hear in HR and people analytics for a fictional organisation called Innovative Enterprise. We’ll start with the story, introduce the data, and then apply One Model’s data-informed storytelling framework to the story to show how our platform easily weaves the narratives together. This is a common story that HR teams are asked to tell around employee experience and the impact that a positive work environment can have on the overall business. Story alone: Within Innovative Enterprise, while we have a diverse workforce, this diversity is yet to permeate our leadership effectively. Our leadership team, although competent and committed, does not fully represent the diverse perspectives present within our broader team. This lack of representation in leadership could potentially influence our culture and engagement levels. Data alone: Internal data at Innovative Enterprise shows that while 49% of our workforce identifies as ethnically diverse, only 15% of our leadership does. Recent industry studies that the people analytics team analysed indicate that organisations with diverse leadership teams outperform those without by 35% in terms of innovation and creativity. Moreover, organisations that boast diverse leadership report a 25% higher employee satisfaction score compared to companies with less diverse leadership teams. Data story: At Innovative Enterprise, the lack of diversity in our leadership team becomes evident. Our internal data reveals that while our workforce is 49% ethnically diverse, only 15% of our leadership reflects this diversity. It's clear we're falling short, and this is a challenge that we share with many organisations across our industry. However, industry data provides a clear directive: organisations with diverse leadership teams are more innovative and creative by 35%. They also report a 25% higher employee satisfaction score, indicating a more engaged and motivated workforce. This compelling combination of our internal situation and broader industry data paints a powerful argument for enhancing diversity, equity, and inclusion at the leadership level. The data provides clear guidance — it's time for us to take action. Ready to learn more This example from Innovative Enterprise demonstrates the power of data-informed storytelling in HR. For more impactful stories and detailed analysis, download our eBook Why Data-Informed Storytelling Is the Future of HR to explore additional examples and learn how One Model can help your organisation tell compelling, data-driven stories.
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7 min read
The One Model Team
If you're preparing for people analytics, there’s a lot to do before you hire that first data scientist. To build the right foundation for success, there are five important steps you should follow that don’t even involve data, insights, or statistics. Following these steps will help you establish and support an efficient and impactful people analytics practice at your organisation. 1. Find your why Understanding why you're pursuing people analytics is vital to your journey. This not only means identifying the specific business needs that would benefit from a better understanding, deeper insights, or more precise analysis of your workforce, but also exploring the underlying reasons behind those needs. You could start by asking questions like: What are the biggest challenges or pain points we're facing as an organisation? What are the key areas where we could improve our workforce, and how would we measure success? What are the most critical business decisions we need to make, and what do I need to know to help us make them more effectively? What are the specific gaps in our knowledge that we need to fill in order to make better decisions? Without taking the time to find the why for your organisation, you risk getting lost or going off course before you even begin. By finding your why early and holding onto it through the process, this will keep you focused throughout your people analytics journey. 2. Look upstream When starting your people analytics journey, it’s important to remember that the data you’ve generated is only as good as your processes and technology. There’s a flow we like to think about from process to tech to data to analytics. When people analytics teams run into challenges, there’s likely an upstream challenge in one of these steps to address. Begin by examining your processes. Technology is only as good as the process it’s automating, so if your processes are poorly designed and documented, your technology is unlikely to be implemented correctly. Technology should reflect how you want your business to run. If it doesn’t, you’ll likely end up with incomplete or incorrect data flowing out of the technology — making it difficult or impossible for people analytics teams to create value.This is not to say “don’t start on people analytics until the rest is done”. People analytics teams can absolutely provide great value, and some of the best teams out there are scrappy with what they have on hand. This is more of an acknowledgement of the flow and a callout that if you want long-term success of your people analytics team and to unlock that next level of value, you’ll have to address these upstream challenges. A strong people analytics leader will also be able to help you identify and navigate these challenges upstream. So begin by ensuring that your processes are well-designed and documented. Next, double check on your technology implementation and ensure that it matches your processes. Finally, check in on the data. The data ultimately doesn’t lie, so it will tell you if the processes and tech are clean. Doing so will ensure that data flows smoothly and accurately from the technology preparing you for analytics. 3. Address data management Another early focus for starting down the path of people analytics is data management. Without data, there’s not much for people analytics teams to do. It’s the oil to the people analytics engine. We’ve seen a number of teams get started, but then plateau around a lack of good data. At times the resources to fix data problems sit outside of HR, which makes it all the more important to navigate and commit that resource request up front when pursuing people analytics. Making sure your data is accessible is critical, but raw data extraction is also only the beginning. A robust workforce-specific data model, proper data architecture blending your different systems data, and HR-led workforce data privacy and workforce data governance are also part of your people analytics foundation. This may require marshalling what are typically scarce internal resources, capabilities, and priorities from IT or data engineering teams to ensure that your data is clean, systematically organised, and readily analysable. Or you can save those internal resources by working with people analytics platforms like One Model. We were founded to make this upstream challenge easier. We provide named data engineering resources, have experience developing business-specific workforce data models, and provide the data foundation that people analytics teams need to thrive. If you skip this step, you may experience the following problems: Missing data: Without the right data management structure in place, you may find it difficult to extract the data you need for a given project. This can lead to incomplete or incorrect data and difficult analysis. Slow data: Improper data management can leave you with only monthly (or quarterly!) snapshots and that pace just doesn’t reflect how fast your business moves — let alone back-dated changes, which are frequently found in HR. Inability to build predictive models: Data management is critical to building predictive models. To develop predictive models, you need to extract data in a very specific way (e.g. time-stamped changes). It’ll be difficult or even impossible to build accurate and effective models without this proper data management. By addressing data management early on in your people analytics journey, you can avoid these symptoms and ensure that your people analytics initiatives are successful. To learn more, here are five tips for getting HR data extraction right. 4. Set the tone Setting the tone at the top is crucial for demonstrating that data-driven decision making is the way forward. This involves garnering support from your organisation's senior leaders, as well as regular reminders, activities, and actions from the CHRO or HR head. If you’re in a leadership position, setting the standard that data is required for new projects and investment decisions goes a long way. Cultivating a data-minded culture will trickle down from the top, setting a precedent for the entire organisation. Without this high-level endorsement and sustained backing, making significant strides in people analytics can prove challenging. 5. Find help Consider engaging with a seasoned people analytics leader either full-time or as a consultant to spearhead your people analytics initiatives and education within your function. Experienced people analytics leaders, with their unique combination of data analysis skills, HR orientation, ethical understanding, and team management expertise, can provide invaluable guidance. They’ll work to ensure alignment between your analytics efforts and broader business objectives. Remember to also tap into the people analytics community. This strong and enthusiastic network can provide invaluable support. Engage with professionals on LinkedIn, ask questions, and use the expertise of vendors in the space. The team here at One Model is always willing to connect and assist at every stage of your people analytics journey. New to people analytics or ready to enhance your existing program? Either way, our eBook People Analytics 101 covers everything you need to know about establishing a strong people analytics foundation for smarter HR strategies and meaningful change across your organisation.
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6 min read
James Morales
We’re celebrating! One Model is an ISO-certified company and has recently completed the latest certification: 27001:2022. What does it mean to be ISO-certified? ISO certification provides voluntary third-party validation that a company's internal systems align with internationally recognized standards for quality and consistency. The International Organization for Standardization (ISO), a non-governmental entity responsible for developing and publishing these standards, ensures that businesses worldwide adhere to best practices. ISO compliance provides a structured approach to identifying, managing, and reducing information security risks. It helps organizations systematically assess threats and vulnerabilities and implement appropriate controls to mitigate them. The Importance and Rigorous Process of Becoming ISO-Certified Achieving ISO 27001:2022 certification is no small feat. This rigorous process involves comprehensive audits, meticulous documentation, and a thorough evaluation of an organization's information security management system (ISMS). It's not just about ticking boxes but ensuring every aspect of data security is up to international standards. This certification demonstrates a commitment to continuous improvement and accountability in managing sensitive information. For People Analytics vendors, being ISO-certified means they are dedicated to protecting your data with the highest level of security. It’s a clear signal that they are serious about maintaining robust data protection practices, giving you peace of mind that your information is in safe hands. Is Your People Analytics Vendor ISO-Certified or Simply ISO “Adjacent”? Don’t be fooled. Your People Analytics vendor may claim to follow ISO 27001 standards or they may even be certified – but with an earlier version (27001:2013). The absence of a current certification may lead you to think that it doesn’t matter, that... - It’s just a cherry on top of your data security, and not all that critical. - Not much has changed in cyber fraud in the 9 years since the previous certification. - If it was really important, they WOULD have it (and maybe even brag about it) The fact is, cyber fraud is ramping up exponentially. Now, simply being certified to the most current standard (27001:2022) may not even be enough. A certification may only cover a single system within their organization. To safeguard security effectively, it's imperative to demand certification that encompasses the entire organizational scope, leaving no room for ambiguity or vulnerability. Who’s Minding the Data? Take it a Step Further with a CISSP While it’s not required for ISO certification, if you’re really taking security seriously, it’s good to know whether or not your People Analytics vendor’s Information Security Officer is a Certified Information Software Security Professional (CISSP), which is the gold standard in cybersecurity certifications. One Model is Now ISO27001:2022-Certified We think passing the rigorous verification process is a big deal and we’re proud to say One Model has recently completed the challenging ISO 27001:2022 certification! And, with One Model, you’ll find that we take your HR data security seriously… As the Information Security Officer at One Model, I’m a certified CISSP. We don’t sell your data. Your data never leaves our company. We have data servers in key regions, like the US, Ireland, Canada, and Australia. Only approved, background-checked, full-time employees have access to your data. Your data never leaves your One Model instance. Explore our infographic: IT security risks in the People Analytics space and how One Model works to limit those security risks. Leading companies like John Deere, Blackrock, Coinbase, Kellogg, and Colgate-Palmolive trust One Model’s cutting-edge analytics to elevate their HR strategies and superior security protocols to keep their data safe. To explore how One Model’s ISO-compliant software can solve your People Analytics challenges and lock down your security concerns, reach out with your questions or request a demo. Connect with One Model Today!
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4 min read
The One Model Team
What should your first people analytics project be? Many teams start with employee attrition because it has clear outcomes, it has a direct impact on the company, and attrition data is already in your HRIS. So to understand the five steps you need to follow during people analytics projects, let’s walk through an example of how Penelope, a fictional people analytics practitioner, might approach an employee attrition project, from start to finish. Step 1: Define the problem The first step in any people analytics project is to define the problem you want to solve. In this case, the problem is employee attrition. Specifically, we want to understand why employees are leaving the company and what we can do to reduce attrition. "As an HRBP, I noticed a trend of high employee turnover in the company. I began to investigate why employees were leaving and how we could reduce this trend. My goal was to identify the underlying causes of this issue and develop a plan to address it," says Penelope. Step 2: Gather the data The next step is to gather the data you need to analyse the problem. In this case, you'll need data on the employees who have left and their reasons for leaving (if available). This data can often be found in your HRIS, as well as employee surveys or exit interviews. "To gather the necessary information, I dove into the company's HRIS system, as well as employee surveys and exit interviews. I collected data on employee demographics, job history, performance metrics, and reasons for leaving. I made sure to gather as much relevant information as possible to ensure a comprehensive analysis," shares Penelope. Step 3: Analyse the data Once you have the data, it's time to analyse it. There are a variety of statistical methods you can use to analyse attrition data, including survival analysis, logistic regression, and decision trees. But you can also start with descriptive methods. Your choice of method will depend on the nature of your data and the questions you want to answer, and you don’t always need advanced methods. "I took a look at attrition trends across each of the major groups within the company. Using descriptive statistics, I found that some teams were experiencing higher attrition than others within similar business units. I wanted to identify why the attrition rate was high, so I looked for factors that were strongly correlated with attrition," notes Penelope. Step 4: Tell the story After analysing the data, it's time to tell the data story. This is where data visualisation and data storytelling come in. You'll want to create charts, graphs, and other visualisations that help you communicate your findings to stakeholders. You'll also want to craft a narrative that ties the data together and explains what it means for the company. "Using the results from the data analysis, I created charts, graphs, and other visualisations that I could use to communicate my findings to stakeholders. I crafted a narrative that brought my business knowledge into the story and explained the factors contributing to the high attrition rate and the steps we could take to address it. I presented the data and narrative to the company's leadership team," explains Penelope. Step 5: Implement solutions Finally, it's time to implement solutions based on your findings. This might involve changes to HR policies, changes to compensation structures, or changes to management practices. Whatever the solution, it should be informed by the data you've gathered and analysed. "Based on the data and narrative, I recommended changes to HR policies, compensation structures, and management practices. I presented the recommendations to the company's leadership team and worked with them to implement the changes. Over time, we saw a decrease in the attrition rate and an increase in employee satisfaction," says Penelope. Overall, attrition is a great starting point for any people analytics team. It's a universal problem that every company faces, and the data is often readily available. By analysing attrition data, you can gain valuable insights into your workforce and make data-driven decisions that improve retention and reduce turnover. New to people analytics or ready to enhance your existing program? Either way, our eBook People Analytics 101 covers everything you need to know about establishing a strong people analytics foundation for smarter HR strategies and meaningful change across your organisation.
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6 min read
Pria Shah
One Model is revolutionizing the landscape of people analytics with the introduction of One AI Assistant. Designed to make your people data more accessible and actionable, One AI Assistant uses generative AI to deliver quick and intuitive insights for anyone in your company. What sets One AI Assistant apart is its commitment to transparent AI. We built One AI Assistant specifically to circumvent and control for hallucinations that other AI tools suffer from. It shows its work, enabling your team to make informed, data-driven decisions quickly with confidence. Get Instant Answers with One AI Assistant One AI Assistant embodies the core mission of One Model – we believe your people are your greatest asset. We want to empower you with the insights you need to truly understand your workforce, leveraging all the information you have to enhance better business outcomes and drive success. One Model excels in data orchestration, extraction, and modeling, supported by a team of top-notch data engineers. By integrating One AI Assistant, we've made it easier than ever to access and utilize this data foundation and get answers about your workforce, instantly. Whether you have a simple or complex request, One AI Assistant allows you to interact with your data effortlessly. Receive precise answers accompanied by auto-generated visualizations, through an interface that is both modern and intuitive. Imagine this: Simple Request: "Show me our current headcount" Complex Request: "Show me hires for the past 12 months broken out by gender in the financial services department with a forecast” With One AI Assistant, adding metrics and modifying selections is straightforward. Our user-friendly interface ensures that you can generate insights with ease, enhancing the overall efficiency of your data analysis processes. Empower everyone on your team with self-service capabilities. Creating data visualizations is now as simple as typing a few words, making data analysis accessible to a broader range of users — from generalists to seasoned analysts. Not ready for a full deployment just yet? No problem. Our tools are fully configurable to align with your security and privacy requirements. You can roll them out at your own pace, deploying to the right people whenever you’re ready. Powerful features such as drill through, forecasting, and the ability to pin content to Storyboards enhance the analytical and collaborative experience. One AI Assistant is immediately intuitive, requiring no training, and empowers individuals within organizations to access, visualize, and understand workforce data. This ease of use enhances collaboration and informed decision-making at all levels. Powered by Generative AI The core of One AI Assistant is powered by the sophisticated language understanding of large language models. These models interpret your input, identify key metrics, dimensions, and time selections, and use a vector database to match your requests with your data accurately. This process ensures that the answers you receive are based specifically on your data, not generic knowledge. One AI Assistant is built to prevent hallucinations, delivering only accurate and relevant insights based on your tailored data model, ensuring trust and reliability in every response. Integration Across One Model One AI Assistant's generative AI capabilities are accessible from anywhere within One Model. Whether you're working within a Storyboard or navigating different pages, you can generate charts and insights without disrupting your workflow. This integration enhances the overall user experience, making data-driven decision-making more efficient. Transparent and Secure With One AI Assistant, the query structure is displayed alongside the resulting visualization, providing complete transparency. It adheres to One Model's industry-leading role-based security, ensuring that users only access data they are authorized to see. Ethical and Configurable AI We believe in "ethical AI, with you in control." One AI Assistant is configurable, allowing administrators to manage access and set parameters according to your company's needs. You can control which metrics and dimensions One AI Assistant is able to access as well as which users can access the new feature. Continuous Learning and Improvement One AI Assistant is designed to improve over time. A built-in feedback loop allows users to indicate whether the results are accurate. Users can easily add detailed notes, which will help improve models and inform enhancements. Your Data, Real Answers One Model has always been a leader in data integration, modeling, security, and machine learning for people analytics. With One AI Assistant, we've made these insights more easily accessible to a wider audience. One AI Assistant empowers everyone from HR professionals to team managers to make data-driven decisions based on answers they can trust, leading to better business outcomes. Discover how One AI Assistant can help you work faster, smarter, and empower your entire organization. Experience the future of people analytics today - One AI Assistant is included with all enterprise licenses.
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13 min read
Richard Rosenow
For HR teams, especially new people analytics leaders, a hidden danger lurks in the shadows that can significantly hinder success: It's your Workforce data architecture. This danger comes from a substantial blind spot for organizations between HR and IT, making it difficult for new people analytics leaders to be successful and for HR teams to effectively leverage data. Understanding and bridging this gap is essential for unlocking the full potential of workforce analytics, which is increasingly vital in today's data-driven business environment. Troubleshooting the People Analytics Ecosystem When an HR organization embarks on a journey into people analytics, or when a new people analytics leader establishes a team, one of the first tasks is understanding and assessing the HR data landscape. Alongside stakeholder meetings, team assessments, and making the case for the necessary tech stack, evaluating the data infrastructure is crucial within the first 90 days. Initially, teams might try to manage with available system reports and surveys. Those teams end up relying heavily on manual data wrangling, which in turn brings human error, bias, and friction into processes and often involves complex and messy spreadsheets. Maintenance of those manual systems will eventually hold the team back. This approach carries significant risks, as it is prone to errors and inefficiencies. Moreover, if the key person managing these processes goes on leave or resigns, the entire operation could fall apart, leaving the team holding the bag on an impenetrable data model. When it comes to advanced analytics, the main trouble HR finds itself in at these companies is that people analytics teams can’t run on raw data from system reports alone. In order to reach beyond reporting and into analytics, people analytics teams require architected data, which means raw data must be converted into usable metrics and dimensions. An investment in data architecture forms the bedrock upon which advanced analytics and insightful decision-making are built. Access to clean, well-architected data is essential for the success of People Analytics teams. Bridging the “Invisible Gap” in Building a Solid People Analytics Data Infrastructure So the people analytics leader starts their journey: What data do I have, what data do I need, and what technologies produce data across our workforce ecosystem (HRIS, ATS, Survey, etc). Who do I have that can help me? But they quickly face a two-fold political problem: 1. On the HR side Their leaders and peers on the HR leadership team may be just starting to get familiar with analytics, and data architecture is a step beyond that comfort zone. HR education generally doesn’t cover data engineering and, to give that some credit, why should it? Most HR leaders will not need to engage in data architecture conversations. And to that point, most people analytics professionals are even downstream from these conversations or have to learn it on the job, too. There are very few, if any, courses for HR professionals on the nuances of workforce data architecture. Additionally, data engineering for analytics is a unique need specifically and almost entirely for people analytics within an HR team. People analytics regularly centralizes and handles this work on behalf of their HRLT peers, which inadvertently hides this work – and the pain of this work – from their peers. 2. On the IT side One might assume that IT or central data teams could provide the necessary support for people analytics leaders. While this is true in some organizations, the reality often is that IT and enterprise engineering teams, despite their data expertise, lack understanding of the unique nuances of slow-changing dimensions of workforce data and HR processes. That’s why we provide resources to help, like this 5 Tips for Getting Data Extraction Right blogpost. Additionally, IT teams are frequently overwhelmed with demands from various departments such as product, marketing, sales, and finance, making it challenging to prioritize HR-related data projects. The other hard truth is that we are still in a political reality where teams outside of HR don't readily recognize the value or prioritize this work, as we illustrate in The Little Red HR Team: A modern retelling of a timeless classic. So the people analytics leader, who needs workforce data to be extracted, architected, and modeled to do their assigned job, now has a problem. Why this gap is even more detrimental if you’re moving toward AI in HR Navigating Data Architecture Hurdles Securing buy-in, resources, and priority for data architecture work can be challenging, especially when it's often hidden from key teams and not part of the typical job description. Historically, people analytics leaders have faced two main options, each with its drawbacks: 1. Educate and influence campaign To get this work done, the people analytics leader embarks on an extended period of education for both HR and IT to explain what's going on and why they need to spend time, resources, and priority building an analytical data warehouse, not just reports from the core HRIS. This is thankless work trying to upskill and educate teams who do not want to know or need to know about this area to do their day jobs. These campaigns are long journeys. 2. Just get it done The people analytics leader advances into this “invisible work” by themselves or with the team they have, and just tries to get it done. People analytics leaders take the work on, upskilling in data engineering and doing the best they can. This results in a “good enough” but ultimately shaky foundation. And while that’s happening, people analytics has to wait until you have data to work with. So you put your head down and get work done. Unfortunately, when it's done or “good enough,” – and this is the hardest part – no one else will notice. The first option means you lose the critical window of time when new leaders need to show effectiveness. But the second option means you lose visibility and guarantee a long term problem with maintenance. Potentially even more dangerous for a new leader. With both options, success is far from guaranteed. Both HR and IT teams just want you to get your work done; they don't necessarily want to learn about why their current setup of technology is not working. There's good news, though. The vendor landscape supporting people analytics has been evolving to meet this need. How One Model Helps Move the World Forward One Model is uniquely designed to address the 'invisible work' of data engineering and data architecture in people analytics. This was clearly demonstrated in our work with Elastic, a leading tech company. The One Model platform enabled Elastic to streamline their data processes and significantly enhance their people analytics capabilities. Read more about our partnership with Elastic. Data Orchestration One Model stands alone when it comes to the levels of support we offer for data architecture. Our data orchestration layer is One Model’s crown jewel within the product suite. One Model seamlessly extracts, transforms, and loads your data into a secure, tailored data model within our People Data Cloud (effectively, a sophisticated data warehouse built specifically for your people data). The automation we establish ensures that your data is consistently updated daily without the need for manual intervention. By eliminating the need for manual data loads or loading files yourself, we provide a reliable and efficient solution for maintaining up-to-date, high-quality data for analytics. This was a game-changer for Elastic, enabling them to maintain accurate data without the burden of manual updates. Additionally, our platform features direct connectors that go beyond mere extraction of raw files or reports, providing fully modeled data ready for analytics. Whether dealing with flat files or complex data sources, our system integrates and unifies data into a cohesive analytical model, which updates daily, and streamlines your access to data. Data Engineering Support And you're not going at this alone or upskilling your team with additional expensive training to make this happen. One of the biggest reasons I chose One Model when I was a buyer in people analytics was that One Model provided data engineering support as part of the subscription. Named resources support your team, but above and beyond that support, the One Model platform and One Model team members maintain the data pipelines. No more calling IT teams that don't prioritize HR and no need to hire unique and expensive resources for data engineering. With One Model, you will have a partner you can call who not only picks up the phone, but who cares about your success in this “invisible” space. In Elastic's experience, this support allowed their team to focus on strategic analytics rather than getting bogged down in the technical details of data engineering. Seamless Integration from Connection to Dashboard Most importantly, this orchestration happens quickly and securely. You don't have to spend months or years trying to unlock your data. We can extract and create a tailored data model for your HRIS rapidly – from connection to dashboard! This quick implementation enabled Elastic to quickly transition to leveraging high-quality analytics, accelerating their time to value, and enhancing their overall data strategy. One Model stands as the global leader in our space, uniquely positioned among people analytics providers as the premier partner for data architecture. Don’t feel like you have to navigate the complexities of data architecture alone. Partner with One Model and leverage our expertise to unlock the full potential of your workforce data. Reach out to us today to see how we can transform your data management and analytics capabilities. Glossary of Terms When exploring the complexities of data architecture and engineering, it's helpful to familiarize yourself with key terms frequently encountered in this field. The following glossary provides a concise overview of essential concepts and terminology: Data architecture: The overarching strategy, rules, and principles governing the collection, organization, transformation, and storage of data in a specific environment. Raw data: Unprocessed digital information extracted from a technology, often in a format that's difficult to understand without processing. Data integration: The process of combining data from different sources and providing users with a unified view of these data; sometimes referred to as ETL, which is to Extract data from a source, Transform it to fit your needs, and Load it into the end system. At this point, it becomes an analytical data model (see #6). Trending data: Data showing changes and patterns over a specified period of time, often used to predict future events or behaviors. Data warehousing: A large store of data collected from a wide range of sources within a company and used to guide management decisions. Analytical data model: A set of interconnected tables (or fact tables) ready for use in analytics. (a.k.a. a Galaxy schema) Unified data model: A framework that unifies multiple data types from different sources into a consistent and universally accessible format. Download a resource for your IT team that helps explain why they should care about people analytics. Why Tech Leaders Prefer One Model's People Analytics Platform Download today
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5 min read
The One Model Team
A well-crafted data-informed story can effectively influence decision-making, foster understanding, and drive meaningful change within the organization. A data-informed story blends the art of storytelling with data-driven insights, creating a compelling narrative that resonates with the audience and inspires action. Here's a framework for how to develop data-informed HR stories: Business Objective: Setting the Stage Every compelling data-informed story begins with a clear business objective. It's essential to know what you want to convey and the actions you want to inspire from your audience. Defining your objective gives direction to your story, shaping its structure and maintaining its focus. A well-articulated objective ensures your story remains purposeful and impactful, driving the narrative towards your desired outcome. “Using people data to get to a scientific insight is only half the battle. If you can't step back and crisply describe your findings in terms of business impact, you quickly lose the room, lower credibility, and break trust with business leaders.” — Ian O’Keefe, Head of Talent Analytics and Data Science, Amazon Evidence: The Backbone of Your Story Your story's credibility stems from its findings: both data evidence and the story context. Data Evidence: Collect and analyse data pertinent to your objective. This data acts as the backbone of your story, supporting your narrative and revealing valuable trends, patterns, and insights. It's the facts and figures that make your story believable and persuasive, reinforcing your arguments and enhancing your story's validity. Story Context: Context adds depth to your data, making it meaningful and relevant. Explain why your data matters, its relation to broader organisational objectives, and its direct impact on your audience. This context helps your audience comprehend the data's significance, allowing them to connect the dots between raw data and its implications. Visualization: Bringing Your Data to Life Visualising your data helps to clarify and accentuate your key messages. Rather than presenting raw data or lists, craft clear and engaging visual representations of your data. This could involve charts, infographics, or diagrams, which enable your audience to quickly grasp the information and easily identify the patterns or trends you're emphasising. Narrative: The Art of Engaging Your Audience Narrative is the act of weaving together data and insights into a compelling story that resonates with the audience and inspires action. By using an engaging narrative, relatable examples and analogies, and emotional appeal, HR professionals can effectively communicate the human impact of organisational decisions and drive meaningful change. “To infuse more storytelling into People Analytics, understand the business and people context, use narrative techniques and visualisations to present data engagingly, and go beyond data by exploring the human factors driving it. Enhancing storytelling in this field can significantly boost its impact on business outcomes.” — Tony Truong, Vice President of People Strategy and Operations, Roku Engaging Narrative: To captivate your audience, weave your data and insights into a compelling narrative. Ensure your story flows logically, featuring a beginning, middle, and end, each part reinforcing the key message you wish to convey. Relatable Examples and Analogies: Examples and analogies act as bridges between complex data and familiar concepts. By relating your data to real-life scenarios or recognisable concepts, you make it more accessible and understandable for your audience, making your story more relatable and engaging. Emotional Appeal: The magic of storytelling lies in its ability to evoke emotions. Incorporate elements that resonate with your audience on an emotional level. This could involve personal anecdotes, inspiring stories, or connections between the data and the organisation's values and goals. “People Analytics insights have an easier path to landing as a compelling story if quantitative findings are combined with qualitative findings. Pulling anecdotes from HR and non-HR leaders, managers, and employees in your business lines is a validating and powerful storytelling device.” — Ian O’Keefe, Head of Talent Analytics and Data Science, Amazon Interactivity: A Living, Breathing Story Data stories are not static monologues but dynamic dialogues. Build your stories in a way that allows you to be prepared for follow-up questions and additional requests. Consider building your data stories in platforms where you can treat them as living documents, flexible and adaptive, fostering interactivity and ongoing engagement. This approach will enrich your narrative, keeping it relevant and resonant over time. Action: The Impetus for Change The goal of any data-informed story is to inspire action. Conclude your story with a clear call to action, outlining what steps you want your audience to take based on the insights presented. This crucial step ensures your story doesn’t merely inform but also drives engagement, leading to tangible change. Ready to learn more? Download our eBook Why Data-Informed Storytelling Is the Future of HR to explore additional examples and learn how One Model can help your organization tell compelling, data-driven stories.
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7 min read
Dennis Behrman
Richard Rosenow, One Model’s VP of People Analytics Strategy, spoke at People Analytics World in London this year on how the people analytics role has evolved. Watch the video or read our "Cliff’s Notes" below. Richard Defines People Analytics People analytics is a multifaceted field that encompasses many things. So it’s helpful to break it down into three key components: the Community, the Act, and Function. Community: The community is the heart of people analytics. It centers people analytics as a movement of people who want to make the world of work better with data. The Act: The use of data to support workforce decisions. Everyone in the business participates in the act of people analytics, including managers, leaders, and HR professionals. This practice has been around informally since the 1940s, highlighting its longstanding importance. Function: The formalization of people analytics as a business unit has been around for about 15 years, with pioneers like Jeremy Shapiro and Tom David Port leading the way. This function continues to evolve, adapting to the changing needs of the business world and supporting data-informed workforce decision-making. What is People Analytics? Learn more. Overview of the People Analytics Function Richard's experiences in the field of people analytics have given him an inside look. One unique aspect of people analytics he points out is the absence of a centralized governing body. This lack of a formal structure allows for continuous growth and evolution. It’s important to give ourselves and each other grace as we navigate and develop this dynamic field. Perhaps for the same reason, people analytics faces several common challenges, including lack of budget, data acquisition and quality issues, resistance to change, and disconnection within the company. Understanding and addressing these challenges is crucial for the continued growth and success of people analytics. One critical aspect of overcoming these challenges is understanding and connecting within what we call the People Data Supply Chain. By improving visibility and connectivity across different levels with HR, we can address many of the problems that arise. People Data Supply Chain One of the first tasks of people analytics is finding usable data, which means reaching upstream. The quality of the data inevitably leads to a focus on technology. As the people analytics leader moves into technology, a lack of standards in org structure are often revealed. And ultimately, at the very top, we see inconsistencies in strategy. If we don't decide what we're going to do and what we're going to do well, how does the operation know what to build? How do we set up our tech? How do we get our data out? And how do we do analytics? Besides the problem of unactionable data, inefficient and disconnected functions and disrupted data flow within the supply chain creates friction and politics. Connecting the Functions As the first one in people analytics to point out this infrastructure, its limitations, and its opportunities, Richard stresses that the sequence within the People Data Supply Chain is crucial. Often, there is good visibility above but poor visibility below, creating disconnects. Understanding this sequence and ensuring smooth transitions between levels can significantly reduce problems. Integrating these functions can lead to smoother operations and more effective people analytics practices. And a well-established data supply chain is especially imperative before implementing generative AI in HR. Emerging Role: Workforce Systems Leader The variety of job titles in people analytics is staggering; Richard has, in fact, identified over 2,600 unique titles. But In the midst of all this change, he has seen a new position emerging that combines people analytics with tech, ops, and strategy. Currently called by many names, this workforce systems leader reduces politics and provides a viable alternative to reporting directly to the CHRO. This new role offers an excellent career path for people analytics leaders. It allows them to leverage their unique insights and experiences to drive meaningful change within their organizations. Moving Forward As we continue to learn and grow in the field of people analytics, Richard reminds us that it’s crucial to be kind to ourselves and each other. Sharing insights and experiences within our community will drive our collective progress. Considering the People Data Supply Chain is essential for effective people analytics practices, we’ll be releasing an in-depth exploration of the topic soon. If this resonates with you, let’s continue this important conversation. Together, we can shape the future of people analytics and drive meaningful change in our organizations. Speaking of the future, Richard Rosenow covers these timely topics in greater depth in the webinar below. Take a listen!
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6 min read
The One Model Team
To say that HR is undergoing significant transformation is quite an understatement. But in the shadow of AI/ML, people analytics, and other massive splashes, two familiar foundations are shifting: HR team structures and what HR teams are focusing on. Here's a flyover of what's at stake. 1. Evolving HR Team Structures HR team structures are evolving to bridge traditional functions with analytics, technology, and strategic planning. It's important to know what's changing and how your business can adapt: Impact of Layoffs - Layoffs, especially in tech, force HR teams to rethink their strategies. Some companies downsize, while others use this time to attract top talent, leading to more diverse and adaptable teams. Recommended Approach: Use layoffs as an opportunity to reassess and restructure your HR team to align with new team focuses (below). Focus on bringing in diverse skills and expertise to create a more resilient and adaptable team. Optimal Team Size - There’s a growing belief that HR teams can be more effective with a smaller, well-defined team size. Bigger isn't always better; the right team size can enhance efficiency. Recommended Approach: Evaluate your organization’s specific needs to determine the optimal team size. Prioritize quality over quantity to build a lean, efficient team. Platform Approach - Modern HR platforms are reshaping team structures by automating routine tasks and streamlining workflows. This shift allows HR teams to focus more on strategic insights and less on manual processes. Recommended Approach: Invest in comprehensive HR technology platforms that offer automation and integration capabilities. This can free up your team to focus on strategic tasks and improve overall efficiency. New Emerging Roles - At the same time that some roles are becoming redundant or obsolete, new ones are forming to oversee or bridge gaps in new processes. We're seeing people analytics leaders morph into entirely new roles that span across HR functions. This cross functional people analytics position goes by many names, but we're calling it Workforce Systems Leader. Recommended Approach: Stay adaptable, proactive, and informed. Embrace emerging roles like the Workforce Systems Leader to optimize your HR processes and keep your organization at the forefront of industry trends and advancements. Joining a people analytics community can be very helpful in the midst of ongoing evolution. Stay tuned as we address the implications, functions, and ongoing shifts of roles in this industry. 2. Shifting HR Team Focuses As team structures change, so do their priorities. HR must now be focusing on three key areas: data infrastructure, productivity analytics, and skills and workforce planning. Data Infrastructure - A strong data foundation is crucial for advanced analytics and AI. Efficient data management helps HR teams create actionable insights that drive business forward. Recommended Approach: Invest in advanced data management tools and provide training for HR staff to ensure high-quality data and effective use of analytics. Productivity Analytics - The shift to remote and hybrid work has made productivity analytics essential. HR needs to measure productivity accurately and understand what influences it, especially in new work environments. Recommended Approach: Implement productivity tracking software and regularly analyze the data to refine remote work policies and improve employee performance. Skills and Workforce Planning - Integrating skills data into workforce planning is becoming vital. HR must understand the impact of specific skills on workforce dynamics and align this knowledge with company goals. Recommended Approach: Conduct a skills inventory and use advanced workforce planning tools to align skill development initiatives with the company’s strategic objectives. It's not necessarily easy, but by embracing these and other changes we've identified this year, HR departments can improve their effectiveness, foster collaboration, and drive significant business outcomes. Ready to get ahead of these shifts and redefine the impact of HR in your organization? Download this new resource today to take a deeper dive into all 6 of this year’s top emerging trends for people analytics.
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5 min read
Admin
Greenhouse is a robust applicant tracking system, but with the flood of data that gets generated, information about your candidate pipeline, recruiter efficiency, and conversion rate can get lost. There are foundational graphs and reports available out of the box. But many organizations need more highly-advanced analytics to drive talent lifecycle insights. That’s why One Model created Advanced Analytics for Greenhouse, an innovative offering that empowers users to unlock the full potential of their Greenhouse data. What is Advanced Analytics for Greenhouse by One Model? We've created this analytics service specifically for Greenhouse customers to help you get the most out of your investment. From automated data extraction, to insight-rich visualizations, to robust querying and reporting, everything in the service was designed by Greenhouse experts for Greenhouse users. We provide a Quick Start deployment, broad user support library, and a complete self-administration toolkit with a low cost of ownership. Advanced Analytics for Greenhouse helps you answer critical questions, including: What is the average application count per job? What is our gender diversity in applicants and offers? How long does it take to make an offer? What is the forecast for opened and filled jobs? How many open jobs do recruiters have? Why choose One Model’s Advanced Analytics for Greenhouse? Greenhouse helps people-first companies hire for what’s next by powering all aspects of attracting, hiring, and onboarding top talent. And One Model offers an end-to-end, flexible recruiting analytics platform that extends Greenhouse reporting beyond the basics. By combining the power of both Greenhouse and One Model, here are 7 incredible benefits users can expect from Advanced Analytics for Greenhouse: 1. Efficient Deployment Using our flexible people analytics platform and extensive knowledge of Greenhouse software, we’ve designed a Quick Start deployment process to get you up and running quickly. 2. Automated Updates and Managed Data Pipelines Say goodbye to worrying about data updates. One Model provides automatic data refreshes to ensure users have latest information on their recruiting outcomes. Get notifications when your updates are processed successfully. 3. Ensured Security Data security is paramount. With Advanced Analytics for Greenhouse, administrators can rest assured that their data is safe as it moves through our secure data platform, backed by robust security protocols and ISO Certification. 4. Full Administrative Control Your Advanced Analytics for Greenhouse administrators get access to an easy-to-use and secure interface to control user set up, define user capabilities, and manage data access rights by department or metric. 5. Data-Driven Storytelling Our analytics solution is feature-rich but not complicated. It allows users to go beyond reporting to develop and deliver visualizations which answer questions that matter. They can design, create, and share information with their stakeholders with ease. 6. Hidden Patterns Revealed One Model's exploratory data toolkit enables users to delve deep into data, uncovering valuable patterns and trends across time that drive recruitment success. You can answer complex questions through drag and drop functionality without coding. 7. Competitive Edge Gained It’s not easy to find and hire the right talent, but with One Model’s Advanced Analytics for Greenhouse you can gain deeper recruiting and hiring, giving your company a competitive edge in a challenging labor market. Unlock New Horizons of Success If you want to revolutionize your talent acquisition analytics and gain powerful insights, Advanced Analytics for Greenhouse is the answer. It's an opportunity to take your recruiting game to the next level, ensuring your HR and people analytics teams have the data-driven tools they need to succeed. Want to learn more? Watch our demo and explore this page to learn more about Advanced Analytics for Greenhouse. Or fill out the form below to request more information.
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4 min read
The One Model Team
Have you been tasked with proving the ROI of your people analytics program? Start here. Traditional return on investment (ROI) calculations often fall short in capturing the full value of people analytics. While efficiencies and cost savings are important, they represent a narrow view of people analytics' potential and value. If you've been asked to defend your people analytics business impact, you must start with a broad approach before diving into the weeds. These 3 mindsets are just the beginning, but they're critical. 1. Understand the Limitations of Traditional ROI Metrics Traditional ROI calculations typically focus on two components: estimated savings through system efficiencies and reductions in attrition, or faster time-to-fill job postings. While these metrics are useful, they can be misleading. Establishing a direct cause-and-effect relationship is tricky. For example attributing savings from reduced attrition doesn't tell the whole story, especially in a volatile job market. Additionally, measuring people analytics effectiveness may require factoring in the cost of implementing advanced technologies. So it's important to reinforce that the value of people analytics infiltrates the entire workforce experience and efficiency, which should not be measured strictly in financial terms. 2. Embrace a Holistic Perspective, But With a Laser Focus The mission of people analytics is to foster continuous improvement in talent decisions, leading to better organizational outcomes. So people analytics should be evaluated based on its ability to drive better talent decisions across the organization. This broader perspective encompasses not only financial outcomes, but also benefits various stakeholders, including employees, customers, and the community. By focusing on the overall impact on organizational effectiveness and stakeholder satisfaction, people analytics can be seen as a critical driver of long-term success. This approach encourages investments that enhance the quality of talent decisions and support the organization's strategic goals. 3. Introduce a Simplified Value Model A more practical and effective approach to measuring people analytics value is through a 3-pronged framework: Utilization: Tracks how often people analytics content is used. Leaders regularly engaging with people analytics deliverables, such as dashboards and reports, indicates that members of your team want and are finding value in workforce data. User Level: Assigns high value to senior leaders. If a CEO frequently uses a workforce dashboard, it's likely delivering valuable insights that inform decision making. Tracking engagement levels across different user groups can highlight which tools are most effective and where improvements are needed. Deliverable Level: Evaluates the potential impact of the people analytics content by measure outcomes and decisions influenced by these deliverables. For example, a report that leads to a successful strategic initiative demonstrates high value. By focusing on key users and high-impact deliverables, this model ensures people analytics teams align and prioritize their efforts to meet organization needs. We're Here to Help Of course, these are just the beginning steps in the complex task of assessing the effectiveness of your people analytics program. If you're ready to dive into the specific metrics and tools that will help you make a solid case for people analytics based on data, we're here to help. Download our comprehensive guide, Measuring the Value of People Analytics. You'll discover the various lenses you need to look through when calculating people analytics ROI in general, as well as specific formulas for key metrics. Plus, you'll see how we calculate the value of a small people analytics portfolio based on the value-utilization framework. Get the Equations and Key Metrics You Need:
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5 min read
Matthew Wilton
People data is the lifeblood that fuels insights and drives strategic decisions. Yet, for many leaders, extracting meaningful data from complex systems like Workday can be a daunting task. One Model's Workday Connector is designed to turn this challenge into an opportunity, providing a powerful solution that stands out in the crowded market. Here's why it’s a game-changer for technical people analytics leaders. The One Model Advantage: Beyond Brute Force At its core, our Workday API Connector is built on a deep understanding of the intricacies of Workday. Unlike competitors who might rely on inefficient methods—such as pulling data for every employee every day—One Model has developed a sophisticated approach that is both clever and efficient. Intelligent Data Retrieval With a brute force approach querying a year's worth of data for a single employee requires 365 requests to the Workday API. For a 1,000 employee company this means to get a full year's data will require 365,000 API requests. Workday’s API returns data in large, complex XML files and API requests can take seconds to receive a response. For the 1,000 employee company, even if the API responds to every request in 1 second, it will take over 4 days to pull all the data from the Workday API. This brute force method does not scale and is not practical, especially for larger enterprises. Our solution? We focus on significant data change points, intelligently identifying the moments when meaningful changes occur in an employee's record. This approach not only reduces the volume of data processed but also ensures that we capture the most critical updates. The Self-Healing Data Model: Scalability and Accuracy One Model’s unique self-healing data model is a standout feature that ensures accuracy and consistency in your analytics. Here's how it works: Intelligent Identification: By leveraging our deep understanding of the nuances of data locations and changes, our connector identifies and extracts only the necessary data points. This minimizes the load on Workday’s API and speeds up the data retrieval process. Error Detection and Correction: Our system automatically detects discrepancies and back-dated changes, correcting them without manual intervention. This self-healing capability ensures that your data remains up to date and accurate, even if historical changes are made. Dynamic Processing: The connector dynamically adapts to changes in the Workday API, ensuring continuous, reliable data extraction without interruption. Comprehensive Data Support: From Raw Workday Data to Analytical Models One Model goes beyond mere data extraction. We transform raw data into analytical models, providing actionable insights rather than just raw numbers. Our approach integrates custom fields and user-defined reports, ensuring that even the unique aspects of your data are captured and analyzed. Integration with Custom Reports For those unique data points that aren't covered by standard API calls, One Model supports the integration of custom reports. Customers can create custom reports in Workday, which our connector then pulls and integrates into the overall data model. This flexibility means that no piece of data is left behind, giving you a comprehensive view of your workforce. Unmatched Support and Stability Our Workday Connector isn't just a tool; it's a platform-based service. Through the platform, we offer continuous monitoring, maintenance, and support to ensure your data pipeline remains robust and reliable. Beyond the platform, our team is on hand to address any issues, making sure that your focus remains on deriving insights, not on troubleshooting data pipelines. Handling Workday Data Updates with Ease Workday’s frequent updates can pose challenges, but One Model’s connector is designed to handle these seamlessly. By using versioned API endpoints and dynamic data processing, we ensure that changes in Workday’s data model do not disrupt your analytics operations. Why Choose One Model? In a market where many solutions promise easy data extraction but fall short on delivering comprehensive, scalable, and accurate data models, One Model’s Workday Connector stands out. Here’s why: Scalability: Efficient data retrieval methods that scale with your organization. Accuracy: Self-healing models that ensure data integrity. Flexibility: Integration of custom reports and fields. Support: Continuous maintenance and monitoring from a dedicated team. We have many customers and current prospects that have come to us to solve their challenges in accessing, obtaining, and maintaining a historic data load from Workday. With our Workday Connector, you get more than a Workday data export – you get it in a form that drives meaningful, actionable insights. Unlock the full potential of your people data with One Model. Connect with us today or download our Workday People Analytics guide to learn more about our connection to Workday and how it can transform your analytics capabilities.
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2 min read
Christy Green
Effective workforce listening is critical for HR professionals. Listening at scale involves gathering and analyzing data from various channels to understand the workforce better. As a key listening tool, employee surveys provide valuable insights into employee sentiment, behaviors, and overall satisfaction. However, transforming survey data into actionable outcomes can be challenging. This is where One Model’s new Qualtrics API connector comes into play. One Model’s Qualtrics API integration simplifies survey data extraction and analysis by streamlining the consolidation and mapping of survey information with other HR data for comprehensive analysis and strategic planning. Simplify Data Acquisition with Unified Data Models One of the primary benefits of the new Qualtrics API connector is its ability to simplify data acquisition through a unified data model. Traditionally, organizations have struggled with the laborious task of mapping varying survey questions and consolidating data for meaningful analysis. The new API reduces the number of manual steps and potential for errors. Integrate Survey Results with Key HR Metrics The Qualtrics API integration enables users to integrate survey results with key HR metrics. By doing so, it facilitates advanced analytics and strategic planning across different departments and time periods. This integration provides a holistic view of employee engagement and performance, allowing organizations to understand the impact of HR initiatives on retention and performance over time. Enhance Employee Engagement and Performance The new API connector plays a crucial role in boosting employee experience by providing deeper insights into employee sentiment and behaviors. By integrating survey metrics with HR metrics, organizations can develop targeted engagement strategies, enhance the effectiveness of HR initiatives, and ultimately strengthen company culture. This comprehensive analysis helps in identifying areas for improvement, driving stronger performance, and increasing employee retention. Key Benefits Save time: Extract and prepare data in fewer steps. Create a better culture: Support personalized employee experiences and strategic planning by using detailed insights from integrated data. Go beyond surface-level data: Gain deeper insights into engagement and performance with machine learning and statistical analysis. For a deeper understanding of the integrated framework for workforce listening, explore One Model’s comprehensive approach that includes conversations, surveys, and systems data in their blog post here.
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6 min read
Dennis Behrman
How does Human Resources help a business succeed? Like it or not, success in today’s organizations hinges on creating exceptional employee experiences. That means HR holds the key to achieving this critical objective. In this Hacking HR podcast episode, Enrique Rubio and Richard Rosenow, our VP of People Analytics Strategy, explored how HR data can transform workplaces into more human-centered environments. Yet, many HR professionals grapple with data anxiety for a variety of reasons. Listen in on their conversation or enjoy our “Cliff’s Notes” to learn how HR and people analytics professionals can overcome these challenges and embrace a data-driven approach to focusing on people. Why Building a Human-Centered Workplace Requires Data Enrique: We’ve been investing in conversations about empathy, kindness, compassion, feedback, mental health, wellness—all things that create a human-centered workplace. How can we implement these values in the workplace using data? How do we measure that it’s working well? Richard: Data has transformed HR’s ability to listen and engage in meaningful conversations at scale. Historically, HR professionals excelled at listening, but data now allows us to listen to larger populations effectively. For instance, in a company of four thousand employees, it’s impossible for leadership to talk to everyone personally. Data helps us understand and address the needs of employees by identifying patterns and insights that we might miss otherwise. It’s about listening at scale and making informed decisions based on those insights. Overcoming Data Anxiety Enrique: There’s a sentiment among HR professionals who feel they joined the field to work with people, not to dive into data, math, and technology. How do you address these concerns? Richard: The good news is that the technical burden on HR is decreasing. With advancements like ChatGPT, HR professionals don’t need to become data engineers. These technologies handle the heavy lifting, allowing HR to focus on strategic and consultative roles. Learning basic data literacy and understanding how to use data effectively is crucial, but the need to learn complex technical skills like SQL is diminishing. Today, the goal of successful human resource management is to leverage technology to enhance HR’s core strengths in understanding and supporting people. Real-Life Impact of Data in HR Enrique: Do you have any examples where data truly delivered value in creating a human-centered workplace? Perhaps looking into absenteeism versus engagement, or something similar? Richard: One memorable example is from my time as an HRBP for a large retail population experiencing high attrition. We collaborated with a professor researching job embeddedness, a measure of how well employees fit into their roles and communities. By running surveys before and after implementing a targeted program, we were able to decrease attrition significantly. This experience highlighted the power of using data to design effective HR programs and measure their impact, reinforcing the importance of HR success metrics. Surprising First Steps Enrique: It can be challenging to know where to start with integrating analytics into HR practices. What would be your first steps? Richard: Focus on connection and confidence. Start by making connections between HR metrics and business outcomes. Understand how HR activities impact operational results and find ways to measure these connections. Additionally, build confidence in your data. Reliable data allows HR to make informed decisions and advocate for necessary changes. At OneModel, we help HR leaders build unified data models, providing the confidence needed to understand and drive business success. Identifying HR Success Metrics Enrique: One common issue is investing time and resources into HR projects without setting up indicators of success. How can HR professionals ensure they have the right indicators? Richard: It’s crucial to set up indicators of success early on. Engage with analytics teams from other departments, if needed, to establish these indicators. While measuring complex human aspects like well-being can be challenging, finding proxy indicators and triangulating data can provide meaningful insights. For example, asking employees if they have a best friend at work can be a good proxy for workplace happiness, which can be linked to engagement and productivity. How One Model Helps Successful human resource management involves combining data insights with a deep understanding of human behavior, allowing HR professionals to develop programs that enhance employee satisfaction and business performance. One Model makes this possible by enabling listening at scale and efficiently providing deep data insights never before available. We enable HR teams to turn data into meaningful “stories” that drive action and growth. Want to focus on people not data? Learn how to tell better data stories with One Model.
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6 min read
Richard Rosenow
David Green, a powerhouse in the people analytics world and a wonderful friend, is celebrating a major milestone, and the whole people analytics community is here for it. Congratulations, David Green, for 10 years of inspiring people analytics professionals with your Data Driven HR Monthly Newsletter Why Not Listening to David Green Should Carry a Health Warning Richard Stein, Chief Growth Officer at Amazing Workplace, recently observed on LinkedIn: “There is a reason why David Green is #1 and not listening to him should carry a health warning!” This statement captures David's impact on the market. David Green is not merely a renowned expert in people analytics, data-driven HR, and the future of work; he stands as one of the most influential figures in the HR community today. Throughout his career and in his latest role as Executive Director at Insight222, David has helped thousands of practitioners find our space of work and supported hundreds of global member organizations in creating value and enhancing employee experiences through the development and application of people analytics. David’s influence spans numerous platforms, reaching hundreds of thousands across his channels: David Green on LinkedIn (go follow him now!) MyHRFuture blog Digital HR Leaders Podcast Digital HR Leaders YouTube Channel Data-Driven HR Monthly Newsletter Excellence in People Analytics, a book co-authored with Jonathan Ferrar A Decade of Insights This month, David celebrates a significant milestone: 10 years of producing his popular newsletter, a roll-up of at first annual, then quarterly, and, as of late, monthly takeaways and links to the best articles and content coming out across people analytics. The Data Driven HR Monthly newsletter has grown into a media empire of sorts and a critical resource for the HR and people analytics community. Each issue delves into the latest trends in people analytics, digital HR, and the future of work, providing a curated selection of noteworthy articles, research findings, and practical advice from industry leaders. His inaugural roll-up on LinkedIn, "The 20 best HR Analytics articles of 2014", is still a must-read. It continues to hold true as a Who's Who of leaders changing the world of people analytics today, and many of the articles highlighted there are relevant a decade later (for better or for worse!). The newsletter's consistent engagement highlights how readers from across HR and beyond find value in his insights to stay informed and drive organizational transformation. It stands out for its comprehensive coverage and its role in fostering a well-informed and forward-thinking HR community. Noteworthy Mentions Green has been recognized with several notable awards and accolades in the field of people analytics and HR. Some of his key recognitions include: The Top 10 HR Influencers of 2024 (HR Cap, January 2024), ‘The 100 most influential people in HR’ (The HR Weekly, January 2021), and for the third year in succession, the ‘Top 100 HR tech influencers’ (HR Executive, May 2021). Additionally, we included Ferrar and Green’s book Excellence in People Analytics in our One Model Virtual Library (our recommended reading list) and were honored to be mentioned in it. To the Future David Green's contributions to the field of people analytics are lasting and foundational. David has shaped the way organizations and leaders across our field harness data to enhance human resources and overall workplace efficiency. He has made the world of work a better place through his efforts. As the HR world continues to evolve, we look forward to many more years of his valuable insights.
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6 min read
Dennis Behrman
Anyone who analyzes data knows there's always a need to drill into reports to answer the questions that pop up. One Model is the only people analytics platform that allows you to drill into literally anything and everything, as if your siloed enterprise data sources were a single source of truth. Working with Metrics, Dimensions, and Time Explore is a powerful tool designed to help you perform powerful ad-hoc analysis on your One Model storyboards, reports, and visualizations. Here's a quick look at how the Explore tool works. Select Your Metrics Metrics are quantifiable measures used to understand the results or outcomes that you observe in your business. The Explore tool presents the entire list of metrics available to you based on your organization's metrics library and the access permission associated with your user profile and your group/team membership. You can add and remove metrics by dragging and dropping them from your metrics library to your metrics selection fields. Your metrics library contains all of the direct and derived values that are used to tell the stories hidden within your data. To learn more about how metrics are established in your One Model instance, check out this article or this video. (You may need to login with your One Model account to view Help Center content.) Pick Your Dimensions Dimensions are attributes or categories by which data can be grouped. Dimensions organize data into meaningful sections for comparing. For example, in a turnover report, dimensions could include rank, business unit, performance rating, and so on. To sub-group your data even further, you might want to add pivoted dimensions, which help you compare groups by more than one attribute. One Model's Explore tool allows you to drag and drop any number of dimensions into your report to see an analytical picture with more detail. Mind Your Time Model Time modeling is perhaps the trickiest and most important activity that happens on the One Model platform. Since time is a constant, your data analysis depends on the most comprehensive coverage of observable and measurable events for analyzing data over different periods. In theory, time subdivides infinitely. But in practice, most analysts and decision makers prefer to view time within a standard set of available lenses such as days, months, quarters, and years. But since months, quarters, and years can have different numbers of days within them, it is critical to getting time right to understand your business in the most accurate way possible. It's important for these cumulative measures to "add up" or "sum to the right number" when aggregated (or drilled through) at scale. It's equally important for data about events to be captured at various time intervals. For example, a group of employees who are currently high-performing rock stars may inform a decision today about high performers. But in reality, many of those rock stars may have been groupies in the past. One Model has no peer when it comes to the most effective application of time series analysis. Here's why. I created the Sankey diagram below with fake data to show a point. Observe how none of the more than 4000 high performers at the end of 2021 remained high performers at the end of 2023. So any analysis conducted in 2024 that uses the pool of 2023's high performers to infer multi-year trends would be an incomplete and possibly flawed analysis of the company's high performers. Most other approaches don't account for the question of "how it looked" in the past. Explore Explore's Unrivaled Speed to Insight Your organization needs the most accurate and current information to make the most informed talent decisions. The Explore tool is one of many keys to telling the stories within your data. Approachable & Intuitive One Model's Explore tool features a professional-class user interface designed to cater to both casual and highly technical users. This balanced design ensures that casual users can easily navigate and utilize the tool without feeling overwhelmed, while technical users have access to advanced functionalities and customization options. The interface’s adaptability fosters a productive environment for all users, enabling them to swiftly uncover insights and make data-driven decisions. Consistent Metrics Definitions Paired with Flexible Dimensional Pivots The Explore tool ensures the consistent application of organization-wide metrics definitions and offers the flexible application of dimensions, enabling users to tailor analyses to their specific needs. By presenting a cohesive and accurate picture of organizational data, the Explore tool enables faster and more reliable insights, accelerating the overall time-to-insight. Better, Faster Insights to More Decision-Makers Around Your Organization One Model's Explore tool excels in its ability to deploy sound insights to any team or decision maker within an enterprise. By seamlessly integrating with various data sources and offering robust reporting features, the tool ensures that actionable insights are readily accessible to all relevant stakeholders. No other people analytics platform drives more data-driven decision-making better than One Model, thanks to tools like Explore, which empower organizations to make informed decisions quickly and efficiently. Essential Questions to Ask When Selecting an AI-Powered HR Tool Learn the right questions to ask to make the right decisions as you explore incorporating AI in HR.
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7 min read
Gina Calvert
RedThread Research has identified 7 skills verification methods that range from simple to more complex. In Part 1 of this 2-part job skills assessment series, we dive into the 4 simplest and most common job skills assessments. In Part 2, we examine 3 complex forms of skills verification that lean heavily on benchmarks and data. RedThread members may access the full report authored by Heather Gilmartin and Dani Johnson. As the prevalence of skills-based recruiting grows, HR leaders are beginning to grapple with how to verify skills in order to ensure their data is accurate. They’re discovering that evaluating job skills is more complex than merely defining roles and hoping to find perfect matches. Decision-makers must weigh a variety of factors to determine the most suitable verification approach for their needs. You’re likely using some of these tactics to authenticate skills, but which are right for each role? And when should you level up to new ones? 1. Self-Assessment If you’re looking for simple ways to verify skills, having employees and applicants affirm their own expertise is the second most common approach, according to RedThread. This is most typically seen in job applications, employee resumes, and interviews. But just because it’s popular doesn’t mean it’s effective. While widely used, self-assessments can be unreliable. Discrepancies can occur for several reasons, including poor self-awareness, overconfidence, unintentional "self-presentation" bias, or, more seriously, candidate fraud. Many studies support the notion that people are notoriously inaccurate in subjective evaluation compared to objective measurements. Additionally, RedThread notes that this approach lacks specificity of the level of skills and doesn’t contribute to the company’s skills data set. That’s not to say there’s no place for worker self-reviews. As long as leaders recognize the limitations and risk, self-assessments can be a good, low-cost first step in identifying top talent early on. Giving potential employees an opportunity to showcase their abilities and skills contributes to a better hiring experience. 2. Performance Feedback / Informal Observation In this verification type, an observer validates skills through an informal set of standards using various modes of feedback and reviews. According to RedThread’s report, 37% of surveyed organizations use performance feedback in their skills verification processes - the single most-used method by a wide margin. This is possibly because even before adopting a skills-based recruiting strategy, performance feedback was already being used. These evaluations offer valuable insights into an employee's understanding and reveal any knowledge gaps by reflecting their overall performance over time or within a specific project. This approach contrasts with formal assessments, which isolate feedback to a single, often stressful event or test. One significant downside to note in this type of job skills assessment is that the observer’s feedback can be subjective and influenced by personal biases. 3. Formal Observation The key difference between formal and informal observation is that formal observation employs a specific framework to assess employee skills. A formal, structured set of standards empowers managers to develop the ability to hold difficult conversations. It enables the clear identification of areas of improvement, and it provides a foundation for coaching and knowledge transfer that helps improve performance levels. Even beyond actual performance and skills, observation can provide insight into so-called “soft skills,” such as how they handle pressure, adapt to new challenges, and interact with colleagues. It’s important to invest in the time and training needed to carry out effective, unbiased observation. Observers should factor in the possibility that employee apprehension may result in inconsistent results. Additionally, observation might not capture all aspects of an employee’s capabilities. 4. Formal Assessment Think tests, simulations, and sandboxes. RedThread reports that 53% of respondents who use formal job skills assessments do so because of compliance and regulatory requirements for certain roles, including necessary certifications or credentials. Formal assessments can be very valuable. They increase objectivity, help clarify the role for applicants (who may be defining the skill differently than you do), provide leaders with data, and save time for recruiters. However, they don’t always align with the role or tell you what you need to know. Paying attention to assessment quality is critical for the best outcomes in skills verification. Upskilling Your Career Skills Assessment Approach In this first installment of our exploration into skills verification approaches, the basic methods we’ve discussed serve as a foundational step. It's important to recognise, however, that these initial methods, while effective up to a certain point, might not suffice for roles requiring deeper or more specialized skill verification. And as the skills trend continues to evolve, leaders will increasingly desire more confidence, accuracy, or granularity in their skills data. In Part 2 of this series, we explore 3 more rigorous and comprehensive approaches to meeting the evolving demands of talent acquisition and employee upskilling programs. One Model: Skills-Based Recruiting Depends on Data One Model provides a people analytics platform that enhances skills-based recruiting by leveraging data-driven insights to identify skill gaps and predict future talent needs. We help organizations make more informed hiring decisions and better align their recruitment strategies with their business objectives. Learn how to build a people data platform that will allow you to do better skills-based hiring.
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Featured
5 min read
Gina Calvert
In Part 1 of our series on job skills assessments, we explored 4 simple ways to verify skills as identified by RedThread Research. RedThread members may access the full report authored by Heather Gilmartin and Dani Johnson. In Part 2, we delve into 3 sophisticated techniques that leverage both internal and external data to ensure a more accurate job skills assessment approach. As the landscape of skills-based recruiting expands, it becomes evident that some roles and contexts demand more nuanced and data-intensive verification methods than others. 1. Comparison to External Benchmarks When verifying skills, it’s crucial to measure them against established external standards. Yet, according to RedThread, only 11% of organizations do so. Benchmarking helps companies understand how their candidates' skills stack up against industry standards. In addition to providing a clear perspective on talent level relative to the broader market, it helps the organization future-proof their talent strategy and competitive edge. However, relying solely on external benchmarks may overlook unique aspects of a company’s culture or specific job roles that require customized skill sets. This approach also assumes that industry standards are up-to-date and sufficiently granular for an organization’s needs, which may not always be the case in fast-changing industries. Effective benchmarking relies on advanced skills intelligence tools, thus requiring an investment in technology or access to benchmarking data. As with other verification methods, benchmarks are most effective when used in conjunction with internal assessments. These platforms can integrate with existing HR systems to provide deeper insights and real-time data that help refine benchmarking efforts against industry standards. 2. Inference from HR Data Skills prediction based on HR data involves analyzing information from HR technology systems to infer employee skills. AI models predict employees’ skills based on a range of data sources. It’s quick, effective, and doesn’t require much employee involvement, RedThread explains. They report that 13% of employers currently make use of this career skills assessment method. This method uses historical data, such as past job performances, training records, and employee interactions, to predict skill levels and identify potential gaps. As it continues to evolve, the accuracy of skill predictions generally increases with the number of data points processed by AI. While powerful, this approach can be limited by the quality and completeness of the data collected. Biases in historical data can also lead to skewed predictions, making it essential to continuously update and review data inputs to ensure accuracy and fairness. HR data on industry skills is typically purchased through Human Resource Information (HRIS), Learning Management (LMS), Talent Marketplaces, Applicant Tracking (ATS), and Performance Management systems. Such systems enhance the accuracy of skills predictions by utilizing machine learning models which improve as they process more diverse and comprehensive data sets. 3. Inference from Work Data Using work system data to measure skills involves analyzing real-time data from work processes and outputs. By evaluating the quality, efficiency, and creativity of the work produced, organizations can gain a precise understanding of an employee's practical skills. This method requires sophisticated data analysis tools and expertise. It is also more complex than using HR data because it demands advanced technical integrations and substantial cross-functional collaboration to identify relevant metrics for specific skills. However, RedThread concludes that this is the only skills verification method that offers real-time insights into daily work and enables decisions at scale, based on performance data. This is where One Model shines, by seamlessly integrating with multiple data sources across the organization, enabling a more holistic and real-time assessment of employee skills based on actual work outputs. How One Model Partnerships Elevate Job Skills Assessment with Advanced Data-Driven Approaches Lightcast is a leading expert in the labor market. They collect and process a wide array of data, including job postings, resumes, and work history profiles. This data is aligned to job titles and skills classifications every two weeks. By merging Lightcast's extensive knowledge of the external labor market with One Model's ability to unlock people data, organizations can gain business insights relative to industry-wide talent trends. A One Model partnership empowers HR teams to: Enhance the consistency of data in reporting by adding standardized titles to current roles Analyze talent headcount, career paths, retention with accuracy Better align skills with job roles to enhance skills knowledge and plan for the future Ready to spend your time sharing insights, not integrating your people data? Learn how One Model integrations can help you see the whole picture. Or get a peek under the hood at how One Model could specifically benefit your organization. Request a demo.
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4 min read
Richard Rosenow
The buzz around Artificial Intelligence (AI) in the workplace is growing louder by the day. As organizations worldwide attempt to harness this revolutionary technology, particularly in the realm of Human Resources (HR), a fundamental question arises: Is our workforce data truly ready for AI and Machine Learning (AI/ML)? The Reality of Data Readiness for AI and ML In our modern business environment, HR teams are making use of workforce data for a variety of purposes. Traditionally, these teams had focused on extracting data for reporting in the form of monthly extracts or daily snapshots. This approach, while useful for traditional needs, falls short of the data needs for AI and ML. That’s because data preparation for AI isn’t just about collecting and storing data to review later; it's about curating data in the right way to effectively train sophisticated models. AI tools today are highly complex and capable of predicting patterns with remarkable accuracy. However, vast amounts of high-quality, curated data are required to effectively train those models. The quality and relevance of the data are critical for the fine-tuning needed for specific tasks or domains like our use cases in HR. The Need for a Paradigm Shift From this perspective, most HR datasets and HR data stores that we had previously prepared are not ready for AI and ML (whether it's generative AI or "traditional" predictive AI). Without appropriately prepared training data, the algorithms we hope to launch will fall short in their learning. Potential benefits of AI in HR—from recruitment optimization to workforce alignment with business goals—could remain untapped or, worse, lead to unintended consequences if models are trained on poor or incorrect data. Preparing your HR team for this new phase of work isn’t just about adopting new technologies; it's a paradigm shift in how we think about and handle data. This is even more pivotal in the areas of MLOps and LLM operations when we try to deploy these models at scale in a repeatable fashion. We’re going to start to hear more about these terms and the operational needs of machine learning in the near term future and it’s HR’s responsibility to stay on top of the nuances in this space. The First Step: Preparing and Unlocking Your Data Data extraction is one of the most essential parts of preparing for AI and ML. We address the foundational importance of this step, robust data preparation and management, in our blogpost 5 Tips for Getting Data Extraction Right. It explores in greater detail these 5 action steps: Prioritize and align extracted data with the needs of the business Be thoughtful about what you extract Build the business case to pull more Automate your extractions Extract for data science, not just reporting The paradigm shift and these tips can help HR teams more effectively and efficiently adopt AI practices that will drive business value and insights. Why One Model Stands Out in People Analytics AI The final key in preparing for AI and ML is having the right technology in place to build a fine-tuned model that meets your company’s unique needs. One of the main reasons I joined the One Model team stems from their foresight and commitment in this area. Due to that investment, we're now the only people analytics vendor with a machine learning platform that runs on a data model tailored to your firm, not just last-minute AI features. This distinction is vital. And "One Model" isn’t merely about preparing data for AI models; it’s an end-to-end platform encompassing data management, storytelling, model creation, evaluation, deployment, and crucially, audit-ready and transparent tools. Our platform empowers HR teams to manage and deploy customized ML models and MLOps effectively, beyond the traditional scope of data engineering teams. The dialogue around AI, ML, and MLOps in HR is already in full swing. Staying informed and engaged in this conversation is crucial. If you wish to delve deeper or discuss strategies and insights in this space, I, along with the One Model team, am more than willing to engage. We're keen to hear how your team is navigating the intricate landscape of MLOps in HR. Essential Questions to Ask When Selecting an AI-Powered HR Tool Learn the right questions to ask to make the right decisions as you explore incorporating AI in HR.
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5 min read
Dennis Behrman
For employees, recognizing a bad company culture isn’t difficult. Their feelings of being overwhelmed, frustrated, unvalued, and unsupported serve as clear indicators. When employees experience these emotions, it’s a red flag that something is amiss within the organization. Leaders may see the downstream effect of a negative culture in the workplace in various ways. They might see low productivity, high employee turnover rates, and a general low return on investment (ROI) for the organization. Our VP of Sales and Solutions Architect Leader Phil Schrader discussed this topic with our friends at Culture Curated. Partners Season Chapman and Yuli Lopez shared several common ways they see leaders contributing to toxic workplace cultures. #1 You’re Fostering an Environment of Disconnection Yuli pinpointed siloes as a significant issue that impacts culture. “When there is poor work culture, you see it reflected in not enough connectivity between departments or peers. Siloes impact the way work is getting done.” The lack of connectivity not only affects internal operations but also has a tangible impact on customer satisfaction and organizations’ bottom line: A Towers Watson study found that strong internal communication strategies can lead to a 47% higher return to shareholders compared to the least communicatively effective firms. According to Forbes, siloes are often a trickle-down effect of conflicted leadership.The #1 key to solving this problem that plagues most organizations and creates a toxic workplace culture? Transparent communication. #2 - You Haven’t Defined Your Organization’s Core Identity Season notes that as an organization evolves, defining its core identity is crucial. It boils down, she says, to “being honest about what you want, what you need, and what competencies and behaviors you need your employees to display.” For example, while growth and innovation may have once defined a company, there may come a point when consistency and predictability become essential. If leaders fail to define this new season for their teams, results can be disjointed and a poor work culture results. With no clear sense of their organization’s purpose and identity, employees can struggle to connect their individual roles to the broader mission. This disconnect hampers motivation and engagement, ultimately affecting overall organizational performance. Conversely, a well-defined core identity is the compass that guides an organization toward success. It aligns teams, fuels innovation, and ensures a cohesive, purpose-driven workforce. #3 - You're Eroding Trust and Teamwork Every organization goes through seasons where employees are “in the trenches,” so to speak, when the work is challenging and collaboration is a must. Season shares that in healthy organizations, employees jump in and work together. Believing in and removing obstacles for each other has a catalyzing effect on the team and the results. However, where teams exhibit unhealthy competition, distrust, disengagement, or failure to communicate, a toxic work culture is born. Leaders can unintentionally foster these negative conditions by withholding information, showing favoritism, being disorganized, and failing to recognize and support their teams. On the other hand, when leaders model the collaborative, encouraging spirit they want to see in employees, they positively shape team dynamics, building trust and nurturing motivation. How One Model Helps Create Healthy Organizational Culture People Analytics is the answer to many culture challenges. The One Model People Analytics platform empowers HR leaders to effectively use their workforce data to understand and manage virtually every aspect of the employee experience. From Data to Decisions: What Is People Analytics?
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29 min read
Richard Rosenow
Welcome to the One Model resource page for books about People Analytics. This is meant to be a living document, so if we missed one of your favorites, please don't hesitate to reach out to Richard Rosenow with a recommendation. He's always looking to add to the library. Join the One Model Summer Book Club During June, July, and August, the One Model team and friends across the industry will be participating in an interactive summer book club. Connect with us on LinkedIn and join in on the conversation #peopleanalyticsbookclub. July People Analytics Book Pick: The Algorithm: How AI Decides Who Gets Hired, Monitored, Promoted & Fired & Why We Need to Fight Back Now by Hilke Schellmann "The Algorithm is fascinating to me because it caused a stir with the general public about how AI was changing the world of work, but I heard very little community discussion within People Analytics on this book. As this book is a journalistic deep dive into the algorithms and predictive tools that are used and monitored by many professionals in people analytics, I want that to change. I'm looking forward to discussing this book with all of you and the author in our July book club." - Richard Rosenow Join the conversation on LinkedIn. August People Analytics Book Pick: How to Measure Human Resource Management "This book, by Jac Fitz-enz and Barbara Davison, is a cornerstone of our field. It was groundbreaking when it was first published in 1984 and remains incredibly relevant today. Fitz-enz's insights into quantifying and evaluating HR practices were years ahead of their time, laying the groundwork for the sophisticated analytics we use now." - Richard Rosenow Past Book Club Books: June People Analytics Book Pick: Creepy Analytics by Dr. Salvatore V. Falletta "This book is already on my bedside table, and I cannot wait to dive in. As we continue to explore the power of people analytics, I find it essential to keep an eye on ethical HR practices." - Richard Rosenow Meet the Author Webinar - Register to hear the recording! Join the conversation on LinkedIn. People Analytics Essentials - Reading Starter Kit When it comes to books about people analytics, we are finally at a point where we have the luxury of having too many choices. The virtual library is well stocked and all of these books come highly recommended and vetted by our experts, but if you had to start somewhere, we recommend the following 7 books to kick things off. Work Rules - Best “painting the dream” “Work Rules” by Laszlo Bock is the first book I recommend to folk looking to learn more about People Analytics. Written by Google's former SVP of People Operations, this book offers a behind-the-scenes look at Google's unique approach to attracting and retaining top talent. Through compelling anecdotes and evidence-based insights, Bock presents actionable strategies for building a dynamic, innovative, and people-centric organization. Work Rules has held the top spot on my book recommendation list for a long time as it’s the right blend of real-life case study and inspiration. It’s not too technical, but you leave feeling excited to learn more about the space. Whether you're in HR and new to this people analytics space or looking for ideas about how to infuse people analytics into your existing practices, "Work Rules!" is a great introduction in how it paints the picture of what people analytics can look like at scale within an organization. Moneyball - Best at “building excitement” "Moneyball" by Michael Lewis (the movie is great, but the book is better!) serves as an unexpected guide for people analytics leaders. While it tells the story of the Oakland Athletics baseball team's innovative use of statistics, the takeaways from the book reach far beyond baseball. The application of data analytics to assess baseball players and make strategic decisions on the field makes for an easy transition to talking about how people analytics can assist business decisions. For those new to people analytics or HR, "Moneyball" offers relatable examples of how data-driven decisions can lead to surprising and effective outcomes. Its engaging narrative can even serve as a conversation starter with business leaders outside the typical HR function, demonstrating how unconventional thinking about talent practices, paired with data analysis, can lead to success. "Moneyball" is not only a gripping story but a playbook for those seeking to introduce people analytics into their organization. Excellence in People Analytics - Best at “Overall introduction” "Excellence in People Analytics" by David Green and Jonathan Ferrar is the classic resource now within the people analytics field. Their book is specifically tailored for those in the people analytics field or those seeking to embark on this fascinating journey. The authors, both renowned experts in the field, offer a comprehensive guide to understanding, implementing, and excelling in people analytics within an organization. With a blend of theoretical frameworks and practical case studies, the book provides a holistic view of how people analytics can drive better decision-making and foster organizational success. Ideal for HR professionals, analytics leaders, and business executives, "Excellence in People Analytics" will help set the stage for people analytics and inspire you to leverage data in innovative ways to enhance people processes. Sensemaking - Best at “reminding you that humans still matter” "Sensemaking" by Christian Madsbjerg is a thought-provoking exploration of the human context in the era of data and analytics. Madsbjerg argues for a balanced approach that marries data with a deeper understanding of human behavior, culture, and emotion. I go back to read Sensemaking every couple of years to remind myself to focus on the people in people analytics. Knowing what data can do for an organization is important, but it’s just as important in a people analytics role to understand the limitations of data. For those entering the field of people analytics or looking to expand their HR perspective, "Sensemaking" provides a unique standpoint, emphasizing that not everything can be reduced to numbers. It encourages readers to blend analytical thinking with empathy, intuition, and cultural awareness. This approach can lead to more nuanced and effective decisions in people management. "Sensemaking" is a must-read for those who wish to infuse their analytical work with human insights and achieve a more sophisticated and holistic understanding of the people they serve. Cartoon Guide to Statistics - Best at “Stats without fear” The "Cartoon Guide to Statistics" is a breath of fresh air for anyone who has ever felt bewildered by statistics. Whether you're a people analytics leader or an HR professional looking to dip your toes into the world of data, this lighthearted and engaging guide speaks plain English and turns complex statistical concepts into digestible and even enjoyable lessons. As someone who came to statistics later in life, this book was a blessing and I can’t recommend it enough. Through witty cartoons and crystal-clear explanations, the book proves that relearning statistics as an adult doesn't have to be a daunting task. In fact, it can even be funny! A breakthrough resource for those who may struggle with traditional statistical texts, the "Cartoon Guide to Statistics" offers a welcoming entry point to the crucial world of data analysis. The Fundamentals of People Analytics: With Applications in R - Best at “mastering the stats” While leading people analytics teams at Experian, Mastercard, Robinhood, and Roku, Craig has somehow also found time to teach, give back to the people analytics community, and write a full statistics textbook, end to end, with people analytics at the core. The Fundamentals of People Analytics: With Applications in R is what happens when a true practitioner sees a problem, no great statistical resources with HR folk in mind, and applies himself to it fully. The result is a masterwork guide to statistics for the people analytics professional. If you are looking to learn statistics for HR or build your confidence in the applications in HR, look no further than this book. Talent Intelligence - Best at “Talent intelligence” "Talent Intelligence" by Toby Culshaw explores the field of talent intelligence, an area adjacent to, but one that is starting to appear to be distinct from, people analytics. Overviewing the world of both internal and external talent markets, Culshaw's insights provide a deep understanding of how to strategically approach talent acquisition and talent management through data-informed practices. This book is an ideal recommendation for those involved in recruiting or those in people analytics seeking to expand their perspective. Whether you're an HR leader or a professional looking to understand the broader landscape of data-informed HR, "Talent Intelligence" offers a comprehensive guide to leveraging data to make informed talent decisions. It's an eye-opener for anyone wanting to deepen their understanding of the ever-evolving landscape of talent in today's business world. The field of People Analytics In this section are books that cover the full field of People Analytics. These are excellent overviews that share a range of topics from what this field is, to how to go about it, to case studies in the space. A great place to start for folk looking to learn more about how analytics is used to better understand the workforce. Excellence in People Analytics - Jonathan Ferrar and David Green The Power of People - Nigel Geunole, Jonathan Ferrar, and Sheri Feinzig People Analytics for Dummies - Mike West Introduction to People Analytics - Nadheem Khan and David Milnor People Analytics: How Social Sensing Technology Will Transform Business and What It Tells Us About the Future of Work - Ben Waber Work Rules - Laszlo Bock HR analytics: The What, Why, and How - Tracey Smith Predictive Analytics for Human Resources - Jac Fitz-enz and John Mattox II Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness - Alec Levenson Data-Driven HR: How to Use Analytics and Metrics to Drive Performance - Bernard Marr Human Capital Analytics - Gene Pease, Bryce Byerly, and Jac Fitz-enz The Practical Guide to HR Analytics: Using Data to Inform, Transform, and Empower HR Decisions - Shonna Waters The Basic Principles of People Analytics: Learn how to use HR data to drive better outcomes for your business and employees - Erik van Vulpen The New HR Analytics: Predicting the Economic Value of Your Company's Human Capital Investments - Jac Fitz Enz Specialized People Analytics Similar to the first section, this section is for books that cover a broad overview of the space, but for a more narrow vertical within the space. These are books that still touch people analytics, but specialize is sub-topics such as DEI, L&D, Workforce Planning and Talent Intelligence. Great for learners who want to go deep on a given topic or transition into people analytics from their prior field. Talent Intelligence - Toby Culshaw Inclusalytics: How Diversity, Equity, and Inclusion Leaders Use Data to Drive Their Work - Victoria Mattingly, PhD, Sertrice Grice, and Allison Goldstein Agile Workforce Planning: How to Align People with Organizational Strategy for Improved Performance - Adam Gibson Strategic Workforce Planning: Developing Optimized Talent Strategies for Future Growth - Ross Sparkman Next Generation Performance Management: The Triumph of Science Over Myth and Superstition - Alan Colquitt Learning Analytics: Measurement Innovations to Support Employee Development - John Mattox II, Mark Van Buren, and Jean Martin Adaptive Space: How GM and Other Companies are Positively Disrupting Themselves and Transforming into Agile Organizations - Michael Arena Positioned: Strategic Workforce Planning That Gets the Right Person in the Right Job - Dan Ward and Rob Tripp People Analytics focused Analytics, Data Science, and Statistics With the maturity of the people analytics space, we've seen a rise in textbooks covering the fundamentals of HR analytics from an anaytics technical perspective or statistical overview of the space. If you are looking to brush up on your technical knowledge or just starting down your journey with statistics and looking for an HR analytics textbook, you're in the right place. The Fundamentals of People Analytics: With Applications in R - Craig Starbuck Handbook of Regression Modeling in People Analytics: With R - Keith McNulty Handbook of Graphs and Networks in People Analytics: With R - Keith McNulty Introducing HR Analytics with Machine Learning: Empowering Practitioners, Psychologists, and Organizations - Austin Hagerty and Christopher Rossett Doing HR Analytics - A Practitioner's Handbook With R Examples - Lyndon Sundmark Storytelling with Data - Cole Nussbaumer Predictive HR Analytics: Mastering the HR Metric - Dr. Martin Edwards and Kristen Edwards General Analytics, Data Science, and Statistics The benefit of people analytics being a younger discipline in the analytics field is that we have many other disciplines that have gone down this path ahead of us. We can learn from analytics and statistics books across many disciplines and bring that knowledge back to people analytics. Here is a sample of books that come up frequently when speaking to people analytics leaders about their favorites from outside the field. Competing on Analytics: The New Science of Winning; With a New Introduction - Thomas Davenport and Jeanne Harris Weapons of Math Destruction - Cathy O’Neil The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World - Pedro Domingos Naked Statistics: Stripping the Dread from the Data - Charles Wheelan The Signal and the Noise: Why So Many Predictions Fail--but Some Don't - Nate Silver Cartoon Guide to Statistics - Larry Gonick Statistics in Plain English - Timothy Urdan Understanding Humans At the end of the day, we are people analytics, not just analytics, and with that comes a real need to understand our subject area - people! These are some of the goto books in the space and some of my favorites when it comes to engaging with the social sciences. They are hand picked for being engaging, thoughtful and educational reads. Moneyball - Michael Lewis Sensemaking: The Power of the Humanities in the Age of the Algorithm - Christian Madsbjerg Humanizing Human Capital: Invest in Your People for Optimal Business Returns - Stela Lupashor and Solange Charas Irresistible: The Seven Secrets of the World's Most Enduring, Employee-Focused Organizations - Josh Bersin The Model Thinker: What You Need to Know to Make Data Work for You - Scott Page The Undoing Project: A Friendship That Changed Our Minds - Michael Lewis Thinking, Fast and Slow - Daniel Kahneman Superforecasting - Phillip Tetlock, Dan Gardner Evidence-Based Management: How to Use Evidence to Make Better Organizational Decisions - Eric Barends and Denise Rousseau Humans at Work - The Art and Practice of Creating the Hybrid Workplace - Anna Tavis and Stela Lupashor Misbehaving - Richard Thaler It’s Not Complicated - Rick Nason Want to learn more from an expert about the space? Let's schedule time to chat. Measuring HR We won't get far with people analytics if we don't have well defined inputs, outcomes, or measures within HR. Understanding how HR is measured is critical to a people analytics team success. There is a full second library worth of HR measurement books out there and this section just scratches the surface. The HR Scorecard: Linking People, Strategy, and Performance - Dave Ulrich, Mark Huselid, Brian Becker Human Resource Management: People, Data, and Analytics - Talya Bauer, Berrin Erdogan, David Coughlin, and Donald Truxillo Investing in People: Financial Impact of Human Resource Initiatives - Wayne Cascio and John Boudreau Victory Through Organization: Why the War for Talent is Failing Your Company and What You Can Do About it - Dave Ulrich, David Kryscynski, Wayne Brockbank, and Mike Ulrich Investing in People: Financial Impact of Human Resource Initiatives - John Boudreau, Wayne Cascio, and Alexis Fink The ROI of Human Capital - Jac Fitz Enz Positioned - Dan Ward, Rob Tripp HR Technology Last but not least, the HR technology space has its own set of fantastic resources. We won't get very far in people analytics without technology to produce data, so understanding this space is critical. Here are some standout HR technology books from the past few years. Introduction to HR Technologies: Understand How to Use Technology to Improve Performance and Processes - Stacey Harris Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce - Ben Eubanks I, Human - Tomas Chamorro-Premuzic Talent Tectonics: Navigating Global Workforce Shifts, Building Resilient Organizations and Reimagining the Employee Experience - Steve Hunt Finished this list? Check out One Model's whitepapers and ebooks. Also, did we miss your favorite? Recommendations are welcome! Send Richard Rosenow your recommendations and we'll add to the list. Learn how to up your game with One Model's people analytics software.
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5 min read
Dennis Behrman
In the rapidly evolving landscape of Human Resources (HR), where technology and automation are reshaping the way businesses operate, the role of HR tech influencers has taken on paramount importance. As organizations navigate this dynamic environment, insights from trusted HR influencers have become indispensable in making informed decisions, adopting innovative tools, and embracing data-driven strategies. A testament to this influence is the recognition of Richard Rosenow with the "2024 Top HR Tech Influencer" award. The Role of HR Influencers in Technology Adoption HR departments across the globe have been in the process of transitioning for nearly 2 decades. From a subjective people operation to one of the most important analytical business assets. At the forefront of this transformation are the HR influencers whose insights and expertise have been guiding HR leaders through the emerging technology jungle and helping expand the roles inside their teams. in their HR strategies. The annual list of "Top 100 HR Tech Influencers," curated by Human Resource Executive and the HR Technology Conference & Exposition, serves as a testament to the pivotal role these influencers play in shaping the HR technology landscape. This list, now in its sixth year, comprises a diverse array of professionals, including analysts, consultants, and practitioners, who collectively represent the vanguard of HR technology thought leadership. Their contributions extend beyond their respective fields, encompassing thought-provoking perspectives, innovative solutions, and a deep understanding of the synergy between technology and HR. How do you become an HR Influencer? Becoming an HR influencer entails a journey of expertise, innovation, and consistent value delivery. Richard Rosenow's path to becoming an HR influencer exemplifies this process. With a background rooted in HR and technology, Richard leveraged his real-world experience building and leading people analytics teams to create insightful content and share actionable strategies across various platforms. Through engaging articles, speaking engagements, and thought leadership on LinkedIn, he demonstrated a deep understanding of HR technology's evolving landscape. Richard's dedication to staying updated on industry trends, sharing real-world solutions, and fostering meaningful connections established him as a trusted voice. His deep sense of caring and creating goodwill has also made him a friend to many. By consistently adding value, addressing pain points, and offering innovative perspectives, he garnered a dedicated following. One Model is honored to have Richard on our team. As a previous customer and evangelist who exemplifies our values, we could not be happier to have him on our team. More about the 2024 HR Tech Influencer Award The year 2024 marks a pivotal juncture in the realm of HR, with automation and generative artificial intelligence redefining traditional workplace dynamics. These advancements have underscored the need for HR operations to be optimized for efficiency, agility, and adaptability. HR departments are increasingly turning to technology to streamline processes, make informed decisions, and enhance overall organizational performance. The selection process for the "Top 100 HR Tech Influencers" list is rigorous and thorough, spearheaded by the editorial team at Human Resource Executive in collaboration with the HR Technology Conference organizers. The primary objective is to identify individuals who possess the transformative power to reshape how technology is leveraged within the HR industry. Rebecca McKenna, senior vice president of the HR portfolio at ETC, emphasizes the significance of this year's cohort, especially given the rapid advancements witnessed in HR technology. These influencers stand as beacons of reliable guidance, offering organizations across the globe profound insights and dependable advice. Interested in talking to Richard and the One Model team? Let us know! In conclusion, in a world where technology and HR are intricately intertwined, HR influencers have emerged as essential conduits of knowledge and innovation. Richard Rosenow's upcoming recognition underscores the significance of their contributions, reminding us that the path to HR excellence is paved by those who illuminate the way forward.
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Featured
5 min read
Dennis Behrman
Transitioning to a data-driven HR system can be daunting. So our VP of Sales and Solutions Architect Leader, Phil Schrader, met up with Yuli Lopez, Partner and Principal at Culture Curated, to discuss best practices for HR leaders embarking on or looking for guidance on that journey. Yuli describes her own mindset to this transition in the video below. Read on for a few additional tips for anyone going through or leading this level of change and upheaval. Embrace a Growth Mindset Adapting to digital systems is a learning curve for everyone involved. It’s crucial to approach this transition with an open mind, ready to embrace new methodologies and technologies. Yuli was two steps ahead in this regard. “[As an HRVP], it was exciting to be able to have information that I had been chasing,” she said. For teams or individuals feeling nervous about or resistant to using data in HR, she encourages new users to “jump in and put time on your calendar for just exploring.” She’s right. Venturing beyond familiar or unexpected information can unveil insights you weren’t even aware you needed. This exploration can help users begin to understand how data stories are constructed from visibility into the details impacting employees. Of course, a willingness to experiment works best in a culture where mistakes are seen as opportunities for growth rather than failures. A growth mindset not only enhances individual capabilities but collectively elevates the organization. Practice Adaptability The transition to using data in HR is less a straight line and more a series of learning opportunities, commonly thought of as obstacles. In this journey, embracing change and having the ability to pivot your HR mindset is paramount. Leaders who quickly adapt to the unexpected and use every challenge as a stepping stone towards innovation will cultivate a flexible environment. Open dialogue will be the norm, ensuring that every team member feels they have a voice in this transformative process. Adaptability is undergirded by two key characteristics (Source) that leaders must both personify and incentivize employees to develop: Emotional Resilience: The inner strength required to navigate through challenges and preserve mental and emotional health during times of change. Personal Responsibility: The commitment to proactively manage how we react to change, ensuring we are in control of our own development and progress. In addition to technical training, coaching on the dynamics of change and change management can be useful. Prioritize Collaboration Digital transitions benefit significantly from diverse perspectives and expertise, not to mention a strong, collaborative team. Engaging team members in the planning and implementation phases ensures that the digital solutions adopted are user-friendly and genuinely address the needs of the organization. Yuli notes that it’s important to partner with other departments. “There may be other aspects you’re not thinking about. If you go to them with a hypothesis, together you may be able to draw unexpected insights. This collaborative approach not only facilitates smoother adoption but also strengthens the sense of ownership among staff, fostering a supportive environment for change. Create a Strong Visionary Perspective Vision casters are like seasoned captains navigating through uncharted waters. They have a keen eye on the distant horizon, focusing on the incredible benefits that lie ahead. For a data transition, that could be delivering impactful insights across your organization and easily translating workforce data into cost allocations. These visionaries don't just keep these exciting perspectives to themselves; they share them, painting a vivid picture of the future and recruiting buy-in for an efficient process. As both cheerleaders and coaches rolled into one, these leaders are in the trenches, reminding everyone why the upheaval of transition is worth it. They champion and model patience and persistence, highlighting what every step closer to using data in HR means for the team, the organization and clients. How One Model Helps These mindsets are fundamental for HR leaders guiding their departments through the digital transition, but the technology of choice plays an enormous role in the outcomes of the journey. One Model provides the people analytics solution technology that orchestrates everything decision makers need to be able to quickly make brilliant workforce decisions.
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6 min read
Tony Ashton
The recent announcement by SAP SuccessFactors to sunset its legacy People Analytics product leaves SAP SuccessFactors customers facing significant uncertainty. The sunset signals the deprecation of some* key reporting technologies used by SAP SuccessFactors —Canvas, Classic, Table, and Tiles and Dashboard Reports. With these SAP SuccessFactors reporting tools being shut down, businesses reliant on them face potential operational hiccups in their decision-making processes. However, this development also presents a unique opportunity to explore superior alternatives that can ensure continuity, innovation, and enhanced analytics capabilities. In other words, it could be the perfect time to consider an upgrade. Why Settle When You Can Ascend In contrast to SAP SuccessFactors’ winding down approach, maybe it’s time to look for a forward-thinking analytics and reporting platform that does double duty: Addresses the immediate gaps left by the SAP SuccessFactor updates and provides a robust foundation for future growth. Look for a solution that offers: Advanced Analytics and Reporting: Leveraging state-of-the-art technology to deliver deep insights and customizable reporting capabilities that grow with your business. Seamless Integration: Effortlessly merge data from various sources, including SAP SuccessFactors, ensuring a smooth transition and continuity of operations. Future-Proofing Your Analytics: Ensure that your chosen solution’s analytics capabilities evolve to meet future challenges head-on with continuous updates and a commitment to innovation. Learn more about getting People Analytics out of SuccessFactors and your other HR tools. Transitioning to a More Capable and Dynamic Solution The journey from SAP SuccessFactors' legacy reports to a more sophisticated and comprehensive analytics platform like One Model can be seamless and transformative. Get started by: Conducting an Analytics Audit: Understand your current analytics and reporting needs and how they might evolve. Evaluating One Model’s Offering: Explore how One Model’s features and capabilities align with your business objectives. Planning for Migration: Leverage One Model’s support and resources for a smooth transition, ensuring minimal disruption to your operations. While the deprecation of SAP SuccessFactors’ legacy reporting tools marks the end of an era, it also opens the door to embracing a more advanced, flexible, and comprehensive analytics solution like One Model's SuccessFactors People Analytics Solution. By choosing to upgrade, organizations can not only overcome the challenges posed by SAP SuccessFactors’ transition but also position themselves for stronger, more data-driven success in the future. Is it time for an upgrade? Embrace the future of analytics with One Model—where innovation, integration, and insight come together to drive your business forward. Note: After recording this video we noticed that SAP SuccessFactors had deferred a couple of their deprecation announcements. Table Reports and Canvas Reports will stay around for a while longer, while Classic Reporting and the Tiles & Dashboards are still being deprecated. This illustrates the complex data structures and variety of different technologies at play in the SAP SuccessFactors reporting landscape remains a challenge. For a complete answer to this, come and have a chat with us. We'd love to show you a better solution. Let us know you're interested, and we'll reach out to schedule time.
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5 min read
Dennis Behrman
We asked our friends at Culture Curated why organizations should have a strong focus on human resource compliance. That led to a more foundational question: What is an organization’s culture? Culture: Ping Pong Tables or Compliance in HR? In the quest to boost workplace culture–and thus performance, our initial instinct might be to think of adding fun elements, like a ping pong table in the break room. However, the journey to improving corporate culture delves much deeper than surface-level entertainment. It begins with the bedrock of strong human resource compliance. In fact, “Any good culture is going to be built on the foundation of strong compliance,” says Season Chapman, Partner & Principal Consultant of Culture Curated. “It’s about how we must treat people.” But compliance isn't just about adhering to HR compliance laws or procuring a human resources compliance solution. It's about establishing a framework within which people are treated fairly and decisions are made responsibly. This foundation of compliance in HR is essential, not just for its own sake, but as the ground floor upon which the rest of the company culture is built. Laying Your Culture’s Foundation: Accountability and Belonging Moving beyond the notion that culture is merely about having fun, culture is–at its core–about accountability, achieving results, and fostering trust among team members. But how do we shift the conversation towards these deeper aspects of culture? The answer lies in starting with human resources compliance as the base layer. Drawing from psychological principles, humans seek a sense of belonging and connection. They want to feel aligned with the company's mission and vision. The secret to that goal starts with a focus on building meaningful relationships with employees and fostering a sense of belonging and support. In today's workplace, the concept of psychological safety is paramount for cultivating a culture where employees feel confident in sharing ideas. This safe space is critical for a vibrant, innovative workplace culture. Starting the Journey Towards a Balanced Culture So, how does an organization embark on this journey towards a culture that balances fun, compliance, and psychological safety? According to Yuliana Lopez, Partner & Principal Consultant of Culture Curated, “The starting point is an organizational assessment.” She explains that such assessments gauge the current state of compliance and how employees feel about their work environment and relationships with peers. This comprehensive evaluation can identify areas for improvement and set the stage for developing a culture that not only meets legal requirements but also fulfills and inspires its workforce. Are You Ready for the Coming Wave of AI Regulation for Human Resources? How One Model Helps With Compliance Foundations One Model assists with compliance by providing an integrated analytics platform designed to manage and analyze workforce data according to legal standards and best practices that includes: Offering advanced analytics and reporting capabilities that enable compliance with regulatory requirements. Prioritizing robust data security and privacy measures to protect sensitive information and comply with data protection regulations. Featuring role-based access controls so that only authorized personnel have access to sensitive data, in order to maintain continuous compliance with labor laws, and occupational safety and other standards. Providing customizable dashboards to monitor key compliance indicators, from wage and hour laws to benefits regulations and beyond. While the allure of quick fixes like a ping pong table may seem like an easy way to boost morale, the real work in improving culture goes much deeper. By establishing a strong foundation of compliance with human resource compliance solutions like One Model, organizations can lay the groundwork for a positive culture. This foundation enables leaders to enhance performance, foster genuine connections, and support the well-being of every employee. Wondering about compliance in the world of AI and Machine Learning? We’ve got you covered. 1. Understand how ethics are changing in a world with AI. Read more. 2. Be prepared with regulations coming to HR. Join the Regulations and Standards Masterclass today. Learning about AI regulations and standards for HR has never been easier with an enlightening video series from experts across the space sharing the key concepts you need to know.
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7 min read
Steve Hall
Reporting specialists and data analysts are often required to predict the future for stakeholder groups. They do this through a variety of models, including forecasting and annualization. Although both methodologies aim to predict future values, their applications and the mathematical logic behind them vary significantly, catering to different business needs. What is Annualization and Its Significance? Annualization is a mainstay for finance and accounting but there are situations where it may be useful in HR contexts. It can be used to estimate year-end values for turnover rates, total new hires, and job openings filled, based on current data. Annualization works well when: There is little volatility in the metric across time periods There is little seasonality in the metric The metric is not likely to trend upward or downward during the course of the year Simplify Annualization with One Model One Model streamlines the computation of annualized metrics. By selecting the "Year to Date" option and "Annualize" in "Time Functions," the system will only consider the current year's data, offering a clear example of annualization at work. The Case for Forecasting Forecasting provides several benefits over annualization. While annualization typically only considers data points from the current year, forecasting can: Utilize data from a much wider time frame and range of data points Factor in seasonal fluctuations and trends Provide a more nuanced view of potential future states with confidence intervals, which is especially valuable for HR metrics that exhibit variability (e.g., number of hires, number of terminations, and termination rates). Simplify Forecasting With One Model One Model simplifies forecasting with its Embedded Insights feature. Just create a time-series line graph for your metric and use the feature to extend your forecast to the year's end. Increasing the number of data points, by adjusting the time metric from monthly to weekly or daily, for instance, can enhance forecast accuracy by capturing shorter-term cycles that may be present in the data. Including data from at least 30 data points will improve the accuracy of your forecasts and if annual seasonality is present, including data covering two or more years will also improve accuracy. You can adjust forecast parameters to align the final forecast period with the year-end. After running the forecast, simply click on the last data point in the visualization to see the forecasted value and its confidence interval. For more complex situations where the current year data pattern is expected to shift relative to last year’s pattern, One AI can be used to create a predictive model that incorporates additional internal and external features to improve accuracy. Making the Choice: Annualizing or Forecasting? Annualization and forecasting each have their strengths and weaknesses. Deciding between them depends on your data and your stakeholders’ needs. Sometimes a rough approximation is good enough; other times, a precise estimate or a range of values (e.g., a confidence interval) will be required. Annualization Forecasting Only considers data from the current year Can leverage data from multiple years Only needs a single month of data to start the estimation process One Model will need at least 4 data points to produce a forecast, but forecast accuracy suffers with so few data points unless the metric progresses in a very linear fashion Does not adjust for seasonality or trend Accounts for trends and seasonality Very simple approach requiring little input regarding computations and easy to understand More sophisticated approach that may prompt questions from end-users (luckily One Model provide embedded information describing the forecast) Estimates made early in the year are likely to be inaccurate Estimates made early in the year are likely more accurate than Annualization, especially when data from the prior year are utilized Will always underestimate or overestimate if a trend is present Can produce more accurate results even when trend is present Alternatives and Strategic Adjustments Alternatives like the 12-month rolling average provide another strategy for estimating year-end values, accommodating changes anticipated over the year. For specific metrics, like annual turnover, manually adjust the year-end prediction using expert analysis or the expected effects of internal actions. Depending on the metric being forecasted, it may also be reasonable to manually adjust the year-end value based on general projections for the year. For instance, to predict next year's annual turnover, start with the current end-of-year rate and refine it using projections from external experts or by considering the expected effects of internal measures aimed at reducing turnover. One Model Simplifies Forecasting and Annualization You might encounter scenarios where estimating year-end values for a metric is necessary. Although predicting these values with absolute precision is challenging, One Model can generate reasonable estimates, bearing in mind that sudden changes mid-year could significantly affect forecast accuracy. In practice, forecasting, particularly with One Model's Embedded Insights, tends to be more effective than annualization, especially at the start of the year. However, the accuracy of forecasting is impacted by decisions related to data inclusion and model parameters. Forecasting may also require a bit more effort to maintain, albeit minimal. Fortunately, One Model simplifies the use of both annualization and forecasting. In fact, using both methods to create estimates can be practical. When the results are close, opting for the annualized figure might be preferable for its simplicity. If results differ, the underlying data should be evaluated and the method that best aligns with the data’s characteristics should be used. One Model has you covered regardless of the situation you face and the approach you prefer or choose.
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8 min read
Richard Rosenow
When considering implementing a people analytics solution into your organization, an important first step is to consider if you should buy an out-of-the-box solution, build one yourself from scratch, or buy a flexible solution you can build upon. If you choose to build on your own, have you considered the ongoing maintenance requirements and costs you’ll encounter over time if you choose to build on your own? If you choose this DIY approach, you’ll have to constantly allocate valuable internal resources towards updating the system and keeping it running — pulling your teams away from more strategic and impactful work. Instead, you could partner with a trustworthy people analytics vendor to take that maintenance off your team’s hands. Let’s dive into what maintaining a people analytics solution entails and why it’s so important. Then, we’ll explore how choosing the right vendor can help you ditch the DIY drama and keep your people analytics solution running smoothly. The Continuous Journey of Maintenance The allure of developing an in-house people analytics solution is often marred by underestimating the ongoing commitment required for maintenance (as my colleague, Shiann, learned from her own in-house development lessons). Unlike the initial setup, maintenance is a continuous journey, marked by the need to adapt to new technologies, regulatory changes, and evolving organizational needs. The pitch from internal teams who want to build their own systems that a central pool of resources will keep the analytics platform updated often falls short when confronted with the reality of constant evolution in HR systems and practices. The Complexity of Maintenance Maintenance encompasses much more than fixing bugs or updating software; it involves adapting to new data sources, integrating evolving HR technologies, and ensuring all systems remain aligned with organizational objectives. The challenge compounds when internal teams are tasked with maintaining a system built from scratch, as they must juggle maintenance on top of fire drill tasks, innovation, and the strategic redirection of HR practices. Vendor Advantages: Specialization and Scalability Vendors specializing in people analytics bring a wealth of experience and resources dedicated to the development, deployment, and maintenance of people analytics solutions. Their focus on HR technologies and data models allows them to offer solutions that are not only up-to-date with the latest trends and technologies but also scalable to accommodate organizational growth and changes in HR practices. Expertise and Efficiency People analytics vendors are equipped with specialized teams that understand the nuances of HR data, ensuring that maintenance is not just about keeping the system running but optimizing it to deliver actionable insights. Economies of Scale By serving multiple clients, vendors can spread the cost of maintenance, research, and development across their customer base, allowing for more significant investments in innovation and security. Proactive Evolution Vendors continuously update their platforms to incorporate new features, integrations, and best practices, ensuring that the analytics solution remains at the forefront of HR technology. Navigating Vendor Selection and Partnership While the benefits of partnering with a vendor are clear, not all vendors are created equal. It's crucial to conduct due diligence to ensure that the selected vendor has a proven track record, a robust maintenance and support system, and the flexibility to adapt to your organization's unique needs. Experience and Compatibility Look for vendors with experience in the systems you need (HRIS, ATS, survey, etc.) and those who have successfully navigated the complexities of integrating diverse HR data sources into a unified model. Support and Maintenance Model Understand the vendor's approach to maintenance — whether it's a named resource tracking your account or access to a central pool of experts. Ensure that their support system aligns with your organizational needs and expectations. Subject Matter Expertise Review the vendor’s leadership team and customer teams for a background in HR or the people analytics space. There are many data vendors out there, but there are only a few that focus on and care deeply about what it means to work in HR. That nuanced understanding shows up in how they care about your needs, what new HR support tools are on the roadmap, and how they spend their time developing solutions. Scalability and Adaptability The chosen vendor should demonstrate the ability to scale their solution in line with your organizational growth and the agility to adapt to emerging HR technologies and practices. You don’t want to have to switch vendors later in your people analytics journey once you realize they can’t handle more complex tasks. Why One Model Is Your Maintenance Partner for People Analytics When it comes to the crucial role of maintenance in people analytics, partnering with a vendor like One Model offers a comprehensive and streamlined approach that can significantly enhance your team's efficiency and focus. Here's how One Model stands out as a true partner to HR and people analytics teams with maintenance tasks: Seamless Data Pipeline Maintenance One Model proactively manages data pipeline maintenance, especially in scenarios where a vendor changes their API — which happens often. This adaptability ensures that your analytics operations remain uninterrupted and consistently reliable, removing the burden from your internal teams to monitor and adjust to these external changes. Data Engineering Support Included With One Model, break-fix solutions and ongoing data engineering support are integral parts of the subscription service. This means your team has continuous access to expert assistance for any technical issues that arise, ensuring minimal downtime and optimal performance of your analytics platform. Integrated Platform Workflow One Model's platform is designed to work in harmony, ensuring that changes in the data orchestration tools People Data Cloud™ are immediately reflected in the data storytelling front end and OneAI advanced analytics toolkit. This integration eliminates the common headache of fixing broken dashboards due to data table changes, enabling a smoother workflow and more reliable data visualization. Monitored Site Reliability Ensuring the reliability of your people analytics platform is paramount, and One Model takes this responsibility seriously. By putting One Model in charge of site reliability, we provide peace of mind that your analytics tools will be available when you need them, supporting on-demand access to workforce insights. Focus on Analytics, Not Software Maintenance By taking on all software-related aspects of the build and maintenance, One Model allows your team to focus on what they do best: deriving meaningful insights from people analytics. This division of labor maximizes the value your team brings to strategic decision-making, consulting, and insight-creation, without being bogged down by the technical complexities of software maintenance. Learn why more enterprises are turning away from proprietary solutions Read the Evolution of the Buy vs. Build Conversation today The Case for Vendor Partnerships The decision to partner with a vendor for people analytics should not be taken lightly. It involves weighing the benefits of access to specialized expertise, efficiency gains, and the ability to stay ahead of HR technology trends against the perceived control and ownership benefits of an in-house solution. However, when considering the long-term implications, particularly in the realm of maintenance, the argument in favor of vendor partnerships becomes compelling. Maintenance is not merely a technical challenge; it's a strategic imperative that ensures the people analytics platform remains relevant, effective, and aligned with organizational goals. In this context, vendors offer a partnership that transcends the mere provision of technology; they become collaborators in the journey towards achieving HR excellence. In conclusion, as organizations navigate the complexities of modern HR practices, the choice of partnering with a vendor for people analytics offers a strategic advantage. It ensures access to cutting-edge technology, specialized expertise, and a scalable solution that evolves in tandem with the organization. The maintenance of a people analytics platform is a journey best undertaken with a partner like One Model who brings not only technology but also a commitment to innovation and excellence in the field of HR analytics.
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6 min read
Dennis Behrman
In a timely conversation on DEI data, Phil Shrader of One Model and Season Chapman and Yuli Lopez of Culture Curated shed light on the importance of diversity and inclusion data analytics. While strides have been made in leveraging people analytics to propel the DEI movement forward, they reveal a stark reality: The journey towards achieving comprehensive diversity data standards is far from over. What’s Missing and What’s Present in Your DEI Data? As we delve deeper into the complexities of gathering DEI data, it becomes evident that significant gaps in what is collected hinder progress toward truly inclusive environments. Critical areas needing attention and improvement include: Performance reviews and gender bias Season Chapman highlighted a concerning statistic: In a significant study, 66% of women received negative personality-related feedback in performance reviews, compared to less than 1% of men. (Source) This discrepancy not only exposes a gender bias but underscores the need for a more nuanced approach to evaluating performance and collecting performance data. By systematically analyzing both the written and verbal components of reviews, organizations could begin to identify biases entrenched in their evaluation processes. Ageism and Strength-Based Diversity The often overlooked dimension of age bias, dubbed by the American Psychological Association as 'the last socially acceptable prejudice,’ highlights a gap in DEI initiatives’ predominant focus on racial and gender bias. Season also highlighted the tendency to emphasize weaknesses rather than strengths in organizational cultures. Incorporating strength-based analytics into DEI strategies could revolutionize how talents are matched with roles, fostering a more inclusive and productive workplace environment. Do you track and measure these 4 diversity metrics? Awareness of DEI Data Bias Types The above examples and many others highlight the significant potential for bias in data and data collection. Bias can exist within current data due to a variety of factors, including but not limited to: Historical bias can exist when past data, such as male-dominated hiring patterns, favors men over women for certain roles. Representation bias can occur when data used to train an algorithm may over- or underrepresent some groups. An example of this is found with facial recognition tools that produce higher error rates for certain groups. Measurement bias can happen when data that is collected disproportionately values behaviors or achievements that are more accessible to a particular group. Algorithmic bias can result when algorithms use their own predictions to make future decisions, which can replicate and even amplify existing biases in the dataset. It’s important to note that there’s no such thing as completely bias-free data. (Source) But we must seek to mitigate bias in our analytics by choosing effective technology, increasing our awareness of how it occurs, and applying safeguards. 3 Key Considerations in Advancing DEI Through Analytics Exploring the landscape of diversity data reveals three pivotal areas essential for effective DEI strategies: Accurately interpreting and applying DEI data: To achieve this, organizations can use advanced analytics and visualization tools that enable stakeholders to see beyond the surface-level numbers. This enables them to identify underlying patterns and insights that drive targeted, effective DEI interventions. Ensuring data collection methods capture the full diversity of an organization: This involves developing and implementing data collection strategies that are inclusive of all identities and experiences, thus mitigating biases that could skew the understanding of the organization's diversity landscape. Addressing privacy, confidentiality, and bias in data and algorithms: Organizations should establish multidisciplinary ethics committees that regularly review data collection, analysis practices, and algorithmic decisions for biases. This oversight ensures continuous alignment with ethical standards and promotes fairness and equity in all AI-driven DEI decisions. How One Model Supports DEI Initiatives Modern enterprises must do more than just track hiring metrics; they need to deeply analyze diversity data to drive genuine improvements. Leveraging people analytics software like One Model enables organizations to reduce bias and harness insights for crafting policies that foster long-lasting diversity and inclusion. Our clients use One Model's powerful analytics to visualize and monitor their DEI journey, establishing robust strategies that not only report but actively shape a more inclusive workplace. Ready to project your diversity in 5 years? One Model can calculate that for you.
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4 min read
Richard Rosenow
The world of people analytics is at a crossroads. On one side, the potential for data-driven decision-making in HR is incredible, offering insights that can transform organizational dynamics and employee engagement. On the other, a stark reality exists: a significant gap in the talent pool, especially when it comes to finding talent ready to tackle the data engineering side of people analytics. This gap isn't just a minor inconvenience; it's a major roadblock for HR departments aiming to leverage the full power of data analytics. Let's unpack why this is a critical issue and how companies like One Model are addressing it. Talent Challenges in Building In-House Solutions Developing an effective people analytics platform is no small feat. It requires an end-to-end team with a diverse set of skills, from data engineering and data science to HR expertise and software development. But finding individuals who possess these skills is a daunting task that often requires extensive time and resources to source, recruit, and onboard. Once onboarded, the innovation gap can become quickly apparent. Data engineers and data scientists thrive on solving novel complex problems, but we’ve seen the maintenance and iterative improvement of in-house HR technology can lead to disengagement and high turnover for this group. Especially given the rare blend of skills these professionals possess and high market demand. Moreover, every hour spent by your HR or IT team on developing, troubleshooting, and maintaining an in-house analytics solution is an hour not spent on value creation or strategic initiatives. As organizations grow and change, so too do their analytics needs. Building a solution that can scale and adapt with these changes without significant additional investment is a formidable if not impossible challenge, often straining resources further. Platforms are not a one-and-done investment. The Strategic Advantage of Vendor Partnerships Partnering with a people analytics platform vendor like One Model brings a wealth of experience and a team of experts who are continuously engaged in the development and refinement of the platform. This immediate access to expertise translates into scaled reporting and sophisticated analytics capabilities that are ready to use on day one. By starting with a vendor, organizations can keep internal resources focused on strategic priorities, leveraging the headstart provided by the vendor rather than getting bogged down in technicalities. Vendors operate at scale, serving multiple clients with the same infrastructure. This allows them to offer powerful analytics capabilities at a fraction of the cost it would take to develop similar functionalities in-house. Additionally, vendors are motivated by competition and client’s needs to continuously innovate and improve their offerings, ensuring organizations benefit from these innovations without additional investment. One Model: A Case Study in Vendor Excellence When it comes to overcoming the talent challenges of building and maintaining a sophisticated people analytics platform, One Model stands out. Not only do we offer incredible careers for data engineers, working on challenging and impactful projects across the analytics space, but we also maintain an incredible retention rate for that talent. Our approach to dedicated support means that the data engineer who implements your solution often stays on to support your subscription, offering deep familiarity with your organization's specific needs and challenges. Our leadership, including our CEO who comes from a data engineering background, ensures that our solutions are not only technically advanced but also perfectly tailored to the real-world needs of people analytics. This level of expertise and commitment positions One Model as a partner who understands the intricacies of people analytics from a data engineering perspective — making us an attractive, cost-effective, and strategically sound alternative for organizations looking to leverage the full power of people analytics without the challenges of staffing. Conclusion While building a people analytics solution in-house from the ground up may seem appealing, the practical challenges and talent implications often make partnering with a vendor the safer choice. One Model offers a history of success, expert support, and an innovative platform that continues to evolve to meet your organization's needs, ensuring you remain at the forefront of HR technology revolution. Choose One Model, where data engineering talent meets HR innovation, and let us help you leverage insights to attract, retain, and develop top talent effectively. Learn how One Model can help you.
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5 min read
Richard Rosenow
In today's dynamic job market, the transition to skills-based hiring is gaining momentum. This approach focuses on evaluating candidates based on specific skills rather than traditional factors like education and work history. However, as HR professionals, it's essential to recognize that skills-based recruitment can only reach its full potential when built upon a solid job title taxonomy. The Missing Link: Job Taxonomy A job taxonomy or job architecture is like the foundation of a house – essential for stability and structure. It's a framework that classifies jobs based on a variety of factors and needs. Think of it as a common language that allows everyone in your organization to clearly understand the definition of roles and their place in the bigger picture.. This is an important start because if we don’t have agreed-upon language to talk about the hierarchies of roles, even your most basic reporting fails. Without a clean job taxonomy, you could easily find yourself struggling to report on something as basic as "engineering talent." In the past, teams might have managed without a perfect job title taxonomy, but those days are long gone. With the growing complexity of the workforce, an increase in HR technologies, and the need for firm foundations for people analytics, a well-structured job taxonomy is now essential. Addressing the Pitfalls in Skills-Based Approaches Unfortunately, there seems to be a growing misconception that skills-based hiring methods somehow eliminate the need for clean taxonomy and data architecture. This oversight is akin to skipping your vegetables – it might seem tempting, but it's not sustainable. This fallacy is based in part on the limitations of current skills-based hiring itself and the need for more case studies in practice. A good starting point includes: Recognizing the Path It can be helpful to see skills-based hiring not as the perfect, new, fully-formed solution for workforce management, but as a step in the evolution from education-based and job-title-based approaches. Both of those prior methods were shortcuts that never got granular enough to really capture human capability. While a skills-based approach is an improvement, it’s still simply a shortcut to understanding human capabilities. Right now, it loosely conveys "we're going to be more careful" in assessing candidates against the actual requirements of the job. The question is whether organizations have the data architecture to support it. And are they getting the buy-in from other business functions to capture the true value of becoming skills-based? Improving Our Shared Language Skills-based hiring isn't just about evaluating skills for individual positions; it's about identifying critical skills that drive business growth and introducing language that clarifies the space. For example, expected skills should align with job profiles, while assessed skills reflect individuals within your company. And it doesn't seem like anyone is even talking about 'potential skills' yet. These distinctions are crucial for clarity. Balancing Granularity and Hierarchy Just as we would say "workspace" instead of listing every item on our desk, skills-based hiring requires a balance between granularity and hierarchy. While detail is necessary for technological advancements, we still need the broader terms for everyday conversations about work. For example, it would be helpful to list all the stuff on and around our desk to an organizational consultant who was helping us tidy up, but "workspace" is sufficient for most conversations. The same thing applies to skills. In some cases, saying "People Analytics" skills is more practical than listing specific roles like data analysis, storytelling, data engineering, consulting, or research. But in others, it could cause confusion to try to have a discussion at that level. We need that granularity of an individual skill to enable tech advances (e.g. talent marketplaces, job matching, talent assessments). But we still need the hierarchy and rollups of the skills into roles and job families to continue our day to day conversations about workforces. Both are required to make skill conversations meaningful. Think of Job Taxonomy as a Verb It can’t be overstated that job taxonomy isn't a one-and-done task; it's a living entity that evolves with your organization and pays out dividends over time. It should perhaps be thought of as an ongoing verb, not a one-time noun. And a clean taxonomy’s pivotal role in various HR functions – from workforce planning and compensation analysis to talent acquisition and learning and development – highlights even further how important it is. Unfortunately, its initial price tag can appear high enough that some teams have trouble forecasting the benefit. Job title taxonomy is tied into so many projects, though, that it's a must-have as soon as you can get it. Without solid taxonomy, integrating skills in particular into the recruitment process becomes a daunting or impossible task. For now, starting with static expected skills for current jobs, updated quarterly or even annually, would be a massive first step from a profile-based view of the world and unlock a lot of new opportunities. It's good to start small in this space. The Starting Point: Standardized Job Taxonomy In the meantime, perhaps we can somehow translate the fervor around skills-based hiring into conversations about meaningful data architecture and data engineering funding. While not as glamorous, data standardization is an indispensable foundation for the success of skills-based recruitment, the glue that holds it together. One Model helps by helping you set up a true people data platform that is customizable and transparent. Learn how to build a people data platform that will allow you to do better skills-based hiring.
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9 min read
Steve Hall
In organizational management, span of control plays a key role in defining how streamlined and agile a company can be. Understanding the Span of Your Manager-to-Employee Relationships At its core, span of control refers to how many people a manager or supervisor directly oversees. The optimal number depends on a variety of factors including job type and job level, and most organizations set targets using rules of thumb and experience. The span of control metric helps determine if the organization is structured appropriately, with too large a span of control leading to ineffective management and manager burnout, and too small a span of control leading to inefficiency. To calculate your average span of control, divide the total number of direct reports by the total number of supervisors. For instance, if there are 100 direct reports to 10 supervisors, the average span of control is 10. Exploring the 2 Types of Span of Control In the context of organizational structure, span of control is classified as either wide or narrow. Each type presents unique advantages and challenges, so it is not a one-size-fits-all proposition. The choice between a wide and narrow span of control depends on various factors, including: The nature of the organization's work and its structural preferences Industry norms Complexity of tasks Managerial capacity Job level Both wide and narrow spans have their place, even across departments and job levels within an organization. The key is to find a balance that maximizes efficiency, promotes effective management, and aligns with the organization's overall goals. Wide Span of Control In a wide span of control, a single manager supervises many subordinates. This structure is often seen in companies with flat organizational structures, with fewer layers between the top and bottom levels and a shorter chain of command. Wide structures are also more common at lower levels in organizations. Features: Low supervision overhead costs Prompt response from employees Improved coordination Suitable for repetitive or low-skill tasks Advantages: Encourages delegation of authority Facilitates better manager development Ensures clear policies Promotes autonomy among subordinates Fewer levels in the managerial structure Cost-effective Suitable for larger firms and repetitive tasks Well-trained subordinates Disadvantages: Risk of supervisors being overburdened Potential loss of control for superiors Need for highly qualified managing employees Hindered decision-making Increased workload for managers Unclear duties for team members Confusion among subordinates Management challenges in large teams Reduce manager-employee interactions Narrow Span of Control Conversely, a narrow span of control is characterized by a manager overseeing a smaller number of subordinates. This approach is prevalent at the top or middle management levels, especially when tasks are complex and require more support from superiors. Features: Ideal for new managers to gain supervisory experience Beneficial for managing remote or diverse teams Necessary for jobs requiring frequent manager-employee interactions Useful in new operations and for employee training Advantages: Easier communication and management in small teams High specialization and labor division Better opportunities for staff advancement Direct supervision by managers over each subordinate Effective communication between subordinates and managers More layers in the management structure for easier control Improved management control and effective supervision Disadvantages: The potential of stifling of employees' creativity due to excessive manager control Slower decision-making in extended hierarchies Limited cross-functional problem-solving Higher costs due to more managerial positions Delays in information transmission and decision-making The Challenge of Manual Span Management Effective span management is a balancing act, nearly impossible to achieve without technology. Strong span management requires examining spans vertically, horizontally, and over time; this creates a complex situation that is not easily or effectively handled without well-orchestrated data. Span Management Impacts A high manager-to-employee ratio might lead to insufficient attention to each team member, potentially affecting employee development and performance. Conversely, a low ratio could indicate inefficiencies and a bloated organizational structure that erodes profitability. Span Management in Different Industries Span management requires a tailored approach, as the ideal ratio varies by industry and job function. In labor-intensive industries, a higher ratio is often more manageable, whereas in knowledge-based sectors, a lower ratio might be preferable to ensure quality supervision and mentorship. Seasonal Staffing Certain industries or departments may experience fluctuations in workload at different times of the year, necessitating a flexible approach to span management. During peak seasons, managers may need to handle more direct reports or delegate responsibilities more effectively, while in slower periods, they may focus on training and development. A dynamic strategy can maintain efficiency without compromising the quality of supervision or employee growth. The Role of HR and Analytics in Span of Control Human Resources plays a critical role in monitoring and adjusting the span of control. HR can track this metric in real-time by using analytics tools to help maintain an optimal balance. People analytics software like One Model offers capabilities to analyze and adjust management span of control across various levels and departments, ensuring organizational efficiency and employee satisfaction. Data-Driven Span of Control Analysis Span of control analyses help organizations identify optimal structures and make precise staffing decisions in response to changes over time. Using people analytics tools, HR can dissect span of control across different dimensions such as department, geography, and manager level. Analysts should examine span of control: Both vertically and horizontally, and over time Relative to gross and net revenue Relative to employee-related outcomes such as engagement and retention It is not practical or effective to evaluate and manage span of control manually; this is an area where robust data can be used to drive effective decision making and optimize outcomes. However, to kickstart this analysis, even basic data from a core HCM or HRIS system can be enlightening. Metrics like span of control and organizational layers are akin to stepping on a scale — they provide immediate feedback on the state of your organizational structure. Within this discussion, key metrics such as span of control trends and visualization of layers and organizational units are invaluable. One crucial metric, for instance, is the number of managers with only one or two direct reports. This simple statistic can reveal much about the nature of your management structure. These insights are essential for keeping talent management processes aligned with business reality. If your current team or technology cannot readily provide these views, it may be time to reconsider your approach and tools. It took our team under 5 minutes to find the ratio between managers and non-managers. How long will it take your team to answer Question #38 on the People Analytics Challenge? Setting Targets for Span of Control Setting the right targets for span of control involves considering various factors, including industry norms, organizational structure, and management levels. A higher ratio may be effective for frontline or production roles, while senior management might require a lower ratio to strategize and lead effectively. Organizations often set their span of control targets based on industry benchmarks, aiming for a median that balances efficiency and managerial attention. Variations in span of control targets can be set for different organizational units, such as contact centers, corporate offices, and field operations. But the best organizations strive to surpass industry norms and link span of control metrics with outcomes of interest such as efficiency, profitability, employee engagement, and voluntary turnover. By doing so, they can optimize span of control to drive desired outcomes. Mastering Span of Control with One Model Understanding and effectively managing the span of control is crucial for any organization seeking to optimize its structure for maximum efficiency and employee development. With One Model, organizations can gain the insights needed to make informed decisions about their management structures, ensuring they are well-equipped to adapt to changing market demands and internal growth dynamics. One Model also supports next-level span-of-control analytics by allowing organizations to link span-of-control with operational metrics, moving the organization from descriptive analytics into the realm of optimization. After all, blindly following industry benchmarks won't ensure optimization within the organization. One Model is equipped to support optimization through the modeling core HRIS data, employee engagement data, employee performance data, and operational data related to production, safety, and financial outcomes. If you aren’t using a tool to measure and track span of control, you’re missing out. If you aren’t linking span of control to business metrics that matter, you’re really missing out.
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6 min read
Richard Rosenow
In the rapidly evolving field of People Analytics, a pressing roadblock has come to the forefront: the need for remote eligibility in senior roles. This isn't just a passing trend; it's a strategic imperative shaped by market realities and the nature of People Analytics itself. Let's dive into why every organization looking to lead in People Analytics should consider making their senior roles, if not all of their People Analytics roles, remote eligible. 1. The Scarcity of Senior People Analytics Leaders The first point to consider is the tight talent market of senior People Analytics leaders. In cities big and small, from New York to San Francisco, the pool of top-tier professionals in this niche field is still small. My experience in talent intelligence and location strategy shows that expecting to find a world-class leader in your immediate vicinity is wishful thinking. Opening up the Search for Remote Talent With a remote search, organizations can open their roles to a wider, more diverse range of candidates. This approach isn't just about filling a position; it's about finding the matched people analytics leader for your organization who can bring the right perspectives and drive innovative strategies in People Analytics. 2. Talent Density and the Geographical Challenge People Analytics, a relatively nascent and specialized field, overall lacks the talent density seen in more established areas of HR like recruiting or compensation. This reality requires a more tailored approach to building and leading teams, usually involving multiple sites and sometimes sites in multiple countries. Increasingly, People Analytics teams are distributed, with components in multiple locations or even outsourced, which essentially establishes the team as a remote team. “If one person on the team is remote, the team needs to act like a remote team” - Darren Murph (Remote Work Expert) The Case for Remote Leadership In such a scenario, anchoring a leader to a single location is counterproductive. A leader's effectiveness in People Analytics hinges on their ability to manage and integrate their team. Remote work facilitates this by allowing the People Analytics leader to lead by example, demonstrating what it means to be remote at the company. 3. People Analytics Teams: Pioneers of Remote Work Research A critical aspect often overlooked is that People Analytics leaders are not only avid followers of the academic work in this area but also that they are likely to be the pioneers of remote work research. Over the past five years, these senior leaders and their teams have studied and understood the nuances of remote, hybrid, and in-person work models. The Informed Choice of PA Leaders People Analytics leaders are making informed personal choices based on their research and understanding of work models. They're increasingly opting to stay put or seek remote roles, knowing full well the impact and potential of remote work arrangements. This trend isn't just about personal preference; it's about leading by example and embracing what they've learned through their research. 4. The Wide Reach of People Analytics People Analytics is not confined to a single department, function, or stakeholder; it spans across the entire organization (even outside of HR). Senior leaders in this field need to interact with various stakeholders across different departments and locations. Remote Work: A Practical Necessity Given this broad scope, the traditional model of a leader bound to a single office location becomes impractical. Whether it's through phone, video calls, or email, much of the People Analytics leader's role already functions in a remote capacity as they interact with a variety of stakeholders globally on a daily basis. Formalizing this as a remote role eligible role acknowledges the existing operational reality. 5. The Relocation Resistance Among PA Leaders In my interactions with job seekers and executive candidates that we’ve spoken to as part of the One Model People Analytics roles page project, a clear trend emerges: top talent is increasingly reluctant to relocate. They are turning down roles that require them to move or just not engaging with those recruiters. This isn't just a preference; it's a decisive factor in job selection. The Untapped Talent Pool There is a significant talent pool waiting for remote opportunities. Organizations not offering remote options for positions like PA Leader, PA Director, or VP of People Analytics are missing out on this talent. This isn't about accommodating personal preferences; it's about accessing the best in the field. Join the conversation on Linkedin. Conversation with feedback from PA Leaders Summary: A Call to Action for the HR Community The evidence is clear: the future of successful People Analytics builds lies in remote eligibility for hiring. While there are arguments for in-person roles, maybe for junior staff (largely unproven), the need for remote eligibility in senior positions is undeniable. As an HR community, we must recognize and adapt to this reality to connect the best talent to the right teams. Embracing Remote Work It's time to rethink how we approach senior roles in People Analytics. By embracing remote work, we can tap into a broader talent pool, foster innovative leadership, and align with the forward-thinking nature of People Analytics. Post your Senior People Analytics roles as remote opportunities! People Analytics Roles. Employers: Need a secure people analytics platform that ensures you can have a remote workforce? Reach out for a demo of One Model.
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17 min read
Richard Rosenow
The landscape of People Analytics and HR Technology is rapidly evolving and staying on top of the latest trends and insights is crucial for professionals in this field. To understand where other experts are turning for their insights and inspiration in 2024, we surveyed people analytics practitioners. Our aim? To discover which conferences are on their radar - the ones they plan to attend and those they aspire to make it to someday. Let's dive into the results of this survey, revealing what's hot on the conference circuit this year! Who's in the Spotlight? Role of Respondents We had a diverse group of practitioners from a number of disciplines, but a vast majority were working on or for people analytics teams and projects (100+ of the responses). A handful of HR tech and HR Ops leaders who did not have People Analytics teams also replied. We had a small number of vendors/consultants and academics take the survey as well. While these groups were not the primary focus of our analysis, their presence reflects the conferences' role in business development, networking, and the diverse perspectives in the field today. Seniority Breakdown Director+: A third of the respondents held positions at the director level or higher. This substantial representation emphasizes the strategic importance these conferences hold for senior decision-makers. Manager / Sr. Manager: Approximately a quarter of respondents were in managerial roles (either Manager or Senior Manager). It's worth noting that in many developing people analytics teams, these roles might be the highest-ranking members as the function evolves. Individual Contributor: The remaining respondents (~40%) identified themselves as Individual Contributors, constituting the most represented group. This not only underscores the active interest and participation of operational-level professionals in people analytics conferences but also accurately reflects the structure of seniority within People Analytics teams. Naturally, due to organizational design, there will be more Individual Contributor representation than Director/Manager. Company Size Breakdown 20,000+ Employees: This is the largest segment, comprising about half of the respondents. It suggests that major corporations view these conferences as crucial for their people analytics strategies and initiatives. These are also likely the teams with available budgets for professional development. 5,000-20,000 Employees / 1,000-5,000 Employees: Roughly 20% of respondents belong to each of these ranges. This underscores the importance of these conferences for large organizations that have established people analytics functions but aren't as big as the largest corporations. Less than 1,000 Employees (a. <1,000): The smallest segment unsurprisingly comes from smaller organizations. We’ve heard that in these organizations, employees often have multiple roles or teams that they balance, making it difficult to find time to attend these conferences. Top Conferences on the Radar Drumroll please, here are the top planned conferences! Planned Conferences Four conferences were identified by more than 20% of practitioner respondents as events they would attend. Several other conferences fell within the 10-15% range. However, for simplicity, we've only listed conferences that garnered 20% interest or higher below. There are indeed many excellent conferences (more listed below), but these are the standouts this year: SIOP Chicago Roughly 1/3 of those who replied to the survey planned to attend SIOP! This stood out by far from the other conferences. For some in the people analytics space, this may come as a surprise, but for those who have attended SIOP in the past, this makes a lot of sense. SIOP attendees are loyal. SIOP is the annual gathering of the Society for IO Psychology professionals (but open to anyone interested) and sees upwards of 5,000 IO Psychologists descend on a new city each year for four days of intensive conference activities. With around 10 concurrent sessions every hour, there is content for everyone, leading to a healthy dose of FOMO. Specifically, SIOP provides a fantastic experience for people analytics leaders and practitioners. I had the opportunity to attend SIOP in 2023, and it was memorable for its rigorous debates, insightful discussions, and excellent networking opportunities. The sessions I attended on AI ethics, employee listening, recruiting analytics, and assessments were some of the best in-person content I've experienced. Additionally, the impromptu conversations in the hallways with new and old friends were incredibly valuable. If you’d like to learn more about SIOP and how a People Analytics team may benefit, please read my review here: A People Analytics Journey to SIOP! I am thrilled to attend again in 2024 and have the privilege of presenting a Machine Learning Operations masterclass with Rob Stilson and Derek Mracek (more details to follow). If you're planning to attend, please let me know! Local PA Meetups Chosen by a third of respondents and in a close second to SIOP, local meetups interest is still going strong (and it feels like it’s rising). NYC and Bay Area still lead the pack as the earliest meetups and strongest communities, but we’ve seen dozens of meetups spring up (in the US at least) over the past few years (including Pittsburgh here in my backyard!). As part of participating in this survey, I'll be connecting people with others in their local community to initiate more meetups. So, stay tuned for updates. People analytics can often feel isolating for small teams. Therefore, I urge everyone reading this to take note of your local meetup and try to attend if possible! We've also included a comprehensive list of known local meetups at the end of this blog post (jump to end of blog) Wharton People Analytics With approximately a quarter of respondents, Wharton stands out as one of the few conferences in the US solely dedicated to people analytics and not affiliated with a vendor. Now in its 11th year in 2024, the Wharton conference is academically rich and rigorous. Although I haven't personally attended before, I'm looking forward to participating in March this year! HR Technology Conference and Exposition (Las Vegas) Rounding out the top four, a quarter of respondents indicated that HR Tech in Vegas is the place to be, underscoring both how significant technology choices are to People Analytics teams, but also the density of talent that makes its way to Vegas for HR Tech. With nearly 10k attendees, the vendor floor is a spectacle and an exciting way to see the showcase of technology supporting the people analytics and broader HR space. If you’re a PA leader who also oversees or interacts with Tech, it’s a must-attend event each year. Wish List Dreams We also asked respondents which conferences were on the practitioners' wish lists. Four conferences stood out that People Analytics practitioners wish they could attend: Wharton People Analytics A repeat from the list above, about half of the respondents wish they could attend the Wharton People Analytics conference. As mentioned, Wharton has been a staple in the community for well over a decade now. It’s unclear why more folk can’t make it to Wharton PAC, but I’ll make sure to take rigorous notes later this year and will report back on insights and takeaways. Make sure to subscribe to our newsletter to hear more throughout the year! People Analytics World - London (Tucana) Well over a third of respondents wish they could attend People Analytics World London. PAW London is a dedicated gathering of people analytics practitioners put together by Tucana. It’s a staple in the field and always draws mature people analytics teams and world-class speakers. Tucana has also recently branched out to supporting workforce planning and a number of other events globally and is a leading provider supporting the PA community. Definitely one to try to attend if you can! Insight222 Global Executive Retreat Nearly a quarter of respondents wish they could attend the Insight222 Executive Retreats, but compared to many others on these lists, these executive retreats are invite only. Insight222 is the premier membership organization for people analytics teams and from what I hear, these events are meticulously planned, organized, and executed. Bravo to the Insight222 teams for curating these experiences and if you ever change your mind about speakers from outside vendors coming to speak… you know where to find me. Gartner ReimagineHR Rounding out the top 4 is Gartner ReimagineHR. Gartner ReimagineHR is a premier conference for HR leadership with a specific focus on CHROs and CHRO directs. The quality of conversation is high and the maturity of teams is elevated. I missed this one in 2023, but after hearing reports from folk who attended, it’s not one I’ll miss again. Looking forward to attending this one in 2024 too. Those are the most popular events, but many world-class events were not mentioned. We've compiled our list below and appreciate all who submitted people analytics conferences. If you found this helpful, please let us know. If it proves beneficial, we'll compile a similar list again next year. One Model + Lightcast + Worklytics = The Talent Intelligence & People Analytics Summit And we here at One Model have got some of our own events coming together in 2024! The main one to highlight is a roadshow we’re putting together with our friends at Lightcast and Worklytics. The Talent Intelligence & People Analytics Summit is traveling to a few select cities in the US across 2024, starting with Austin, Texas on February 7th! It’s not too late to register. Finally, I hope to see you out there in 2024! Make sure to follow me and the One Model page to stay connected to us out in the field! 2024 events Follow One Model on LinkedIn and check out our events page. Transform US | 11-13 March 2024 | Las Vegas Wharton People Analytics | 14-15 March 2024 | Philadelphia SIOP Annual Conference | 17-20 April 2024 | Chicago People Analytics World - London | 24-25 April 2024 | London Unleash America (Las Vegas) | 7-9 May 2024 | Las Vegas 9th Annual People Analytics Summit (Toronto) | 14-15 May 2024 | Toronto TALREOS | 16-17 May 2024 | Chicago Irresistable 2024 (Bersin) | 20-23 May 2024 | Los Angeles Oracle Ascend | 17-20 June 2024 | Las Vegas SHRM Annual Conference & Expo | 23-26 June 2024 | Chicago People Analytics Exchange (IQPC) | 25-27 June 2024 | Minneapolis HR Analytics Summit (London) | 4 September 2024 | London Workday Rising | 16-19 September 2024 | Las Vegas HR Technology Conference and Exposition (US) | 24-27 September 2024 | Las Vegas Unleash World (Paris) | 16-17 October 2024 | Paris SuccessConnect (SAP) | 28–30 October, 2024 | Lisbon Gartner Reimagine (Orlando) | 28-30 October 2024 | Orlando HR Analytics and AI Summit (Berlin) | 24-26 November 2024 | Berlin Is the wait too long? You don't need to wait till the next event to talk to One Model (although we're excited to see you in person). Connect with us today. 2024 Meetups And now time for the meetups! These meetups happen frequently throughout the year, so the best wya to be involved and stay involved is to connect with their local site / meetup / LinkedIn group. Where we can, we’ve included some details about how to connect and when there was not a site yet available, we’ve added in local organizers. Brisbane (AU): March 27th at 8pm AEST (Link to event) New York: https://lnkd.in/gbfu_Mjc (Jeremy Shapiro / Stela Lupushor) Bay Area: https://lnkd.in/gnrgRBnH (Annika Schultz / Mariah Norell) Chicago: https://lnkd.in/ghgc3EDb - (Chris Broderick) Philadelphia: https://lnkd.in/g-bWmX5y - (Fiona Jamison, Ph.D.) Pittsburgh: https://lnkd.in/eCdP7KFC (Ken Clar / Richard Rosenow) Minneapolis: https://lnkd.in/eS2aUH3W (Stephanie Murphy, Ph.D. / Mark H. Hanson) Seattle: Bennet Voorhees / Marcus Baker / Philip Arkcoll Denver: Kelsie L. Colley, M.S. ABD / Zach Williams / Gabriela Mauch Boston: Hallie Bregman, PhD / Noel Perez, PMP Dallas: Jordan Hartley, MS-HRM / Cole Napper Austin: Ethan Burris / Roxanne Laczo, PhD Houston: Amy Frost Stevenson, PhD / Jugnu Sharma, SHRM-CP Atlanta: Sue Lam Nashville: Dan George Orlando: James Gallman / Danielle Rumble, MBA Omaha: Justin Arends Salt Lake City: Willis Jensen Toronto: Danielle Bushen / Konstantin Tskhay, PhD Washington DC: Rewina Bedemariam Portland: Rosanna Van Horn
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Featured
12 min read
Steve Hall
Understanding the subtleties of organizational promotion dynamics offers a window into career advancement opportunities or lack thereof and can uncover both desirable and undesirable organizational outcomes. Key among these insights is the internal promotion rate. This metric goes beyond mere numbers to reveal the depth of employee development, engagement levels, and the effectiveness of human resources strategies in fostering high performers and nurturing talent for higher-level job roles. Analyzing Organizational Health Through Promotion Metrics The internal promotion rate, calculated as the percentage of employees promoted in a given period, can offer human resources professionals a clear view of their employee development opportunities. Strong promotion rates tell us that development opportunities are available and being acted upon, and we can use these rates to identify areas of celebration and opportunity with the organization. Promotion activity is linked with retention as employees who receive promotions are encouraged to stay through increased pay and responsibility, and it shows other employees that growth, development, and advancement opportunities are provided by the organization. Real-time promotion data becomes a strategic asset in understanding how promotions influence employee morale and retention, and allows HR leadership to craft promotion protocols and goals. Here are several analyses where promotion rates could be used: Career Path Ratio - Used to gauge the success of internal grooming of managers, performance management process controls or compensation cost management. Cross-Function Mobility - This metric identifies skills development of high-potential employees through diversity of experience. Promotion Speed Ratio - Once you have the promotion rate, you can divide total tenure to understand how fast employees are being promoted. Upward Mobility - You can divide promotions by internal movements to understand your upward mobility paths compared to all. Performance to Promotion ratio - This will help you understand how performance ratings correspond to promotions. Gender and Ethnic Diversity Staffing Breakdown - Examining gender and diversity breakdowns with hierarchical levels and promotion rates can help identify and address any "glass ceilings" that may be present, offering a more comprehensive understanding of gender and diversity dynamics within the workforce. Turnover Breakdown - Assessing turnover rates in tandem with gender and ethnic diversity breakdowns and promotion rates is crucial for gaining a holistic perspective on the organization's gender- and diversity-related challenges and opportunities, contributing to a more nuanced analysis of workforce dynamics. Promotions into higher level positions, achieved through performance management cycles, non-competitive moves, or competitive moves, reflect strong individual performance and readiness for greater responsibility. They serve as recognition and rewards for outstanding performance within an organization. While a high promotion rate suggests robust performance and managerial strength, it could also result from flawed performance management or succession planning processes. Conversely, a low rate may indicate a lack of qualified internal talent or organizational constraints that make it difficult for internal talent to move upward. Low rates could also reflect a bias towards “buying” external talent. Analyzing promotion rate requires considering an employer's performance management policies for accurate interpretation. Ultimately, the internal promotion rate is a multifaceted indicator, reflecting how effectively an organization nurtures its talent and commits to long-term development while upholding diversity and inclusion principles. How to Calculate Promotion Rate The internal promotion rate is a straightforward yet revealing metric. First, you need the right data to get the right answers. Locate both your core workforce and mobility data. Formula: Promotions / Average Headcount * 100 For example, suppose your organization has 500 employees on average for the period and 50 employees were promoted within a year. The internal promotion rate would be: 50 / 500 x 100 = 10% However, this calculation only provides a surface-level understanding. Organizations must explore how promotion rates vary across different demographics and departments to gain deeper insights. How to Track Promotions Across Diverse Demographics Understanding how to track promotions effectively can provide crucial insights beyond mere statistical data; it highlights the diversity and inclusivity of an organization's workplace practices. By examining promotion rates across various workforce segments, including diversity groups, job roles, business units, age groups, and tenure groups, organizations can better understand their approach to career advancement and how it impacts different demographic groups. Diversity Groups and Career Advancement When analyzing promotion rates among diverse groups, it becomes possible to spot biases or disparities. This is crucial for ensuring that career advancement opportunities are equitable and accessible to all employees, regardless of their background. Today's businesses must monitor hiring metrics and analyze diversity reporting effectively to make meaningful changes. People analytics software can remove bias and give companies data to support workplace diversity and inclusion programs and policies. One Model customers like Colgate use our people analytics software to build powerful visuals to track and communicate their progress, increase workplace inclusion at every step, and build an enduring diversity-rich strategy. Job Roles, Business Units, and Growth Opportunities The potential for career growth and progression within an organization can vary significantly across job roles and business units. For example, a department experiencing rapid expansion may witness higher promotion rates as roles broaden and the demand for new leadership surges. This dynamic highlights the importance of strategic HR functions like succession planning in shaping career pathways and organizational resilience. Succession planning is more than just a process; it's a strategic effort to identify critical positions and groom potential successors for these key roles. When a vital role becomes vacant, organizations with robust succession planning can promptly fill the gap with a capable and prepared individual, enhancing operational continuity. This proactive approach ensures organizational readiness for future changes and signals to employees a clear trajectory for growth and advancement within the company. Effective succession planning intertwines seamlessly with people analytics. Clarifying objectives and progress is essential to shape the future workforce strategically. This clarity is best achieved through metrics that track and measure the effectiveness of succession strategies. Metrics and analytics can provide insights into the readiness of potential successors, the distribution of talent across the organization, and the impact of training and development initiatives aimed at preparing employees for future roles. Another crucial aspect of succession management is its ability to boost employee motivation. Employees are more engaged and motivated when they perceive opportunities for growth and advancement within their current organization. Seeing a well-defined path to potentially step into key roles enhances their commitment and drives their performance, aligning their personal growth aspirations with the organization's strategic goals. It took my team 5 min 16 sec to pull these charts together. How quickly can you complete the People Analytics Challenge? Age, Tenure, and Upskilling Younger employees or those with shorter tenures tend to have steeper promotion trajectories than their more experienced counterparts. Early career persons will need to be promoted more frequently as they begin to master their discipline. The time between promotions tends to increase as higher job levels are achieved. The time it takes to go from Specialist to Manager is typically much shorter than going from VP to SVP. PwC’s Global Workforce Hopes and Fears Survey found the biggest priorities for younger workers are training, development, flexibility, autonomy, and transparency on social issues. One key distinction among Generation Z workers (ages 18 to 24) is that they are more vocal in their demands than older generations. Specifically, they are more than twice as likely to ask for a promotion in the next year (38% of Gen Z and 37% of Millennials, compared to 16% of Baby Boomers). High-potential employees, especially, are keen to embrace new challenges, learn, and grow. Providing robust professional development and training opportunities boosts employee confidence and enhances their value to the employer. This investment leads to higher performance, satisfaction, and productivity, positively impacting the internal promotion rate. Organizational Culture and Structure The shared values, beliefs, and behaviors within an organization significantly influence employee experiences and, by extension, their promotion prospects. A positive workplace culture correlates with higher sales, profits, and productivity, while a negative culture can drive high turnover. HR professionals can assess employee performance against the organization's culture and values to understand how workplace culture affects employees. Changes in organizational structure have been shown to impact employee performance directly. HR can be pivotal in shaping workplace culture to enhance promotion rates. This involves fostering open communication, transparency, and respect across the organization and ensuring all employees feel included. Company values should be reinforced during onboarding and through ongoing training and leadership programs. Limitations of Promotion Rate Metric This metric falls short of providing a comprehensive evaluation of promotions in relation to other internal movements, such as transfers. It does not delve into the nature of promotions, whether they transpire within the routine course of performance reviews or if they involve transitions into higher-level positions within different organizational units. Furthermore, it lacks specificity regarding the hierarchical level at which these promotions occur. Importantly, it does not shed light on the consequential aspects of promotions, such as changes in compensation or increased responsibilities that often accompany these advancements in an individual's career trajectory. In order to obtain a more nuanced understanding of the dynamics at play, a more comprehensive assessment that encompasses these facets would be necessary. Streamlining Promotion Tracking with One Model How promotions are tracked within an organization is just as crucial as the data itself. An effective tracking system not only gathers data but also segments it meaningfully. This is where One Model plays a pivotal role, offering an advanced yet user-friendly suite of tools that effortlessly disaggregate data across various essential metrics. Simplifying Complex Data with One Model — One Model’s platform is designed to ease tracking and analyzing promotion data complexities. With its sophisticated, intuitive interface, HR professionals can quickly segment and analyze data across departments, gender, diversity, and cohort groups. This comprehensive approach enables organizations to gain deeper insights into their promotion dynamics and make more informed decisions. Departmental Breakdown Made Easy — Understanding promotions within specific departments is critical to identifying growth opportunities and potential areas for improvement. One Model allows for a detailed departmental breakdown, clarifying which areas excel in employee advancement and which might need a more focused approach. Gender and Diversity Analysis for Inclusivity — A crucial aspect of modern HR is fostering an inclusive workplace culture. One Model simplifies the process of conducting thorough gender and diversity analyses. By providing clear insights into promotion rates across different demographic groups, One Model helps businesses ensure that all employees, regardless of gender or background, have equal growth opportunities. Cohort Group Analysis for Targeted Development — Cohort group analysis, such as examining promotion rates among new hires or high-potential employees, is vital for shaping effective talent development strategies. One Model’s tools enable HR professionals to perform nuanced analyses of various cohort groups, helping to tailor development programs and career progression pathways that align with individual and organizational goals. The OPM Promotion Calculator and GS Promotion Rate While discussing promotion rates, tools like the OPM promotion calculator, particularly relevant in government jobs utilizing the General Schedule (GS) system, are worth noting. Although indirectly related, such calculators can offer benchmarks for understanding promotion norms in broader industries. The Significance of a Comprehensive Analysis More than merely calculating the average promotion rate is required. A comprehensive analysis, considering all the segments above, is imperative. It ensures a fair and inclusive work environment and helps align workforce development with organizational goals. Understanding and effectively managing internal promotion rates is a multifaceted process. Organizations must go beyond merely calculating these rates; they should deeply analyze variations across demographics and departments. Tools like One Model facilitate this process, providing a seamless solution for tracking and analyzing promotion data, ensuring that every aspect of workforce development is aligned with broader business objectives. Ready to take your analysis to the next level? Request a demo and watch me breakdown promotion rate live.
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Featured
10 min read
Eliza Fury
Different teams, different vibes! As the holiday season approaches, the pressure to find the perfect gifts for coworkers can sometimes be overwhelming. Fear not, because we've curated a list of trendy and thoughtful presents tailored to various job categories. Whether you're shopping for a product manager, UX designer, HR professional, analyst, programmer, or tech support guru, we've got you covered. Product Manager Presents: At One Model, we have amazing Product Managers. I know they are always looking for ways to stay organized and love interacting with their peers. Therefore, I think these might be great presents for these coworkers. Desk Calendar - Sometimes old fashion project management gets the ideas flowing. Moleskin Notebook - A product manager will never hesitate to take down a note in style. Monitor Memo Board - When you have a lot of input to juggle, sometimes you want your notes close at hand. Virtual Team Building Experience - Foster team spirit with a virtual team-building work activity. Since we have a virtual team, we tried this service. UX Designer Delights: To thank your designers for all their hard work, why not get them something to stimulate their creativity? Book - Fuel their artistic soul with a beautifully crafted book of design inspiration. Universal Principles of UX and The Design Everyday Things are highly revered options. Digital Drawing Tablet - Boost their creativity with a state-of-the-art digital drawing tablet. Design Thinking Card Game - Spark creativity with a game that challenges designers to think outside the box. HR Heroes' Treats: HR is the bread and butter of any company, so we thought these little bits and pieces would be perfect: Human Resources Shirt - Growing companies are doing a lot of recruiting and an HR team member can appreciate this shirt. Online Course Subscription - Invest in their professional development with an online course subscription. Don't have much to spend? Send them to the HR Regulation Masterclass. Check out our blog dedicated to funny HR gift ideas. Analyst Appreciation: Funny Mug - Analysts and data scientists like to stay sharp. You can't go wrong with a fun mug. Premium Coffee/Tea Sampler - Speaking of stay sharp, that mug needs some fuel. Check out this world-traveled coffee sampler and this gourmet tea collection. Data Visualization Art - Telling the story visually is an art, and books like this are definitely inspirational. Cool earplugs - To identify key insights sometimes you need to block out the world. Sudoku puzzle book - Dare I say that Sudoku is one of the most fun ways to play with numbers. If you want to get more than a book, check out this sudoku game. Programmer Picks: If you are a programmer, you'll know programmers can never have enough coffee or goofy socks. This is why our gift list is filled with these things, along with some other goodies. Coding Socks - Keeping your feet warm in style. Check these fun ones out. Computer Programmer Coffee Mug - Like analysts, programmers also like to keep sharp lest they miss a semi-colon. Here is a fun mug. Floppy Disk Coasters - Don't let that coffee mug leave a ring on their desk. Check out these retro tech coasters. Coding-Themed Hoodie - Keep them cozy and stylish with a hoodie featuring clever coding graphics. Coding Puzzle Box - Challenge their coding skills with a puzzle box, like the rubics cube. Laptop Cooling Pad - Keep their tech cool and stylish with this cooling pad for their laptop. Tech Support Treasures: If you're the one employee who feels like they call their tech support person a little too much, why not get them something sweet from our list below: Funny tech support shirt - Here is a great idea for the holidays. Humorous coffee mug - Give them a reason to smile in the morning with a fun gift. Coffee Mug Warmer - Don't let their coffee get cold while trying to figure out your issue. In the department of thoughtful gestures, small gifts for coworkers can make a big impact. These carefully curated presents for coworkers not only show your appreciation but also reflect the unique interests and roles within your team. So, dive into the joy of gifting, and spread smiles across the office. Whether giving for the holidays, birthdays, anniversaries, or just because, you can't go wrong with one of these gifts. In closing, I want to leave you with a riddle: What is a single gift for the entire office that creates a measurable positive impact for every team? Answer: One Model One Model democratizes data to every people leader improving retention, employee experience, and internal promotions. Ask your CHRO or People Leader to check out One Model. Ask One Model to Reach Out to Your People Leader
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Featured
10 min read
Phil Schrader
Succession planning is a strategic HR function. Its purpose is to map out key positions in the organization and identify potential successors who are (or will be) ready to step into those key positions when they become vacant. Organizations with effective succession planning programs are more resilient. When a critical role is vacated, they already know who can step up and fill the role. Succession management also boosts employee motivation because they can see a path forward within the organization. Strategic HR activities like this go hand in hand with People Analytics. In order to effectively plan for the future, you need clarity around what you want to accomplish and whether you are improving.. Metrics help you create that clarity. How many of our plans have successors? How ready are they? What’s our bench strength? Are our successors representative of the wider talent pool? So let’s dig in and talk about that union of strategy and analytics. How do you measure your succession plan readiness, and what are the key metrics for succession planning and leadership development? Measuring Succession Planning First, here is an "oldie but a goldie" video walking through the succession planning process. Second, here are the key elements of measuring succession planning. Scope: What are the critical roles that require identified successors. Ideally, your program covers all non-entry level roles, but time is scarce so prioritize. Coverage: Given the scope above, do you have plans set up for all critical roles? Readiness: Have you evaluated each successor’s readiness for each plan they are in? Remember that one person might be a successor for multiple positions, and they might be more ready for some roles than others. Readiness can be categorized in high-level groupings. For example, “Ready Now”, “Ready in < 1 Year”, and “Ready in > 1 Year”. Bench Strength: Given completeness and individual readiness, how strong is your bench? Can you fill all critical roles? Is it still strong if you net out the successors, i.e. account for people who are selected in multiple plans. Diversity: Does your plan make full use of the available talent in the organization? Have historical tendencies caused you to overlook strong successors because they have different backgrounds and experiences from the incumbents? Will your leadership ranks become more or less diverse when your plans move into action? It took me 44 minutes and 56 seconds to pull together the metrics above to answer Question #25 from the People Analytics Challenge. Let me show you the full Succession Dashboard. Connect with us today! Key Metrics (with Definitions) Here are the key metrics you can use to address the strategic questions above. Percent of Leaders with a "Ready Now" Successor Bottom line. What does your successor coverage look like right now? Count up the number of leaders who have a successor that is ready now. Divide that count by the total number of roles in your succession planning program (see Scope above). For example, if you have 10 positions that you’ve identified as needing a successor and you have a ready now successor for 7 of those roles, then your percentage of leaders with a ready now successor is 70%. Now flip that number around and say to yourself, “Ok if one of our really key people left today, there’s a 30% chance that we’d have no one ready to take over that position.” Don’t let that be you. Use the detailed data from this calculation to create an operational list of the positions without a successor. Then work the list! Gross and Net Bench Strength The first metric tells you how ready you are to move on from one key person. Gross and Net Bench strength give you a sense of how resilient your organization would be in the face of multiple changes. Technical note: These calculations will assume that your program has set out to have 3 successors identified for each key role. Gross Bench Strength: Total successors divided by total successors needed, ignoring whether the successors are used in multiple plans. Net Bench Strength: Total successors divided by total successors needed, only counting each successor once. i.e. taking into account whether the successors are used in multiple plans. So let’s look at these calculations together. Let’s say you have 10 key roles and you have determined that you should have 3 successors for each. That means your total successors needed is 30. Now go through your plans and add up all the listed successors. Perhaps you have 26. That means you have 26 successors out of the 30 you need making a gross bench strength of 87%. Awesome. Ok. Now let’s get more nuanced. Let’s deduplicate the list of successors. Maybe there are 2 high potentials in that pool who are listed on all 10 plans. Extreme example but useful for our illustration. That means that there are really only 8 unique successors. That makes your net bench strength 8 / 30 or 27%. This difference between a gross bench strength of 87% and a net bench strength of 27% tells you that you have good immediate coverage but low resiliency. You can effectively respond to 1 or 2 people leaving, but beyond that, your bench will be depleted. Incumbent vs. Successor Diversity % Generally speaking, today’s organizations are looking to take full advantage of their available talent by ensuring that traditionally underrepresented groups are considered for advancement. A simple way to check on this progress is to compare the representation numbers of your incumbents to the representation of your successors. Let’s suppose the current pool of employees in key roles is 10% diverse while your pool of successors is 20% diverse. This is a signal that your succession planning process will contribute to greater diversity in your key positions in the future. Remember to align your successor diversity metrics with the key groupings defined by your organization’s DE&I program. These could include gender, ethnicity, or other employee attributes. Promotion Rate and Time on Bench If you make progress on the metrics above, then you’ll be leading your organization into a more resilient future. Good job! But remember, resilience is great for the organization, up to a point. Remember that the high potential employees in your plans have their own career goals. If they feel stuck on the bench, they’re likely to find their next role outside the company. If you are so resilient that you could back up all your key leaders for the next 25 years, then you are fooling yourself. Those high potential employees listed on your plans will be long gone by then. So keep an eye on the promotion rate of your internal candidates over time. (Number of promotions / average headcount). They’ll be making their own estimates as well. Alternatively, you might calculate the time on bench for your successors. When one of your successors leaves the company, check to see if they were on the bench too long. Or just ask them in your exit interview. Pay particular attention to the time on bench for your diverse successors. It’s not enough to say, “Look at how diverse our bench is!” if those candidates are continuously passed up for the next big job. Using Successor Metrics to Support People Strategies The metrics above are just a starting point. The key to strategic HR and people analytics is a willingness to ask important questions and use data to answer those questions. Ideally your succession planning process fits into a larger talent management vision that is supported by a wide range of interconnected datasets and measures. For example, you may be ready to fill key roles with external candidates. Your time to fill for similar positions will help you know if that’s a reasonable backup strategy. Alternatively, your employee pulse survey data and turnover by attrition analyses may indicate that you are having a hard time retaining diverse employees. Perhaps this will link back to the time on bench calculations discussed above. You are unlikely to find meaningful answers in a single data source, so invest in building the right underlying data architecture to connect data from succession plans, core HR, recruiting, engagement, compensation, and other workforce data. At the same time, keep the strategic focus in mind so that you’re not just doing analytics for analytics sake. Come back to the important questions like, “If we lost someone in a key role today, what’s the percent chance we’d be totally flat footed with no idea how to replace them?”
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Featured
26 min read
Shiann Weiss
Are you looking for a way to bring joy to an HR professional close to your heart? The right gift can crack a smile on their face and make them feel special. That could even mean including a gift for their future "little people analytics superstar". Since the holiday season is coming, we know you're likely having a busy period. If you're doing a bit of gift-giving for the holidays and struggling to find out what someone on your team might like, we have you covered. So, whether you have one person to get gifts for or multiple, we know you're bound to be scratching your head. Therefore, to help you out, One Model has compiled a shortlist of thoughtful and funny HR gifts to get you started. Shop by HR Gift Categories Office Accessories Games & Activities Pet Lovers More Fun Than Serious Reading Kids' Gifts for their Mini-Me People Analytics Kids Books Business Toys Games for Future Inclusion Leaders Looking for a gift for the entire company - Check us out. Office Accessories Permission Slips , Office Citations, or Witty Women Can't Be Nice Notepads Tea and Coffee Mugs Recruiters Swear, HR can't fix crazy, Did you Document It?, Data Analyst, Relax I have a spreadsheet Tumblers and Happy Hour glasses Because I used my HR Voice Glass, There's no crying in HR, Analyst- I'm not arguing, Because payroll, HR hows your day wine glass, I'm a data analyst Decision Makers Logo Paperweights and Wheels or the classic Magic 8 Ball Other fun desk accessories Desk signs like "When You Excel..", custom job title plague, or Don't Make Me Use My HR Voice Candles like "Smells like this could've been an email" or "Smells like Traitor Just Kidding" Or even funny HR pens and notebooks Games & Activities Charty Party - Get you're chart on! #CultureTags For the cook Recipes from around the world For the pet lover in HR It's not that they like their furry friends more than humans, it's just that they are easier to deal with. For those into Learning and Development > Dog puzzle toy For those who bring their dog to the office > Dog suit and Laptop Chew toy For those that express themselves through pets > Cat Moody or Dog Moody desk cards Coloring Books - There are so many options Snarky and Humorous for Adults #HRLife Coloring Book Data Analyst Color Book Fun Reads Save the serious books for birthday presents. Am I Overthinking This (A Great Pairing with the Decision Makers Above) You Can't Make This Sh*t Up!: Tales From the HR Crypt Surrounded by Idiots Per My Last Email... Want to couple a funny book with something more stimulating? Check out the book recommendations from Richard Rosenow's People Analytics Library. Also, check out our people analytics children's books. Gifts For Those Future People Analytics Superstars People Analytics Kids Books A is for Analytics The Great Graph Contest Machine Learning for Kids: A Project-Based Introduction to Artificial Intelligence I Am Human: A Book of Empathy Bayesian Probability for Babies HR Business Toys for Kids Play Office Worker with Complete Pretend Workstation Build an Office Doll House Office Accessories & Spaces Office Dolls Fun Games for Future DE&I Leaders Around The World Trivia Friends & Neighbors Helping Game Stone Soup Board Game Hopefully, this gives you some ideas and helps your holiday shopping season go a little bit smoother. Everyone loves a laugh during the holidays whether it be HR gag gifts or other cheeky gifts for HR professionals. One Model is committed to helping every individual at an organization be successful by empowering them with the people insights they need to make the best decisions for that organization. Want to get the entire company a gift? Check out what One Model can offer:
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Featured
18 min read
Lauren Canada
Human resources (HR) departments play a crucial role in shaping a company's success by managing its most valuable asset — its workforce. But traditional HR practices often rely on gut feelings, intuitions, and subjective observations, which can result in bias and poor decision-making. People analytics, also known as HR analytics, offers a data-driven approach to understanding and optimising the workforce's performance, productivity, and engagement. What Is People Analytics? The people analytics definition is essentially the process of collecting, analysing, and interpreting workforce data to gain insights into HR practices' effectiveness and improve decision-making. What does people analytics involve? It involves using various data sources, such as employee surveys, performance metrics, turnover rates, and other HR-related data, to measure and analyse different HR aspects. HR professionals can leverage this data to identify patterns, trends, and relationships that are otherwise invisible, enabling them to make informed decisions that positively impact the workforce and the organisation's bottom line. While the terms "analytics," "reports," and "business intelligence" are sometimes used interchangeably, they are not synonymous. Analytics involves the systematic analysis of data to uncover meaningful patterns and insights, whereas reports refer to structured presentations of data in a summarised format. On the other hand, business intelligence encompasses a broader scope, including the collection, analysis, and interpretation of data to support strategic decision-making. So, what is people analytics? The people analytics definition goes beyond general analytics, reports, and business intelligence by focusing specifically on the analysis of HR-related data and the extraction of insights pertaining to the workforce. Unlike generic analytics, people analytics centres around human-centric data, such as employee demographics, performance metrics, and engagement surveys. It delves deep into the behavioural aspects of work, uncovering correlations and patterns that provide valuable insights into talent management, employee engagement, and workforce planning. With people analytics, you can gain a more holistic understanding of your workforce and make data-driven decisions tailored to HR needs. What Does People Analytics Involve? Data Collection What does people analytics involve? People analytics involves the seamless collection of relevant HR-related data from a multitude of sources, including HR systems, employee surveys, performance evaluations, and other pertinent data repositories. Other data sources include employee benefits, employee turnover rates, and workforce demographics. The goal is to collect as much data as possible from various sources for a more comprehensive and accurate view of the workforce. Data Cleansing The data collected from various sources often contain errors, inconsistencies, and missing data that can lead to flawed insights and ill-informed decisions if not addressed. Once the data is collected, people analytics software will clean, validate, and transform it into a format suitable for analysis. This process can also include consolidating data from multiple sources, standardising data formats, and filling in missing data. Data Analysis In this step, data is analysed using statistical methods, machine learning algorithms, and data visualisation tools. This enables HR professionals to identify patterns, trends, and relationships that are critical to understanding the organisation's workforce and answer important HR-related questions, such as: What factors contribute to employee turnover? What skills and attributes are required for high-performing teams? What training and development programs are most effective in improving employee performance and productivity? Through this analysis, HR professionals can identify potential issues and opportunities, allowing them to take proactive measures to address them. They can also explore different scenarios and test hypotheses to make more informed decisions about the workforce. Data Visualisation Data visualisation tools are crucial in this process, allowing HR professionals to communicate insights to stakeholders effectively. These tools can take many forms, from simple charts and graphs to complex dashboards that display a wide range of data. HR professionals can use visualisation tools to identify patterns and trends in the data, spot anomalies, and explore correlations between different variables. They can also use them to compare data across various departments, locations, or time periods, tell compelling stories, and generate persuasive reports. Effective data visualisation can be an essential factor in the success of people analytics initiatives because it makes data digestible — enabling stakeholders to grasp complex concepts and insights quickly and easily. Get the Ultimate Download Whether you're new to people analytics or ready to enhance your existing program, this eBook covers everything you need to know about establishing a strong foundation for a successful people analytics function that leads to smarter HR strategy and meaningful change across your organisation. Four Key People Analytics Trends 1. Revolutionising the Role and Function of HR People analytics is reshaping the HR function from being primarily administrative and operational to becoming a strategic partner in driving business outcomes. By leveraging advanced analytics tools and techniques, HR teams can extract valuable insights from data, enabling them to make data-driven decisions that align with organisational goals. This transformation empowers HR professionals to shift their focus from transactional tasks to strategic initiatives, such as talent acquisition, retention, and development. 2. Transforming HR Business Interactions People analytics provides HR teams with the ability to deliver data-backed insights to business leaders, fostering more meaningful and impactful conversations. HR professionals can then effectively communicate the impact of HR initiatives on key business metrics, such as revenue, productivity, and profitability. This transformation strengthens the partnership between HR and other business functions, positioning HR as a valuable contributor to overall business strategy and success. 3. Elevating the HR-Employee Connection People analytics is also driving a transformation in the HR-employee relationship. By analysing employee data, organisations can gain deeper insights into employee sentiments, preferences, and needs. This data-driven approach enables HR teams to personalise employee experiences, tailor development programs, and create a more inclusive and engaging work environment. The result is a stronger bond between HR and employees, as HR professionals can better understand and meet the individual needs of employees, leading to higher levels of engagement and satisfaction. 4. Enhancing the Quality of People Analytics With the advancements in AI and machine learning, HR teams can unlock more sophisticated and accurate insights from complex data sets. Predictive analytics models can forecast future workforce trends, identify potential attrition risks, and even recommend personalised learning and development opportunities for employees. These enhanced insights enable HR professionals to be more proactive and strategic in their decision-making, optimising talent management strategies and improving overall organisational performance. Embracing these four trends empowers HR teams to become strategic partners in driving organisational success, creating a more data-driven, agile, and employee-centric HR function. Choosing the Right People Analytics Tool: 3 Key Metrics When selecting the right people analytics tool for your organisation, it's essential to consider three key metrics to make an informed decision. These metrics will enable you to evaluate the effectiveness, usability, and compatibility of the tools with your HR objectives. 1. Data Integration Capabilities Ensure the tool can seamlessly integrate with your existing HR systems, such as your HRIS, performance management software, and learning management system. The ability to aggregate data from various sources is vital to obtain a comprehensive view of your workforce and maximise the insights derived from the analytics tool. Look for a tool that offers flexible and efficient data integration capabilities to support your data-driven decision-making processes. 2. Analytical Capabilities Evaluate the tool’s capabilities in data analysis, statistical modelling, and predictive analytics. Consider the range of analytics techniques and algorithms it offers, as well as its ability to generate actionable insights. Robust analytics capabilities enable you to uncover patterns, trends, and correlations within your HR data, facilitating strategic workforce planning, talent management, and employee engagement initiatives. Look for a tool that aligns with your specific analytical requirements and provides advanced analytics features to address your organisation's unique challenges. 3. User-Friendliness and Accessibility Consider the user interface, ease of use, and the availability of user-friendly dashboards and visualisation features. The tool should empower you to navigate and extract meaningful insights from the data effortlessly. Accessibility is equally important. Ensure the tool is accessible across devices and provides secure data access to authorised users. Look for a tool that prioritises user experience and provides intuitive interfaces to maximise adoption and utilisation across your HR teams. One Comprehensive Solution for Data Integration One Model offers a comprehensive people analytics platform that integrates data from multiple sources, making it easier for HR professionals to gain insights into their workforce. The highly customisable platform allows organisations to tailor their HR data needs to their specific requirements. One Model's platform can also save organisations significant amounts of money. For example, Paychex, a leading provider of HR outsourcing solutions, used One Model's platform to expand its people analytics capabilities, resulting in an 800% savings over the cost of an internal build. Why Is People Analytics Important? Why should you invest in people analytics? Why is people analytics important? At the core, HR analytics means driving better, faster talent decisions at all levels of the organisation. You need to invest resources in HR data to drive and accelerate this mission. The value of people analytics should be judged by the quality of talent decisions that are being made across the organisation and the ROI of those decisions on the business. With the right people analytics tool, users can quantify and measure the ROI of people analytics on an organisation, including cost savings, employee retention, new hires, and more. Below are several core benefits of people analytics: Improved HR Practices: People analytics tools enable HR professionals to make informed decisions based on data rather than subjective observations or intuition. HR analytics means more effective HR practices that are aligned with the organisation's goals and objectives. Better Workforce Management: By analysing workforce data, HR professionals can identify skills gaps, training needs, and performance issues, allowing them to take corrective actions to improve workforce management. Increased Employee Engagement: HR analytics can help identify factors contributing to employee engagement, such as job satisfaction, work-life balance, and career growth opportunities. By addressing these factors, organisations can improve employee engagement and reduce turnover. Higher Return on Investment: By optimising HR practices and improving workforce management, HR analytics can help organisations achieve a higher return on investment (ROI) and improve their bottom line. The Role of AI in HR According to Bersin’s research, a mere 2% of HR organisations actively utilise people analytics. This presents a significant advantage for innovative businesses looking to tap into this field and leverage its potential. People analytics profoundly impacts how HR functions by transforming recruitment, performance measurement, compensation planning, growth mapping, learning, and retention management. In fact, studies by Deloitte indicate that people analytics is rapidly becoming the new currency of HR, providing benefits such as increasing job offer acceptance rates, reducing HR help tickets, and optimising compensation. As the new currency, people analytics brings a wealth of benefits to HR professionals, enabling them to enhance key aspects of their work. HR analytics is evolving from a one-time initiative to becoming a real-time, easily adaptable tactic that offers immense benefits for HR as processes scale with business needs. HR analytics means HR teams can make data-driven decisions that result in more successful recruitment outcomes, streamlined HR processes, and better alignment of compensation practices with employee performance and market trends. This shift towards people analytics as the new currency signifies its increasing importance and its pivotal role in shaping the future of HR practices. At the core of people analytics is artificial intelligence (AI). AI allows HR professionals to analyse vast amounts of data quickly and accurately. AI-powered HR analytics can even inform candidate screening, performance evaluation, and workforce planning, freeing HR professionals' time to focus on higher-value activities. AI can also provide predictive insights, allowing them to anticipate workforce trends and take proactive measures to address them. How People Analytics Has Evolved People analytics has evolved significantly over the past few years, thanks to advances in technology and data science. Although managing humans may be the most complex aspect of work, other humans have been the primary means of interpreting and managing them thus far. But this is gradually changing, with computers beginning to provide more nuanced and targeted support for managing humans. People analytics is now becoming an expected way to enhance HR teams' decision-making, with more and more teams relying on this function daily. Initially, HR analytics primarily focused on HR reporting and compliance, such as tracking headcount, turnover, and diversity metrics. But as technology and data science advanced, it’s now more sophisticated, enabling HR professionals to gain deeper insights into the workforce's performance, productivity, and engagement. As a result, organisations that embrace HR analytics are gaining a competitive edge by making data-driven decisions that positively impact their bottom line. As more HR leaders become aware of the advantages of people analytics and these teams learn to integrate it into their function, they will recognise its benefits and embrace it as an essential part of their work. The Stages of People Analytics Maturity To truly understand the question, “What is people analytics?” you also need to know that people analytics is a journey, and organisations can be at different stages of maturity. The spectrum of people analytics maturity consists of four stages: Descriptive Analytics: Organisations at this stage use basic HR metrics to describe what has happened in the past, such as headcount, turnover, and time to fill vacancies. Diagnostic Analytics: At this stage, organisations use data to diagnose the reasons behind HR-related issues, such as high turnover or low productivity. Diagnostic analytics involves identifying patterns and relationships in data to understand the root causes of problems. Predictive Analytics: Organisations at this stage use data and AI to predict future HR trends and outcomes, such as workforce demand and supply, turnover, and performance. Predictive analytics enables organisations to take proactive measures to address potential issues before they occur. Prescriptive Analytics: Organisations at this stage use data and AI to prescribe specific actions to improve HR outcomes. Prescriptive analytics involves recommending specific HR interventions to achieve specific goals and objectives, such as training and development programs or employee engagement initiatives. Leveraging the Latest People Analytics Solutions The latest people analytics solutions enable organisations to delve deeper into the behavioural aspects of work, better understand the cause-effect relationship between various human and non-human aspects at work, and make data-driven decisions. There are three key points to make the most of a people analytics solution: Identify and quantify the relevant data to be analysed. Stay updated on the latest industry trends. Create clear end goals when implementing these solutions. Additionally, HR professionals must continually update and upskill their knowledge and capabilities to ensure that the organisation can optimise the latest people analytics offers and effectively leverage the latest trends for a more productive and satisfied workforce. Why One Model Beats The Competition One Model stands out as the best-in-class people analytics solution on the market. With its comprehensive platform, organisations gain access to a robust suite of tools and features designed to streamline data collection, analysis, and visualisation. The customisable nature of One Model empowers HR teams to tailor their analytics needs to fit their unique requirements, enabling them to extract actionable insights that drive strategic decision-making. Unlock the full potential of your HR analytics capabilities with One Model. Book a demo today to discover how One Model can revolutionise your people analytics journey, helping you uncover valuable workforce insights and propel your organisation towards greater success. Request Your Time to Meet with Us.
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Featured
5 min read
Joe Grohovsky
In a recent editorial (here), Emerging Intelligence Columnist John Sumser explains how pending EU Artificial Intelligence (AI) regulations will impact its global use. A summary of those regulations can be found here. You and your organization should take an interest in these developments and yes there are HR legal concerns over AI. The moral and ethical concerns associated with the application of AI are something we must all understand in the coming years. Ignorance of AI capabilities and ramifications can no longer be an excuse. Sumser explains how this new legislation will add obligations and restrictions beyond existing GDPR requirements and that there is legislation applicable to human resource machine learning. The expectation is that legal oversight will arise that may expose liability to People Analytic users and their vendors. These regulations may bode poorly for People Analytics providers. It is worth your while to review what is being drafted related to machine learning and the law as well as how your current vendor addresses the three primary topics from these regulations: Fairness – This can address both training data used in your predictive model as well as the model itself. Potential bias toward things like gender or race may be obvious, but hidden bias often exists. Your vendor should identify biased data and allow you to either remove it or debias it. Transparency – All activity related to your predictive runs should be identifiable and auditable. This includes selection and disclosure of data, the strength of the models developed, and configurations used for data augmentation. Individual control over their own data – This relationship ultimately exists between the worker and their employer. Sumser’s article expertly summarizes a set of minimum expectations your employees deserve. When it comes to HR law, our opinion is that vendors should have already self-adopted these types of standards, and we are delighted this issue is being raised. What are the differences between regulations and standards? Become a more informed HR Leader by watching our Masterclass Series: Why One Model is Preferred when it comes to Machine Learning and the Law? At One Model we are consistently examining the ethical issues that are associated with AI. One Model already meets and exceeds the Fairness and Transparency recommendations; not begrudgingly but happily because it is the right thing to do. Where most competitors put your data into a proverbial AI black box, One Model opens its platform and allows full transparency and even modification of the AI algorithm your company uses. One Model has long understood the HR law and how the industry has an obligation to develop rigor and understanding around Data Science and Machine Learning. The obvious need for regulation and a legal standard for ethics has risen with the amount of snake oil and obscurity being heavily marketed by some HR People Analytics vendors. One Model’s ongoing plan to empower your HR AI initiatives includes: Radical transparency. Full traceability and automated version control (data + model). Transparent local and model level justifications for the predictions that our Machine Learning component called OneAI makes. By providing justifications and explanations for our decision-making process One Model builds paths for user-education and auditability for both simple and complex statistics. Our objective has been to advance the HR landscape by up-skilling analysts within their day-to-day job while still providing the latest cutting edge in statistics and machine learning. Providing clear and educational paths to statistics is in the forefront of our product design and roadmaps, and One Model is just getting started. You should promptly schedule a review of the AI practices being conducted with your employee data. Ignoring what AI can offer risks putting your organization at a competitive disadvantage. Incorrectly deploying AI practices may expose you to legal risk, employee distrust, compromised ethics, and incorrect observations. One Model is glad to share our expertise around People Analytics AI with you and your team. High level information on our OneAI capability can be found in the following brief video and documents: https://bit.ly/OneModelPredictiveModeling https://bit.ly/OneModel-AI https://bit.ly/HR_MachineLearning For a more detailed discussion please schedule a convenient time for a personal discussion. http://bit.ly/OneModelMeeting
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Featured
11 min read
Shiann Weiss
Over the course of my career, I’ve had the privilege of working with several awe-inspiring developers and building some pretty cool proprietary tools before I arrived at One Model. Every project was unique, and I experienced first-hand the rub between business, development, and internal end-users. From working toward MVP (minimally viable product) and beyond to switching away from proprietary solutions, I’ve seen both success and failure along the way. Here are just three failed IT projects from my trove of stories that I feel everyone could learn from. I hope that my experiences can help us all consider the possibilities and rethink building a proprietary people analytics solution. The Build from Scratch At a previous company, we had a special, custom report generated for all of our clients that we built from two data sources: publicly scraped information and connected account-based search engine data. We had two critical needs that made a proprietary solution very appealing: If we solved for the collection and transformation of the data, we could drop our unit price from 6-8 cents per unit to 2-3 cents per unit. With the number of units we needed to purchase on a weekly basis, this would make a considerable impact on our bottom line. Compared to other store-bought solutions, we felt the visualizations of our reports were just too unique. If we couldn't replicate those reports exactly and reduce the resources needed to generate them, a build wasn't going to be worth it. What happened? We evaluated what was needed, created the MVP, and worked through a series of sprints to create enhancements and fix some minor bugs. MVP took us about half a year, and we worked on enhancements for another few quarters. It worked great - For a while. About two years later, our development team was busy implementing some exciting new clients, building advanced features on other systems for our bread-and-butter clients, as well as making new in-house software development enhancements for other parts of the org. At this time, our reporting tool was working as expected… until it wasn’t. The data source broke, and we had to find and code a new solution. All of a sudden, an item non-existent on the roadmap became a big problem that we needed to fix asap. It created a strain for both the business and the development team. What did I learn about DIY? When it's proprietary, you own it. It may seem easy to build something, but companies often fail to plan for ongoing maintenance and prepare to fix major issues. It's too easy to neglect a tool that seems to be working for the team. Discover how you can get the best of both worlds when you buy and build. The Rebuild from Scratch I worked for a company with an efficient CMS proprietary system that we used internally to manage our clients, including some of the most recognized brands in apparel, sports equipment, and toys. When I joined the company, this tool had been used for nearly a decade. The development team often compared the code to a giant Band-Aid ball — so many patches had been put in the code that it was now almost impossible to update something without causing a lot of problems in other places. After I had spent several years working directly with this tool, the founder of the company, who was one of the original developers, had a new concept that would revolutionize how we managed our clients’ content. We assembled a small but mighty team, with seasoned members specializing in ideation, development, project management, and end-user experience. We laid out a three-week Scrum process that would keep the project on track. What happened? The board cut funding after seven months. In this case, the board paused funding, the lead developer left, and the project died. It was really unfortunate because the permissions, data structure, and communication mechanisms between different parts of the tool were in the final stages of development. When other top developers were brought in from other parts of the company to review it, they were impressed with the quality of work and how much was there. However, the support, both to build and from the board, was gone. What did I learn about DIY? The process and concept were really cool, but ultimately, I learned that the boring stuff is often what takes the longest to build and funding can dry up or be shifted towards a new project. Like building a house, the bones are the most important part and can eat up a huge chunk of your funds — even though that’s not the part you actually see. People can also optimistically underestimate the effort it takes to get the backbone of any project stood up. The Failed Build and Switch to Buy I was brought into a company specifically to work with their proprietary marketing automation platform. It allowed me to put all clients into the same strategy but use their own unique messaging from their unique email addresses and phone numbers. It had safeguards to reduce the possibility of client branding cross-contamination. It created scalability with a measurable/adjustable strategy while still allowing for highly customized messaging. However, there was a problem, the proprietary tool was built without reporting. Also, working inside the tool was cumbersome and increased the risk of human error. To counter this, there were multiple checks involving marketing and development before anything could be updated within the tool. What happened? Change requests were worked into a queue for the development team, and they worked on some enhancements as they had time against other business needs. The problem was development was already spending a lot of time just helping with day-to-day operations in the tool, and it became harder to justify additional time commitment for the tool. Often if the tool broke, it required our top talent to figure out and fix the issue. The company ultimately decided to buy a flexible platform that provided a proven starting point and empowered us to build customizations within it. We brought in consultants, evaluated companies, and noted requirements. We needed a custom implementation, but we wanted to see if there was an option that would allow us to do the more complex projects we always dreamed of creating. We purchased and stood up enough to start the existing automations in the new environment. Then over the next year, end-users and developers worked to make customizations in the newly purchased CRM and Marketing Automation tool to meet our needs. In the end — success! We were finally able to build the strategies that we wanted, and the tool was regularly updating and becoming better. We also had a support team beyond our development team, and our capabilities exponentially grew. What did I learn about DIY? Sometimes it seems like a good idea to do it on your own, especially when you have such amazing talent internally. However, your team is ultimately interpreting end-user needs who may not have the full vision for all their needs. Also, while your development team is good, they may not have the exact experience to build that specific type of solution, and therefore the code may not be as flexible as it needs to be to accommodate future requests. So while it will work (because your dev team is amazing), you’ll quickly discover that MVP is not really MVP, and you are stuck with something that needs a lot of Band-Aids. Buying and then building upon that tool — now known as build+ — set us up with a flexible solution and high-quality support team. Why Do We Gravitate Toward In-house Development for Internal Tools? Building your own HR analytics software is really a funny concept when you actually stop and think about it. You wouldn’t have your field workers build their own cars to go to each event. You don’t have IT build computers for your company. You buy the cars and the computers. You wouldn’t ask your team to reinvent Microsoft Office either. It is unrealistic to expect your developers to create something great when comparatively non-DIY, 3rd party solutions took thousands of build hours from people who have spent decades working in their fields. Data transformation and machine learning are the same when it comes to people analytics solutions. Compared to a DIY solution, One Model accelerates time to value in an organization and becomes usable in just a few weeks. Plus, One Model acts as a strategic partner with a skilled team of data engineers and experienced customer success practitioners who share the people analytics journey with you. Learn more here. So why is building so hard? What challenges will your developers face? 1. Your HCM may handle data differently than you expect and, therefore, you’ll have to do work to put that data into an analytics-ready table format. For example, Workday combines your data with business logic. Therefore, most of its data is in the form of snapshots over time. To answer any question related to time, or filtered by a period, you need to pull every possible snapshot and stitch them together into a proverbial “flipbook”. 2. In order to connect old data sets or pair them with complementary systems, like surveys or learning management tools, work will be required to merge the data with appropriate keys to ensure dates align for proper analysis. 3. You did your best creating the requirements, but your HR team is not a tool designer. It is more likely that factors will not be considered and key requirements missed. This is the number one reason your IT team will never be finished building this solution. After something has been built, a seemingly simple request, like a breakout or grouping, can require significant rebuilds. You’ll be saying, "Technically it works as designed, but every new question requires a rebuild and takes so much time. Our HR analysts can't even do a voluntary turnover graph." Your IT team wants a solution that offers the best of both worlds, so you can buy the right starting point and then easily customize within it. Your team wants you to look at One Model. Connect with us today. One Model offers a best of both worlds approach and lets you bypass all the headaches and start making people decisions based on your data. With pre-built storyboards and step-by-step predictive analysis tools built-in, you own the transformed data and your development team can use One Model as an HR data consolidator for all your HR tools. They can own the transformation logic while your team works on answering the questions currently burning a hole in your soul. Plus, One Model is flexible — so your teams can build and customize within the platform to fit your organization’s unique needs. Read our whitepaper to learn more about this best of both worlds approach.
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Featured
6 min read
John Carter
When examining the workforce dynamics of an organization, it's common to fixate on revenue-generating roles. After all, these positions are directly responsible for bringing in profits. However, focusing solely on revenue-centric roles leaves out a significant chunk of the workforce: the non-revenue employees. The Role of Non-Revenue Employees While non-revenue employees might not directly contribute to the financial bottom line, their contributions are foundational to the organization's success. They constitute the vast business “machinery” that powers the organization, supports revenue-generating roles, and ensures smooth business operations. In fact, they can represent more of your workforce. These include roles in HR, IT, administration, and many other indirect revenue employees who maintain the infrastructure of a business. Non-revenue units keep the operations of a business running. Imagine a product-based company without a logistics team to ensure timely deliveries or a multinational enterprise without HR personnel to manage its vast workforce. The value of non-revenue-producing departments becomes clear when you consider the chaos that would ensue in their absence. Non-revenue employees often introduce efficiency, stability, and scalability into an organization. They identify bottlenecks, streamline processes, and ensure that the revenue-generating departments can operate at peak productivity. Indirect revenue employees may not directly contribute to sales, but they directly influence revenue by performing at a high level of customer satisfaction, meeting or exceeding CSAT goals, reducing churn and creating referenceable champion customers. It took me 10 minutes and 15 seconds to create this breakout. Want to see me do it live? Fill out the form, and let’s connect our teams. The Value of Non-Revenue Units in People Analytics While non-revenue-generating (NRG) roles may not directly influence the new sales revenue stream, they are foundational to an organization's long-term success. Here's why: Holistic Workforce Analysis: An organization only gets a skewed view of its workforce by concentrating on revenue-producing roles. People analytics should consider every layer and department to ensure a balanced strategy for talent acquisition, retention, and development. Reducing Churn in Non-Revenue Departments: Turnover in non-revenue producing departments can be just as detrimental as in sales or business development. For instance, frequent changes in the support and client services roles leads to a loss of inherent knowledge, long ramp up times and loss of confidence with customers reflecting in low CSAT scores, while turnover in HR can impact talent management strategies. Organizations can reduce churn, stabilize operations, and indirectly boost revenue by applying people analytics to these non-revenue units. Identifying Opportunities for Upgrading Skills: As businesses evolve, the roles of non-revenue employees change. People analytics can help identify the need for new skills or training in these non-revenue units, find employees with the skills already and utilize those people, ensuring they continue to support the company effectively and saving money in the long term (training and recruitment costs). The dilemma often faced revolves around headcount — is it worth investing in these indirect revenue employees? The perceived short-term pain of increasing payroll for NRG employees often becomes a deterrent. As leaders, it's tempting to don many hats, especially with constrained budgets. But in doing so, are leaders truly optimizing their own roles? An organization's head, tasked with vision, direction, and often direct revenue-generation through donations, can get tangled in the intricacies of non-revenue units, thereby diluting their effectiveness. The Opportunity Cost with Non-Revenue Departments Convincing a board to hire for NRG roles, especially in medium or smaller organizations, is not straightforward. How you frame the argument is key. One approach is the opportunity cost perspective. By calculating an executive director's (ED) hourly pay and then juxtaposing that against time spent on non-revenue-producing department tasks, organizations can discern the real costs. For instance, if an ED earning $70,000 annually spends 10 hours weekly on tasks better suited for an NRG role, that's an annual cost of $17,498. If reallocating those 10 hours could generate more than this amount, it’s a stronger case for hiring specialized staff. While it's not always as black and white, this method provides tangible metrics, bridging the gap between HR and finance in understanding the worth of non-revenue employees. Ultimately, the emphasis should be on the organization's health and growth. While NRG roles might not bring in direct revenue, their contribution allows revenue-generating sectors to flourish. The Future of Non-Revenue Employees in Business Strategy The line between revenue-generating roles and non-revenue employees is blurring. As businesses increasingly adopt interdisciplinary strategies, the contributions of non-revenue units become more intertwined with revenue outcomes. For example, an effective marketing campaign (often considered a cost center) can significantly boost sales, making it an indirect revenue employee function. The bottom line? While the spotlight often shines brightest on revenue-generating roles, the silent machinery of non-revenue employees is what keeps a business thriving. It's time we acknowledge the importance of non-revenue producing departments and give them the attention they deserve in our people analytics endeavors. Want to see if your people analytics team can answer the top questions asked of HR as fast as us? Download the people analytics challenge!
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Featured
6 min read
Dennis Behrman
The One Model team just returned from Las Vegas after an exciting HR Tech 2023. We launched some new products, evangelized the basics of connecting data from around the organization, and partied with our new partner Lightcast. Watch our team recap and see some fun images from our time at the show or scroll down to read our takeaways. Great Minds in HR Tech Shared a Ton of Insights HR Tech 2023 offered something for everyone, across many roles. It was great to see so many HR analytics and people analytics evangelists and enthusiasts. It's an inclusive community that offered incredible diversity of thought and experience. My colleague Richard Rosenow noted that an evolution of the field was actively taking place at HR Tech, specifically toward model governance. I personally told dozens of visitors to our booth that sooner or later, regulators and auditors are going to be asking questions about how a decision was made. Leaders in that room should be prepared to show every aspect of their data-driven decision process in a trustworthy and explainable way. Our HR Tech Conversations from the Expo Hall Check out our entire event interview playlist on Youtube. We Felt the Energy of HR Tech An event in Vegas always has a high level of energy, but our feeling was that organizations are buzzing about the opportunity to build the very best workforce through productivity and well-being. Most people know that people analytics is the path to every workforce story. About 70% of the folks we spoke with said their team is interested in People Analytics and were actively looking for solutions that provide great insights. Do the Basics Right There was so much talk about skills and generative AI, but many companies haven't finished with the basics of enterprise data orchestration. Many companies still struggle organize all of their people data. The cool stuff is difficult or impossible without a data foundation of well-connected enterprise systems. Lots of Hype Around Generative AI It seemed as though most software vendors were discussing their own generative AI technologies. I felt some enthusiasm from would-be technology buyers, but most are rightfully concerned about transparency and accountability as AI regulations become more likely. Most vendors have a very common large language model implemented, but we've seen analysis that shows generative AI isn't a reliable interpreter of quantitative data. In that study, only 70% of the answers that the AI generated were correct. So Many Opportunities for Fun One Model really allows our customers to get out of the late-night data crunching and come out and have a good time. Several companies were so excited about the prospect of having a scalable people analytics solution that they even joined us and our partners at a special VIP event. So, if late-night data crunching is your current reality, it's time to explore the capabilities of One Model. Continue your People Analytics journey with One Model. Schedule a demo!
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7 min read
Dennis Behrman
Few tasks can be as perplexing — and oddly satisfying — as the alchemy of turning headcount numbers into meaningful cost allocations by work days in a month and then having the option to break it down by department or any other variable you desire. With business demands rapidly evolving, the age-old adage of "time is money" has never been more accurate. Yet navigating the complexities of cost allocation, also referred to as overhead allocation, and crafting the perfect cost allocation plan can be a Herculean task. As you may know, cost allocation involves the identification and allocation of expenses to various activities, individuals, projects, or any relevant cost-related entities. Its primary objective is to equitably distribute costs among different departments, facilitate profitability calculations, and establish transfer pricing. Essentially, cost allocation serves as a means to gauge financial performance and enhance the decision-making process. Since your employees are by in large your greatest investment, understanding their cost allocation on many levels has immense benefits. As Phil shows in the video above, One Model makes this process seamless — and it’s all thanks to the power of our data orchestration model. Learn more about our People Data Cloud Platform. The Changing Landscape of HR Data It is no longer enough to get a holistic cost allocation from your headcount. Organizations across the globe need to be able to slice and dice their data to really understand how those costs are changing over time and how to best build a thriving workforce. Traditional views showing headcount over time are excellent starters, but the main course? That's translating those numbers into actionable cost insights. After all, understanding not just the size but also the cost of your workforce over time is the key to informed decision-making for both finance and operations teams. For example, slicing and dicing dynamic cost allocation over time, like total days in month breakout and broken down by department, supervisor hierarchy level, or by length of time employed can lead to insights that can change policy or articulate critical headcount needs. How does One Model accomplish this? One Model possesses unique capabilities that can transform your traditional headcount chart into a sophisticated cost analysis tool. What makes us unique? It all has to do with the data model. Once your data is modelled, you gain access to a variety of metrics that you can use as is or modify to fit your specific business needs. Diving into your compensation grouping of metrics, you can replace the “headcount, end of period” metric with “headcount, beginning of period” or append it with the “average salary, end of period” metric. Delving deeper, the real magic happens as One Model enables you to convert that average salary into a robust cost allocation strategy. With the dynamic "compensation cost daily allocation" metric at your disposal, it's like having a personal assistant that adjusts effortlessly to varying time durations, including accommodating leap years. Furthermore, One Model recognises the fluctuations in costs, especially during shorter months or leap years, ensuring a more precise and insightful view of your financial landscape. This capability allows you to make more informed decisions and gain a deeper understanding of your organisation's financial dynamics. Segmenting Cost Allocation Metrics Each organisation is akin to a mosaic, with numerous sections and subdivisions. With One Model, you can delve into each segment, examining the cost allocation intricacies at every level. The insights gleaned can empower both finance and operations professionals, offering clarity in strategy and resource allocation. Why is Overhead Allocation such an important metric? Cost allocation is crucial for various reasons in business and financial management. Here are four key reasons why it's important to pay attention to cost allocation: Fairness and Equity Overhead allocation ensures that costs are distributed fairly among different departments, products, or projects. This fairness is essential for budget allocation and growth in each department. Performance Measurement Allocating costs accurately allows for better measurement of the performance of different departments or business segments. By attributing costs to specific activities, it becomes easier to identify areas of inefficiency and make necessary improvements. Profitability Analysis Cost allocation helps in determining the profitability of products, services, or business units. This information is invaluable for making strategic decisions about resource allocation, product pricing, and business expansion. However, read our other considerations when breaking down revenue in our average revenue per employee blog. Resource Allocation When costs are allocated appropriately, organisations can allocate resources more effectively. It helps in identifying where additional resources are needed and where resources might be overallocated, leading to cost savings. Visualising Cost: The Power of Representation One Model lets you visualise your cost allocation journey over time through detailed charts. While this can present a plethora of data, each data point offers invaluable insights. For those who prefer a more structured representation, a tabulated view can provide clarity. All you need to do is create a data set that shows the amount of cost to allocate, along with the start and end dates of that allocation. From current headcount to cost allocation for recruiting, the process to get the answer is the same. For example, if you spent $10,000 on job advertisements on LinkedIn from Jan. 1, 2018, to Dec. 31, 2018, One Model can efficiently allocate that spend per day throughout the year. This becomes very useful when combined with other metrics over periods of time. For example, I can compare what I'm spending on LinkedIn with the number of applications I receive from LinkedIn during that period. This yields a "Cost Per Application" metric that I can use to compare the effectiveness of LinkedIn relative to other sources. The Takeaway If the daunting task of juggling countless spreadsheets, numbers, and formulas sounds all too familiar, there's a better way. One Model is designed to transform the perplexing world of cost allocation and overhead allocation and creating a tailored cost allocation plan into a more straightforward, efficient process. So, if late-night data crunching is your current reality, it's time to explore the capabilities of One Model. Let us show you how One Model does this 1:1
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1 min read
Lauren Canada
This infographic dives into what the IT security risks are in the people analytics space, how they can impact your business financially, legally, or otherwise, and how One Model works to limit those security risks. Click here to view the full infographic! Click here to view the full infographic!
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10 min read
Joe Grohovsky
John Sumser, one of the most insightful industry analysts in HR, recently wrote an article providing guidance on the selection of machine learning/AI tools. That article is found HERE, and can serve as a rubric for reviewing AI and predictive analysis tools for use in your people analytics practice or HR operations. Much of our work day is filled with conversations regarding the One Model tool and how it fits into an organization's People Analytics initiative. This is often the first practical exposure a customer contact has using Artificial Intelligence (AI), so a significant amount of time is invested in explaining AI and the dangers of misusing it. Good Questions to Ask About Artificial Intelligence Solutions - And Our Answers! Our product, One AI, delivers a suite of easy-to-use predictive pipelines and data extensions, allowing organizations to build, understand, and predict workforce behaviors. Artificial Intelligence in its simplest form is about automating a decision process. We class our predictive modeling engine as AI because it is built to automate the decisions usually made by a human data scientist in building and testing predictive models. In essence, we’ve built our own automated machine learning toolkit that rapidly discovers, builds, and tests many hundreds of potential data features, predictive models, and parameter tuning to ultimately select the best fit for the business objective at hand. Unlike other predictive applications in the market, One AI provides full transparency and configurability, which implicitly encompasses peer review. Every predictive output is not only peered reviewable within a given moment of time but also for all time. This post will follow a Q&A style as we comment on each of John’s 12 critical questions to ask an artificial intelligence company. 1) Tell me about the data used to train the algorithms and models. Ideally, all data available to One Model is used for feeding the machine learning engine - the more the better. You cannot overload One AI because it is going to wade through everything you throw at it and decide which data points are relevant, and how much history it should use, and then select, clean, and position that data as part of its process. This means we should feed every single system we have available into the engine from the HRIS, ATS, Survey, Payroll, Absence, Talent Management - everything and the kitchen sink as long as we’re ethically okay with its potential use. This is not a one size fits all algorithm; each model is unique to the customer, their data set, and their target problem. The content of training data can also be user-defined. Users define what type of data is brought into the modeling process, choosing which variables, filters, or cuts will be offered. At any time if users want to specify how individual fields will be treated, they have the ability to do so with the same types of levers as you would have in creating your own model externally. 2) How long will it take for the system to be trained? The scope of data and the machine learning pipeline determine training time. The capacity to create models is intrinsically available in One AI and training can take anywhere from 5 minutes to 20+ hours. For example, we automatically schedule re-training a turnover prediction model for a 15k employee-customer in the space of 45 minutes. 3) Can we make changes to our historical data? Yes, data can be set to be held static or use fresh data every time the model is trained. One AI acts as a data science orchestration toolkit that automates the data refresh, training, build and ongoing maintenance of the model. Models are typically scheduled to potentially refresh on a regular basis e.g. monthly. With every run extensive reports are created, time-stamped, and logged so users can always return to summary reports of what the data looked like, the decisions made, and the performance of the model at any given time. 4) What happens when you turn it off? How much notice will we receive if you turn it off? One AI models and pipelines are completely persisted. They can be turned on and off with no loss of data or logic. We are a data science orchestration toolset for building and managing predictive models at scale. Is AI being offered in a solution for your HR Team? Download our latest whitepaper to get the questions you should ask in the next sales pitch when someone is trying to sell you technology with AI. 5) Do we own what the machine learned from us? How do we take those data with us? Yes, customers own the results from their predictive models, and those results are easily downloaded. Results and models are based upon your organizations data. One Model customers only see their own results, and these results are not combined with other data for any purpose. All the decisions that the machine made to select a model are shown and could be used to recreate the model externally as well. 6) What is the total cost of ownership? Predictive modeling, along with all features of our One AI product, are inclusive within the One Model suite subscription fee. 7) How do we tell when the models and algorithms are “drifting”? Each predictive model is generated and its results are fully transparent. Once a One AI run is finished, two reports are generated for review: Results Summary – This report details the model selected and its performance. Exploratory Data Analysis – This report details the state of the data that the model was trained on so users can determine if the present-state data has changed drastically. Models are typically scheduled to be re-trained every month with any new data received. The new models can be compared to the previous model using the output reports generated. It is expected that models will degrade over time and they should be replaced regularly with better performing models incorporating recent data. This is a huge burden on a human team, hence the need for data science orchestration automating the manual process and taking data science delivery to scale. 8) What sort of training comes with the service? One Model’s customers are trained on all aspects of our People Analytics tool. Training is offered for non-Data Scientists to be able to interpret the Results Summary and Exploratory Data Analysis reports so they can feel comfortable deploying models. A named One Model Customer Service Manager is available to aid and provide guidance if needed. 9) What do we do when circumstances change? One AI is built with change in mind. If the data changes in a way that breaks the model or the model drifts enough that a retrain is necessary, users can restart the automated machine learning pipelines to bring in new data and create a new pipeline. The new model can be compared to the previous model. One AI also allows work to occur on a draft version of a model while the active model is being run in production. 10) How do we monitor system performance? The Results Summary and Exploratory Data Analysis charts provide extensive model performance and diagnostic data. Actual real-world results can be used to assess the performance of the model by overlaying predictions with outcomes within the One Model application. This is also typically how results are distributed to users through the main analytics visualization toolsets. When comparing actual results against predictions, One Model cautions users to be aware of underlying data changes or company behaviors skewing results. For example, an attrition model may identify risk due to an employee being under-trained. If that employee is then trained and chooses to remain with the organization, then the model may have been correct but because the training data changed results can’t really be compared. In the case of this employee their risk score today would be lower than their risk score from several months ago prior to training. The action to provide additional training may indeed have been a response from the organization to address the attrition risk, and actions like these that are specifically made to address risk must also be captured to inform the model if mitigation actions have taken place. The Results Summary and Exploratory Data Analysis reports typically build enough trust in cross-validation that system performance questions are not an issue. 11) What are your views on product liability? One AI provides tooling to create models along with the reports for model explanation and interpretation of results. All models and results are based exclusively on a customer’s own data. The customer must review the model’s results and choose to deploy and how they use those results within the organization. We provide transparency into our modeling and explanations to provide confidence and knowledge of what the machine is doing and not just trusting a black box algorithm is working (or not). This is different from other vendors who may deliver inflexible canned models that were trained on data other than the customers or are inflexible to use a unique customer data set relevant to the problem. I would be skeptical of any algorithm that cannot be explained or its performance tracked over time. 12) Get an inventory of every process in your system that uses machine intelligence. Each One Model customer decides how specific models will be run for them, and how to apply One AI. These predictive models typically include attrition risk, time to fill, promotability, and headcount forecast. Customers own every model and the result generated within their One Model tool. One AI empowers our customers to combine the appropriate science with a strong awareness of their business needs. Our most productive One AI users utilize the tool by asking it critical business questions, understanding all relative data ethics, and providing appropriate guidance to their organization. If you would like to learn more about One AI, and how it can address your specific people analytics needs, schedule some time with a team member below.
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6 min read
Phil Schrader
It’s always good news when a prospective One Model customer tells me that they use SuccessFactors for recruiting. Given that HR technology in general and applicant tracking systems in particular seldom involve feelings of pleasure, my statement bears a bit of explanation. I wouldn’t chalk it up to nostalgia, though like many members of the One Model team, I had a career layover at SuccessFactors. Instead, my feelings for SuccessFactors recruiting are based on that system’s unique position in the evolution of applicant tracking systems. I think of SuccessFactors as the “Goldilocks ATS”. On one hand, SFSF doesn’t properly fit in with the new generation of ATS systems like SmartRecruiters, Greenhouse, or Lever. But like those systems, SFSF is young enough to have an API and for it to have grown up in a heavily integrated technology landscape. On the other hand, SFSF can’t really be lumped in with the older generation of ATS systems like Kenexa and Taleo either. However, yet again, it is close enough to have picked up a very positive trait from that older crowd. Specifically, it still manages to concern itself with the mundane task of, ya know, tracking applicant statuses. (Yeah, yeah, new systems, candidate experience is great, but couldn’t you also jot down when a recruiter reviewed a given application and leave that note somewhere where we could find it later without building a report???) In short, SFSF Recruiting is a tweener and better for it. If you are like me, and you happen to have been born in the fuzzy years between Gen X and Millennials, then you can relate: you're young enough to have been introduced to web design and email in high school, and old enough to have not had Facebook and cell phones in college. So let’s take a look at the magic of tracking application status history using data from SuccessFactors RCM, an applicant tracking system. While it seems like a no-brainer, not all ATSs provide full Application Status history via an API. Since it's basically the backbone of any type of recruiting analytics, it's fortunate that SuccessFactors does provide it. For those of you who want to poke around in your own data a bit, the data gets logged in an API object called JobApplicationStatusAuditTrail. In fact, not only is the status history data available, but custom configurations are accounted for and made available via the API as well. This is one of the reasons why at One Model we feel that without a doubt, SuccessFactors has the best API architecture for getting data out to support an analytics program. Learn more about our SuccessFactors integration. But there is something that not even the Goldilocks ATS can pull off -- making sense of the data. It’s great to know when an application hits a given status, but it’s a mistake to think that recruiting is a calm and orderly process where applications invariably progress from status to status in a logical order. In reality, recruiters are out there in the wild doing their best to match candidates with hiring managers in an ever-shifting context of business priorities, human preferences, and compliance requirements. Things happen. Applicants are shuffled from requisition to requisition. Statuses get skipped. Offers are rescinded. Job requisitions get cancelled without applicants getting reassigned. And that’s where you need a flexible people analytics solution like One Model. You’ll probably also want a high-end espresso machine and a giant whiteboard because we’re still going to need to work out some business logic to measure what matters in the hectic, nonlinear, applicant-shuffling real world of recruiting. Once we have the data, One Model works with customers to group and order their application statuses based on their needs. From there, the data is modeled to allow for reporting on the events of applications moving between statuses as well as the status of applications at any point in history. You can even look back at any point in time and see how many applications were at a particular status alongside the highest status those applications eventually made it to. And yes - we can do time to fill. There are a billion ways of calculating it. SuccessFactors does their customers a favor by allowing them to configure how they would like to calculate time to fill and then putting the number in a column for reporting. If you're like most customers though, one calculation isn't enough. Fortunately, One Model can do additional calculations any way you want them-- as well as offering a "days open" metric and grouped dimension that's accurate both current point in time as well as historically. “Days in status” is available as well, if you want to get more granular. Plus, on the topic of time to fill, there’s an additional tool in One Model’s toolkit. It’s called One AI and it enables customers to utilize machine learning to help predict not only time to fill, but also the attributes of candidates that make them more likely to receive an offer or get hired. However, that is another topic for another day. For today, the good news is that if you have SuccessFactors Recruiting, we’ll have API access to the status history data and customizations we need to help you make sense of what's going on in recruiting. No custom reports or extra connections are required. Connecting your ATS and HRIS data also means you can look at metrics like the cost of your applicant sourcing and how your recruiters are affecting your employee outcomes long term. So here’s to SuccessFactors Applicant Tracking System, the Goldilocks ATS. Ready to get more out of SuccessFactors? Click the button below and we'll show you exactly how, and how fast you can have it. **Quick Announcement** Click here to view our Success with SuccessFactors Webinar recording and learn how to create a people data strategy!
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9 min read
Dennis Behrman
Human resources (HR) management has become more critical in today's rapidly evolving business landscape. HR departments face the challenge of attracting, retaining, and nurturing talent while ensuring the organization's success. To address these demands, HR management platforms have emerged as invaluable tools. However, implementing AI-powered people analytics solutions has transformed HR platforms, empowering organizations to make data-driven decisions and optimize their practices for improved efficiency and effectiveness. With AI-powered people analytics platforms, organizations can leverage insights and trends to enhance their HR strategies, leading to better talent decisions and organizational outcomes. AI-Powered HR Management Platforms AI is changing the landscape of HR management by augmenting and automating various tasks. Based on Society for Human Resource Management research, AI adoption for HR tasks was particularly widespread among larger companies, with 42% of firms employing at least 5,000 workers utilizing AI in 2022. While having specialized data analysts is still crucial for effectively utilizing AI, user-friendly tools increasingly empower employees across all roles to perform data analysis. HR analytics tools examples leverage advanced algorithms and machine learning to analyze vast data and make intelligent recommendations. Some key use cases of AI in HR processes include: Recruitment and Candidate Screening HR professionals prioritize streamlining the recruitment process, and AI technology is crucial in achieving this goal. By automating job advertising, AI helps save time and optimize campaigns for better results. AI algorithms measure outcomes, predict future trends, and reduce costs. Furthermore, AI addresses unconscious bias by reaching a diverse candidate pool and engaging passive candidates. It automates routine tasks, provides feedback, and ensures transparent communication, enhancing the candidate experience. AI in job advertising improves efficiency, diversity, and the recruitment experience for potential employees. Employee Onboarding and Training AI platforms revolutionize employee onboarding and training by automating administrative tasks, offering personalized onboarding plans, and providing interactive learning experiences. They streamline paperwork, documentation, and scheduling, ensuring a smooth organizational transition. AI platforms offer diverse online training resources and use machine learning to analyze performance and suggest personalized skill development. Moreover, they facilitate knowledge sharing and collaboration through natural language processing and chatbots. These platforms enhance efficiency, effectiveness, and employee experience during onboarding and training processes by leveraging AI technologies. Performance Management and Feedback One of the key benefits of AI platforms in performance management is their ability to capture and analyze vast amounts of data. By leveraging machine learning, they identify patterns and correlations, providing managers a comprehensive understanding of individual and team performance. These platforms automate performance evaluations, offer real-time feedback, and track key performance indicators, facilitating ongoing feedback and coaching conversations. They also provide personalized recommendations for improvement, suggesting relevant training programs and resources based on individual strengths and career goals. With AI platforms, organizations can optimize performance management processes, empower employees to drive their development and foster a culture of continuous improvement. Predictive Analytics for Workforce Planning Through the analysis of historical data, HR analytics software can identify patterns and trends in workforce behavior, such as employee turnover rates, skill gaps, and recruitment success. This enables organizations to make accurate predictions about future workforce demands and make proactive decisions to address potential challenges. AI platforms also consider external factors such as market trends, economic indicators, and industry forecasts to provide a holistic view of the workforce landscape. By incorporating this external data into predictive models, organizations can anticipate talent supply and demand changes and align their workforce planning strategies accordingly. AI-Powered People Analytics Solution in HR Management In the swiftly evolving business landscape, staying ahead requires more than mere intuition; it demands insights derived from data. AI-Powered People Analytics Platform is a transformative tool poised to redefine how organizations understand and nurture their most valuable asset: their people. Seamlessly adopting advanced AI capabilities with comprehensive workforce data, this platform unlocks a deeper understanding of employee dynamics. According to Straits Research, as of 2022, the worldwide people analytics market was estimated at $2.58 billion, and it is projected to reach $7.67 billion by 2031, exhibiting a CAGR of 12.88% during the forecast period of 2023-2031. From predictive analytics that shapes strategic decisions to personalized development paths that amplify individual growth, embark on a journey where data-driven precision meets human-centric leadership. Discover how this platform redefines success by empowering companies to cultivate thriving, resilient, and engaged teams. Key aspects of people analytics in HR management include: Employee Engagement and Retention Through analyzing various data sources such as employee surveys, performance data, and employee feedback, people analytics can identify patterns and trends related to engagement. It aids organizations in recognizing gaps and issues related to engagement and retention, measuring progress, and establishing objectives to enhance employee engagement and retention strategies. Through data analysis concerning turnover rates and mobility efforts, organizations can pinpoint trends that affect engagement and retention, uncover any underlying biases, and develop precise approaches for improvement. Diversity and Inclusion Initiatives People analytics empowers organizations to improve corporate culture and drive diversity and inclusion initiatives by leveraging data and insights. Organizations can identify gaps and set goals by analyzing employee demographics, representation, and inclusion metrics. People analytics helps uncover biases in talent processes and enables organizations to develop strategies for fair and equitable practices. Additionally, it measures the impact of diversity and inclusion initiatives on employee experiences and outcomes, allowing organizations to make data-driven adjustments. Ultimately, people analytics provides valuable insights to foster inclusive workplaces where all employees feel valued and empowered to contribute their unique perspectives. Succession Planning and Talent Management People analytics is vital in talent management and strategic workforce planning within organizations. By analyzing employee performance, skills, and potential, people analytics provides valuable insights for identifying and nurturing high-potential employees for future leadership roles. It helps organizations create talent pipelines by identifying skill gaps and developing targeted development programs. People analytics also aids in succession planning by enabling data-driven assessments of potential successors, allowing organizations to make informed decisions for key positions. With the help of people analytics, organizations can effectively manage and develop their talent, ensuring a smooth transition of leadership and fostering a culture of continuous growth and development. AI-Driven Insights for Informed Decision-Making Utilizing AI algorithms, which can dissect intricate data sets, yields valuable insights that can be acted upon. HR professionals stand to benefit significantly, as these insights empower them to execute well-informed judgments regarding recruitment, performance assessment, and the cultivation of talent. Implementing AI-driven analytics enables a strategic approach to HR, fostering enhanced decision-making across hiring, performance management, and talent development. Predictive Analytics for Identifying HR Trends and Patterns According to McKinsey, 70% of corporate leaders regard people analytics as a top priority. Organizations are placing a strong emphasis on understanding the skills and capabilities of their workforce. This proactive approach empowers them to preemptively tackle hurdles, fine-tune workflows, and execute impactful HR strategies. By harnessing AI-driven insights, HR leaders gain the ability to discern underlying dynamics, ensuring that their initiatives are both finely tuned and aligned with evolving organizational needs. Enhanced Employee Experience Through Personalized Recommendations AI-powered people analytics platforms can provide personalized recommendations to employees, such as learning and development opportunities, career pathways, and wellness programs. This improves employee engagement and satisfaction. Additionally, AI-powered HR platforms integrated with enterprise learning management systems can go beyond traditional training and development initiatives. Enterprise learning management systems can recommend wellness programs and resources that promote employee well-being, including mental health support, fitness activities, and stress management techniques. By addressing the holistic needs of employees, an enterprise learning management system contributes to a healthier and more productive workforce, fostering a positive work environment. Harnessing the Power of AI-Powered Analytics Platforms for Organizational Success The AI-powered people analytics software is revolutionizing HR management platforms. By harnessing the power of artificial intelligence and data-driven insights, HR professionals can make more informed decisions, improve employee engagement, and enhance overall organizational performance. These advanced platforms enable the automation of repetitive tasks, enabling HR teams to focus on strategic initiatives and personalized employee experiences. Moreover, AI-driven predictive analytics tools for HR can provide valuable insights into workforce trends, enabling proactive talent management and effective succession planning. As organizations embark on this transformative journey, the collaboration between technology and human expertise will shape the future of HR, driving innovation, productivity, and success in the workplace. Learn more about One Model's people analytics software.
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Featured
10 min read
Phil Schrader
The One Model difference that really sets us apart is our ability to extract all your messy data and clean it into a standardized data catalog. Let's dive deeper. One Model delivers people analytics infrastructure. We accelerate every phase of your analytics roadmap. The later phases of that roadmap are pretty fun and exciting. Machine learning. Data Augmentation. Etc. Believe me, you’re going to hear a ton about that from us this year. But not today. Today we’re going to back up for a minute and pay homage to an absolutely wonderful thing about One Model: We will help you clean up your data mess. Messy Data? Don't distress. Josh Bersin used this phrasing in his talk at the People Analytics and the Future of Work conference. From my notes at PAFOW on Feb 2, 2018: You know there are huge opportunities to act like a business person in people analytics. In the talk right before Josh’s, Jonathan Ferrar reminded us that you get $13.01 back for every dollar you spend on analytics. But you have to get your house in order first. And that’s going to be hard. Our product engineering team at One Model has spent their careers figuring out how to pull data from HR systems and organizing it all into effective data models that are ready for analytics. If your team prefers, your company can spend years and massive budgets figuring all this out... Or, you can take advantage of One Model. When you sign up with One Model: 1) We take on responsibility for helping you extract all the data from your HR systems and related tools. 2) We connect and refine all that data into a standard data catalog that produces answers your team will actually trust. Learn what happened to Synk when they finally had trust. Big data cleansing starts with extracting the data from all your HR and related tools. We will extract all the data you want from all the systems you want through integrations and custom reports. It’s part of the deal. And it’s a big deal! For some perspective, check out this Workday resource document and figure out how you’ll extract your workers’ FTE allocation from it. Or if Oracle is your thing, you can go to our HRIS comparison blog and read about how much fun our founder, Chris, had figuring out how to get a suitable analytics data set out of Fusion. In fact, my coworker Josh is pulling some Oracle data as we speak and let me tell you, I’m pretty happy to be working on this post instead. Luckily for you, you don’t need to reinvent this wheel! Call us up. We’ll happily talk through the particulars of your systems and the relevant work we’ve already done. The documentation for these systems (for the most part) is out there, so it’s not that this is a bunch of classified top-secret stuff. We simply have a lot of accumulated experience getting data out of HR systems and have built proprietary processes to ensure you get the most data from your tools. In many cases, like Workday, for example, we can activate the custom integration we’ve already built and have your core data set populated in One Model. If you go down that road on your own, it’ll take you 2 - 3 days just to arrange the internal meeting to talk about how to make a plan to get all this data extracted. We spent over 10,000 development hours working on our Workday extraction process alone. And once you do get the data out, there’s still a mountain of work ahead of you. Which brings us to... The next step is refining your extracted data into a standardized data catalog. How do you define and govern the standard ways you are going to analyze your people data? Let’s take a simple example, like termination rate. The numerator part of this is actually pretty straightforward. You count up the number of terminations. Beyond that, you will want to map termination codes into voluntary and involuntary, exclude (or include) contractors, etc. Let’s just assume all this goes fine. Now what about the bottom part? You had, say 10 terminations in the given period of time, so your termination rate is... relative to what headcount? The starting headcount for that period? The ending headcount? The average headcount? How about the daily average headcount? Go with this for two reasons. 1) It’s the most accurate. You won’t unintentionally under or overstate termination rate, giving you a more accurate basis of comparison over time and the ability to correctly pro-rate values across departments. See here for details. And 2) If you are thinking of doing this in-house, it’ll be fun to tell your team that they need to work out how to deliver daily average headcounts for all the different dimensions and cuts to meet your cleaning data requirements. If you really want to, you can fight the daily average headcount battle and many others internally. But we haven’t even gotten to time modeling yet, which is so much fun it may get its own upcoming One Model Difference post. Or the unspeakable joy you will find managing organizational structure changes, see #10. On the other hand, One Model comes complete with a standard metrics catalog of over 590 metrics, along with the data processing logic and system integrations necessary to collect that data and calculate those metrics. You can create, tweak, and define your metrics any way you want to. But you do not have to start from scratch. If you think about it. This One Model difference makes all the difference. Ultimately, you simply have to clean up your messy data. We recognize that. We’ve been through it before. And we make it part of the deal. Our customers choose One Model because we're raising the standard and setting the pace for people analytics. If you are spending time gathering and maintaining data, then the yardstick for what good people analytics is going to accelerate away from you. If you want to catch up, book a demo below and we can talk. Tell us you want to meet. About One Model: One Model helps thriving companies make consistently great talent decisions at all levels of the organization. Large and rapidly-growing companies rely on our People Data Cloud™ people analytics platform because it takes all of the heavy lifting out of data extraction, cleansing, modeling, analytics, and reporting of enterprise workforce data. One Model pioneered people data orchestration, innovative visualizations, and flexible predictive models. HR and business teams trust its accurate reports and analyses. Data scientists, engineers, and people analytics professionals love the reduced technical burden. People Data Cloud is a uniquely transparent platform that drives ethical decisions and ensures the highest levels of security and privacy that human resource management demands.
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9 min read
Stephen Haigh
As a people analytics professional, you may be tasked with explaining the business case for a people analytics solution to your coworkers and leadership. To do this, you must be prepared to address any potential objections they may have. If you're wondering how to sell your ideas up the chain of command or across departments, here are some tips to help you prepare for those conversations, navigate any obstacles, and bring the right people analytics platform into your organisation: Talking to your CFO and CIO Most HR leaders already understand the importance of people analytics. The leaders who typically create the most hurdles for investing in people analytics are CFOs and CIOs. Let’s explore each. Discussing people analytics solutions with your CFO CFOs are most concerned about budget and how the expense will impact the company’s bottom line. Suppose a budget has not been secured yet. In that case, it will be challenging to convince the CFO to invest in people analytics solutions when the company already spends a significant amount on other platforms. Therefore, you need to restructure your business case as an investment that will lower the overall people expense by giving insights into areas that deliver clear ROI such as reducing attrition, informing the talent acquisition efforts across internal and external resource, feeding a more data led workforce planning strategy and equipping people leaders with the insights they need to make their area of the business more efficient. Connecting business benefits to a return on investment is key. Discussing people analytics solutions with your CIO or CTO Generally, technology leaders of larger organisations want their tech teams to be building the company’s tech solutions. They don’t want their resources to go overlooked or underutilised. So it's understandable that CIOs or CTOs might be concerned about purchasing a product that they don't have to build themselves. They may also have reservations about the new solution’s level of security and compliance. To address these concerns, it's important to emphasise that investing in people analytics software, like One Model, doesn't mean giving up control of the data transformation process. Instead, it allows tech teams to focus on other critical tasks across the business. A few quick conversations with key members of that team will let you know which benefits to focus on when positioning to leadership. Whether you’re talking to a CFO or CIO, the best way to build an effective business case for people analytics is to understand your audience’s concerns and needs. Let’s go over strategies to make your conversations the best they can be: How to Build Your HR Analytics Business Case 1. Work on your positioning. To successfully advocate for people analytics in the workplace, practising and refining your approach is important. This means anticipating potential objections from your manager as well as your cross-functional colleagues and proactively addressing those concerns in your discussions. The best way to uncover these is to ask them some casual questions one-on-one like, “Have you heard of people analytics? What do you think about it?” As we mentioned earlier, you need to know your audience and understand what they need to hear to fully embrace your ideas. You also need to consider what they don’t need to hear. Ask yourself, what are the top three points they need to hear and structure your conversations around that key information? Need help with your positioning? We'd love to help! Connect with us today. Many HR leaders know their reputation is on the line when choosing an enterprise solution. Some go with the most popular tool, but popularity doesn’t necessarily ensure performance. 2. Use data to demonstrate potential impact The most critical aspect of creating an HR analytics business case is to demonstrate how investing in it can make an impact on business outcomes such as employee engagement and retention, customer satisfaction, and financial performance. Showcase data points that support your business case for people analytics, such as market trends or research studies conducted by reputable organisations that show the value of HR analytics solutions. For example, generally speaking, each position that turns over can cost an organisation 33% of that position’s annual salary. Do you know how to calculate the cost of turnover at your company? Is that a metric you can pull quickly? If not, it may even be worth explaining how big of a project that is as an example of why investing in a tool is worth the budget. 3. Invite your team to consider the alternatives. You always want what's best for the team and business, so it's important to encourage discussion and be open to alternative options and new ideas. Invite the team to help participate in problem-solving the company's needs by comparing people analytics with a different type of solution. This approach not only invites discussion and debate, but also ensures that everyone feels heard and valued. By working together to co-create solutions, you can achieve better outcomes and win support more easily. Plus, by involving everyone from the beginning, we can build stronger, more cohesive teams committed to the company's shared goals. Talk through the alternative options of DIY, using a pre-canned vendor, or investing in One Model. Present each option's benefits, drawbacks, and value to the team, then encourage an open discussion and friendly debate to persuade your boss and colleagues. 4. Share examples of successful implementations. Circulating case studies and success stories from other organisations is a great way to show the value that people analytics can bring to your business. Show how they overcame similar objections, used data effectively, and achieved tangible business results. 5. Gather Allies You should also identify key allies who can help champion your idea and offer support. Understanding where people will back you up can help you handle objections. Again, the best way to get this support is by having one-on-one conversations starting with open, generalised questions. Additionally, it's important to emphasise how your suggestion to adopt a people analytics solution aligns with the organisation's values and strategy and will ultimately benefit your manager and the company. By carefully considering and addressing these factors, you'll be well-positioned to gain support for your ideas. 6. Focus on benefits, not features. Paint a picture of what life would be like after successfully implementing people analytics within the business — how it could make operations more efficient, cost less time and money (through automation and AI), ensure DE&I goals are met, and improve business outcomes. This will help people visualise and rationalise how HR analytics can benefit the business and make them more likely to buy in. Establishing a framework for moving forward After holding these conversations, the process doesn't necessarily end with a simple approval or rejection. If you get the green light, congratulations are in order! It’s time for you to get to work on implementing a people analytics solution. But if your idea is rejected, don't be discouraged. Most innovators experience numerous rejections before eventually succeeding. Rejection is simply a part of the process, not the end of it. Regardless of the outcome, use this as an opportunity to understand the reasons behind the decision. This requires asking questions and seeking clear feedback from the decision-maker. For acceptances, this will give you points to come back to later down the road. For rejections, understanding their arguments and potential areas of concern will allow you to identify ways you can re-frame your solution differently. Understanding the thought process behind either decision will help you gain support from your ideas in the workplace on future projects. In some cases, this feedback may even prompt the decision-maker to reconsider their position. If nothing else, seeking feedback can create a shared vision and establish a framework for moving forward. If you remain open to collaborating with others and working to address the issues that led to the rejection, there is a greater likelihood that leaders and managers will eventually commit to your idea and get on board. For a great example of another company that had to sell internally and is now winning, read our Tabcorp case study.
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10 min read
Dennis Behrman
Ever play with a Magic 8 Ball? Back in the day, you could ask it any question and get an answer in just a few seconds. And if you didn't like its response, you could just shake it again for a new prediction. So simple, so satisfying. Today's HR teams and businesses obviously need more reliable ways of predicting outcomes and forecasting results than a Magic 8 Ball. But while forecasting and predicting sound similar, they're actually two different problem-solving techniques. Below, we'll go over both and explain what they're best suited for. What is HR forecasting? Remember the Magic 8 ball? At first glance, the Magic 8 ball "predicts" or "forecasts" an answer to your question. This is not how forecasting works (at least, for successful companies or HR departments). Instead, HR forecasting is a process of predicting or estimating future events based on past and present data and most commonly by analysis of trends. "Guessing" doesn't cut it. For example, we could use predictive forecasting to discover how many customer calls Phil, our product evangelist, is likely to receive in the next day. Or how many product demos he'll lead over the next week. The data from previous years is already available in our CRM, and it can help us accurately predict and anticipate future sales and marketing events where Phil may be needed. A forecast, unlike a prediction, must have logic to it. It must be defendable. This logic is what differentiates it from the Magic 8 ball's lucky guess. After all, even a broken watch is right two times a day. What is predictive analytics? Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and trends that could potentially predict future outcomes. It doesn't tell you what will happen in the future, but rather, what might happen. For example, predictive analytics could help identify customers who are likely to purchase our new One AI software over the next 90 days. To do so, we could indicate a desired outcome (a purchase of our people analytics software solution) and work backwards to identify traits in customer data that have previously indicated they are ready to make a purchase soon. (For example, they might have the decision-making authority on their people analytics team, have an established budget for the project, completed a demo, and found Phil likeable and helpful.) Predictive modeling and analytics would run the data and establish which of these factors actually contributed to the sale. Maybe we'd find out Phil's likability didn't matter because the software was so helpful that customers found value in it anyway. Either way, predictive analytics and predictive modeling would review the data and help us figure that out — a far cry from our Magic 8 ball. Managing your people analytics data: how do you know know if you need to use forecasting vs. predictive analysis? Interested in how forecasting and/or predictive modeling / predictive analytics can help grow your people analytics capabilities? Do you start with forecasting or predictive modeling? The infographic below (credit to Educba.com - thanks!) is a great place to compare your options: Recap: Should you use forecasting or predictive analysis to solve your question? Forecasting is a technique that takes data and predicts the future value of the data by looking at its unique trends. For example - predicting average annual company turnover based on data from 10+ years prior. Predictive analysis factors in a variety of inputs and predicts future behavior - not just a number. For example - out of this same employee group, which of these employees are most likely to leave (turnover = the output), based on analyzing past employee data and identifying the indicators (input) that often proceed with the output? In the first case, there is no separate input or output variable but in the second case, you use several input variables to arrive at an output variable. While forecasting is insightful and certainly helpful, predictive analytics can provide you with some pretty helpful people analytics insights. People analytics leaders have definitely caught on. We can help you figure it out and get started. Want to see how predictive modeling can help your team with its people analytics initiatives? We can jump-start your people analytics team with our Trailblazer quick-start package, which really changes the game by making predictive modeling agile and iterative process. The best part? It allows you to start now and give your stakeholders a taste without breaking the bank, and it allows you to build your case and lay the groundwork for the larger scale predictive work you could continue in the future. Want to learn more? Connect with Us. Forecasting vs. Predictive Analysis: Other Relevant Terms Machine Learning - machine learning is a branch of artificial intelligence (ai) where computers learn to act and adapt to new data without being programmed to do so. The computer is able to act independently of human interaction. Read Machine Learning Blog. Data Science - data science is the study of big data that seeks to extract meaningful knowledge and insights from large amounts of complex data in various forms. Data Mining - data mining is the process of discovering patterns in large data sets. Big Data - big data is another term for a data set that's too large or complex for traditional data-processing software. Learn about our data warehouse. Predictive Modeling - Predictive modeling is a form of artificial intelligence that uses data mining and probability to forecast or estimate more granular, specific outcomes. Learn more about predictive analytics. Descriptive Analytics - Descriptive analytics is a type of post-mortem analysis in that it looks at past performance. It evaluates that performance by mining historical data to look for the reasons behind previous successes and failures. Prescriptive Analytics - prescriptive analytics is an area of business analytics dedicated to finding the potential best course of action for a given situation. Data Analytics - plain and simple, data analytics is the science of inspecting, cleansing, transforming, and modeling data in order to draw insights from raw information sources. People Analytics - All these elements are important for people analytics. Need basics? Learn more about people analytics. About One Model One Model’s people analytics solutions help thriving companies make consistently great talent decisions at all levels of the organization. Large and rapidly-growing companies rely on our People Data Cloud™ people analytics platform because it takes all of the heavy lifting out of data extraction, cleansing, modeling, analytics, and reporting of enterprise workforce data. One Model pioneered people data orchestration, innovative visualizations, and flexible predictive models. HR and business teams trust its accurate reports and analyses. Data scientists, engineers, and people analytics professionals love the reduced technical burden. People Data Cloud is a uniquely transparent platform that drives ethical decisions and ensures the highest levels of security and privacy that human resource management demands.
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14 min read
Lisa Meehan
The way a company structures its workforce is crucial to its success. Workforce structures determine how employees are organised, how work is delegated, and how communication flows throughout the organisation. Workforce structures refer to the way a company organises its employees, financial responsibilities, and the relationships among them. It provides a framework for managing and coordinating work activities. There are several types of enterprise structures and your organisation uses several of them, so let’s talk through different ones and see how you can visualise them. Types of workforce structures Most workforce structures can best be displayed as an org chart. An organisational chart, or org chart, is an essential tool for any enterprise structure as it provides a clear and concise visual representation of the hierarchy, roles, and relationships among employees. It enables employees to understand where they fit into the organisation and how their role contributes to the overall goals of the company. Functional One of the most common structures in business today, a functional organisational structure groups employees according to the functions they perform, such as marketing, accounting, or operations. This allows for specialised expertise in each function, where everyone has a defined role and clear lines of communication. Location and structural overlay It may be that only a division of the company is broken up into location-based structures. For instance, this can be common in sales or HR talent acquisition departments where you have an East, Northeast division of responsibility. Supervisor hierarchy You may have also heard of position hierarchy or supervisory hierarchy. This is a slight modification of the traditional Hierarchical model. You can see these a lot in support or in places within the organisation where approvals are needed. Hierarchy establishes the connection between a superior and the subordinates within an organisation. The supervision hierarchy report exhibits the designated supervisor, presenting their immediate reports, followed by their respective reports, and so on. It encompasses the option to include the employee number, along with the name and job title of each individual, based on the chosen level of supervisory depth. A supervisor hierarchy shows who reports to who. It refers to the structure of reporting relationships within an organisation, where supervisors are responsible for overseeing the work and performance of their subordinates. In a typical supervisor hierarchy, each supervisor has a team of employees reporting directly to them. Often for the people running those units, there is a 1:1 but that’s not always perfect. The supervisors themselves report to higher-level managers or executives, forming a chain of command. The reporting relationships follow a top-down approach — with information, instructions, and feedback flowing from higher-level supervisors to lower-level employees. This hierarchical structure ensures clear lines of authority, accountability, and efficient communication within the organisation. Cost centre structure hierarchy The cost centre structure refers to the total collection of different cost types, including both fixed and variable expenses, that constitute the overall expenditures of a business. This is where the financials are run. It’s normally wrapped up in the chart of accounts. Organisations use the cost centre structure to establish pricing and pinpoint opportunities for minimising costs. This is typically how the finance system works and who is financially accountable for the funds that they spend. This can be different from who runs the business units. Therefore, this view can often be out of alignment with the structural hierarchy. To put it simply, it’s because Finance runs the financials and HR runs the business structure. This type of view often coincides with internal company political struggles. Why? Finance likes to be in control of its space and typically doesn’t like HR veering into it. But if HR can get a cost structure into a people data view, it’s typically a good thing. For instance, this will allow the finance team to get activity- or project-based accounting, or the total cost of the project including the hard numbers and people resources to make real assessments on the ROI of various initiatives. You can only get this view when finance and people data are combined. Matrix structure While not easy to visualise, this structure is really important to get right. A Matrix workforce structure generally refers to a type of organizational setup where employees are assigned to multiple reporting lines or managers simultaneously, as opposed to a traditional hierarchical structure where each employee reports to only one manager. In a matrix workforce, individuals are part of cross-functional teams and can work on various projects simultaneously, often with different sets of colleagues and supervisors. The matrix structure is most often used in large, complex organizations that handle multiple projects simultaneously and require a high degree of collaboration across departments. It is commonly found in industries such as technology, engineering, consulting, and pharmaceuticals. Additionally, matrix structures are prevalent in multinational corporations, where teams need to coordinate and work across different geographical regions. How does One Model help? As you can see, getting different views of the various structures within your business can have profound impacts on your understanding. One Model creates alignment for customers, so they can pivot between those different views with the included people insights. This is really important so you can create a mapping between your financial structures and people structures to become the translator that brings that world together within the organisation. Senior leaders typically want to see where all the money is being spent and where the people are so they can make informed decisions. So views that bring this data together make One Model incredibly valuable to our current customers. Our products empower you to change the view with a click of a button, so you get a complete view of what is actually going on. You can also cross-tabulate those views and link them together. Want to see One Model in action? Watch this quick demo video. How security plays into analysing workforce structures A basic organisational breakout may not be too concerning, but once you start applying analytics to your charts to get a better understanding of how key insights or talents are distributed throughout your organisation, you run into issues. That’s why having a tool like One Model with strong roles-based security that locks sensitive information to specific roles allows you to create a public view that instantly keeps your data safe. Security plays a crucial role in analysing workforce structures by focusing on access controls, user authentication, data protection, security awareness, incident response, vendor and third-party risk, and compliance with regulations. By incorporating security considerations into workforce analysis, organisations can identify vulnerabilities, mitigate risks, and establish a robust security foundation for their operations. Explore the evolution of workforce models Want to learn more about the evolution of workforce planning models over the past four decades and the key role that enterprise segmentation plays in achieving great analytics? Watch our webinar with Peter Howes, a thought leader and pioneer in the field of analytics and strategic planning models. He discusses how these structures have changed to a more strategic approach that’s focused on meeting the needs of the business. 7 benefits of incorporating people analytics into your workforce structures Incorporating people analytics into various workforce structures can provide organisations with valuable insights and significant benefits. People analytics, also known as HR analytics or workforce analytics, involves gathering and analysing data about employees to make informed decisions and improve organisational performance. Here are seven ways incorporating people analytics can positively impact workforce structures: 1. Data-Driven Decision-Making: People analytics paired with workforce structure views allows organisations to base their decisions on objective data rather than relying solely on intuition or anecdotal evidence. By overlaying workforce data on top of various structures, organisations can gain insights into critical aspects such as employee performance, engagement, turnover, and productivity to quickly see where trouble resides in the business. These data-driven insights enable more informed decision-making in areas like talent acquisition, talent development, succession planning, and performance management. 2. Talent Acquisition and Retention: People analytics inserted into your workforce structure views can highlight where your most loyal and high-performing employees exist. Seeing this allows you to identify the most effective recruitment channels, evaluate candidate profiles, and predict the likelihood of candidate success — so your team can build impactful strategies. By analysing data on employee turnover and retention, organisations can better visualise the factors influencing attrition rates and develop targeted retention strategies. It can also facilitate the identification of high-potential employees for succession planning and talent development initiatives. 3. Performance Management: Incorporating people analytics into an enterprise structure allows organisations to evaluate employee performance objectively and uncover great leaders and employees. By analysing performance data, organisations can identify top performers, evaluate goal attainment, and provide targeted feedback and development opportunities. People analytics can also help uncover performance patterns and trends, enabling managers to make data-driven decisions regarding promotions, rewards, and recognition. 4. Employee Engagement and Satisfaction: Organisational structures paired with people analytics provides a map of employee engagement levels, job satisfaction, and factors that impact overall employee experience. This will quickly allow you to understand the health of various teams within your business. By analysing data from employee surveys, feedback platforms, and other sources, organisations can identify areas for improvement and take proactive measures to enhance employee engagement and satisfaction. 5. Workforce Planning and Optimisation: Workforce hierarchy paired with people analytics plays a vital role in strategic workforce planning and optimisation. By analysing workforce data, organisations can assess their workforce's current and future needs, identify employee gaps, and develop strategies for workforce development and succession planning. People analytics can also help optimise workforce structures by identifying areas of organisational inefficiency or redundancy, enabling resource allocation and restructuring initiatives. 6. Diversity and Inclusion: Where do your DE&I community members reside in your org? Which areas of the business are most diverse? Incorporating people analytics into your workforce structure can support diversity and inclusion efforts by analysing demographic data. This allows organisations to assess representation, identify potential biases, and implement targeted diversity and inclusion initiatives. 7. Predictive Analytics and Future Insights: People analytics enables organisations to leverage predictive analytics to forecast future trends and outcomes related to the workforce. By analysing historical data, organisations can identify patterns and make predictions about attrition rates, talent shortages, skill requirements, and workforce needs. These insights allow proactive planning and decision-making, ensuring the organisation is prepared for future workforce challenges. In summary, workforce structures already exist in your organisation, the question is can you use them to better understand your business and create efficiencies? If you can’t, or if the process is a major project for your HR team, then you need to consider people analytics software like One Model that empowers you to transform how your leaders make decisions. We’d love to show you how One Model can help your organisation make better talent decisions. Request a demo today!
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5 min read
Richard Rosenow
The performance management process can be a source of frustration and wasted time for many businesses. A 2014 Deloitte University study found that 58% of companies surveyed believed their performance reviews were a waste of time. This sentiment has not improved in the years since. In response, Deloitte and other major companies, including Accenture, Adobe, GE, Goldman Sachs, IBM, Microsoft, and SAP, had at the time abandoned traditional annual performance reviews in favor of more effective approaches. At the time, data from Towers Watson shows that 14% of companies have already eliminated performance ratings, with an additional 24% considering doing the same. These are large, enterprise companies making the switch to new performance management methods. These are well-established businesses that carefully consider and test new programs before fully implementing them. These companies discovered that alternative methods can provide more valuable insights into employee performance and that the resources and time dedicated to performance reviews can be better allocated elsewhere. While there are many arguments for abandoning traditional performance reviews, the increased velocity of employee performance metrics for employees is a key factor to consider. By collecting and analyzing data in a more timely manner, businesses can make faster and more informed decisions about their employees to drive the performance of the company. What is data velocity? Data velocity is one of the 3Vs of Big Data. The other two are X and Y. The concept of data velocity refers to the speed at which data is collected and analyzed. Traditionally, HR data has been collected at a relatively slow pace, with annual performance reviews being a common example. But this slow process means that data collected can quickly become incomplete, outdated, and subject to biases (recency), making it less useful for informed decision-making. For performance management, higher velocity data would mean multiple data points throughout the year instead of one annual performance review. But to increase the velocity of HR data, companies may need to adopt new technologies and approaches that enable more frequent and efficient data collection and analysis. Traditional systems that handled annual performance reviews may not make the transition to a higher velocity approach. This might include the use of specialized systems for check-ins and pulse surveys or working with HR tech startups that specialize in real-time performance management. If you look closely at any of the companies that have dropped annual performance reviews, they aren't actually eliminating performance management or even the review process. Instead, they’ve adopted technologies that enable them to switch to a high-velocity alternative. Accelerating performance management to the next level Think about your FitBit or smartwatch if you have one. If it only told you once a year how many steps you’d taken, it wouldn't give you much insight into how to change your habits. The critical piece of that technology is the velocity of the data collection. That enables you to know when you've been sitting too long reading HR analytics articles and that you should get up and take a walk. When you can collect higher velocity data, the time gaps between data points shrink, which then lets a learning algorithm better understand the data. When an algorithm can make sense of your data across time, that's when you can start to make predictions or better segment the employee population. The use of higher velocity data in HR can greatly improve the accuracy and effectiveness of employee performance analytics. By collecting data at a faster rate, businesses can better understand how performance metrics for employees change over time and identify trends and patterns faster throughout the year. This can lead to more informed decision-making and the ability to make predictions or segment the employee population. While these approaches may involve significant changes, they can ultimately provide more valuable insights into employee performance and drive business success. Why has the velocity of HR data lagged behind other fields? The lack of progress in increasing the velocity of HR data collection and analysis can be attributed to the challenges of changing established practices and the difficulties of collecting data from employees. Not to mention that HR departments are stretched to their limits in terms of data collection and may not have the resources, tools, or capacity to gather data at a faster pace without technological support. Without the right technology, it’s difficult to implement a high-velocity performance management system that can provide accurate, timely insights into employee performance. Traditional methods are not sufficient for statistically sound, bias-free analysis (some companies are still recommending post-it notes in a drawer to record employee achievements). In order to effectively collect and analyze work performance data in real-time, HR departments need access to digital technologies specifically designed for this purpose. This can be frustrating for HR professionals who are eager to adopt modern, data-driven approaches to performance management. But as new technologies are developed and made available, it’ll be easier for HR departments to implement high-velocity performance management systems that drive business success and improve employee performance. "As we strive to improve performance management" "In our efforts to harness the power of HR data" "As we move towards more data-driven approaches to performance management" "In our pursuit of high-velocity HR data" "As we continue to evolve our performance management strategies" As the field of HR evolves and the demand for high-velocity data increases, companies have seemingly three options: build their own technology in-house, purchase a solution from a vendor, or risk falling behind. But with One Model, companies have a fourth option: build+. They get the benefits of starting with a robust system as well as the ability to customize and make the solution fit their needs 100%. Learn more about build+ in One Model's latest whitepaper. Add image and cta for whitepaper.
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13 min read
Nicholas Garbis
At some point, every successful People Analytics team will develop a meaningful partnership with the Finance organization. Unfortunately, this partnership is usually not easily achieved and it's quite normal for initial alignment efforts to last for a couple of years (or more!). We are delighted to repost this insightful blog post authored by Nicholas Garbis on May 4, 2021. Revisiting his valuable insights will help us all foster a deeper understanding of how HR and Finance can collaborate more effectively. A new or maturing People Analytics team may fail to recognize the effort level required and not prioritize the work needed to establish this critical partnership with Finance. They do so at their own peril. The day will inevitably arrive when a great analytics product from the PA team will be dismissed by senior leaders when they see the foundational headcount numbers do not match. The PA team will be lacking in a clear explanation that is supported by the CFO and Financial Planning & Analysis (FP&A) leaders. But why is this the case? And how can HR and People Analytics teams do a better job of establishing the partnership? Analyzing the analytics conflicts between finance and HR Lack of alignment on workforce data At the heart of the issue is a lack of alignment on the most basic workforce metric: headcount. Both Finance and HR teams are often sharing headcount data with senior leaders. In many companies, the numbers are different. This creates distrust and frustration, and I will contend that, given Finance’s influence in most organizations, the HR team is on the losing end of these collisions. End result is that the organization spends time debating the figures (at a granular level) and misses the opportunity to make talent decisions that support the various company strategies (eg, growth, innovation, cultural reinvention, cost optimization). While headcount is at the foundation, there are several other areas where such disconnects arise and create similar challenges: workforce costs, contingent workers, position management, re-organizations, workforce budgets/plans, movements, etc... Solving the basic headcount alignment is the first step in setting the partnership. Source of the Disconnect: "Headcount Dialects" and "Dialectical Thinking" The disconnect in headcount figures is nearly always one of definition. Strange as it may sound, Finance and HR do not naturally count the workforce in the same way. It's as if there is a 'headcount dialect" that each needs to learn in order to communicate with the other. Therefore, if they have not spent some intentional, focused time on aligning definitions and processes, they will continue to collide with each other (and HR will fail to gain the trust needed to build an analytics/evidence-based culture around workforce decisions). The dialectical thinking challenge is for Finance and HR to recognize that the same data can be presented in (at least) two different ways and both can be simultaneously accurate. It is for the organization to determine which definition is considered "correct" for each anticipated use case (and then stick to that plan). Primary disconnection points Two primary areas of disconnect are the definition of the term “headcount” and whether a cost or organizational hierarchy is being used. Definition of “Headcount”: There are several components of this, underscoring the need for alignment when it comes to finance headcount vs HR headcount. Using Full-Time Equivalent (FTE) or Employee Count: Employees that are working less than full-time are often in the system with FTE values of 1.0 (full-time), 0.5 (half-time), and every range of fraction in between. The Employee Count, on the other hand, will count each employee as 1 (sometimes lightly referred to as a “nose count” to distinguish it from the FTE values). In some companies, interns/co-op employees are in the system with FTE value of 0, even though they are being paid. Determining Which Status Codes are to be Included: Employees are captured in the HR system as being active or inactive, on short-term or long-term leave of absence (LOA, “garden leave”), and any number of custom values that are used to align with the HR processes. In many companies, the FTE values are updated to align with the change in status. Agreeing on which status codes are counted in "headcount" is required for setting the foundation. Organization versus Cost Hierarchy: The headcount data can be rolled up (and broken down) in at least two ways: based on the organization/supervisor hierarchy structure or based on the cost center/financial hierarchy. Each has its unique value, and neither is wrong -- they are simply two representations of the same underlying data. It’s quite common that insufficient time has been spent in aligning, reconciling, and validating these hierarchies and determining which one should be used in which situations. Organization Hierarchy: This is sometimes called the “supervisory hierarchy” and represents “who reports to whom” up the chain of command to the CEO. This hierarchy is representative of how work is being managed and how the workforce is structured. Each supervisor, regardless of who is paying for their team members, is responsible for the productivity, engagement, performance, development, and usually the compensation decisions, too. Viewing headcount through the organization hierarchy will provide headcount values (indicating the number of resources) for each business unit, each central function, etc... The organization hierarchy is appropriate for understanding how work is being done, performance is being managed, the effectiveness of leaders and teams, and all other human capital management concerns. It is also useful in some cost-related analyses such as evaluation and optimization of span-of-control and organization layers. Cost Hierarchy: This is sometimes referred to as “who is paying for whom” and is rarely in perfect alignment with the organization hierarchy. There is a good reason for this, as there are situations when a position in one part of the organization (eg, research & development) is being funded by another (eg, a product or region business unit). In these cases, one leader is paying for the work and the work is being managed by a supervisor within another leader's organization. I have seen "cross-billing" situations going as high as 20% of a given organization. When headcount is shown in a cost hierarchy, it indicates what will hit the general ledger and the financial reporting of the business units. It has a valid and proper place, but it is mostly about accounting, budgeting, and financial planning. Which business unit is right? The truth is that as long as you have all the workforce data accurately captured in the system, everything is right. This sounds trite, but it puts emphasis on the task at hand which is to determine a shared understanding and establish rules for what will be counted and how, which situations will use which variations, and what agreed-upon labeling will be in place for charts/tables shared with others. Some organizations that have a culture of compliance and governance could set this up as part of an HR data governance effort (where headcount and other workforce metrics would be defined, managed, and communicated). Going further, there is a need beyond the Finance and HR/People Analytics leader to socialize whatever is determined as these running rules across the Finance and HR organizations. These teams all need to be aligned. How does One Model help finance and HR collaborate? With a People Analytics solution like One Model in place, the conversations between HR and Finance can be had with much more clarity and speed. This becomes easier because, within One Model all of the workforce data is captured, data quality is managed, and all related dimensions (eg, hierarchies, employee attributes) are available for analysis. Two examples of content that is specifically designed to facilitate the Finance-HR alignment discussions are: Headcount Storyboard. Setting up a storyboard which shows headcount represented in multiple ways: FTEs versus employee counts, variations of which statuses are included/excluded, etc. This information becomes readily comparable with the metric definitions only a click away. Even better, the storyboard can be shared with the Finance and HR partners in the discussion to explore on their own after the session. One Model is the best tool for counting headcount over time. Hierarchy Storyboard. Providing views of the headcount as seen using the supervisor and cost hierarchies side-by-side will help to emphasize that both are simultaneously correct (ie, the grand total is exactly the same). This can also provide an opportunity to investigate some of the situations where the cost and organizational hierarchy are not aligned. In many cases, these situations can be understood. Still, occasionally there are errors from previous reorganizations/transfers which resulted in costing information not being updated for a given employee (or group of employees). With the data in front of the teams, the discussion can move from “Which one is right?” to “Which way should be used when we meet with leaders next time?” When you have One Model, you can bring HR and Finance together faster and more easily ... and that helps you to accelerate your people analytics journey. Need Help Talking to Finance? Let us know you'd like to chat.
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5 min read
Phil Schrader
Analytics is a funny discipline. On one hand, we deal with idealized models of how the world works. On the other hand, we are constantly tripped up by pesky things like the real world. One of these sneaky hard things is how best to count up people at various points in time, particularly when they are liable to move around. In other words, how do you keep track of people at a given point in time, especially when you have to derive that information from a date range? Within people analytics, you run into this problem all the time. In other areas, it isn’t as big of a deal. Outside of working hours (sometimes maybe during working hours), I run into this when I’m in the middle of a spreadsheet full of NBA players. Let's explore by looking at an easy-to-reference story from 2018. Close your eyes and imagine I’m about to create an amazing calculation when I realize that I haven’t taken player trades into consideration. George Hill, for example, starts the season in Sacramento but ends it in Cleveland. How do you handle that? Extra column? Extra row? What if he had gotten traded again? Two extra columns? Ugh! My spreadsheet is ruined! Fortunately, One Model is set up for this sort of point-in-time metric. Just tell us George Hill’s effective and end dates and the corresponding metrics will be handled automatically. Given the data below, One Model would place him in the Start of Period (SOP) Headcount for Sacramento and End of Period (EOP) Headcount for Cleveland. Along the way, we could tally up the trade events. In this scenario, Sacramento records an outbound trade of Hill and Cleveland tallies an inbound trade. The trade itself would be a cumulative metric. You could ask, “How many inbound trades did Cleveland make in February?” and add them all up. Answer-- they made about a billion of them. Putting it all together, we can say that Hill counts in Cleveland’s headcount at any point in time after Feb 7. (Over that period Cleveland accumulated 4 new players through trades.) So the good news is that this is easy to manage in One Model. Team Effective Date End Date Sacramento 2017-07-10 2018-02-07 Cleveland 2018-02-08 --- The bad news is that you might not be used to looking at data this way. Generally speaking, people are pretty comfortable with cumulative metrics (How many hires did we make in January?). They may even explore how to calculate monthly headcount and are pretty comfortable with the current point in time (How many people are in my organization). However, being able to dip into any particular point in time is new. You might not have run into many point-in-time scenarios before-- or you might have run into versions that you could work around. But, there is no hiding from them in people analytics. Your ability to count employees over time is essential. Unsure how to count people over time? Never fear. We’ve got a video below walking you through some examples. If you think this point in time stuff is pretty cool, then grab a cup of coffee and check out our previous post on the Recruiting Cholesterol graph. There we continue to take a more intense look beyond monthly and yearly headcount, and continue to dive deeper into point-in-time calculations. Also, if you looked at the data above and immediately became concerned about the fact that Hill was traded sometime during the day on the 8th of February and whether his last day in Sacramento should be listed as the 7th or the 8th-- then please refer to the One Model career page. You’ll fit right in with Jamie :) Want to read more? Check out all of our People Analytics resources. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Its newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data.
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4 min read
Dennis Behrman
Now that many of our customers have complete control and access to their data like never before, they're exploring how to tell better data stories. A fun way to explore this topic is to look at the great examples from the past. Minyard’s example shows us how to tell a story with data Let’s take this example forward to one of history's most famous data stories, Minard’s visualization of Napoleon’s 1812 march into Russia. Edward Tufte, a renowned expert in data visualization, praised Minard's visualization of Napoleon's 1812 march. In his book "The Visual Display of Quantitative Information," Tufte referred to Minard's graphic as "probably the best statistical graphic ever drawn”. Although not directly HR (other than in a dark way, a visualization of workforce attrition over time), we can still analyze how this visual fits into the framework of six effective data storytelling elements and apply those lessons to HR storytelling: Business Objective: In the context of HR, the objective was to convey a message and inspire action. Minard's visualization powerfully demonstrates the disastrous consequences of Napoleon's march, highlighting the importance of understanding the impact of decisions on people. Evidence: Minard's visualization uses data from multiple sources, such as the number of soldiers, their geographic locations, and temperature. In HR data storytelling, this would translate to gathering relevant data from various sources like employee engagement surveys, performance metrics, or attrition rates, to support the narrative. Visuals: Minard's visualization is a clear, engaging visual representation of complex data. Similarly, HR professionals should utilize data visualization tools to create visually appealing and easy-to-understand representations of workforce data. Narratives: Minard's map tells the data-informed story of the march's progression and the resulting loss of soldiers. In HR data storytelling, a compelling narrative should weave together the data and insights, making them relatable and memorable for the audience. Interactivity: While Minard's visualization is static, you could imagine leaders in the armed forces looking for cuts of this data by troop category, demographics, and nationality (pre-GDPR). Interactivity would have allowed Minard to engage quickly with the graphic to see different cuts of the data. HR professionals adapt their data stories based on the audience's questions and feedback, making the story more engaging and dynamic. Action: Minard's visualization serves as a cautionary tale, prompting leaders to consider the consequences of their decisions. In HR data storytelling, ending the story with a clear call to action can drive engagement and ensure the story leads to meaningful change within the organization. By analyzing Minard's data storytelling example in the context of the simple six-element storytelling framework, HR professionals can gain valuable insights on how to create data-informed stories that effectively communicate the human impact of organizational decisions and inspire meaningful change. Check out 8 Essential People Analytics Dashboards 1 Image Source Ready to tell better data stories with your people analytics data? Download our Data Storytelling eBook today.
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5 min read
Dennis Behrman
HR leaders - are you ready? Starting from Wednesday, July 5th, enforcement begins for Local Law 144 and Department of Consumer and Work Protection (DCWP) Rules. These regulations specifically address the use of Automated Employment Decision Tools (AEDT) found in software used during the job application or promotion process for employers and candidates residing in New York City. Understanding the new AEDT regulation Now, let's break it down in simpler terms. The law and regulations cover AEDT, which is basically any process that uses machine learning, statistical modeling, data analytics, or artificial intelligence to provide simplified output like scores, classifications, or recommendations. These tools are meant to assist or even replace discretionary decision making by humans in the employment process. To comply with these new requirements, employers need to take a few steps. First, you'll need to figure out if any of the software your company uses for hiring or promotions falls under the category of AEDT. In other words, if it's helping with decision-making by "scoring," classifying, or recommending candidates or employees in NYC. This law is broader than similar laws in Illinois and Maryland that focus on facial-recognition software, so it covers commonly used HR software. If you do use software with AEDT, here's what you need to do: (1) make sure a bias audit has been conducted; (2) provide at least 10 days' notice to applicants or employees that AEDT will be used; (3) explain the qualifications the AEDT will consider during assessments; (4) disclose the data source and type of AEDT being used, along with your data retention policy if it hasn't been shared elsewhere; and (5) inform applicants or employees that they have the right to request an alternative means of assessment or a "reasonable accommodation" under other laws. But wait, there's more! The initial bias audit is just the beginning. Employers are also responsible for conducting an AEDT audit annually. And here's the kicker: the results of the bias audit need to be published on your website before using the AEDT. These audits have to be conducted by "Independent Auditors" who are unbiased and not financially connected to the employer or the software vendor. Read more here. Now, let's talk penalties. If you violate any of these requirements, you could face civil penalties. The first violation can result in a $375 fine, while subsequent violations could be at least $500, with a maximum of $1,500. There are separate penalties for violations of the notice and audit requirements. Keep in mind that this new regulation is in line with the EEOC's guidance issued in May 2023, which also requires bias audits, notice, and opt-out provisions. It's all part of the effort to ensure fairness in employment decisions. And it's not just happening in NYC – other jurisdictions are considering similar bills and regulations related to AI and employment decisions. Want to learn more about these regulations and others around the world impacting HR? Take our Regulations and Standards masterclass and be a leader in the space. Understanding your options I think a lot of companies not using One Model are going to have some big challenges. From our experience, most AI tools are like a black box. You cannot get a clear understanding of what is being used in the models that generate the insights you're using. One Model is the complete opposite. Our models are only built from your data. They have outputs that tell you exactly what data is being fed into the models and, in turn, are customizable. In addition, One Model customers benefit from being able to build models beyond talent acquisition including retention, diversity, and more. Would you like to see our AI in action? Meet with us today! Understanding the bigger global picture On a global scale, the EU's Artificial Intelligence Act is also making progress. The European Parliament recently adopted its official negotiating position on June 14, 2023. This Act covers the use of AI in various areas, including employment. If it goes into effect, AI used for employment purposes would likely fall under the "high-risk" category and face greater regulation. So, as HR leaders, it's important to stay informed about these evolving regulations. Make sure to review your software tools, conduct the necessary audits, and provide the required notices to applicants and employees. And keep an eye out for any developments in your local jurisdiction or at the EU level. Good luck navigating these changes!
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4 min read
Dennis Behrman
Phil Schrader and Stephen Haigh had an opportunity to attend the People Analytics World Conference in London April 26-27, 2023. During their visit, Phil was asked to give a public demonstration of how HR analytics software works. While we can't speak for other people analytics tools, we can speak to One Model. The crowd was mesmerized and had lots of questions at the end that you definitely have to watch. Join Phil as he walks through data import, export, and all the magic in between — even showing in real time how an AI model is built exclusively on your data. Phil, always cheeky and fun to watch, is a great teacher in all the things you should look for when assessing which people analytics tool is right for you. Compared to other HR analytics tools on the market, you'll quickly see that One Model is more transparent, easier to use, and more open than any other option on the market. Want your own personal tour of One Model? Request time to meet today. During the video, Phil walks us through each of these layers: The Consumer Layer: At the top of the platform, users, such as HR Business Partners, can access data, insights, and storyboards through a user-friendly interface. The storyboard feature allows users to interpret data visually and navigate through various tools like Explore, Storyboards, and Data. These tools enable users to slice and dice analytics, explore heat mapping, and gain insights into different data sources. From Consumer to Analyst Layer: One Model's flexibility empowers users to transition from the consumer layer to the analyst layer effortlessly. Here, analysts can customize the views, rearrange elements, and dive deeper into the data. With simple clicks, they can transform data into charts, change metrics, and connect multiple systems to gain a holistic view. Configuring Metrics and Data Engineering: As analysts continue their exploration, they can configure metrics according to their organization's specific requirements. They can modify calculations, adjust inclusion/exclusion criteria, and create unique views tailored to their audience. Furthermore, One Model offers transparency into data engineering, allowing analysts to delve into the underlying data models, processing scripts, and data sources. Unleashing the Power of Data Science: Finally, One Model empowers advanced analysts and data scientists to build predictive models. With the augmentation feature, analysts can create and maintain multiple models, evaluate their performance, and put them on schedules. The platform provides a guided walkthrough for model building, enabling users to define their objectives, select relevant metrics, and generate predictions. The prediction capabilities extend to specific employee segments or the entire population.
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10 min read
Richard Rosenow
What’s the difference between talent intelligence and people analytics? As I speak with people analytics leaders, HR tech vendors, and research analysts, this question comes up a lot. To help clarify the difference, I've developed a quick two-step trick, complete with real-life examples. Read on and let me know what you think. Why is there confusion between people analytics and talent intelligence? Since HR Tech 2022, talent intelligence (TI) has been on fire in the HR tech branding space. There was an incredible market valuation of a few TI companies that woke up the market, and since then there has been a “run on the brand”. This created a lot of noise and complications as companies that weren’t doing TI started calling themselves “TI vendors”. But we’ve seen that “generative AI” has taken the crown for this latest hype cycle — meaning the pretenders are quickly changing their banners to #GAI and we’re seeing the true talent intelligence companies remaining. The core talent intelligence platforms seem to be Lightcast, TalentNeuron, Claro, SkyHive, Revelio, People Data Labs, Draup, Horsefly, and LinkedIn Talent Insights. They gather and generate labor market data, make sense of resume data, social profile data, government data, and job posting data, and make it available for use by teams who analyze the market. We’ve also seen a rise in talent intelligence teams this past year that are distinct from people analytics (PA) teams. Toby Culshaw’s Talent Intelligence Collective is a distinct community with their own conference and conversations. I would suggest that it’s common to find folk who are part of both communities, but we’re starting to see the TI community come into its own and separate from the people analytics community. People analytics had similarly separated from IO psychology in the past 20 years. While there’s a lot of overlap and people participating in both communities, they’re becoming more distinct over time. Lastly, I’ve heard one too many people just give up and say these words mean the same thing. I think that could be true for workforce analytics / HR analytics / people analytics, but talent intelligence is proving distinct. Naming something is the first step to understanding it, so if we blur names, we blur our understanding. I’ve found two big tricks to defining these terms: Differentiating the function from the act Focusing on the second word Trick 1: Differentiating the function from the act The first step is to clarify whether we are referring to the business unit or department that performs the work (the function) or the act of doing the work itself. Function Act People analytics The business unit responsible for centralized people analytics, typically within HR, but not necessarily. The act of performing people analytics with workforce data, performed by anyone in HR, management, or leadership roles. Talent intelligence The business unit responsible for centralized talent intelligence, usually within recruiting organizations. The act of performing talent intelligence, typically done by sourcers, recruiters, facilities, or strategy teams. We often see people analytics as an umbrella term that encompasses workforce planning, people strategy, and sometimes even compensation and HR technology. This doesn't necessarily mean that all of these teams engage in the act of people analytics, but they are all part of the same function or business unit. Not knowing the difference between the function and the act of doing something can cause confusing, semantic arguments. The name of the function? Honestly, it doesn't matter. The name of the act? That's important because it helps us understand who is doing what, whether it needs to be centralized or decentralized, and to distinguish the work from the skills required to do it. So when someone asks you what you think PA or TI are, make sure you clarify the function of the business unit vs the act of doing the work first. Trick 2: Focusing on the second word The second trick, and the most important one, is to ignore the first word and focus on the core meanings of “intelligence” and ”analytics” in the human resources space. Simply put: Intelligence - the ability to gather and combine data about the world to support decisions Analytics - the ability to make decisions based on insights from data We now have real starting places for our definitions. Focusing on the second word highlights the true difference between “intelligence” and “analytics”. These are distinct words with unique contexts that they bring into conversations. By bringing back the first words and ensuring that we stick to the definitions of the second word, we get a clear definition of the two spaces: The term talent intelligence refers to the act of gathering and combining data about the labor market and talent to inform decisions. The term people analytics refers to the process of making decisions based on insights from data we have about our people. To illustrate how these definitions of talent intelligence and people analytics play out, let’s consider two examples: Talent intelligence: A global company is planning to expand its operations to a new region. By leveraging talent intelligence, they can gather data about the local labor market, including the availability of skilled professionals, salary expectations, and competitor presence. This information helps the company make informed decisions about where to establish their new office and how to attract top talent. People analytics: An organization is experiencing high employee turnover rates. Using people analytics, they can analyze workforce data to identify patterns and trends, such as which departments have the highest turnover and which employee demographics are most affected. Armed with this information, the organization can make better decisions and develop targeted retention strategies to improve employee satisfaction and reduce turnover. These examples may still hold confusion, except in the largest organizations where a people analytics or talent intelligence function are both represented and responsible for distinct areas, but ideally this language can help define the space. Taking this further Let's take this thought exercise further. Another benefit of the approach of separating the first and second words is that it can be mapped onto a 2x2 matrix, which uncovers further insights. Intelligence Analytics Talent Talent intelligence - the act of gathering and combining data about the labor market and talent in the world to support decisions Talent analytics - the action of making decisions with insights from data we have about our talent and labor markets. People People intelligence - the act of gathering and combining data about our people to support decisions. People analytics - the action of making decisions with insights from data we have about our people. Playing this out, there are people analytics (function) teams that do all four of these tasks. Some also do 3/4, 2/4, or 1/4 and some of these tasks are centralized by another team or just decentralized within the business unit still. Knowing which is which before digging into a conversation with someone on names and tasks is critical. Unraveling the Mystery By embracing these two handy tricks, we've successfully untangled the web of confusion surrounding talent intelligence and people analytics. Remember, the key is to differentiate between the function and the act, and to focus on the core meaning of the second word. Talent intelligence solutions are all about gathering and combining data on the labor market and talent, while people analytics revolves around making decisions with the data we have about our people. With these distinctions in mind, we can avoid misunderstandings and promote effective communication in the HR and talent management world. Our handy 2x2 grid further showcases the range of functions and acts that people analytics teams can perform, emphasizing the versatility and breadth of their work. By fostering a comprehensive understanding of talent intelligence and people analytics, organizations can better harness the power of their workforce data to drive informed decision-making and achieve their business objectives. So, go ahead and spread the word — it's time to put this newfound clarity to good use! What are you doing for talent intelligence and people analytics? Let's have a conversation.
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7 min read
Ryan Sauve
The financial services industry has undergone seismic shifts in recent years, from pandemic-driven changes to remote workforces and economic turmoil to increased emphasis on employee diversity. Financial services HR departments have their hands full as they navigate these unprecedented challenges while striving to ensure employees are kept up-to-date with rapidly evolving markets and technology. Financial services are also notably one of the most important drivers of economic growth and stability and employ millions of people in different roles — from front-line customer service to back-office data entry. Banks, investment banks, and insurance companies (among other financial services) are complex and require various technical and human skills for these roles. Despite this complexity, they have traditionally been managed with little understanding or insight into their workforce, relying on outdated HR systems and manual processes which do not capture the nuances of people’s performance. Until now. To successfully meet the demand for continual reskilling, many finance companies are investing in data visualization and people analytics software to navigate the skills gap, a hybrid work environment, and an ever-changing economic landscape. HR in the World of Finance With the financial industry's pivotal role in steering our economic system, it is of paramount importance to consider HR. Unlike other industries where land, capital, and enterprise are commonly found as equal players alongside labor; within the financial space, there is an outsized reliance on its workforce whether trading, consulting, or selling. This fact has made effective HR work vital in seeing these organizations thrive. By connecting with employees at all levels, from executive board members right through to senior managers (along with those “backstage” workers who ensure things run smoothly), HR ensures efficient working operations necessary for success today and into tomorrow. In general, the most successful companies will be ones that are employees-centric. This means even greater pressure on HR departments. In fact, a recent survey revealed that 64% expect more strain due to increased hybrid work environments, with 18% expecting a significant increase in their workload. As such, people analytics in finance will be a priority for many companies in 2023 and beyond. Hearing From Your Knowledge Workers at Scale A Workplace Culture 2018 report found that 71% of professionals say they would be willing to take a pay cut to work for a company that has a mission they believe in and shared values. In addition, 70% of U.S. workers would not work at a company if they had to tolerate a negative workplace culture. Financial services people analytics provides an opportunity for organizations to gain meaningful insights into their workforce. It allows them to understand how their employees work best and how they can improve engagement levels to drive business performance. People analytics in finance can provide insights into employee performance, engagement levels, and attrition rates, allowing companies to predict how best to deploy their staff to achieve desired outcomes. Identify Burnout and Avoiding Costly Attrition People analytics in the finance services industry is also helping organizations understand their workforce better, enabling them to identify potential burnout risks and intervene before it’s too late. By identifying struggling employees or those at risk of leaving, these firms can take preventative measures such as providing additional support or re-orienting tasks to help avoid costly attrition. Manage Resources and Improve Business Outcomes Financial services companies often have high revenue per employeeaverages due to the complexity of their operations and the specialized skillsets required to perform them. Financial institutions such as banks, investment bankers, and insurance companies require a wide range of technical and human skills to function properly. Financial services are also expensive for customers, meaning that these firms can charge more for their services than other industries. This allows these firms to generate higher revenues from fewer employees than other sectors. Therefore, attrition is more closely related to Business Outcomes than other industries. We’re also under pressure to ensure that our business is fair and equitable. As our industry tends to be more regulated having diversity metrics had hand and even tracking them ensures that we’re hitting the mark. That is exactly why we see so many financial services industries maturing in people analytics faster. I believe this is because they can easily see how this makes them more competitive and improve their bottom line. Nurturing Employee Knowledge and Skills People analytics can also play an important role in helping financial services organizations nurture the knowledge of their employees and ensure they are performing at their peak. By layering talent profiles, learning development metrics and employee backgrounds, firms can create and monitor targeted training programs and development plans to ensure that teams have the right skillsets to meet the ever-evolving demands of the industry. They can even compare trained employees to untrained employees and see how the segments are performing! Finding Top Performers and Improving Retention Strategies Bank HR, in particular, has a great deal to gain from financial services people analytics, as it can be used to identify potential high-performers and groom them for critical roles in the organization. Everyone should have a succession plan. Furthermore, banks can use predictive modeling to identify employees that are at risk of leaving or being poached by competitors, allowing the institution to intervene quickly with retention strategies. Analyzing Data to Understand Customer Experience People analytics can also help finance companies better understand the customer experience by allowing them to correlate employee performance with customer satisfaction. By analyzing data from customer feedback surveys, organizations can identify areas where customers may not be receiving the best service or areas where further staff training might be beneficial. Leveraging HR in the Finance Industry Overall, people analytics provides financial services organizations with a new way to gain insights into their workforce, allowing them to make more informed decisions about how best to utilize their resources and improve overall business outcomes. This helps promote a culture of collaboration and innovation within the organization, as well as providing valuable data to inform decisions about talent acquisition, promotion, and succession planning. By leveraging people analytics strategically, firms can ensure their workforce is able to meet the challenges and opportunities of their dynamic markets. Let’s Talk Finances! Connect with us today.
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0 min read
Lauren Canada
This infographic reveals 4 key HR metrics to strengthen your next data story, so you can prevent costly turnover and retain top talent. Start scrolling to explore the piece!
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8 min read
Phil Schrader
I recently sat down with Culture Curated’s Season Chapman and Yuliana Lopez to ask them which metrics were their favourite and Yuliana said net hires. Let’s find out why: Net hires are a critical component of workforce management, as they help organisations determine staffing needs, forecast future headcount, and make informed decisions about recruitment and retention strategies. In this One Model blog post, we’ll explore the concept of net hires, how it’s calculated, and why it’s essential for organisations to track this metric. What are net hires? Net hires, also known as net hiring or net employment, is a measure that tracks the difference between the number of employees who leave an organisation and the number of new employees who are hired during a specific period. This metric provides valuable insights into a company's workforce dynamics, such as the rate of employee turnover, the pace of recruitment, and the organisation's overall hiring needs. This metric is an essential component of workforce planning and management, as it helps organisations to determine staffing needs, forecast future headcount, and make informed decisions about recruitment and retention strategies. For example, if a company hires 50 new employees during a quarter and loses 20 employees during the same period, the net hires for the quarter would be 30 (50 - 20 = 30). A positive net hires value indicates that the organisation is expanding its workforce, while a negative value indicates that the organisation is reducing its workforce. The chart below shows new hires juxtaposed against terminations. Explore several ways to visualize headcount here. I like using One Model for the presentation of this data because you can quickly adjust by any segment or time period to see how the story changes when looking at it from different angles. Calculating net hires To calculate net hires, organisations need to track the number of employees who join and leave the company during a specific period. This information can be obtained from various sources, such as HRIS records, payroll systems, and employee surveys. Once the data has been collected, organisations can use the following formula to calculate net hires: Net hires = Total number of new hires - Total number of terminations For instance, if a company hired 100 new employees and had 50 terminations during a specific period, the net hires for that period would be 50 (100 - 50 = 50). Having trouble balancing headcount with net internal movements? Learn more. Why is monitoring net hires important? Net hire headcount is a critical metric for organisations for several reasons. Firstly, they provide insights into the organisation's overall workforce trends, such as the pace of recruitment, the rate of turnover, and the company's growth trajectory. By tracking net hires over time, organisations can identify patterns and trends in their hiring practices and adjust their recruitment strategies accordingly. Secondly, net hires can help organisations by understanding the rate at which employees are joining and leaving the company, organisations can make informed decisions about their recruitment and retention strategies, including whether to ramp up hiring efforts, invest in employee training and development, or adjust staffing levels in response to changing market conditions. Finally, net hires can also help organisations evaluate the effectiveness of their recruitment efforts. By tracking the number of new hires, organisations can assess the success of their recruitment campaigns and identify areas for improvement. Additionally, by comparing net hires to other metrics, such as employee engagement and retention rates, organisations can gain a more comprehensive view of their overall talent management strategy. Challenges of tracking net hires While net hires are an essential metric for organisations, tracking this metric can be challenging. One of the main challenges is ensuring the accuracy of the data. HR records and payroll systems are prone to errors and inconsistencies, which can lead to inaccurate calculations of net hires. Moreover, tracking net hires requires a robust data infrastructure, including data collection, storage, and analysis tools. Another challenge is defining the period over which net hires should be calculated. Since you are measuring change over time, you could run into a situation where you get a zero result in calculated measures. In this case, having a tool that can understand and make sense of that is important. Organisations also need to determine whether to track net hires on a monthly, quarterly, or annual basis — depending on their specific workforce management needs. Moreover, organisations need to ensure that the period over which net hires are calculated is consistent across all departments and business units, to enable accurate comparisons. Optimising net hires To optimise net hires, organisations need to adopt a data-driven approach to recruitment and talent management. A key way to do that is by using people analytics tools to track and analyse workforce data, including net hires, turnover rates, and engagement levels. Final lessons from Season As you heard in the video, Season doesn’t like looking at one metric or a metric at a single point in time because it’s misleading. With that in mind, we know that net hires mean less if you don’t understand your termination metrics and recruitment rate. Remember to think of all the contributing factors and explore the data at your disposal to create a comprehensive story that creates value for your organization. The power of segmenting headcount In addition to looking at supporting metrics, you should also be segmenting your headcount audience to see if there are trends across departments or geography. Only looking at things as a whole may be misleading. That’s why using a tool like One Model with flexible storyboards is vital to put all the pieces of the same story on the same page. Make sure that a headcount dashboard is one of the first essential dashboards you build. Ready to Learn More? Watch me build this report live. Connect today.
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6 min read
Dennis Behrman
Artificial intelligence (AI) has become an integral part of various industries, revolutionizing the way organizations make decisions. However, with the rapid advancement of AI technology, concerns about its potential and ethical implications have emerged. As a result, governments around the world are preparing to enact regulations to address the use of AI in people decisions. In this blog post, we will explore the scope of these forthcoming regulations and discuss how People Data Cloud can help ensure equitable, ethical, and legally-compliant practices in automated decision-making across organizations. Broad Scope of Regulations While generative AI, such as ChatGPT, has been the catalyst for these regulations, it is important to note that the scope will not be limited to such technologies alone. Instead, the regulations are expected to encompass a wide range of automated decision technologies, including rule-based systems and rudimentary scoring methods. By extending the regulatory framework to cover diverse AI applications, governments aim to ensure fairness and transparency in all areas of decision-making. Beyond Talent Acquisition Although talent acquisition processes like interview selection and hiring criteria are likely to be subject to regulation, the scope of these regulations will go far beyond recruitment alone. Promotions, raises, relocations, terminations, and numerous other people decisions will also be included. Recognizing the potential impact of AI on employees' careers and well-being, governments seek to create an equitable and just environment across the entire employee lifecycle. Focus on Eliminating Bias and Ensuring Ethical Practices One of the primary objectives of these regulations will be to eliminate bias in AI-driven decision-making. Biases can arise from historical data, flawed algorithms, or inadequate training, leading to discriminatory outcomes. Governments will emphasize the need for organizations to proactively identify and mitigate biases, ensuring that decisions are based on merit and competence rather than factors such as race, gender, or age. Ethical considerations, including privacy and consent, will also be critical aspects of the regulatory landscape. Be Prepared. Join the Regulations and Standards Masterclass today. Learning about AI regulations and standards for HR has never been easier with an enlightening video series from experts across the space sharing the key concepts you need to know. A Holistic Approach to Compliance To comply with forthcoming AI regulations, organizations must evaluate their entire people data ecosystem. This includes assessing where data resides, which technologies are involved in decision-making processes, the level of human review and transparency afforded, and the overall auditability of automated decisions. Achieving compliance will require robust systems that enable organizations to monitor and assess the fairness and transparency of their AI-driven decisions. One AI is Your Automated People Decision Compliance Platform As governments gear up to regulate AI in people decisions, organizations must be prepared to adapt and comply with the evolving legal landscape. The scope of these regulations will extend beyond generative AI and encompass a broad range of automated decision technologies. Moreover, regulations will address not only talent acquisition but also various aspects of employee decision-making. Emphasizing the elimination of bias and ethical practices, governments seek to create fair and equitable workplaces. To ensure compliance with AI regulations, organizations can leverage platforms like One Model's One AI, which is fully embedded into every People Data Cloud product. This platform provides the necessary machine learning and predictive modeling capabilities, acting as a "clean room" to enable compliant and data-informed people decisions. By leveraging such tools, organizations can future-proof themselves against audits and demonstrate their commitment to ethical and unbiased decision-making in the AI era. Request a Personal Demo to See How One AI Keeps Your Enterprise People Decisions Ethical, Transparent, and Legally Compliant Learn more about One AI HR Software
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4 min read
Dennis Behrman
Corporate culture plays a pivotal role in driving organisational success, but quantifying its impact has often relied on subjective assessments. However, with people analytics, human resources teams can adopt a scientific approach to measure and predict the impact of corporate culture more effectively. Phil Schrader caught up with Season Chapman and Yuli Lopez, both One Model enthusiasts who have ventured out as founders of Culture Curated, for a captivating dialogue about culture. They share their firsthand experiences of using One Model's software platform to directly measure and address human resources challenges. The Power of People Analytics in Culture Assessment: Traditional methods of understanding corporate culture, relying on gut instinct and after-the-fact observations, can be subjective and limited in providing actionable insights. People analytics, on the other hand, uses data-driven methodologies to uncover patterns and make predictions about culture's impact on business outcomes. By analysing various data points, such as employee surveys, performance metrics, and feedback channels, organisations gain a comprehensive understanding of the underlying factors shaping their culture. Measuring Culture in Real Time Phil Schrader's conversation with Season and Yuli highlights the transformative potential of people analytics in directly measuring and monitoring key drivers of corporate culture. Culture Curated leveraged One Model's software platform, empowering HR teams to collect and analyse vast amounts of data to transform it into meaningful insights. With objective metrics and data visualisations, organisations can now track culture-related issues in real time, enabling proactive interventions to address them promptly. This shift from reactive observations to predictive analysis empowers leaders to make informed decisions that positively impact culture. Predicting and Enhancing Culture The true power of people analytics lies in its ability to predict the outcomes of culture-related initiatives. By examining historical data and identifying patterns, organisations can forecast the potential impact of cultural interventions. This predictive capability allows leaders to develop targeted strategies to enhance their desired culture and align it with their business objectives. People analytics provides evidence-based guidance for redesigning performance management systems, fostering diversity and inclusion, and enhancing employee well-being initiatives. With these insights, organisations can drive meaningful change and achieve their desired results. The Video Dialogue: A Firsthand Account To delve deeper into the transformative effects of people analytics on corporate culture, we invite you to watch the engaging video dialogue between Phil Schrader, Season Chapman, and Yuli Lopez of Culture Curated. In this video, they share their experiences of using One Model's software platform to directly measure and address culture-related challenges. Their insights provide a firsthand account of the power of people analytics in driving organisational success through a data-driven approach to culture assessment. It's All About Attracting and Retaining Top Talent People analytics has revolutionised the way organisations measure, monitor, and improve their corporate culture. By harnessing data-driven insights, leaders can make informed decisions and actively shape their culture to drive success. We trust experts like Season and Yuli when it comes to transforming culture measurement. Ultimately, organisations can create thriving cultures that attract top talent, foster innovation, and achieve sustainable success in today's competitive business landscape. Request a Personal Demo to See How to Measure and Improve Culture with People Analytics
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8 min read
Jamie Strnisha
Given the Great Resignation, recent mass layoffs, and store closures, the past year has shown just how crucial strategic workforce planning can be to overall organisational health and longevity. But it’s not always easy for HR teams to understand where to start when developing workforce planning strategies. So let’s dive into what strategic workforce planning is, how it differs from organisational workforce planning, and how people analytics can transform what your HR team can achieve with your workforce planning strategies. What is strategic workforce planning? Strategic workforce planning in a nutshell is having the right people in the right roles at the right time at the right costs, which can lead to better productivity and lower costs. Strategic workforce planning is one of the most important elements of HR strategy. It helps businesses identify skills gaps, carefully manage resources, benchmark performance against competitors, and ensure proper budget allocation for the organisation. Strategic workforce planning is the process of analysing an organisation's workforce needs, both present and future, and developing strategies to meet those needs. It involves assessing the current workforce, developing scenarios, identifying gaps and future needs, analysing people data to inform decisions, and creating plans to address those gaps and needs. Practising effective workplace planning also involves continuously measuring and monitoring the implementation and effectiveness of those plans against your KPIs and organisational goals. The process typically involves gathering and analysing data about the current workforce, such as employee skills, cost, and demographics, as well as external factors that may impact workforce needs, such as changes in technology, industry trends, and economic conditions. This can include hiring new people, training current employees, and planning for future changes in the workforce. The difference between strategic vs. operational workforce planning Strategic workforce planning and operational workforce planning are two different approaches to managing an organisation's workforce needs. Strategic workforce planning focuses on long-term workforce planning, typically looking at a three- to five-year horizon or beyond. It involves analysing the organisation's strategic goals and objectives and determining the workforce requirements needed to achieve them. This includes identifying the skills, knowledge, and capabilities that will be needed, and creating plans to develop and acquire those resources. Strategic workforce planning is a high-level planning process that is typically undertaken by senior management and HR leaders. Watch a One Model strategic planning session. Operational workforce planning, also known as strategic staffing or headcount planning, is more focused on short-term workforce needs, usually over a six-month to two-year time horizon. It involves short-term management of the workforce and is focused on ensuring that the workforce has the resources it needs to succeed. For example, this may include ensuring that an organisation can staff up to meet seasonal demands (retail at Christmas, farming during the summer, etc.). Operational workforce planning is often carried out by mid-level and front-line managers. How has strategic workforce planning changed over time? Strategic workforce planning has undergone significant changes over time in response to changes in the economy, technology, and social trends. Here are some of the key changes that have occurred: Increased focus on skills: In the past, strategic workforce planning tended to focus on job titles and positions rather than skills-based workforce planning. However, today's strategic workforce planning is more focused on identifying the specific skills and knowledge that are needed for each role. Data-driven approach: Advances in technology have made it easier to collect and analyse workforce data, leading to a more data-driven approach to strategic workforce planning. This allows organisations to make more informed decisions about their workforce needs. Emphasis on flexibility: With the rise of the gig economy and remote work, organisations are increasingly seeking more flexible workforce solutions. This has led to a greater emphasis on strategic workforce planning that can adapt to changing conditions — empowering organisations to optimise which roles should be full-time or contract, or which roles can be hybrid or in-office. Strategic alignment: Strategic workforce planning has evolved to be more closely aligned with organisational strategy, helping organisations ensure that they have the right people with the right skills in the right positions to achieve the organisation’s strategy. Peter Howes does a great job of showcasing how HR workforce planning has changed over time. Watch the recorded webinar. How strategic workplace planning impacts workforce planning Strategic workplace planning can have a significant impact on workforce planning in a number of ways. Here are some examples: Attracting and retaining top talent: A well-designed workplace can be a major factor in attracting and retaining top talent. Promoting collaboration and productivity: By designing a workspace that supports teamwork and communication, organisations can help employees to work together more effectively and reduce workforce productivity issues. Supporting health and well-being: Creating an environment that is focused on keeping employees healthy and reducing stress helps with your long-term planning by ensuring your workforce stays at peak performance. Adapting to changing workforce needs: If an organisation is shifting towards more remote work or hybrid work arrangements, workplace planning can be used to create a workspace that supports those arrangements. What role does people analytics play in strategic workforce planning? People analytics plays an important role in strategic workforce planning by providing data-driven insights into an organisation's workforce. By using data and analytics tools, like One Model, organisations can better understand their current workforce and identify trends and patterns that can inform their workforce planning strategies. People analytics can help organisations to: Identify workforce gaps: By analysing workforce data, organisations can identify areas where they have a shortage of skills or talent, allowing them to focus their strategic workforce planning efforts on addressing those gaps. Forecast future workforce needs: People analytics can be used to project future workforce needs based on factors such as demographic changes, industry trends, and business goals. Optimise workforce efficiency: By analysing workforce data, organisations can identify opportunities to improve workforce efficiency, such as by reallocating resources or adjusting work schedules. Measure the effectiveness of strategic workforce planning: People analytics can be used to track the success of workforce planning strategies over time, allowing organisations to adjust their plans as needed to achieve better outcomes. Increase ROI Businesses can make fast decisions, optimise ROI, and improve customer and stakeholder satisfaction by leveraging data-driven insights into trends and predicting future needs. How One Model supports better strategic workforce planning One Model is a people analytics company that helps organisations transform their workforce data into actionable insights for better decision-making. By leveraging advanced analytics, artificial intelligence, and machine learning, One Model can support better strategic workforce planning capabilities for businesses of all sizes. One of the key strengths of One Model is its ability to integrate data from multiple HR systems, such as HRIS, ATS, LMS, and others, into a single data warehouse. This allows organisations to gain a complete view of their workforce data, eliminating the need to switch between different systems to analyse data. In addition to its advanced analytics capabilities and intuitive interface, One Model also offers a customizable dashboard that allows HR professionals to monitor and track key workforce metrics. With this tool, HR teams can identify areas of concern, measure the success of their workforce planning strategies, and adjust their plans as needed. Overall, One Model supports better strategic workforce planning by providing a single, integrated platform for workforce data analytics, advanced analytics models, and customizable dashboards. This enables organisations to make more informed decisions and, ultimately, achieve their business goals. Discover how One Model’s People Analytics can support your strategic workforce planning.
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5 min read
Joe Grohovsky
"Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat." - Sun Tzu Complex people analytics (PA) projects risk losing sight of what is profoundly important as they endeavor to fulfill all aspirational requirements. Identifying and delivering business insight is their purpose, not simply fulfilling a stakeholder’s tactical wish list of presentation-layer features. However, far too often PA initiatives are launched with requirements dominated by this tactical wish list without a true appreciation for the value of the metrics contained within each report. The funding and focus involved clearly classify these initiatives as Strategic HR projects. Instead of blindly focusing on what presentation tactics will be used, consider first a strategy for building better insights. These strategic conversations should begin with the number (metric/measure) in question. This number is critical and is the cornerstone for all other discussions. This number should be accurate and meaningful. Everything else within a PA initiative is the tactical positioning of that strategic number. Accuracy Without accurate numbers, a reporting effort is wasted. Ask yourself these questions. Is the number derived from trusted, validated source data? Is the source data modeled specifically for your organization? Does your definition of that number align with what will be provided? These questions are more than simplistic check boxes. Consider Headcount, which is the most basic HR measure. Is it based on the Start of the Period, End of the Period, or Average Daily Headcount? Are retroactive changes accommodated? What will happen when introducing additional data sources such as Engagement or Performance? Are you forced to work with templated data and a rigid data model? Interested in learning how to create a stellar People Data Platform? Read our latest whitepaper to understand the steps your team needs to take to create an analytics-ready data platform that will give your team reliable, accurate information that can help propel your people analytics projects toward success. Meaningfulness Not all numbers are equal or valuable. When considering specific metrics, consider these questions. Is this number important on its own, or does it merely provide context? Is it actionable? Considering the above, an easy analogy would be the numbers a physician uses during a patient’s annual physical examination. Those numbers include things like age, height, weight, blood pressure, etc. Age and height are uncontrollable and immune to any action. However, these numbers still provide valuable context for other numbers. Weight and blood pressure would be considered actionable and the focal point for discussion. Once actionable numbers are identified, ask yourself “So What.” Will this insight drive any internal decisions? If not, it is best to focus elsewhere. These questions will determine meaningfulness. Presentation of Numbers After accurate and meaningful numbers are established, a conversation on presentation tactics can occur. Awareness of internal culture and data consumer preferences is critical in this step. Most PA initiatives serve a broad spectrum of data consumers that may involve: HR Business Partners Analysts Center of Excellence Data Scientists Line of Business Managers Self-Service capabilities Senior Executives Each group is best served by providing varying amounts of support, flexibility, and handholding. Common differences for each group would include the decision to provide a summary or detailed data, the amount of context provided, or the amount of supporting documentation required to establish metric validity. Summary It is understandable that PA professionals become fascinated with whiz-bang features contained within presentation capabilities. Ease of data consumption is important, but please realize that it trails behind generating accurate, meaningful numbers. Storytelling your way through People Analytics without substance supporting you is risky. For examples of impactful HR projects, or information on how One Model approaches this topic, please connect with us.
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9 min read
Lauren Canada
Effective workforce and employee listening is one of the most critical skills for HR professionals. To address workforce needs, HR team members must be actively and attentively listening to their employees and workers. Gathering information about the workforce is vital, but that doesn’t mean it’s simple. Listening to your workforce means giving each member respect, time, and attention, so you can truly understand what’s going on and identify the best way to respond. This becomes more difficult in a hybrid workplace and can be complicated at scale for larger organisations. So let’s dive into three ways your HR team can practise effective employee listening at scale. 1. Facilitate more meaningful conversations Speaking to the workforce and using their feedback to support decision-making is how HR really began as a profession. Conversations refer to the 1:1 interactions, observations, and ethnographic tools that HR uses to understand the workforce and what your workforce needs. These are very human tools that can be a powerful method for HR storytelling within an organisation. When conducted effectively, conversations allow HR personnel, managers, and leaders to gain a nuanced understanding of their workforce that technology can’t yet replicate. For instance, it will be a long time before computers can comprehend how grief impacts performance, the unsettling chaos of a reorganisation, or the pride of a promotion. Despite recent advances, empathy, connection, and meaning-making will remain distinctly human domains for some time. On the other hand, bias and human error in conversations is a concern, and there are dangers in relying solely on conversations to inform the HR decision-making process. These are issues that must be thoughtfully planned for and mitigated — so you want to use other employee listening strategies to help validate, verify, and correct for bias in information gathered from conversations. There are three types of conversations that HR teams can use to practise effective employee listening: 1. Formal Conversations These include regular 1:1s, performance reviews, and formal checkpoints that ensure the workforce is heard, managed, and supported. These conversations not only help managers and HR leaders evaluate their employees' performance but also provide an opportunity for the organisation to gather information and better understand the employee experience. 2. Informal Conversations This refers to casual chats that take place around the “watercooler” (in person or remote), where employees can share what's really going on. These conversations can lead to surprising insights about the workplace, culture, and organisation. For instance, employees might discuss work-related challenges, share ideas for improvement, or provide feedback on a topic that you wouldn’t expect. Such conversations can help managers and HR leaders identify potential issues before they become problems. Informal conversations can be a great avenue for HR to gain business context that isn’t captured elsewhere. 3. Ethnographic Research The most formalised version of conversation-based information gathering is ethnographic research — referring to scientific and qualitative research techniques such as observation, participation, and immersion in the workplace to gain cultural and organisational understanding. Ethnographic research can provide a validated and scientifically sound understanding of employee behaviour, well-being, and attitudes, and it can also uncover hidden dynamics and cultural norms that might not come to light through formal or informal conversations alone. By conducting ethnographic research, organisations can gain a deeper understanding of their workforce and tailor their strategies and policies accordingly. 2. Collect information through surveys and forms Engagement surveys and other forms, like performance or training reviews, capture new data that might not be otherwise captured by conversations or other avenues. Surveys are a great method for gathering information from a large number of people quickly. You could spend 30 minutes speaking to every person in your organisation, or you could send a survey that everyone completes on their own time. Surveys can provide a structured, valid, and reliable method to collect information about workforce needs, attitudes, opinions, behaviours, and demographics. Here are three elements you might include in your next HR survey to improve your employee listening strategy: 1. Structured survey questions This includes questions that are answered by a multiple-choice scale like, "How satisfied are you with your current role?" and "Do you feel valued by your employer?". With numeric responses, it’s much easier to parse through and analyse the responses. 2. Open-ended survey questions These questions provide a prompt with a text box for a response. These could include a variety of open-ended topics like “Please tell us about your onboarding experience.” or “Do you have the tools you need to succeed in your role?”. The volume and variety of data that is brought back through open-ended surveys is much higher than structured surveys, so these require further coding or understanding before they can be used in decision-making. 3. Psychometric surveys Psychometric surveys gather information about employees' psychology, attitudes, and sentiments, which can be helpful in understanding variations in trends such as retention and attrition. These questions can be either structured or open-ended, depending on the desired results. 3. Use data from your technology systems As technology is increasingly integrated into workplace operations, your workforce’s interactions with technology can generate a wealth of data about people, processes, and work habits — making your tech stack a powerful employee listening tool. Skilled data engineers, analysts, and data scientists can process this data to extract valuable insights about the workforce. Systems data exists already for nearly every aspect of the work experience today, from hire to termination and from performance management to learning. And it can be collected quickly, passively, and with less bias than conversations or surveys. Plus, when handled correctly, this dataset allows for more sophisticated data techniques and broader perspectives of the organisation as a whole. For an end-to-end approach to employee and workforce listening, which is needed for workforce planning, workforce readiness, or skills gaps analysis, you can use the data within your technology systems. But today’s organisations use so many different technology systems, making it difficult to aggregate this data into an understandable format that can help inform HR decisions. Here are three types of technologies that offer systems data HR teams can use for better employee listening: 1. HR tech This is the traditional tech stack managed by HR tech teams, including systems that handle HR-related processes and programmes (e.g., Core HRIS, ATS, Performance Management, LMS). For example, when a worker is hired, the applicant tracking system (ATS) captures data about their demographics, prior experiences, and the interviewing team's assessment. 2. Collaboration tech Systems capturing collaboration (e.g., Slack, Microsoft Teams, Zoom, Google Docs, etc.) can be powerful tools because they produce information about teams, interactions, and how work gets done within an organisation. Techniques like organisational network analysis can reveal how information flows through an organisation or identify influential individuals. 3. Work tech Work tech refers to technology capturing broad work data outside of HR tech (e.g., procurement systems, code tracking, or attendance). Systems like intranets, timekeeping, expense systems, and ticketing systems fall into this category. These work tech systems also produce data that can be used to recreate, model, and analyse the flow of work in the workplace. By associating these systems with HR tech systems, we can build powerful stories connecting HR data to work outcomes. How One Model supports employee listening at scale One Model is an AI-powered people analytics platform that empowers HR teams to centralise data from multiple technology sources into a single place — for easier analysis and better HR decision making. By bringing all HR data into One Model, HR leaders can get deeper insights into their workforce and perform more effective employee listening at scale. This allows you to listen to your entire workforce from every possible angle, so you can uncover workforce needs, increase engagement, reduce burnout, and address issues in a timely manner. Plus, you can refocus valuable HR time from building dashboards and aggregating data to analysing reports and improving the organisation. Discover how Colgate uses One Model Colgate shares how they used One Model to improve their employee listening strategy, understand their current workforce, and adjust their HR approach to meet their DEIB goals. Or fill out the form to sign up for a One Model demo today! Request a Demo Today!
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6 min read
Richard Rosenow
Once upon a time, in a bustling corporate office, there was a dedicated HR leader who was determined to improve the company's understanding of the workforce. Despite the challenges faced by the HR team, the leader was committed to improving how HR used data for decision-making and decided that getting their workforce data in order so they could make sense of it and analyze it would help the company. Upon researching the space, they decided that investing in a people data platform would best optimize HR processes and bring about positive change. Interested in learning how the HR Leader decided on a people data platform? Check out our whitepaper on the topic to learn more. Download and Read Today The HR leader knew that they needed the support of the other business functions to make this vision a reality. They approached the Data Engineering team, Information Technology team, and Enterprise Analytics team, seeking their assistance in crafting a compelling pitch for the people data platform. "Who will help me gather data and build a strong business case for the people data platform?" the HR leader asked. "Not I," replied the Data Engineering team, busy maintaining complex data pipelines for Finance. "Not I," said the Information Technology team, focused on streamlining the company's vendor landscape. "Not I," responded the Enterprise Analytics team, preoccupied with analyzing key metrics for marketing. Feeling disheartened but undeterred, the HR leader took it upon themselves to build the pitch. They researched the benefits of having a centralized, clean, and well-organized data model, highlighting how a people data platform would enable the HR team to visualize, report on, and analyze HR data effectively. The HR leader emphasized that this investment would not only help HR but would empower the leaders and managers in the company to make data-informed decisions about their workforce. After weeks of hard work, the HR leader completed the pitch but knew that securing the budget wouldn't be easy. They decided to run a pilot project to demonstrate the value of the people data platform to the senior management. "Who will help me with the pilot project to showcase the potential of a people data platform?" the HR leader asked the other business function leaders. "Not I," replied the Data Engineering team, focused on optimizing their data infrastructure. "Not I," said the Information Technology team, busy managing software updates and hardware maintenance. "Not I," responded the Enterprise Analytics team, occupied with supporting the Product team with their dashboards. Undaunted, the HR leader initiated the pilot project on their own, using limited resources and sheer determination. They collected data, created reports, and provided insights that highlighted the platform's potential to revolutionize HR processes. They learned about what was needed to secure HR data and how to best share progress with employees to communicate transparently about the systems. When the pilot project was completed, the HR leader presented the results of the pilot along with their pitch to the senior management. Impressed by the evidence and the potential impact on the company, the senior management team approved a substantial budget for the investment in HR’s very own people data platform. The news spread quickly throughout the company, and soon, the other business functions took notice. Seeing the approved budget, the Data Engineering team, Information Technology team, and Enterprise Analytics team approached the HR leader with newfound enthusiasm. "Can we use your approved budget to build an in-house solution by adding headcount to our teams and activating more licenses on our in-house systems?" they asked, their eyes gleaming with anticipation. The HR leader shook their head and replied, "No, when I asked for your help in building the pitch and running the pilot project, none of you were willing to support the project. I gathered the data, built the business case, executed the pilot, and secured the budget all by myself. This investment is dedicated to the HR team and we will determine how it will be spent on a people data platform." The other business functions couldn't help but feel a pang of regret for not having supported the HR leader earlier. They realized the importance of collaboration and the value of supporting each other's projects. From that day forward, the Data Engineering team, Information Technology team, and Enterprise Analytics team made it a priority to work closely with the HR team, ensuring that the platform launch went off without a hitch and that all departments benefited from the people data platform. The company thrived, as data-driven storytelling spread throughout the company and workforce data was securely and safely distributed to decision-makers, fostering a culture of shared success and mutual support. The moral of the story: Success comes from collaboration and supporting one another, and a company thrives when all its functions work together to support each other’s needs. A bit of a fairy tale ending? Absolutely, but it’s fun to dream. But are you ready to get some help? Reach out to our team for a demo and to learn more about how One Model makes People Analytics easy for HR leaders. You deserve good data and to work with a partner who knows how to help HR get there. We’re here to help.
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4 min read
Taylor Clark
Machine Learning Explainability for Human Resources One AI is the HR industry's leading machine learning and predictive analytics technology because it's flexible, secure, and transparent. Watch Taylor Clark, our Chief Data Scientist, demo a brilliant turnover forecast in seconds in the video below. In Taylor's example, you can see turnover going up over time and then coming back down a little. But how can you inform decisions about which direction the trend could go moving forward? And how can you confidently stand behind that prediction? With One AI, it only takes a click. A powerful forecast in a single click? Yes! Generally, Taylor suggests that people think about turnover trends from multiple contexts and perspectives. So in the linked video, he shares his screen to demonstrate the HR industry's fastest way to analyse turnover. In the People Data Cloud™️ platform, it's a single click on the "light bulb" icon of any table, chart, or graph. Once clicked, you see One AI thinking in the background and doing a lot of math. Suddenly, it produces a forecast. What does the cone mean? The shaded cone over the forecasted zone of the graph is considered the range of uncertainty. It's also known as an uncertainty interval or a confidence interval to mathematicians and statisticians. But here's what it's telling us: the actual results could lie anywhere within this cone when that time comes. The range of possible outcomes is also influenced by the modelling technique that is used by One AI. How can decision makers trust this forecast? Most responsible business leaders need to trust the analyses that they are given. Often, this involves understanding the assumptions and techniques used to generate the analysis. One AI has you covered! The easiest way to win confidence and trust in a forecast with One AI is to simply click on any point within the forecasted range. Then you'll see a pop-up on the screen that shows a whole bunch of information. It includes information such as the upper and lower bounds of the forecast, the different types of algorithms that were used in making the prediction, and even the transformations and data sets used to generate the possible outcomes. You don't need to be a PhD to know that this is the first and only HR technology to offer model explainability and explainable artificial intelligence directly in the reports and as an embedded feature of the analysis. Only One AI is this flexible and transparent. This approach to forecasting is unique in that it can be applied to any table or chart within the People Data Cloud platform. Gone are the days when your data science team needed to recreate the wheel for every single forecast demanded by the business. And gone are the days when you need a data science team for every single type of forecast. Now your modeling teams can build a predictive tool that can be reused and reapplied across a broad range of talent decisions, including attrition, performance, engagement, advancement, and so on. See how One AI takes your forecasting game to the next level. Fill out the form below to get a live demonstration.
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24 min read
Richard Rosenow
Listening at Scale Effective listening is arguably the most critical skill for HR professionals. To address workforce needs, HR team members must be proficient in active and attentive listening. Gathering information about the workforce is as vital to an HR team as air and water. To listen to a member of the workforce is to give them respect, time, and attention, and to hear what is going on. It’s the oldest way we learn. We’ve seen listening programs grow from those roots into programs that survey the full company and beyond. I’ve often referred to people analytics as "decision support for HR," but another phrase I’ve used is that people analytics can be described as "listening at scale." To date, that’s been treated as more a metaphor for how we work with data rather than adopting systems data and people analytics into the listening ecosystem. However, I believe we can take this further and fully integrate the analysis of workforce systems data into an integrated framework for information gathering and listening. Understanding People: A Three-Channel Framework Margaret Mead, an anthropologist, best captured the complexity of working with humans with her quote: “What people say, what people do, and what people say they do are entirely different things." While humorous, I believe this quote can also act as the foundation to inspire an integrated framework for workforce listening. Mead's quote effectively outlines three “information channels” for gathering information about the workforce: conversations, surveys, and systems. I’ve rearranged them slightly for the purposes of this blog: “What people say” = Conversations: People having conversations in the workplace “What people say they do” = Surveys: Respondents assessing themselves and their ideas through surveys “What people do” = Systems: What people actually do in the workplace which can be tracked within HRIS or collaboration technologies (HR Tech / Work Tech / Collaboration tools) And I am very careful to say information channels above. As I’ll detail in this article, conversations, surveys, and systems are where workforce information is generated. Data and insights then flow from those channels to central storytellers and decision-makers. This is an end-to-end view of the HR decision-making process. This is an alternative way to view data to how we usually discuss data in HR. We often hear data described by its topic (e.g. Recruiting data, L&D data, or Comp data), source system (e.g. Workday data, Greenhouse data) or its application (e.g. descriptive, predictive, prescriptive data). This channel view seeks to depict the supply chain of information. Let’s delve deeper into this framework to create a more comprehensive understanding of the workforce. I believe this holistic approach to listening will allow HR professionals to make better-informed workforce decisions that positively impact the organization. Conversations Speaking to the workforce and making use of that information to support decision-making is how we got our start as an HR profession. Conversations refer to the 1:1 interactions, observations, and ethnographic tools that HR employs to understand the workforce. These are very human tools and these tools can be a powerful method for sense-making and storytelling within an organization. When conducted effectively, conversations allow HR personnel, managers, and leaders to gain a nuanced understanding of their workforce that technology struggles to replicate. For instance, it will be a long time before computers can comprehend how grief impacts performance, the unsettling chaos of a reorganization, or the pride of a promotion. Despite recent advances, empathy, connection, and meaning-making will remain distinctly human domains for some time. In the move towards data-driven decision-making, I believe we have underestimated the impact that these conversations can have on decision-making. The anthropological sensemaking that occurs when an experienced HRBP listens to the workforce is unmatched when it comes to quickly understanding cultural dynamics and understanding the core of workforce issues. Bias and human error in this channel of conversations is a well-documented concern and there are dangers in relying solely on conversations to inform the HR decision-making process. These are issues that must be thoughtfully planned for and mitigated, both in how this method is employed, but also the use of other channels to validate, verify, and correct for bias in information gathered from this channel. However, that does not mean that those other channels will replace conversations and conversation still has an important place in decision-making. I see three breakouts within the information channel of conversation: Formal Conversations: These include regular 1:1s, performance reviews, and formal checkpoints that ensure the workforce is heard, managed, and supported. These conversations not only help managers and HR leaders evaluate their employees' performance but also provide an opportunity for information gathering for the organization and for understanding the employee experience. Informal Conversations: This refers to the casual conversations that take place around the “watercooler” (in person or remote), where employees can share what's really going on. These conversations can lead to surprising insights about the workplace, culture, and organization. For instance, employees might discuss work-related challenges, share ideas for improvement, or provide feedback on a topic that you wouldn’t expect. Such conversations can help managers and HR leaders identify potential issues before they become problems, and can be a channel for business context that is not otherwise captured. Ethnographic research: The most formalized version of conversation-based information gathering would be ethnographic research. This refers to the scientific and qualitative research techniques such as observation, participation, and immersion in the workplace to gain cultural and organizational understanding. Ethnographic research can provide a validated and scientifically sound understanding of employee behavior and attitudes, and can also uncover hidden dynamics and cultural norms that might not be apparent through formal or informal conversations alone. By conducting ethnographic research, organizations can gain a deeper understanding of their workforce and tailor their strategies and policies accordingly. Want to see how One Model turns conversation data into analytics? Survey This channel refers to the toolkit around the scaled collection of novel data. I use the word Survey as surveys are a great example, but this channel represents whenever a form is completed to capture novel data that is otherwise not captured by a system passively. This includes engagement surveys and other forms such as filling out performance reviews or feedback forms after trainings. Surveys are a method to gather information from a large amount of people quickly. I could spend 30 minutes speaking to 80 people (a full back-to-back week for me and a 30-minute disruption for every person I speak to) or I could design and send a survey that everyone completes on their own time. Surveys can provide a structured, valid, and reliable method to collect information about workforce attitudes, opinions, behaviors, and demographics. Some breakouts for the survey: Structured survey questions: Questions about the environment, factors in the workplace, and information that the creator wishes to assess. Ideally structured and evidence-based. Questions could include items like "How satisfied are you with your current role?" and "Do you feel valued by your employer?" followed by a distinct multiple-choice scale. Open-ended survey questions: Open-ended survey questions provide a prompt with a text box for a respondent to complete. These questions could include a variety of open-ended topics like “please tell us about your onboarding” or “Are there tools you need to perform your role that you cannot acquire?”. The volume and variety of data that is brought back through open-ended surveys is much higher than structured surveys and these require further coding or understanding before they can be used in decision-making. Psychometric surveys: Psychometric survey questions could be either structured or open-ended, so this is a bit of a false breakout, but it is important to call out as it is a unique type of information gathered about the psychology of the employee in the workplace. Psychometric surveys gather information about employees' attitudes and sentiments which can be helpful in understanding variations in trends such as attrition. Systems The third internal information channel in this framework is systems. As technology is increasingly integrated into workplace operations, the workforce’s interactions with technology generate a wealth of data about people, processes, and work habits. Skilled data engineers, analysts, and data scientists can process this data to extract valuable insights about the workforce. The key advantage of systems data is its readiness for use, as well as the growing volume and speed of its generation. Systems data exists already for nearly every aspect of the work experience today, from hire to termination and from performance management to learning. This broad dataset, when properly extracted and prepared, enables more sophisticated data techniques to be brought to bear faster compared to the other channels. Systems data can also offer a broader perspective of the organization as a whole. Conversation and surveys gather information from each employee from their personal viewpoint, but their perspective may not be broad enough to see organization-wide issues. The view of what is going on end-to-end, which is needed for workforce planning, workforce readiness, or skills gaps analysis, can be generated from this systems channel. Systems data is also valuable because it is largely a passive data source, produced as a byproduct of work conducted through technology. Consequently, it is less subject to biases and limitations of human perception, memory, or interpretation. However, systems data often lack the nuanced information density of business context provided by conversation and survey methods. Additionally, the bias it does have is often embedded in the software design choices which can often be harder to detect and understand. Choices made by programmers regarding UX, data capture, native reports, and interactions available can introduce potential areas for bias in the extracted information. Systems data can be further categorized into three main breakouts: HR tech: This is the traditional tech stack managed by HR tech teams. Systems handling HR-related processes and programs (e.g., Core HRIS, ATS, Performance Management, LMS). For example, when a worker is hired, the applicant tracking system (ATS) captures data about their demographics, prior experiences, and the interviewing team's assessment. Collaboration Tech: Systems capturing collaboration (e.g., Slack, Microsoft Teams). These tools (Slack, Teams, Zoom, Google Docs, etc.) produce information about teams, interactions, and how work gets done within an organization. Techniques like organizational network analysis can reveal how information flows through an organization or identify influential individuals. Work Tech: Technology capturing broad work data outside of HR tech (e.g., procurement systems, code tracking, or attendance). Systems like intranets, timekeeping, expense systems, and ticketing systems. These work tech systems also produce data that can be used to recreate, model, and analyze the flow or work in the workplace. By associating these systems with HR tech systems, we can build powerful stories connecting HR data to work outcomes. Tradeoffs in Information Channels Selecting the right channels for a given decision is vital for success. To do so I see the need to weigh the tradeoffs in trust, effort, and information density. Trust Trust is a key factor in how we interpret information that comes from the various channels. For instance, information from conversations can be difficult to trust, particularly when not everyone involved is present or when they are not recorded, transcribed, or made public. If I talk to my manager about a coworker, my manager will need to verify their side of the story. Even when conversations are recorded, they can still be misleading. Surveys are generally more trusted than conversations due to the structured way that they are delivered. Surveys can have academic ties on their design and are typically more consistent, reliable, and objective than conversations. That said, it can still be difficult to know what someone was thinking when they read a question on their own. Employees may also have an incentive to game a survey or mislead the survey, which can lead to reduced trust. Lastly, systems data is considered to be the most trusted type of data because it is generated as a byproduct and (optimally) unchanged from when it was generated. Unlike conversations or surveys, systems data doesn't rely as much on interpretation as there is not as much subjective context. Instead, the systems channel provides information simply on the actions that have occurred. As a result, systems data is often seen as a more trusted source of information. Effort The effort required to create information from each channel is another consideration. Conversation data is rarely converted into what we think of as data that we could interact with in a spreadsheet (tabular) but is usually synthesized and interpreted by each person who had the conversations. Making sense of many conversations and even having to have many conversations makes this channel high effort to scale. Survey responses are much easier to information due to consistency and the planning involved in creating a survey tool. The data that comes back from closed-ended surveys and many psychometric surveys can be quickly analyzed in tabular formats. For open-ended surveys many of the concerns of conversation come back in, but on a more contained scale. As stated above, the systems channel produces data that is relatively ready to use. While getting data extracted and modeled can require some upfront effort, the effort is more contained and much lower than trying to generate information from language in the prior channels. This low effort to analyze is in part what has made systems data so popular with People Analytics teams. Information Density Density refers to the richness of information each channel provides. Each channel has a certain density of core information, but some channels layer on personal and business nuance, context, and depth. This factor is where conversation shines and where I believe we've underestimated the information channel. Conversations between people are incredibly dense with information passage for core content, but then also including additional streams of information on the pitch of voice, body language, and facial expressions. Open-ended surveys try to address the content nuance but still lag conversation on those other human nuances. The systems channel falls far behind then on this factor as systems data is limited to capturing only predefined data points and largely passive data points. Combining the Tradeoffs One way to mitigate the strengths and weaknesses of these channels is to pull them into a narrative together. For example, systems data can provide a high-level overview of the situation and help frame the story, survey data be used to capture precise additional information needed for a study, and then follow-up, conversations can provide a much deeper understanding of the context of the problems at hand. While combining HRIS output with surveys and conversations can be challenging, translating all three into workforce information is what allows us to pull them both into a coherent narrative. For example, the information generated from the systems channel, “what they do”, may tell a story that there is a high turnover rate among a specific demographic within an organization. Relying solely on systems information, we may jump to the conclusion that this demographic is not a good fit for the company. However, by also listening to "what people say they do" through engagement surveys, we may discover that this demographic is leaving because of a lack of training or career advancement opportunities. Furthermore, if we listen to "what people say" in follow-up conversations between HRBPs and employees, we may identify that there is a particular manager that is not allowing teams to attend trainings. All three channels together create a comprehensive story. There are instances when each channel should also be used independently. Employee relations professionals' investigations may depend solely on conversations, bypassing surveys or systems. Surveys can offer feedback on large-scale events not covered by systems and where conversations are not feasible. Systems data may be all that is needed for a first-pass analysis or exploratory pass at understanding the organization. Example A software development firm leverages information from the systems channel to identify patterns of late-night work activity among its employees. By approaching the data with empathy and understanding, they initiate conversations with affected employees and discover that tight deadlines and unrealistic expectations are causing stress and burnout. As a result, the firm adjusts its project management approach to prioritize employee well-being and work-life balance. They perform a quarterly survey on these topics going forward which finds that the changes they implemented have led to a healthier and more sustainable work environment. Information Channel Framework for Decision Making Let’s introduce a more complex diagram now to house this framework. In the following graphic, I’ve laid out the supply chain from each information channel and how it is converted to information. That information can then be combined in a common form and once it is synthesized and analyzed it becomes stories which inform decisions. Also included in the graphic are the talent strategy for a business (how they want to create strategic advantages with talent) and the experience of the decision maker, which also inform stories. Those two areas have unique influence and in turn are influenced by decisions made by a decision maker. As a final note, this article was focused on internal channels of information, but some additional external channels of information could be external labor market data (information about the context that a workforce sits within) or evidence-based practices (academically validated information). This flow from information generation to how we inform decisions with stories should be top of mind for any team working in employee listening, people analytics, or HR. We ground ourselves when we are reminded that our goal is to support the HR decision-making process to drive business results. Now that we have created a framework and explored the value of combining conversations, surveys, and systems data for a comprehensive understanding of the workforce, let's focus specifically on what it means to bring the systems channel into the conversation on listening. Historically, conversations and surveys have been treated as listening tools, but if the systems channel also generates information about the workforce, we can make the case that we need to listen to employee information through all three channels. Systems data as Listening Here are five ways benefits that an HR team can achieve by blending systems data into the conversation on listening along with a fictional story detailing how this could work in practice: Engaging HRBPs: By embracing systems data as a form of listening, we can make analytics more accessible to HR professionals who may be more comfortable with traditional listening methods. HRBPs are good at listening and this is another way to do what they are good at. By viewing systems as another way to listen, we can reduce initial fears and skepticism that someone feels when they hear “HR Analytics” or “People Analytics” which will help bring HR into the fold, tapping into their strengths. Example - Engaging HRBPs: An HR business partner at a retail organization believes that a new schedule that has been set for employees could be causing work-life balance issues. They have had conversations with a few employees which prompted the investigation and after sending out a work-life balance survey they confirmed the issue. However, leadership was still not convinced, so the HR business partner listened to the data from the time-management system to analyze patterns of absenteeism and tardiness among employees before and after the shifts were changed and they found significant increases in each, which they brought into their story. The HRBP took this story which was informed by conversations, survey, and systems data to the leadership team and it convinced them to make a change to the shift schedule, resulting in improved attendance and employee satisfaction. Integrated storytelling: This framework creates a more integrated approach to analytics, where we can combine the insights gained from systems data with other channels of information to create a more complete picture of the situation at hand. This integrated approach to methods will lead to better workforce decisions as more information can be brought to bear. Example - integrated storytelling: A healthcare organization seeks to improve diversity and inclusion within its workforce. By combining data from employee demographic systems, engagement surveys, and focus group conversations, they created a comprehensive narrative that revealed disparities in career development opportunities for underrepresented groups. As a result, the organization implemented targeted mentorship programs and inclusive leadership training, which fostered a more diverse and inclusive workplace. Strengthens employee trust: Organizations can demonstrate that they value their employees by actively listening to them through various channels, including systems data. By framing systems data as a form of listening and bringing empathy to bear on that, teams can communicate to employees why they are performing analysis and reduce mistrust related to the analysis of systems data. Example: A financial services firm transparently communicates their use of systems data to track employee work patterns in order to optimize team productivity. By sharing this information with employees, explaining how data is protected, and explaining how the data would be used to inform the HR decision-making process, employees felt more involved in the process and trust the company's intentions, which leads to increased engagement and commitment. Reduce debate: Recognizing that all three information channels — conversations, surveys, and systems—are necessary to tell complete human stories fosters a collaborative environment between different teams and functions. This encourages analytics teams, listening teams, and HR business partners to work together to create a comprehensive narrative, rather than focusing on just one aspect of data collection. Example: In a manufacturing company, there is disagreement between HR and operations teams about the most effective way to allocate resources for employee training. By incorporating data from all three channels—systems data on employee performance, survey feedback on training preferences, and conversations with both employees and managers—they are able to reach a consensus that ultimately leads to more efficient training and improved workforce capabilities. Human-centered analytics: Framing systems data as listening to the workforce emphasizes empathy and understanding. We should always remember that behind every data point in HR is a human who has a livelihood, friends, family, and a world outside of work. Approaching systems data as listening to employees reminds analytics teams to respect the human behind the data and ensures that the focus remains on the human aspect, rather than treating employees as data points, which ultimately leads to better workforce decisions. Call to Action As a reminder for all three systems, transparency is key. Employees deserve to know what information is being gathered, how it is used, and who can see or share information that they have provided or that has been collected about them. Proper data privacy controls, data governance, and agreements between company and employee must be established. Without empathy and these protections, all information channels will break down. As HR leaders and People Analytics professionals, we must recognize the value of each channel in capturing the complexity and richness of the workforce's experiences, needs, and perspectives. This framework that takes us from information to decisions also shifts us out of our methodology-based functions (e.g. HRBP holding conversations, Employee Listening doing surveys, and People Analytics working with) and reminds us that our common end goal is informing workforce decisions to drive business results. Upon reading the paper I've gotten the question from a few reviewers around “does this mean the name People Analytics needs to change”. I can see where they're coming from that analytics inspires the stats and management of systems data, but when we look at the core of the word “analytics” it is the science of analysis. I think we are still safe. If we were to go somewhere else someday? I could see us landing on Workforce Decision Support, naming the function on our outcome rather than method, but I don't think that's worth losing the brand we've built under People Analytics today. As we move forward in an increasingly data-driven world, it is crucial that we remain grounded in empathy and the human aspect of decision-making. Understanding and supporting the individuals that make up our organizations is core to who we are in HR. By actively seeking input from the workforce through all available data channels and embracing a comprehensive listening approach, we will be better equipped to drive meaningful change, foster employee trust, and ensure the long-term success of our organizations. Margaret Mead hit a point of truth when she said, “What people say, what people do, and what people say they do are entirely different things.", but we’ll end on another quote from Mead which I’ll pass on to you as you think about the work required to get these three channels speaking together instead of apart at your organization: “Never doubt that a small group of thoughtful, committed, citizens can change the world. Indeed, it is the only thing that ever has.”― Margaret Mead Many thanks to Mike Merritt, Kyle Davidson, Keith Kellersohn, Peter Ward, Beverly Tarulli, Ethan Burris, Shahfar Shaari, Allen Kamin, Anna Tavis, Al Adamsen, Lyndon Llanes and many others for wonderful conversations on this topic and your feedback! I am grateful and reminded daily of what an incredible community we have in the people analytics world. Interested in talking to my team to learn more? Fill out the form below.
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10 min read
Richard Rosenow
People analytics is essential in today's complex business world as it helps organizations make data-driven decisions to maximize the value of the workforce. There are, however, still barriers to adoption, from legal to ethical and from finance to IT. To ensure that people analytics is more accessible to all audiences, HR leaders need to have a nuanced understanding of the audience they’re speaking with, and the needs and interests of those teams. They also need to know this analytics space inside and out in order to stand their ground on the value that it can provide both employees and companies. I had the pleasure of speaking with HRD about this topic and more on a recent podcast. We’ve summarized some of those conversations below, but please take a listen too and let me know what you think. Prefer to Listen to the Conversation? Check out HRD's podcast Q: Why is it important that we reduce the barriers to implementing people analytics systems? A: It is crucial to reduce barriers to people analytics because, at the end of the day, people analytics is about decision support. Organizations need to make data-driven decisions about their workforce quickly and efficiently in today's complex business world. Workforce costs are most of the costs of doing work in many industries, making it imperative to understand how to maximize the value of the workforce. People analytics provides a way to do that. Beyond the cost of doing business, the workforce also is made up of people with families, friends, and rich full lives. It's essential to recognize that they are not just resources to be allocated but valuable contributors to the organization. To care for the workforce, we should be using every tool at our disposal to make better decisions related to the workforce. People analytics helps us do that. This is why you need to develop a plan on how to implement HR analytics before the process begins. Whether it's Workforce Planning (WFP), Diversity, Equity, and Inclusion (DEI), engagement, or breaking down silos, looking at the workforce through the lens of data shines a bright light on the organization. People analytics provides the data and insights to help managers make better decisions, improve employee experiences, and drive business results. Therefore, reducing the barriers to people analytics is critical to unlocking its potential and gaining a competitive edge in today's business world. Q: Why are HR leaders still afraid of people analytics? A: For those holdouts, I would first say give people analytics a second look now that the field has matured. I would also add that I recognize that there is a good reason for some HR leaders to be hesitant. HR professionals have seen many fads come and go over the years, such as competency models, 9 boxes, and stack ranking. HR is complex, so it's difficult to determine what new and shiny interest is real and what's fluff, and what will stick around. Humans might be the most complex thing we manage at work, and for our history at work to date, other humans have been the best way to interpret and manage humans. But this is shifting. Computers are just starting to break through and provide more nuanced and targeted support in that endeavour. People analytics is starting to become expected as a way to augment HR teams' decision-making, and more and more teams are delivering from this function daily. There is also a framing that I’d push back on that people analytics is the "future of HR,". I know this rubs some HR leaders the wrong way too. I would shift this to say that people analytics is a large part of the future of HR, but that people analytics will become HR or will become an automated tool that we use before it replaces the function. By that, I mean that the best parts of HR, the parts that we are most proud of, are still and will still be those human moments, and we do HR for very human reasons. HR analytics systems when run properly inform us around how to be more human and enable us to spend more time doing those things that are important to us as humanity over time. In the long run, those human things will remain and grow even after we implement people analytics systems. I don’t believe that HR leaders are necessarily afraid of people analytics. It's more of an adoption curve that all organizations are going through. People analytics is a valuable tool that can help organizations make data-driven decisions and unlock the full potential of their workforce. As the adoption of people analytics continues to increase and as people analytics teams learn how to integrate it into the function, more HR leaders will recognize its benefits and embrace it as a crucial part of their work. Q: How can we make people analytics more accessible? A: One way I’ve found is that we can make people analytics more accessible by encouraging HR leaders to think of data insights as another form of employee listening. As HR teams have been cut and their ratios have changed, with sometimes a ratio of 1 to 800, it's challenging to speak regularly with 800 people per year. Listening to the workforce through data allows you to listen at scale. I say this too because HR professionals are already good at listening, and framing people analytics as listening is a way to tap into something that HR is already good at. Listening through data can provide insights into employee behavior, preferences, and needs, which can inform HR practices and improve employee experiences. I’ll add too that data cannot do it alone. People are still needed to able to interpret and tell the stories behind the data in order to gain insights and make informed decisions. Therefore, it's essential to provide training and resources to HR leaders to help them understand how to use people analytics effectively. They need to understand how to collect and analyze data, interpret the insights, and use them to inform HR practices. Making people analytics more accessible and encouraging HR leaders to embrace data insights as another form of employee listening can help organizations unlock the full potential of their workforce and improve employee experiences. Q: Where are areas of concern around privacy and ethics and how can HR leaders reassure their employees? A: Areas of concern around privacy and ethics can absolutely arise when implementing people analytics. HR leaders must address these concerns well before starting down the path of using data to inform decisions and to reassure their employees that their privacy and ethical standards will be upheld. Privacy considerations should be in place from the beginning of the people analytics process. It should not be an afterthought, but rather a core component of the team's development, tool rollout, and system setup. Hiring a trained and tenured people analytics leader, someone with experience in the HR subject matter area, or pairing them with someone who has deep HR expertise is an important investment to help an HR team navigate privacy and ethical concerns and to provide guidance to the team. Although IT can be a great partner, they may not have the necessary expertise in privacy and ethical considerations specific to working with workforce data. Knowledgeable folk speaking to the nuance of ethics and privacy around workforce data should lead people analytics, and it's crucial to not have that data cross boundaries. For example, there could be "electric fence" items such as the content of employee emails or sharing DEI data at the row level that would violate privacy, ethical, and legal standards if folk outside of HR had access. When communicating about people analytics, HR leaders should also focus on sharing the positive impact it can have on employees. By using data to understand employee behavior, preferences, and needs, HR leaders can make informed decisions that improve employee experiences. Reassuring employees that their data is used for good can help them buy into data sharing. HR leaders must help employees feel valued and safe when sharing data. In summary, HR leaders must be transparent and upfront about privacy and ethical considerations when implementing people analytics. By emphasizing the positive impact of using data for good and ensuring that privacy and ethical standards are upheld, employees can feel more comfortable sharing their data and trust that their employer has their best interests in mind. Q: How can HR work with finance or IT to help navigate concerns around the cost or need for people analytics? A: I don't think anything gets done in business today without the buy-in of Finance and IT. We live in a unique economic environment and everything we touch is technology. Anyone that thinks they can go alone without IT or Finance leaders is going to be in for a world of issues. That said, many times IT or Finance leaders may not know this space or understand the lens of HR. They have their own concerns and lenses that they bring to business, so it is up to us as HR professionals to communicate and share the value of what we do both on our terms and on their terms to ensure that teams are aligned. It is also important to recognize that Finance and IT leaders are also employees and people leaders who have their own questions and concerns that need to be answered about how workforce data is used. By selling the overall vision of People Analytics and demonstrating how it affects everyone within the company, including their data, their employees, and their decisions as leaders, HR can gain buy-in and support for the initiative from Finance and IT as leaders as well as functional partners. Anyone heading down the path of starting a people analytics function requires collaboration and alignment between HR, Finance, and IT (and many other teams!) to ensure that the initiative is strategic and aligned with the overall goals of the company. Ultimately, gaining support for people analytics early from these partners leads to better decision-making and outcomes for the company. Want to learn more about One Model? Reach out!
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8 min read
John Carter
Effective vs. Ineffective Leaders Successful teams are measured by how well they achieve their set goals and metrics. Effective leaders use the tools available to them to create and set challenging, yet achievable goals. Effective leaders also encourage and enable their teams to be able to reach and even exceed their goals. Ineffective leaders fail to set achievable goals, in many cases, they are too easy just as frequently as they are too hard. Ineffective leaders fail to provide support and encouragement and also fail to recognize when adjustments need to be made. So how do we decipher which leaders are effective and which are ineffective? Well to start, we must first understand that this is not a finger-pointing exercise. This is a tool to uncover both effective and ineffective strategies that can help your management teams perform better. Great management is essential for any business to succeed, and manager effectiveness metrics can be used as a tool to measure success in developing top talent for your organization. These metrics are also crucial in identifying underachieving teams and identifying ineffective leaders who can be educated and uplifted. By tracking the manager performance metrics below, you can identify great leaders and uncover leadership techniques that can be shared. Why are manager performance metrics important? Organizations need managers who can create a productive working environment, communicate effectively with their team, make well-informed decisions quickly and consistently, and lead their team toward the company’s objectives. By tracking performance metrics in people analytics software, companies can discover effective management techniques and reward their top performers. Not only that, they now have a solid benchmark to compare and see if that new program led to measurable improvements. How to find your most effective managers with one visual Wait … Only ONE visual? I thought there were 8 metrics…. Stick with me, I’m not pulling a fast one on you. I’m about to cover each of these 8 metrics in detail, but first I want to make the point that these insights are more compelling when presented together. How to find your best managers, for example, look at the Team Leader Scorecard Example below. Using a heatmap view - we can quickly see how we gain so much more insight when we arrange and visualize them all together. Now, let’s dive into each metric. 1. Headcount growth rate Headcount growth rate is the percentage increase in the number of employees that a manager hires. It’s a good measure of manager effectiveness as it can show how well they can find and retain top talent while continuing to build their team. It is also important to understand what are the drivers behind headcount growth. Increased sales and production or perhaps correcting unbalanced workload distribution. How to calculate headcount. 2. Repeat low performers Repeat low performers are employees who consistently fail to meet set goals and expectations. Tracking this metric is important as it can show how effective a manager is at developing their people and providing them with the skills needed to succeed in their roles. It could also be an indication of a manager setting unrealistic goals and may re-evaluate the goal-setting process. 3. High performer termination rate Effective leaders who view employees as assets can help their company combat resignations by encouraging high-performing employees to stay with the company for longer. By tracking high-performer termination rate, organizations can identify managers who are losing top talent and uncover reasons behind the attrition. They can also seek to understand how effective leaders are succeeding in retaining their top talent and distributing this knowledge among all of their leaders. 4. Termination rate volume Termination rate volume measures the number of employees who leave an organization within a given period. This metric can identify any patterns in a manager’s employee turnover so that organizations can take steps to address any underlying issues causing high turnover rates. 5. Promotion rate By looking at promotions actioned across your organization, you can see the managers and work units with more people than average being promoted. This indicates which managers are best at growing talent. Tracking manager promotion rates is an important metric for assessing manager effectiveness and identifying potential areas of improvement. Also, learn about succession planning. 6. Female representation Female representation is an important metric for assessing manager effectiveness as it shows how well a manager is at promoting diversity and inclusivity within their team. By tracking this metric, organizations can identify any challenges with gender imbalance and take steps to address them. 7. Salary ratio by gender Salary ratio measures the difference in salaries between men and women within an organization. This metric can be used to identify any discrepancies with wage discrimination so that organizations can take steps to address them. Tracking manager salary ratios is important for assessing manager effectiveness and creating a fair and equitable workplace. 8. Diverse retention gap Diverse retention gap is the difference in retention rates between diverse and non-diverse employees. This diversity metric can be used to identify any issues with manager diversity and inclusivity so that organizations can take steps to address them. It took Phil a staggering 51 seconds to pull the chart above together. Take the People Analytics Challenge and see how long it takes you to answer 90 of the top people analytics questions. What about employee retention rate? Employee retention rate measures the percentage of employees who remain with the company after being hired by a manager. This metric is important for assessing manager effectiveness because it shows whether or not they are creating an environment that encourages long-term employment and job satisfaction. This is also a great metric to review and there are lots of ways to talk about it. Reference these articles for more information: Calculate the cost of turnover How recruiters impact employee outcomes How to learn from new hire failure Measuring manager success to stay on top of the game These manager effectiveness metrics represent the key performance indicators (KPIs) of manager success, and if you track them with people analytics software, then you can find the most effective leaders in your company. Keeping an eye on these measures is a great way to ensure that you are developing, retaining, and promoting the highest-performing talent in your organization. With this knowledge in hand, you can make sure that your company is always staffed with the best and brightest leaders possible. Good management in business, or should I say good team management, is key in today’s competitive talent market and these KPIs provide essential data points to measure success so that your team can stay on top of the game. Want to Learn More? Let's Connect. 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2 min read
Phil Schrader
It's an all too common scenario: a rush request coming in from the leadership team, in this case leadership in the finance department. They want to understand some cost impacts with our workforce and how it's changing. But it's Friday afternoon, so I want to try to get this request done as quickly and accurately as I can. Do you want to be able to knock out highly accurate, ad-hoc reports fast? Get a demo of One Model to see how!
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7 min read
Chelsea Schott
Retention rate and turnover rate are two distinct metrics that measure employee longevity in an organization. Understanding employee retention rate is essential for businesses to succeed. How to Calculate Employee Retention Rate Retention rate refers to the percentage of the workforce who remain with the organization for a specific period of time. It measures employee stability and shows the effectiveness of a company's efforts to keep employees engaged and satisfied with their job. Healthy retention rates mean greater expertise, increased productivity, and overall success for any organization. Using data from previous trends or even running an exploratory data analysis can give insight into solutions that make everybody happier in the long run. However, before we dive into the specifics of ways to calculate, it is also important I call out that finding “a retention rate” is only the beginning. In the modern age of people analytics, we need to have a people data platform that allows us to break our data down to draw unique understandings and insights. What does it look like for various groups? What’s the difference between exempt and nonexempt workers? Are our recruiters impacting our new hire retention? To manage your employee retention rate effectively, it’s becoming increasingly important to truly understand what goes into that retention rate. Measuring this can help explain why employees choose to stay and identify key focus areas to keep it that way. So, which retention rate formula to use? Retention rates can be tricky to calculate. There are multiple formulas that can be used for the metric and multiple factors to consider when calculating. One of the most common formulas involves dividing the number of employees at the end of a period by the number of employees at the beginning of a period. A glaringly obvious problem with this formula is the fact that it’s not taking into consideration any new hires or acquisitions taking place during the time period. A company’s retention rate could easily exceed 100% if there were more hires than terminations during the period, which wouldn’t be an accurate indicator of true employee retention. And even if excluding hires from the calculation, the retention rate would be better holistically, but not necessarily as good at a more granular level when considering employees’ internal movements within the company. There are also other retention rate formulas to consider: One Model has dimensions we've created to look back and identify employees who stayed or left the company after a given number of months (typically 6 or 12) from a specific time. We can use this dimension, called Is Future Terminated, along with a headcount metric to calculate the retention rate for a historical time period. The Is Future Terminated dimension is not only helpful for calculating a retention rate, but is also used in One Model’s One AI recipe to determine the likelihood of attrition for groups of employees. Read more about One AI here! Another popular retention rate focuses solely on a company’s new hires. Like the calculation used for One AI, the new hire retention rate will determine how many of the newly hired employees stayed with the company after a certain amount of time. As with the others, this calculation is good to look at new hires in the company overall, but can be complex if you are trying to determine new hire retention rate in certain departments or positions. For example, if an employee is hired in one department and transfers to a new department after six months, but terminates only one month after being in the new department, should the retention rate metric consider the new hire’s attributes – like department – at the time of hire or at the time of termination? Check out Josh Lemoine's blog: Learning from Failure: Why Measuring New Hire Failure Rate is Great! to see even more reasons why new hire calculations are important. The best news is that at One Model, we understand different companies and industries may have different metrics and measures that best represent them. We will work with you on this to determine the best method for your organization, helping you build the metrics that tell your story. One Model makes it easy and Phil shows us how: People Analytics Uncovers Factors Affecting Employee Stability What Can You Do About It? Once you have calculated your retention rate, you then need to determine the factors affecting your employee stability — which is where people analytics comes into play. People analytics generates business benefits and allows you to collect and analyze data on employee behavior and attitudes in order to enhance employee satisfaction. For example, if the data shows that certain departments have a significantly higher retention rate, you can look for common factors, such as the level of managerial support or training the employees receive, that may be contributing to this trend. Another strategy that can improve retention is to use the data to develop targeted workforce engagement programs. People analytics can provide information on what types of benefits, training opportunities, or other incentives are most likely to engage employees and improve retention. By using this information to create targeted engagement programs, you can make the most of your resources and increase your chances of success. You can also compare differences between those who stay versus those who leave and see if there are certain programs/paths that are working better. Employee Retention Rate in the HR Revolution The HR revolution is transforming the way businesses approach employee retention. Companies are investing in new technology and data-driven approaches to help them better understand and engage with their employees. This shift is enabling organizations to develop more effective retention strategies that are tailored to the unique needs of their workforce. By tracking and improving your employee retention rate, you can reduce the costs of turnover, enhance employee satisfaction, and build a more stable, productive, and knowledgeable workforce. Embracing the employee voice and utilizing people analytics software will also help you take a more data-driven approach to retain workers and stay ahead of the competition. Let Us Show You Building Retention Dashboards Today! Request a Demo
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9 min read
Richard Rosenow
It’s very difficult to do people analytics without data. Finding and extracting workforce data to use for analytics is maybe the first and most common challenge that people analytics teams encounter. In this blog post, I’ll share tips I’ve learned about data extraction for HR teams, common challenges involved in extracting data, and best practices for overcoming these challenges. By applying these tips, HR teams can more effectively and efficiently extract data to drive business value and insights. What is Data Extraction? Data extraction is the process of extracting data from one or more sources and transforming it into a usable format for further analysis or processing. It is the "E" in "ETL". In the context of HR, data extraction is an essential process for collecting and organizing data related to the workforce, such as core HRIS, employee demographics, performance data, and engagement data. By extracting this data, HR teams can more effectively analyse and utilise it to make informed decisions and drive business value. Data extraction may involve extracting data from various sources, such as databases, spreadsheets, and HR systems. This is the first in a series we're writing on the people data platform. If you'd like to learn more, Download the whitepaper. Here are 5 Tips to Ensure HR Data Extraction Success 1. Prioritize and Align Extracted Data with the Needs of the Business First and foremost, it is important for people analytics teams to prioritize what data they go after based on the needs and challenges of the business. If the business is experiencing high attrition, start with the HRIS data and build an analysis on termination trends. However, if the business is concerned about understanding remote work, the starting point for data extraction may need to be the survey system to get insights on employee voice back to leadership teams. Delivering against critical business needs adds value to the company, builds trust, and creates the buy-in needed for future projects. There’s a time and a place to pursue novel data to generate insights that the business is not expecting, but without a foundation of trust and a history of delivering against core business concerns that can be a difficult road. When you’re building your data extraction roadmap, start with the data where you can get to value quickly. 2. Be Thoughtful About What You Extract Workforce data is inherently different from other data in the company as underneath each data point is a coworker with a livelihood, career, friends and family, and personal details. It is critical that People Analytics teams be careful about what they extract and that they are thoughtful about use cases for the data. It’s an important ethical decision to make sure the data is private, secured, and safe in storage as well as in the extraction tools and pipelines that get the data into storage. There are ethical approaches you should be thinking about, but we also live in an environment now where there are hard legal requirements related to the extraction and storage of workforce data. Depending on the nature of the data and where you operate, you may be required to comply with CPRA (California), SOX, HIPAA, and GDPR to name a few. Of note, GDPR applies to EU citizens wherever they reside and not just individuals residing in the EU. So if you employ any EU citizens or are considering hiring EU citizens, GDPR regulations are critical when it comes to data extraction. 3. Build the Business Case to Pull More It can be difficult to convince IT teams or central data engineering functions to support HR data extraction. So when you do get someone to assist, there can be a certain anxiety around the idea of “what if I need more”. This can cause a team to over-extract data or pull too much of it too soon. The feeling is understandable. I’ve been there. But as I’ve said before, the people analytics flywheel is a phenomenon that can be realised if you focus on prioritized business problems. This gives you the chance to revisit the data extraction conversation down the road should you need more. Your future arguments for data extraction will be stronger if business needs continue to be the rationale for additional requests for data extraction support. 4. Automate Your Extractions A native report is a report that comes pre-packaged with your HR system. While native reports are helpful to early data extraction wins, they can be difficult to scale and standardise. Native reports tend to have the following effects. They are usually just a subset of the data within the system that are typically pulled through a graphic user interface, which makes them rigid and difficult to repeat. They are prone to time out if you pull too much data or pull too frequently. They may end up looking different depending on which user pulled them due to filters, permission settings, and the effective date range for the data pulled. (HR never closes the books!) Over time, you’ll need to move away from native reports and to an API or another method to extract the data from the system. An API gets you access to the full data set, pulls data more frequently, and introduces standardisation and repeatability by leveraging data extraction tools and relying less on GUIs. APIs never get bored, can be logged and audited, and can run on their own. Automation changes repetitive and high-variance tasks into trusted processes. 5. Extract for Data Science, not just Reporting See the video above to learn more about extracting data from Workday. Meaningful analysis requires more data and often different data than snapshot extraction methods like native reports can provide. Snapshot extraction can handle basics, such as headcount reporting but cannot report what the company looked like on a given day. When you extract your HR data, make sure that you extract what you need for data science and not just your reporting needs. Data science applications require wider data sets and more features. The time component is the most important part of HR data science. An employee might touch 10 different HR systems as he or she joins a company, so the data in each system needs to be joined to the same employee record in a harmonized and sequential order. Make sure that the data in each system is captured at the time of the action with the time stamp. Naturally this creates a “transaction-level” record. Without those transaction records, you can end up with messy data. Examples include data that shows someone being promoted before they were hired or terminated before a transfer. HR is also notorious for back-dating work. Transaction-level records can prevent issues arising from those behaviors. Finally, your data science necessitates extracting the correct components. Prioritise Data Extraction, But Be Aware of the Nuances Are you ready to explore how to extract hr data at your company? Data extraction is an essential part of conducting people analytics. It is important for people analytics teams to prioritize their data extractions based on the needs and challenges of the business, be thoughtful about which data points are extracted, consider automating their data extractions, and be careful about the nuances of the data they extract. Looking to Extract Data Out of Your Specific HRIS Download our Resources Now! Delivering People Analytics out of Workday Delivering People Analytics from Successfactors
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6 min read
Marcus Joseph
In the US, leave is having a moment. From the US President’s State of the Union to New York’s 12 weeks of fully paid parental leave, to the FAMILY Act legislation, leave has been all over the feeds, which is encouraging given the majority of US workers struggle to take advantage of our current policy’s benefits. While most of the coverage seems to focus on longer-term family leave, in today's working environment, paid sick leave is more important than ever. In fact, US workers without paid sick leave could be three to four times more likely to quit their job than comparable workers who have this benefit. This is especially true for hourly versus salaried employees and for female employees who tend to disproportionately carry caregiving responsibilities outside of paid work. For most industrialized countries, sick worker pay is not a critical issue. In fact, 32 of 34 OECD countries guarantee paid leave for personal illness. Who are the two OECD countries holding out? The United States and North Korea. So, let’s dive into the American problem and what it can mean for businesses managing workers in the US. With a “tripledemic” threat of flu, COVID-19, and Respiratory Syncytial Virus (RSV), it’s evident that company sick pay is a critical benefit for companies of all sizes. Even some US government studies concluded that there was a noticeable rise in workers who quit with unpaid leave during 2020. FFCRA Leave and Changing Paid Sick Leave Law Amid COVID-19 The COVID-19 pandemic revealed that paid leave is essential to employee well-being and safety. In the past, paid leave was not considered critical to supporting the American economy. As COVID-19 cases ramped up, allowing workers to stay home or care for their sick family members helped meet real human needs, combat the spread of COVID-19 and mitigate the impact on the American economy. The Families First Coronavirus Response Act (FFCRA) was eventually implemented, which required certain employers to provide FFCRA leave and expanded family and medical leave for specified reasons related to COVID-19. About 25% of US firms did increase their sick leave options and one study found that states, where workers gained increased leave benefits under FFCRA, reported an average of 400 fewer cases of COVID-19 per day. However, 90% of companies reported these increases were intended to be temporary. Since Covid, there also seems to be a renewed interest from the Biden administration to make paid leave a requirement. During the State of the Union 2023, he backed up his claim to stop workers from being stiffed by fighting for paid family and medical leave. His secretary of labor is also calling for better national standards to mark the 30th anniversary of the Family and Medical Leave Act. Need to track Covid illnesses at your organisation? Try our free resource. Rising Turnover Reveals Paid Sick Leave Is Critical to Employee Retention One key reason why our people analytics teams should consider paid sick leave in our turnover models is the impact on retention. Certain populations of workers are much more likely to quit over paid leave. This means that employers who don't offer this benefit are at a disadvantage when it comes to retaining critical team members. The rise in turnover rates is already a nationwide problem. Plus, replacement costs for an employee can be as high as 50% to 60%, with overall costs from 90% to 200%. Offering paid sick leave is not only critical benefit employees look for in a business, but it is also a great way to live out your values of caring about individual well-being and your desire for employees to stay with your company for the long haul. Increase Employee Productivity and Engagement With PTO It’s pretty clear that when employees are out sick, they are not able to work and be productive. However, offering employees the ability to take the time they need to recover without worrying about losing pay will also positively impact productivity levels when those sick team members are back in action and healthy. When employees feel like their employer cares about them and their well-being, they are more likely to be engaged while at work. This leads to improved morale and a better work environment for everyone involved—improving life outcomes for individuals, the bottom line as an organization and your brand as an employer of choice. Ultimately, your standard sick leave policy is a factor your HR analytics team should consider when analyzing retention rates. Understanding how much your average PTO and sick leave is affecting your workforce this cold, flu and COVID season may be the difference between keeping and losing employees and remaining competitive in your market. HR teams should invest in knowing the internal and external story the data tells us and sharing it with leadership. Doing so could help improve employee retention rates, reduce turnover-related costs, and increase productivity in the long run–and help turn leave’s current “moment” into our new norm.
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2 min read
Dennis Behrman
Most large employers are already required by law to ensure that workers are safe and workplace risks are minimised as much as possible. But a new school of thought has emerged around the concept of well-being at work. Whether you've followed this trend closely or this is the first you're hearing of it, well-being has been studied and there is interesting data available about it. We Asked an Expert about Implementing Well-being at Work My colleague Richard Rosenow recently invited his good friend Matt Diabes, a Ph.D candidate at the Carnegie Mellon Tepper School of Business to discuss well-being in incredible new detail. His research demonstrates that well-being is far more complex than ping-pong tables and good pay. Watch their lively and informative discussion to understand what well-being is and how managers and organisations can harness the promise of well-being for great talent outcomes. If your organisation has thousands and thousands of workers whose well-being matters to you, you'll want to be able to measure well-being at your company. Find out how One Model can help you report on well-being and how to achieve organisational well-being goals. Request a Personal Demo to See How Well-being is Measured.
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11 min read
Josh Lemoine
Measuring New Hire Failure Rate in an actionable way and acting on the data will save your company money. In this blog, I'm taking a look at how your organisation can save significant sums of money and minimise workforce continuity risks by measuring and understanding your new hire failure rate. Since recruiting and onboarding new employees is expensive, retaining new employees past their earliest phase of employment is critical. When you reduce new employee turnover you save money. A powerful tool for enabling this change in your organization is measuring New Hire Failure Rate. What is New Hire Failure Rate? New Hire Failure Rate is the percentage of a group of hires that leave the company within a set period of time. More specifically, it's people hired during a specified time period who leave the company within a certain number of months divided by all of the hires from that specified time period. The time to termination is a lever that can be adjusted but generally ranges from 90 days to 2 years. It's a powerful measure because it spans recruiting, onboarding, and employment. A lot of data is captured during each of these phases, lending to a large number of factors available to analyze. Measures similar to New Hire Failure Rate include New Hire Retention Rate and New Hire Turnover Rate. Either one could be substituted for New Hire Failure Rate with a similar value proposition. New Hire Retention Rate is the same thing but the inverse and has a more positive name 🙂. It puts the focus on those who stay rather than those who leave. The New Hire Turnover Rate calculation is a bit easier to perform but the measure can be more difficult to interpret due to it being based on headcount rather than hires. Why is it costly? New hire failure is almost universally a negative thing. Even if you're losing hires who are not a good fit for your company, it's costly. Situations like seasonal holiday hiring at a retailer might be an exception in some cases but can be excluded from your analysis if necessary. Some specific reasons that losing employees early in their tenure is costly include the following: The rate is surprisingly high at many if not most companies. A quick internet search yields numbers in the 20% to 80% range. This article isn't going to cite specific numbers since plenty of other articles already do that and your company is unique. If you were informed though that half of your new hires leave in the first year would you believe it? If I were a leader in the Talent Acquisition or Human Resources areas, I would certainly want to know the rate at my company. Hiring and onboarding costs a lot of money. New hire failure increases the amount of both processes that need to happen. Monetary costs include the following. Talent Acquisition employee salaries Paid sources Training resources Time spent by hiring managers interviewing and onboarding people Companies get little productivity from employees who are not yet up to speed. Employees leaving early in their tenure are leaving before they're productive. People leaving teams is bad for morale of those teams. People in senior leadership leaving can be bad for morale of the entire company. Brand reputation can suffer. Why don't all companies measure New Hire Failure Rate? You'd be hard-pressed to think of a People Analytics metric that's more powerful and actionable than New Hire Failure Rate. So why isn't it usually a key performance indicator for Human Resources and Talent Acquisition teams? Calculating New Hire Failure Rate is surprisingly tricky Hires from a specified time period that terminated within a certain number of months divided by all of the hires from that specified time period sounds easy enough. But you have to ensure that both the numerator and denominator come from the same group of hires. So you need to know the hire date but also the termination date at the same time. And you need the differences between those dates bucketed so that you can adjust the "Time to Termination" between 3 months, 6 months, a year, etc. to find the sweet spot. You also have to offset the group of hires back from the current date to allow enough time to know whether the hire terminated or not. By this, I mean that if you're looking at New Hire Failure Rate within 6 months, you don't want to include hires from the past 6 months since you don't yet know whether they'll terminate within 6 months. New Hire Failure Rate Example: My colleague Phil Schrader, One Model's Solutions Architect, performed this new hire failure rate analysis from scratch in less than 5 minutes. Could you do that with your existing HR analytics today? Take the People Analytics Challenge today! The measure itself isn't actionable unless you know other things about the hire Knowing that your company has a high New Hire Failure Rate highlights that a problem exists but does not help you solve it. In order to improve retention, you need to know as much as possible about the hires who are leaving (and the ones that are staying for that matter). Luckily, companies leveraging modern applicant tracking, onboarding, and HRIS systems have a lot of useful data available. Unluckily, this data is often not available in a useful way. To improve your New Hire Failure Rate, you need to be able to slice it every which way to find the attributes and areas to focus on. Unfortunately.... The hiring process spans two separate teams and often two or more separate systems The Talent Acquisition and Human Resources functions both involve hiring but in most companies, they're two separate teams. Not only that but they often leverage two separate systems (ATS and HRIS) to manage their processes. Even companies who use one system such as Workday to manage both Recruiting and HR suffer from the data from the two functions not being cleanly linked together for analysis. On top of this, there's often data related to onboarding such as survey data. This is extremely valuable data when tied to outcomes like early tenure terminations. Unfortunately, many companies use a survey vendor separate from their ATS and HRIS vendors and obtaining survey results comes with its own set of challenges. How can companies measure it in an actionable way and save money? The first thing you need is a People Analytics team. A People Analytics team services both the Talent Acquisition and Human Resources functions. Since New Hire Failure Rate spans both teams, it's best to have a neutral third party reporting it. This should help prevent false assumptions about the causes of high rates stemming from the other team. There's also the word "Analytics" in " People Analytics", and some analytical prowess will be useful in tracking down the causes. Tracking New Hire Failure Rate is only valuable to a company if they act on the findings. The function of a People Analytics team is to provide actionable insights, so they're well-positioned to maximize the impact of the measure. A People Analytics team needs the right tools in order to be successful. The best tool to measure New Hire Failure Rate is a People Analytics platform. A People Analytics platform provides: All of the data in one place and joined together in one data model (subliminal hint) Core HR data such as Business Unit, Job Level, Location, and Manager Recruiting data such as Application Source, Time to Hire, and Recruiter Candidate Survey results Onboarding Survey results A complex yet intuitive way to deal with time All of the attributes structured into dimensions for grouping and filtering the data A compelling visualization layer for distributing the insights to the people who can act on them Watch my colleague Phil Schrader perform a similar analysis in One Model At this point, it should be clear that performing a one-off analysis of the drivers of New Hire Failure Rate would be very difficult. How can companies achieve even more success? Saving your company money was mentioned in the introduction to this article. In this article, Phil describes how you can leverage One Model to calculate source costs and cost per hire. If you know how much it costs to hire someone, you know how much money you’re losing when they leave the company right away. Being able to go to leadership with dollar figures, even if they’re estimates, can be a very powerful driver of change in your organization. Last but certainly not least, companies can maximize success in measuring New Hire Failure Rate by leveraging Machine Learning. This is a great use case for a causal analysis highlighting drivers of new hire failure. An advantage of performing this type of analysis using machine learning is that it’s far more efficient than doing it manually. A tool like One Model’s One AI is able to take all of the attributes from all of the data sources described in this article and run them through a classification algorithm, returning the most predictive of both new hire failure and retention. It can do this in an intuitive way that doesn’t require Data Science skills. If that sound too tricky, embedded insights in One Model powered by One AI can deliver various onboarding retention statistics right within storyboards. Most things that save you money in the long run require some up-front investment. Measuring New Hire Failure Rate is no exception. Like installing solar panels save you more in the long run than installing water barrels, leveraging a People Analytics team and platform to measure New Hire Failure Rate will be much more impactful than a one-off analysis. This is an opportunity to achieve quantifiable results and further cement the value proposition of People Analytics teams. The answers are closer than you think. Let us show you. Request a Demo
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8 min read
Phil Schrader
Turnover is the strongest signal you get from your workforce. Someone worked here, and — for one reason or another — it didn’t work out. Voluntary termination of employment is a major event, and you need to pay attention to the reasoning (and the data) to help you with employee retention. While some degree of turnover is inevitable, the high cost of losing an employee can have a major impact on your bottom line. So, how do you calculate voluntary termination, and what can you do to combat it? Let's take a closer look. How to Calculate the Cost of Turnover There are a number of ways for calculating the cost of turnover. The most common (but less accurate method) is to multiply the average salary of the position by the number of separations. For example, if you have 10 employees who make an average salary of $50,000 per year and five resign, the turnover cost would be $250,000 ((10 x $50,000) / 2). However, this method needs to take into account the time it takes to find and train replacement employees. A more accurate way to calculate the cost of turnover is to use a formula that factors in recruiting costs, training costs, and lost productivity. Using this formula, the cost of turnover for our example above would be closer to $37,500 (((10 x $50,000) + ($5,000 x 10)) / 2). The ability to calculate voluntary attrition internally will bring a new dimension to your leadership team. However, these benchmarks serve as a baseline for your turnover calculator as there are several other variables and data points to consider, including: Daily rate of the hiring manager’s salary Estimated hours spent interviewing and screening resumes Estimated cost of advertising for the available position Daily rate of departed employee’s salary plus benefits Number of days the position will remain open before you rehire Cost to conduct a background check Daily rate of the hiring manager’s or trainer’s annual salary Total days the hiring manager or trainer will spend with new employee Number of working days in the new hire’s onboarding period It’s also important to factor in position levels. The Center for American Progress (CAP) found that the cost of staff turnover was, on average, 213% of the annual salary for highly-skilled employees. Segment the positions into three different salary levels for a more accurate turnover calculator: average entry level, average mid-level, and average technical salaries. Tracking Voluntary Attrition Over Time The simple truth is that you will not get a full picture of what is happening from a single calculation. It can be time-consuming to calculate on a consistent basis over time manually. You need to see the trends. Create a storyboard to see if trends emerge. You can break down the involuntary and voluntary attrition rate by business unit, location, and organization tenure groupings. You can also quickly see at a glance how turnover rates are changing, where they are high, and whether it’s you or the employee forcing the change. It took me 49 minutes to pull this cost of turnover visual together from scratch. How long does it take you to answer question #59? See how quickly you can take the People Analytics Challenge and answer over 90 of the top questions asked of People Analytics Teams. You Know How to Calculate Churn Rate of Employees and Turnover Cost — Now What? Now that we know how to calculate the cost of turnover let's look at how you can mitigate voluntary attrition in your organization. Use One AI Recipes to Predict Trends and Make Proactive Changes You know the “What” but you really need to know the “Why”. So, run a predictive model on that data to pull out the correlations and understand the why behind the attrition. One AI Recipes will help you predict the likelihood of a person in a selected population voluntarily terminating within a specified period of time. To do this, One AI will consider a number of attributes and will train the model on the population at a defined point in the past. Does distance to the office, time since last promotion, or paid sick leave correlates to a rise in attrition? AI is a tool that helps you make connections and better understand voluntary resignation reasons in order to take specific actions leading to improvement. Improve Hiring Practices Poor hiring practices could easily be one of the reasons why your voluntary attrition rate is high. Be sure to clearly define the skills and experience required for each position and only interview candidates who meet those criteria. Conduct thorough reference checks, and don't hesitate to pass on a candidate if there are any red flags. After analyzing your hiring process, you can incorporate the proper people analytics data to produce the most accurate cost of losing an employee. Promote from Within Another great way to reduce turnover is to promote from within whenever possible. Not only does this show your employees that there are opportunities for advancement within your company, but it also helps reduce training costs because you already have someone on staff who knows your company culture and how things work. Invest in Employee Development Finally, investing in employee development is a great way to reduce turnover rates. Employees who feel like they are learning and growing in their roles are more likely to stick around. Offer professional development opportunities through tuition reimbursement programs or paid memberships to professional organizations. Final Thoughts on How to Calculate the Cost of Turnover Sure we want to understand how much it costs the company. That is the first step in getting leadership to care. However, the real work begins when you understand why people are leaving and can build a plan to curb the costs. People analytics offers real-time labor market intelligence to help businesses identify pain points causing turnover. And considering the high cost of losing an employee and its impact on your bottom line, employee retention is critical in today’s economy. One AI Recipes make creating a predictive model from your people data as easy as choosing the outcome you want to predict and answering a series of questions about the data you want to leverage to make the predictions. The result is a predictive model based on robust, clean, and adequately structured data — all without engaging a data engineer or data scientist. Calculating turnover is the first step toward helping you understand and predict trends, reduce turnover rates, and keep your business running smoothly. Watch Me Build A Turnover Analysis Live Request a Personal Demo Today.
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7 min read
Chris Butler
The employee survey still is perhaps the most ubiquitous tool in use for HR to give their employees a voice. It may be changing and being disrupted (debatable) by regular or real-time continuous listening and other feedback mechanisms. Regardless, employee survey data collection will continue. I am, however, constantly amazed by the amount of power that is overlooked in these surveys. We’re gathering some incredibly powerful and telling data. Yet, we barely use a portion of the informational wealth it holds. Why? Most organizations don’t know how to leverage the confidential employee survey results correctly and maintain the privacy provisions they agreed with your employees during data collection. The Iceberg: The Employee Survey Analytics You're Missing Specifically, you are missing out on connecting employee survey answers to post-survey behaviours. Did the people who said they were going to leave actually leave? Did the people who answered they lack opportunity for training, actually take a training course when offered? Did a person who saw a lack of advancement opportunities leave the company for a promotion? How do employee rewards affect subsequent engagement scores? And of course, there are hundreds of examples that could be thrown out there, it is almost a limitless source of questioning, you don’t get this level of analysis ROI from any other data source. Anonymous vs. Confidential Surveys First, let me bring anyone who isn’t familiar with the difference up to speed. An anonymous survey is one where all data is collected without any identifiers at all on the data. It is impossible to link back to a person. There’s very little you can do with this data apart from what is collected at the time of questioning. A confidential survey, on the other hand, is collected with an employee identifier associated with the results. This doesn’t mean that the survey is open, usually, the results are not directly available to anyone from the business which provides effective anonymity. The survey vendor that collected these results though does have these identifiers and in your contract with them, they have agreed to the privacy provisions requested and communicated to your employees. And a number of survey vendors will be able to take additional data from you, load it into their systems and be able to show a greater level of analysis than you typically get from a straight survey. This is better than nothing but still far short of amazing. Most companies, however, are not aware that survey vendors are generally happy (accepting at least) to transfer this employee-identified data to a third party as long as all confidentiality and privacy restrictions that they, the customer, and the employees agreed to when the survey was collected. A three-way data transfer agreement can be signed where, in the case of One Model, we agree to secure access to the data and maintain confidentiality from the customer organization. Usually, this confidentiality provision means we need to: Restrict the data source from direct access. In our case, it resides in a separate database schema that is inaccessible by even a customer that has direct access to our data warehouse. Provide ‘Restricted’ metrics that provide an aggregate-only view of the data, i.e. only show data where there are more than 5 responses or more than 5 employees in a data set. The definition of how this is restricted needs to be flexible to account for different types of surveys. Manage Restricted metrics as a vendor, preventing them from being created or edited by the company when a restricted data set is in use. Support employee survey dimensionality that adheres to this restriction so you can’t inadvertently expose data by slicing a non-restricted metric by a survey dimension and several other dimensions to create a cut to a population that otherwise may be identifiable. Get Ready to Level Up Employee Survey Analysis! Your employee survey analytics can begin once your survey data is connected to every other data point you hold about your employees. For many of our customers that means dozens of people data sources across the recruit to retire, and business data spectrums. Want to know what the people who left the organization said in their last survey? Three clicks and a few seconds later and you have the results. Want to know if the people you are recruiting are fitting in culturally and which source of hire they were recruited from Or if low tenure terminations show any particular trends in engagement, or culture responses? Or whether people who were previously highly engaged and have a subsequent drop in engagement have a lack of (choose your own adventure) advancement|compensation|training|skilled-peers|respect for management? Literally, you could build these questions and analysis points for days. This is what I mean, a whole new world opens up with a simple connection of a data set that almost every company has. What can I do? Go and check your last employee survey results and any vendor/employee agreements for how the data was to be collected and used. If the vendor doesn’t state how it’s being collected, check with them, often they are collecting an employee identifier (id, email, etc). If you are lucky you might have enough leeway to designate a person or two within your company to be able to run analysis directly. Otherwise, enquire about a data transfer agreement with a third party who will maintain confidentiality. I’ve had this conversation many times (you may need to push a little). If you don’t have data collected with an identifier, check with HR leadership on the purpose of the survey, and the privacy you want to provide employees with and plan any changes for integration into the next survey. This is a massively impactful data set for your people analytics, and for the most part, it’s being wasted. However, always remember to respect the privacy promise you made to employees, communicate how the data is being used and how their responses are protected from being identified. With the appropriate controls, as outlined above, you can confidentially link survey results to actual employee outcomes and take more informed action on the feedback you collected in the employee survey analysis. If you would like to take a look at how we secure and make survey data available for analysis, feel free to book a demonstration directly below. Ready to see us Merge Employee Survey Data with HRIS Data? Request a Demo!
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5 min read
Phil Schrader
The Power of Combining Data Sources Am I weird for having a favorite metric that I always pull once I connect a customer's HRIS and Recruiting data for the first time? Oh well. Let's talk about my favorite merged source metric: First-year attrition by recruiter! I think it's one that can be useful for managing a recruiting function, but it's also a helpful classroom example to explain why we all need to merge recruiting and HR data. Connecting data across HR systems can be a tricky problem, but with the right tools, it is possible to gain valuable insights into employee behavior and business outcomes. In the video above, we explored how One Model can be used to blend data from different HR systems and gain insights into key metrics such as new hire turnover rate. Join the Conversation on Linkedin Segmentation is Key to Understanding Why Additional Data Sources Matter One of the key features of One Model is the ability to quickly break down data into more meaningful groups. Peter Howes will back me up in saying HR data without segmentation is worse than useless. To expand on my video example, by grouping turnover rate by year, we can get a better understanding of the overall trend in general employee retention. Additionally, by narrowing our employee outcomes analysis to specific subsets of employees, such as those who joined the company within the last year (or gender, department, etc), we can gain insights into specific areas of concern, such as early termination rates. But we can get these insights with data from 1 system. What happens when we combine data from say, our recruiting platform? You have the Power When You Join HR Data Sources Another powerful feature of One Model is the ability to connect data from multiple systems, such as recruiting data from your ATS and core workforce data from your HRIS (to use my video example). You can now make discoveries that actually improve processes within your organization. By connecting who has turned over with who actually recruited that person, we can make leadership decisions and work with L&D on potential coaching opportunities. Finding the “Why” After You Merge Recruiting and HCM Data Many people analytics teams (whether through intensive spreadsheet work or quickly using a tool like One Model) can create these insights, but interpreting these insights still requires the nuance and care of an HR analytics leader. Many struggle with providing the “why” behind the data. If you ask a seasoned recruiter they most likely will say that the number one reason is probably related to the applicant feeling mislead in the hiring process and that could increase new hire turnover. But are there other factors at play? Start with an exploratory data analysis and then get sophisticated with an AI engine that non-data scientist can actually use. Overall, the Explore tool in One Model makes it easy to connect data across HR systems and gain valuable insights into employee behavior and success rates. Whether you are an HR professional or a business leader, this tool can help you make data-driven decisions and improve your organization's performance. Want to See Phil Merge More Data? Schedule a Demo Today
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6 min read
Jamie Strnisha
In today's competitive business landscape, it's more important than ever for workplaces to provide value to employees, customers, and investors. Attracting top talent, boosting productivity, enabling innovation, and improving employee experience are all key goals to achieving higher value. Many progressive companies are accomplishing these goals by tracking facility analytics — including attendance tracking and workplace tracking — to make facility improvements. That’s right. Companies use facility analytics to improve their environments and retain top talent and you should too. What is Facility Analytics Facility analytics is the process of collecting data about how a workplace is being used daily. Space utilization data is one type of data that can be collected (through sensors, badge swipe, surveys, or observation studies) and integrated into your overall people analytics data lake. With this workplace analytics data, managers can use it to transition to make proactive, positive changes in the company’s culture and work environment. This can even include transitioning away from assigned workspaces to flexible shared spaces and remote work. Since the pandemic, many companies have made changes, like these, and were able to reduce property costs and optimize public use spaces. After analyzing which departments are using which spaces, changes can even be made to bring teams closer together to improve mobility, increase employee connections and boost productivity. Why Use Facility Analytics and Workspace-Related Data? There may be some that are afraid facility analytics may be too intrusive but, done correctly, it could actually be a critical tool in improving the overall workplace experience. A few examples of what time and attendance people analytics can help track include: The best days for employee gatherings (monitor the days where most employees are in the office). Collaboration among employees by using survey data to track communication and teamwork trends. Employee burnout and workload to determine what generative attributes are leading to turnover. Office movement to know where employees’ desks are versus where they actually worked. Hoteling policy and proximity analysis so managers can see who is using the hotel desks and if additional desks are needed. Energy-saving initiatives, such as changing the temperature or adding motion-sensor lights in unused workspaces. Office activity and meal planning. Contagious illnesses tracking (e.g., COVID-19) to identify and mitigate risks in the organization by knowing who is in the office on any given day. Facility data in people analytics can also be used to track employee productivity. If you find that teams are travelling long distances to meetings, you can move teams and/or encourage online meetings to reduce travel time and increase work availability. In addition, if you see that some people are consistently at the office for long periods of time, you may be able to intervene to prevent burnout. How to Use Attendance Tracking to Future-Proof Your Facility Offices provide social interaction, creativity options, and collaboration. Your goal should be to design a work environment that meets those needs and more. COVID-19 has changed the workplace as we know it. The pandemic gave many people a taste of remote work they never had. With so many employees working remotely, companies are starting to realize that the traditional 9-5 in an office setting may not be necessary. In fact, Forrester Research found that 60% of companies are now utilizing hybrid schedules where employees can work from home and in the office. This "next normal" workplace will require a new way of thinking when it comes to managing employees and facilities. Luckily, when you merge survey data, facility data, and your HRIS, you can start to understand how best to meet business objectives and employee needs by team, cohort, or distance from the office. Ways to Capture Workplace Analytics Wi-Fi Sensors Common Workplace Analytics can be tied to People Analytics and provide a more in-depth understanding of your people. Wi-Fi sensors can be used to track employee movement and usage of common areas. This data can then be used to make adjustments to the layout of the office, as well as cleaning and sanitizing schedules. Additionally, Wi-Fi sensors can be used to send alerts to employees when they enter an area that has been recently cleaned or sanitized. Mobile Apps Mobile apps can be used for a variety of purposes, such as monitoring attendance analytics, sending notifications and alerts, and providing access control to certain areas of the office. Reservation Systems Reservation systems allow employees to book workspace in advance. Additionally, they can be used for location analysis and people analytics (e.g., tracking employee usage of common areas). Badging Data Badging data refers to the workspace-related data collected by security badges that employees wear. This data can be used for a variety of purposes, such as tracking employee movement, identifying trends, attendance tracking, and improving security protocols. Get Maximum Value From Analytics Workspace With One Model Unlock your valuable facility analytics and attendance tracking data with One Model and specialized data modeling that enables you to extract, aggregate, and analyze your data like never before. See for Yourself. Connect with Us Today. Facility analytics is a powerful tool that today's workplaces can use to improve employee experience and boost productivity. One Model seamlessly connects your facility tracking data with other third-party resources — such as a Human Resources Information System (HRIS), Integrated Workforce Management System (IWMS), and surveys about the workspace — to help you improve the workplace and stay ahead of the competition.
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4 min read
Richard Rosenow
People analytics is a rapidly growing field that is helping businesses around the world to better understand and manage their employees. Businesses today are seeking ways to improve efficiency and gain a competitive edge all while retaining top talent. People analytics is an increasingly popular tool that helps organizations achieve these goals by providing insights into the behavior and performance of their employees. This is exactly why business leaders should care about HR analytics. Business Benefits of People Analytics Here are five key benefits that businesses can experience by using people analytics: Visibility People Analytics can be used to get a better understanding of what is happening within your company. By organizing and analyzing data about your workforce, you can gain a clearer picture of what is happening across your organization. This allows you to identify issues and opportunities for improvement, and take action to address them. Listening It is not possible for a CEO to have one-on-one conversations with every employee in the company. However, people analytics allows you to gather and analyze feedback from your employees through surveys, interviews, and other methods to bring workforce stories to life for leadership teams. This allows you to understand the concerns and needs of your workforce and make informed decisions based on this data. Identify Trends People analytics can help you spot larger trends and patterns that may not be immediately apparent to the human eye. For example, by analyzing attrition data, you may discover that employees who live a certain distance from the office are more likely to leave the company. This insight can help you make changes to improve retention and reduce turnover. Cost of Workforce Compensation is the largest single expense for many companies and a key factor in attracting, retaining, and recognizing top talent. People analytics can help you understand how different elements of compensation, such as base salary, bonuses, and equity, impact employee performance and retention. By analyzing this data, you can create a compensation strategy that is effective and fair for your workforce. When you think about it this way, it's really business analytics. In human resources, most of the critical information business leaders need to make decisions is found within their databases. Decision Support Insights produced through people analytics allow you to make decisions based on data rather than gut instincts and assumptions. By analyzing data about your workforce, you can identify opportunities for improvement and make informed decisions that are more likely to lead to success. Overall, people analytics is a powerful tool that can help businesses gain insights into their workforce and make data-driven decisions that drive efficiency, profitability, and retention. By connecting HR analytics to business benefits for your leaders, organizations can understand how to improve their operations, attract and retain top talent, and stay ahead of the competition. Did this help you get internal support? Schedule Time to Talk with Us Today.
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6 min read
Eliza Fury
Like many others, data is not a new concept. If you’re anything like me you’ve grown up with it. Whether it was making your first social media account or making your first online order, you’ve played a part in creating and maintaining data. That’s why it’s not surprising to know that data is leading us into the new Industrial Revolution. The difference with this revolution is that it’s taking place on our computers instead of in factories. The one lesson we can take from the old industrial revolution is the need to ensure people are safe within the work environment. Therefore, it's not surprising that HR analytics will be necessary to ensure a business has a vital human touch. Across our globe HR departments are changing to revolutionize the way they meet the growing demand for skilled workers. In a world where everything is digital, it’s essential that companies make the most of technology to stay ahead of the curve and this is no exception when hiring or upskilling current employees. In general, the most successful companies going forward will be ones that are employee-centric. This means greater pressure on HR departments, as evidenced by a recent survey which revealed that 64% expect more strain due to increased hybrid work environments, with 18% expecting a significant increase in their workload. As such, the HR revolution is a priority for many companies. Workforce Analytics vs. People Analytics: The Evolution of HRM On Gartner, workforce analytics is “an advanced set of data analysis tools and metrics for comprehensive workforce performance measurement and improvement”. HR has evolved to include HR analytics which has, in turn, assisted departments in providing organizions with clearer business insights. This means including people analytics and data analytics to get a holistic view of employees and delving deeper by not just looking into output levels but also attempting to identify longer-term trends within teams across multiple locations for better decision-making regarding workforce management. The first step you should know is the importance of individuals and how they are treated within the workplace. Much like our technology, the terminology has grown to reflect our workplace — people analytics, HR analytics, or workforce analytics — has shown the shift away from viewing employees solely through a production-oriented lens toward recognizing their human potential. This involves providing access to career development opportunities along with life support initiatives like vacation leave so that employees can grow both personally and professionally while still contributing positively in meaningful ways at work. HR in the 4th Industrial Revolution The key to unearthing what makes a successful business, is its people and how they are efficiently managed. Successful businesses give back to it’s people by Identifying top performers and rewarding those that give that sprinkle of extra effort. Focusing on people not only leads to higher rates of retention but also enables businesses to recognize potential in employees while they are still in an entry-level position. In the 21st century, companies that use HR analytics are revolutionaries as they can comprehensively evaluate performance. Workplaces are more complex than one perspective. In this employee revolution, data allows individuals to see unacknowledged high performers and use those insights to reward them — a concept that works hand-in-hand with staff retention. This engagement retains employees and saves professionals the cost of effort involved in replacing staff. How to Create a Better Work Environment One way to elevate top performers is to provide them with opportunities for promotion and recognition. A comprehensive people analytics platform can track how individuals react to certain situations, how they engage with tasks, how quickly they learn new skills, and how consistently they perform over time. HR data can catapult your work environment for the better by allowing you to access who is ready for a promotion or deserving of a bonus. In addition to tracking individual performance, people analytics software also provides valuable insights into how teams interact and processes flow within the organization. Managers can use this information to adjust how they divide tasks, form teams, and incentivize their employees. This can help maximize the value of each individual in the team and ensure that everyone feels like a contributing member. HR revolutionary companies can also potentially see methods that have unintentionally lowered effective and committed employees. By utilizing people analytics software over time, companies can look at HR data related to diversity, learning, and employee experience to give them a foundation of what it means to create a positive and cost-effective environment. Powering Your HR Engine to Make Better Decisions Ideally, all companies should be focusing on spearheading the HR evolution. There are no alternatives that are as effective as data-driven insights, whether it’s assessing employee performance or engagement levels, employers can recognize how to maintain teams that enjoy their work environments and learn from your top performers. Ultimately, your team is like an engine within your factory. A business's goal is to keep that engine running. HR analytics allows you to power that engine and make better decisions — because, without a well-oiled machine, no progress can be made.
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6 min read
Phil Schrader
It can be hard to select the right people analytics projects. There’s no shortage of options to choose from-- turnover risk, career path analysis, pay equity, impact of learning programs, etc. And, there’s also no shortage of hurdles to delivering an impactful analysis. But, there almost always is a shortage of organizational support for people analytics, so if you pick the wrong project, you run the risk of burning through the limited executive support you have. With all that in mind, here are my 3 reasons to consider prioritizing an analysis of recruiting source costs. Reason 1: The data requirements are relatively minimal and generally less sensitive Applicant Sourcing cost analytics minimize two of the main challenges of people analytics: data security and data extraction. While the data is based on job applicant data, the data you need is not overly sensitive. You just need to know how many applications you received from a given source, in a given time frame, and what ultimately happened to those applications. Aside from data validation, you don’t need to dig into the personal details of the job applicants for this analysis. You really shouldn’t hit too many data management, compliance, or security snags with this one. Once you’ve gotten ahold of your application data split by source, you need to gather up your job board pricing and other relevant source costs. While this data may not be tracked in your ATS, it shouldn’t be too hard to gather. You could literally start by grabbing a legal pad and jotting down the approximate annual spend by source. (Ok not a legal pad, but a very simple spreadsheet. We do not currently ingest data via legal pad.) This would be a great time to do a retrospective analysis of 2020 recruiting if you haven’t done so already. On the source cost side, you would just need something like this: That’s it. This could be the entire supplemental data set for 2020 like the example above. In a csv file that’s less than a kilobyte. Once you have this, you can do several things like compare to the number of applicants or the number of hires you had to get to the average cost of recruiting a new employee. Reason 2: Cost-per-hire metrics will come up time and time again. This brings me to reason two of why you should prioritize source cost analytics: it’s an opportunity to build your team’s experience with cost allocation metrics. Being able to efficiently and accurately allocate source costs will serve you very well-- opening the door to progressively more complex and impactful cost of workforce calculations. A couple of years ago, I built out a video that shows how this works in detail in One Model. It’s one of my favorite things about our calculation engine. You can check that out in the video below. The short answer though is that it hinges on being able to calculate recruiting cost per day, then dynamically managing days in period. It’s certainly okay and valuable to just do some back-of-the-envelope calculations at first like “$10,000 spent last year and 5,000 applications received equals $2 per application (or per hire)”, but also very worth investing in the logic and systems that enable you to do this on the fly and drill down to the month, week, day, etc. Recruiting source costs are a good gateway into cost allocation because, as noted above, the underlying data is pretty manageable. Once you can run this for source costs, then you can jump into salaries, benefit costs, etc. Reason 3: It’s an actionable analysis that can save your organization money! The third big reason to prioritize a recruiting source cost analysis is that it will probably pay for itself! Let's face it, recruiting employees is a time-consuming and costly process. It’s easy to help your recruiting team save money once you connect the dots between source spending and recruiting output. All you need to do is stack rank your sources by “cost per” and then reallocate your spend to the ones that perform the best. If your LinkedIn or Indeed rep calls you up and asks why you’re not spending more-- just show them the data. You might find they are willing to give you a better offer! I had front-row seats for an absolute master class in this back at Jobs2web. There I was working with Steve Shaffer, Linda Moller, and some of our early analytics customers (Here’s to you Annette and Brent!). People analytics teams are often criticized for not having enough ROI or dollars and cents style analyzes. You can change that impression with a solid source cost ROI analysis and an early win on saving costs. So there you have it- my argument for why a recruiting source cost analysis is a great “quick win” project for your people analytics program. If you’re curious about the details or underlying math, please reach out and schedule a time to chat. I do love this topic and am always up for a good conversation about recruiting data. Let's Get Your Recruiting Cost Conversation Started
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8 min read
Josh Lemoine
With the introduction of One AI Recipes, One Model has created an intuitive interface for no-code machine learning in people analytics. One AI Recipes (Recipes) are a question-and-answer approach to framing your people data for predictive models that answer specific people analytics questions. Adding this capability to the existing One AI automated machine learning (autoML) platform results in a more accessible end-to-end no-code solution for delivering AI from right within your people analytics platform. We call them Recipes because they walk you through each of the steps necessary to create a delicious dish; a predictive model. Simply select the ingredients from your data pantry in One Model then follow the steps in the Recipe to be guided through the process of creating a successful model. Recipes democratize the production and reproduction of AI models with consistency, accuracy, and speed. Understanding some of the terminology used above and how it relates to One AI will be useful in explaining why Recipes are so useful. What is a no-code machine learning platform? “No-code machine learning platform” is somewhat of a vague term. The definition is pretty straightforward. A no-code machine learning platform is a tool that enables you to apply artificial intelligence without writing any code. It provides a guided user experience that takes business context as an input and produces predictions and/or causal inferences as output. Where it becomes vague is in the range of complexity and flexibility of these platforms. On one end of the spectrum, there are simple-to-use AI builders where the user answers a few questions and is presented with predictions. These tend to only be useful in very standardized use cases. There is often very little transparency into what the machine learning model is actually doing. On the other end are the complex and powerful platforms like Azure ML. Azure doesn’t require writing code and is also very powerful and flexible, but it is also complex. Anyone without a working knowledge of data science would be hard-pressed to create trustworthy models on platforms like this. One AI is aiming at the sweet (no dessert Recipe pun intended) spot on the spectrum. Being designed specifically for people analytics, it allows us to leverage the question-and-answer approach of Recipes. Experienced Chefs can still toss the Recipe aside and cook from scratch though. The One AI kitchen is well stocked with machine learning tools and appliances at its disposal. What is autoML? AutoML is a series of processes or “pipeline” that performs data cleaning and preparation, algorithm selection, and parameter optimization for machine learning. Performing these tasks manually can be labor intensive and time-consuming and requires expertise in data science techniques. AutoML automates these tasks while still delivering the benefits of machine learning. One AI has always provided an autoML pipeline, albeit one where any default setting can be overridden. Even so, there were two areas where we knew we could improve: 1. The data structure for analytic purposes is not the same as the data structure necessary for machine learning. Performing machine learning on data in One Model at times required additional data modeling, a task performed by an expert. 2. Framing up the problem and interpreting the results often required an expert to be involved to ensure accuracy and coherent insights. Recipes address these challenges. Recipes both re-frame the data in a way that a machine learning model can work with and provide a coherent statement that explains both what the model will be predicting and how it will be doing so. How can you benefit from One AI with Recipes? Resource Savings Recipes lighten the load on the technical resources that are likely in high demand at your organization. People analytics is a key strategic business function, yet most people analytics teams aren’t lucky enough to employ Data Engineers, Data Scientists, and Machine Learning Engineers. These teams often fight for the same technical resources as other teams for people who are very talented but can’t possibly possess a deep understanding of all of the different areas of business in the company. Predicting and planning for outcomes has become a key deliverable of people analytics teams, yet they’re often not well equipped to succeed. Companies are increasingly looking for software for automation in HR. Machine learning tools are making great strides in taking business context as an input and producing useful insights as an output. The full realization of this functionality is no-code machine learning platforms. Time Savings With Recipes, time-to-value for machine learning from your people data is substantially reduced. The difference in time required to manually perform this work versus leveraging a no-code machine learning platform is stark. It’s weeks to months vs. hours to days. Even if you have Data Scientists on staff that have the skills necessary to build custom predictive models, they can save time by prototyping in a no-code environment. Interpretability Having the clear statements that Recipes provide that explain what it is you’re predicting and how you’re going about it makes the results easier to interpret. Contrast this with manual machine learning where details can get lost in translation. This prediction statement is in addition to the exploratory data analysis (EDA), model explanation/performance, and Storyboards that One AI provides. One Model also employs a team of experts in the ML and AI space that are available to assist if uncertainty is encountered. Transparency Since One AI is part of One Model, your model configuration and performance data is available in the same place as the predictive or causal data and your people data (at large). Also, your models are trained on YOUR data. These are not “black box” models. At One Model we emphasize making model performance data easily available anywhere predictive data or causal data is included. Compliance As a One Model customer, your potentially sensitive employee data resides in the same place as your machine learning. You do not need to export this data and move it around. On the flip side, the output from your models can be leveraged in your Storyboards in One Model without exporting or moving sensitive data outside of your people analytics solution. The predictive outputs can even be joined to your employee dimensions to help you identify where risk sits. Control and Flexibility Users have the option of configuring data and settings manually in a very granular way. Just because One AI offers a no-code option for creating machine learning models doesn’t mean you’re tied to it. Want to use a specific estimator? You can do that. Want to modify the default settings for that estimator? You can also do that. Recipes just expand the number of One Model user personas able to leverage AI on their data. In Summary One AI Recipes provide a question-and-answer approach to building predictive models that answer key questions in the people analytics space. The resulting democratization of the production of AI models provides benefits including: Resource Savings Time Savings Interpretability Transparency Compliance Control and Flexibility You can have all of this as part of your people analytics platform by choosing One Model. Since you won’t learn about these Recipes by watching Food Network, schedule a demo here: Request a Demo
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Featured
7 min read
Phil Schrader
People analytics provides insight into your organisation’s workforce. Your company’s workforce is at or near the top of your organisation’s expenses and strategic assets. Describing the importance of people analytics is very much an exercise in stating the obvious. For this reason, more and more companies are relying on people analytics, and that reliance is growing even as economic conditions change. In fact, as economic conditions become more challenging, people analytics becomes more, not less, important. Imagine a pilot flying in bad weather. Data on altitude, speed, location, etc become even more critical in that context. So yes, it makes sense to invest in people analytics now, even amidst our current economic concerns. People analytics in a recession is one of the most measurable strategies that HR can pursue. Whether you are hiring during a tight labor market or working through the implications of layoffs and reorganizations, you will want accurate, multi-dimensional, effective-dated, relational analytics ready to guide your decisions. People analytics doesn’t just help organise HR data. It generates faster insights from widely-dispersed HR data to make better talent decisions. For example, your people teams can better manage workforce and staffing levels, maximise productivity, and avoid guesswork about their diversity and inclusion objectives. “New and improved” HR reports alone won’t cut it. With people analytics, your analysts and managers can run exploratory data analysis to connect and understand relationships, trends, and patterns across all of their data. Additionally, the analysis adds context and meaning to the numbers and trends that you’re already seeing. The advantages of people analytics and why you should budget for it in a recession. Advantage #1 - Save money with people analytics. For nearly every business, labor is one of its most significant costs. But human capital is essential to generating revenue. HR analytics provides strategic and tactical visibility into one of your organisation’s most vital resources - its people. When your company uses analytics to manage the right people out, it can also use analytics to help you focus your recruitment efforts. After all, replacement costs for an employee can be as high as 50% to 60% with overall costs from 90% to 200%. For example, if an employee makes $60,000 per year, it costs $30,000 to $45,000 just to replace that employee and about $54,000 to $120,000 in overall losses to the company. HR analytics can also become a strategic advisor to your business to show insights into how your organization is changing. For example, people analytics can track trends in overtime pay, pay rate change for various positions, and revenue per employee (to name a few). While the revenue per employee calculation is a macro number, it’s important for you to be attuned to how it’s changing. Knowing the trends of your revenue per employee can lead directly to asking important questions about your people strategy: Are we investing in people now for future revenue later? Are we running significantly leaner than we have in the past? Are we running too lean? If metrics like revenue per employee or overtime pay are dropping or increasing over time, it could indicate that adjustments need to be made on a departmental level. Advantage #2 - Identify trends affecting morale or productivity. People analytics can also help you identify trends within your workforce that may be negatively affecting your business. HR data can help you pinpoint what is causing the change, and then address these issues early so you can avoid potential problems down the road. For example, Cornerstone used metrics such as policy violations and involuntary terminations to identify “toxic” employees harming the company’s productivity. The findings showed that hiring a toxic employee is costly for employers — to the tune of $13,000. And this number doesn’t even include long-term productivity losses due to the negative effects those toxic employees had on their colleagues. Source. With people analytics, Cornerstone identified common behavioral characteristics of toxic employees and now uses this data to make more informed hiring decisions. This created immediate benefits for their existing employees as well as future advantages as their workforce evolved. Advantage #3 - Recruit and retain top talent. The many benefits of people analytics also include a competitive edge when it comes to recruiting and retaining top talent. By understanding the needs and wants of your employees, you can create a workplace that is more attractive to potential candidates. In a world where data is constantly being updated, it's important for talent acquisition and HR leaders to make informed decisions quickly. HR analytics gives them that power at speed (rather than waiting months before seeing what happened). Using AI to discover related qualities of your top performers can also help your acquisitions team select candidates that will fit well into your culture and start driving results. Advantage #4 - Identify high-performing departments. Another one of the advantages of HR analytics is its ability to pinpoint positive changes as well. HR leaders can track department performance to know when to reward or incentivize employees for their productivity and work ethic. Additionally, it can help you keep your employees happy and engaged, which is essential for maintaining a high level of productivity (and sales). For example, Best Buy analyzed its HR data to discover that a 0.1% increase in employee engagement resulted in more than a $100,000 increase in annual income. Further, AMC’s people data showed that the theaters with top-performing managers earned $300,000 more in annual sales than the other theaters. These HR insights also helped this Fortune 500 company identify top talent and ideal candidates for its managerial positions, which ultimately resulted in a 6.3% increase in engagement, a 43% reduction in turnover, and a 1.2% rise in profit per customer. Identify Trends With Real-Time Labor Market Intelligence Ultimately, HR analytics offers real-time labor market intelligence to help businesses identify pain points causing turnover — something that’s essential in today’s hiring climate infamously referred to as “The Great Resignation.” The rise in turnover rates is a nationwide problem. It’s important for companies to find out why their employees are leaving and then create an effective strategy so they can stop the trend before it gets worse. One Model’s people analytics software can be a valuable tool for any business, especially during a downturn. In short: You should budget for HR analytics as an investment, not a cost. If you’re worried about a recession, you can start performing complex analysis on your data in just a few weeks. Let us show you 1:1
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5 min read
Hayley Bresina
The Rooney Rule is a National Football League policy that requires league teams to interview ethnic-minority candidates for head coaching and senior football operation jobs. The Rooney Rule was established in 2003, and variations of the rule are now actively used in several other industries, including over 100 public companies in the United States. While the Rooney Rule is a well-intentioned start at getting diverse candidates in the door for an interview, research has shown that it falls short of truly moving the needle toward a more diverse organization beyond entry levels in most industries. Organizations using the Rooney Rule are often unaware of the narrow impact a single diverse candidate has on the actual hiring process. For example, if a single diverse candidate is one of four finalists for a job position, they have nearly no chance of getting an offer because they become an outlier, which is inherently a riskier hire. However, the same research suggests the chance increases to 50% when there are two diverse candidates in the finalist pool. Additionally, companies need to be careful that they are not sacrificing quality in order to manufacture diverse candidate pools. Interviewing unqualified candidates to ensure you are following the Rooney Rule will not increase an organization’s diversity and inclusion and will only further perpetuate the myth that diversity hiring programs are symbolic vs. concrete and strategic. One Model can help companies take what we know about the shortcomings of the Rooney Rule and partner in creating meaningful, deliberate diversity slates. Exploring the use of the Rooney Rule in Human Resources The Rooney Rule has been adapted into a talent acquisition strategy called diversity slate hiring. This strategy encourages recruiters to look longer, harder, and smarter for more and higher quality diversity slate candidates in the talent pool, particularly those with diverse backgrounds, experiences, and identities. With One Model, enterprises can track their requisition status to benchmark the percentage of diversity slate candidates over time, per open position, by function and grade level, and more. For Example, Company X utilizes a diversity of slate to ensure it has at least 25-50% gender-diverse candidates and 10-25% ethnically diverse candidates interviewing for a position (depending on local and national diverse candidate pool availability in addition to other internal factors) before making a hire when external situations allow. Tracking its open positions, this company found that only 65% had an ethnically diverse candidate interview for the position. This data allows the company to monitor this percentage, make actionable changes, and ensure its hiring managers enact their diverse slate hiring guidelines to benchmark its diversity goals in 2023 and beyond. Bringing Diversity to Your Interview Panels Tracking diversity in the workplace does not just refer to the diversity slate candidates interviewed for an open position; it also includes the actual diversity of the interviewer. According to recent research by Zippia, out of the 83,233 interviewers currently employed in the U.S., the most common ethnicity is White (55.6%), followed by Hispanic or Latino (22.8%), Black or African American (14.2%) and Asian (4.5%). Bringing diversity to your interview panels is immensely beneficial as it helps you avoid hiring based on shared biases as well as assess diversity slate candidates in a more thorough manner. For Company X, a single One Model storyboard could show that while 74.0% of minorities were brought in to interview for an open position, only 10% met with a minority interviewer in that process. Company X is using the dashboard to track and benchmark its progress for increasing its interviewer panel diversity, in addition to diversity slate candidates. This analysis is especially effective in increasing diverse applicants because new research has found that the applications of candidates from underrepresented backgrounds — which the researchers defined as Black, Latinx, Pacific Islander, Alaskan Native and Native American candidates — went up by 118% when the search chair was also from an underrepresented background. Would you like to see these dashboards in action? Schedule a demo.
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4 min read
Jamie Strnisha
Fundamentally, people analytics is about using research and data to reduce the mistakes of human bias. Consistently collecting and analyzing workplace data is an important step in removing bias from your organization. Today's businesses need to focus on not only monitoring hiring metrics, but also effectively analyzing diversity reporting to make meaningful changes. People analytics software can help eliminate human bias and provide enterprises with the data they need to create programs and policies to support sustainable workplace diversity and inclusion. There is no better example of this than what I’m seeing some of our customers do today. They are using One Model to build powerful visuals to track and communicate their progress in order to increase workplace inclusion at every step and build an enduring diversity-rich strategy. Journey with me as I show you some of the cool things our customers are doing. Tracking Female Representation in the Workplace Over the past few years, we've seen a huge push to prioritize tracking female representation in the workplace. In fact, there were 74 female CEOs employed at America's 500 highest-grossing companies as of March 2022 — up from 41 in June 2021 and 7 in 2002. One Model is allowing enterprises to both better report on their data and also more easily track and monitor changes, determine key KPIs, and see how improvements they’re making internally are affecting the data. One such example includes a 2022 Fortune 500 Company X (name anonymized), who used One Model’s people analytics software to set measurable goals for tracking female representation, diversity slate candidates, interviewer diversity, and more. Company X set an aspirational goal to have 50% female representation by 2025, then used the One Model dashboard to set periodic goals, track performance, and benchmark its growth path. The dashboard allowed Company X to monitor its year-end AIP target, current female representation, deficiencies, and drivers. It also allowed them to answer important questions, including: Are our HR process driving gender equality? Are our hires evenly distributed across genders? How are we trending against our current year-end target? What does our long-term trend look like? One Model would like to help companies take the above approach a step further by breaking down their analysis by grade level to ensure women and diverse racial and ethnic groups are being represented beyond entry-level positions in their organization roles at the level of manager, director, vice president, and beyond. Seeing where their organization currently stands in comparison to their diversity goals while simultaneously analyzing local and national diverse candidate pool availability can allow them to put in place concrete and practical recruiting, hiring, and talent management strategies. These strategies begin to break the cycle of diversity decreasing as grade level increases. Using Data to Give Everyone an Equal Opportunity to Succeed Workplaces can only move the needle if they make diversity reporting and change a strategic priority. With One Model, enterprises can set clear periodic goals and performance measures, benchmark progress, and ultimately make long-term changes. By bringing diversity data to light, businesses can make sure that everyone has an equal opportunity to succeed. Would you like to see these diversity dashboards in action? Schedule a demo today.
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5 min read
Stacia Damron
Is your company meeting its diversity goals? More importantly, if it is, are you adequately measuring diversity and inclusion success? While we may have the best intentions, today’s companies need to be focused on not just monitoring hiring metrics - but effectively analyzing them - in order to make a DE&I difference in the long term. But first, in order to do that, we need to take a look at key metrics for diversity and inclusion success. Let's talk about these diversity KPIs we’re measuring and why we’re measuring them. Without further ado, here’s 4 out-of-the box ways to measure diversity-related success that don’t have to do with hiring - all of which can help you supplement enhance your current reporting. Number 1: Rate and Timing of an Individual’s Promotions Are non-minority groups typically promoted every year and a half when minorities are promoted two years? Are all employees held accountable to the same expectations and metrics for success? Is your company providing a clearly-defined path to promotion opportunities, regardless of race or gender? Every hire should be rewarded for notable successes and achievement, and promoted according to a clear set of criteria. Make sure that’s happening across the organization - including minority groups. Digging into these metrics can help determine those answers and in the very least – put you on a path to asking the right questions. Number 2: Title and Seniority Do employees with the same levels of educational background and qualifications receive equitable salaries and titles? Often, minorities are underpaid compared to their non-minority counterparts. Measuring and tracking rank and pay metrics are two good ways to spot incongruences catch them early – giving your company a chance to correct a wage gap versus inadvertently widening it over time. Quantitative measures of diversity, like this, can help you see trends over time because changing diversity turning radius is a long process. Keep your eye on historically underpaid groups. A fairly paid employee is a happy, loyal employee. Number 3: Exposure to Upper Management and Inclusion in Special Assignments Global studies cited in a Forbes article revealed that a whopping 79 percent of people who quit their jobs cite ‘lack of appreciation’ as their reason for leaving. Do your employees – including minority groups - feel valued? Are you empowering them to make an impact? Unsurprisingly, people who feel a sense of autonomy and inclusion report higher satisfaction with their jobs – and are therefore more likely to stay. Are all groups within the organization equal-opportunity contributors? Bonus: On that note - are you performing any types of employee satisfaction surveys? Number 4: Training and Education Programs and Partnerships In 2014, Google made headlines for partnering with Code School. They committed to providing thousands of paid accounts to provide free training for select women and minorities already in tech. Does your company have a similar partnership or initiative with your community or company? As simple as it sounds – don’t just set it and forget it - track the relevant diversity KPIs that determine success and measure the results of your programs to determine if it is in fact, helping achieve your commitments towards improving diversity. The Summary: Success Comes by Measuring Diversity and Inclusion Hopefully, one of two (heck - maybe all four) of the items above resonated with you, and you’re excited to go tinker with your reporting platform. But wait - what if you have all this data, and you WANT to make some predictive models and see correlations in the data - and you’re all giddy to go do it - but you don’t have the tools in place? That’s where One Model can help. Give us your data in its messiest, most useless form, load it into our platform, and we’ll help you fully leverage that data of yours. Want to learn more? Let's Connect About Diversity Metrics Today. Let's get this party started. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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9 min read
Chris Butler
HR professionals have heard the stories and read the news. Large organisations are having considerable success implementing a people analytics strategy for their organisations. That may leave you wondering what people analytics success can do for your own organisation. Perhaps you fantasise about incredible dashboards, with charts and graphs that are elegant and easy to disseminate across your teams and decision-makers. Maybe you yearn for your organisation’s people data to be governed and protected with the same diligence as other enterprise resource planning (ERP) data strategies. Or perhaps you simply want an end to the back-and-forth that’s associated with custom analysis and forecasting. Wouldn’t it be nice to have an HR data analytics technology that orchestrates everything needed for decision-makers to be able to make brilliant decisions quickly? Envision Winning on HR Analytics It's important to think about how your organisation will win with an HR analytics results approach that encompasses people analytics tools. For example, you will need to make a choice between buying an HR analytics platform or building one. If you choose to build a people analytics platform in-house (or you engage an outside party to build a custom people analytics platform for you, then you are accepting a loss in scalability, reducing time to value, and almost certainly limiting the completeness of your analytics-ready data set. We explain more about this choice in a recent whitepaper. Learn more On the other hand, if you choose to buy an off-the-shelf people analytics platform, you will surely find out that not all solutions are the same. As one of the industry’s most respected people analytics platforms, One Model brings an obsession around customer success that is unique when compared to other solutions on the market. We asked a number of our team here at One Model to share why they’re so passionate about customer success with people analytics. If you’re asking yourself, “What can people analytics do for me?”, keep reading. Building a Product that Creates Success Will Myers, One Model Product Lead Will spends his days making sure that One Model’s People Data Cloud™ people analytics platform delivers the result that our customers expect. He notes that, before you can create brilliant people stories or deliver impactful insights to across your organisation, you must first access the data that is needed, anywhere it may live. That’s tricky because these traditional data sources and repositories often lack the interfaces needed to do this. So you have to trust the team behind the technology to get data orchestration where it needs to be. Delivering Results by Changing How HR Teams Work Kelley Kirkpatrick, One Model Customer Success Lead in Australia Throughout her career, Kelley has seen HR teams collaborate over people data in countless ways. She has a unique perspective when it comes to investing in human resources data analysis technology. In her video, she lets us know that both data and people are key things to “get right” when expanding people analytics capabilities. Transparency drives trust, so Kelley works to ensure that People Data Cloud is the most transparent people analytics tool for her customers. It’s her favourite way to directly access metrics and models built from your data. Quick Turn-arounds Lead to More Wins Nicole LI, One Model Senior UX designer Nicole shares a great example that many technology buyers overlook when selecting a software vendor or technology partner. Most customers expect continuous improvement and rapid innovation. But they rarely get that from large companies. She’s extremely proud of One Model’s approach. It’s exciting to turn around upgrades and new features in a 2-week sprint. As our Senior UX Designer, Nicole thrives on solving problems quickly for her customers. She has some exciting user experience innovations to roll out in the coming months, so stay tuned. Everyone in Your Organisation Wins Jen Lincoln, One Model Customer Success Specialist Jen points out that unlocking your data to all the people leaders in the organisation generates excitement within her customer’s internal teams. One Model is democratising analytics and machine learning, so more people can make better decisions, faster. Have you been able to guess another big One Model strength from these videos? One Model People Make the Difference You can clearly see in these videos that the people we have at One Model make all the difference in your company’s people analytics success experience. We have talented product designers and developers that create unique, innovative tech and customer success champions that roll up their sleeves and do the heavy lifting for all of our customers. Our difference boils down to three strengths: people, platform, and product. I’m honoured to work with every member of the One Model team. We love talking about winning on HR Analytics! Want to have a conversation with a great member of the One Model team? Request Time to Chat with Us Today.
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11 min read
Taylor Clark
The human resources department is a mission-critical function in most businesses. So the promise of better people decisions has generated interest in and adoption of advanced machine-learning capabilities. In response, organizations are adopting a wide variety of data science tools and technology to produce economically-optimal business outcomes. This trend is the result of the proliferation of data and the improved decision-making opportunities that come with harnessing the predictive value of that data. What are the downsides to harnessing machine learning? For one, machines lack ethics. They can be programmed to intelligently and efficiently drive optimal economic outcomes. It seems as though the use of machines in decisions and seemingly desirable organizational behaviors. Of course machines lack a sense of fairness or justice. But optimal economic outcomes do not always correspond to optimal ethical outcomes. So the key question facing human resources teams and technology support is "How can we ensure that our people decisions are ethical when a machine is suggesting those decisions?” The answer almost certainly requires radical transparency about how artificial intelligence and machine learning are used the decision making process. It is impossible to understand the ethical aspect of a prediction made by a machine unless the input data and the transformations of that data are clear and understood as well. General differences between various machine learning approaches have a profound impact on the ethicality of the outcomes that their predictions lead to. So let's begin by understanding some of those differences. Let’s focus on the various types of machine learning models: the black box model, the canned model, and the inductive model. What is a Black Box Model? A black box model is one that produces predictions that can’t be explained. There are tools that help users understand black box models, but these types of models are generally extremely difficult to understand. Many vendors build black box models for customers, but are unable or unwilling to explain their techniques and the results that those techniques tend to produce. Sometimes it is difficult for the model vender to understand its own model! The result is that the model lacks any transparency. Black box models are often trained on very large data sets. Larger training sets can greatly improve model performance. However, for this higher level of performance to be generalized many dependencies need to be satisfied. Naturally, without transparency it is difficult to trust a black box model. As you can imagine, it is concerning to depend on a model that uses sensitive data when that model lacks transparency. For example, asking a machine to determine if a photo has a cat in the frame doesn't require much transparency because the objective lacks an ethical aspect. But decisions involving people often have an ethical aspect to them. This means that model transparency is extremely important. Black box models can cross ethical lines where people decisions are concerned. Models, like humans, can exhibit biases resulting from sampling or estimation errors. They can also use input data in undesirable ways. Furthermore, model outputs are frequently used in downstream models and decisions. In turn, this ingrains invisible systematic bias into the decision. Naturally, the organization jeopardizes its ethical posture when human or machine bias leads to undesirable diversity or inclusion outcomes. One of the worst possible outcomes is a decision that is unethical or prejudicial. These bad decisions can have legal consequences or more. What is a Canned Model? The terms "canned model" or “off-the-shelf model” describe a model that was not developed or tailored to a specific user’s dataset. A canned model could also be a black box model depending on how much intellectual property the model’s developer is willing to expose. Plus, the original developer might not understand much about its own model. Canned models are vulnerable to the same biases as black box models. Unrepresentative data sets can lead to unethical decisions. Even a representative data set can have features that lead to unethical decisions. So canned models aren't without their disadvantages either. But even with a sound ethical posture, canned models can perform poorly in an environment that simply isn’t reflective of the environment on which the model was trained. Imagine a canned model that segmented workers in the apparel industry by learning and development investments. A model trained on Walmart’s data wouldn’t perform very well when applied to decisions for a fashion startup. Canned models can be quite effective if your workforce looks very similar to the ones that the model was trained on. But that training set is almost certainly a more general audience than yours. Models perform better when training data represents the real life population that was targeted and represented in the training set. What are Custom Built Models? Which brings us to custom built models. Custom models are the kind that are trained on your data. One AI is an example of the custom built approach. It delivers specialized models that best understand your environment because it’s seen it before. So it can detect patterns within your data to learn and make accurate predictions. Custom models discover the unique aspects of your business and learn from those discoveries. To be sure, it is common for data science professionals to deploy the best performing model that they can. However, the business must ensure that these models comply with high ethical and business intelligence standards. That's because it is possible to make an immoral decision with a great prediction. So for users of the custom built model, transparency is only possible through development techniques that are not cloudy or secret. Even with custom built models, it is important to assess the ethical impact that a new model will have before it is too late. Custom built models may incorporate some benefits of canned models, as well. External data can be incorporated into the model development process. External data is valuable because it can capture what is going on outside of your organization. Local area unemployment is a good example of a potentially valuable external data set. Going through the effort of building a model that is custom to your organization will provide a much higher level of understanding than just slamming a generic model on top of your data. You will gain the additional business intelligence that comes from understanding how your data, rather than other companies' data, relates to your business outcomes. The insights gleaned during the model development process can be valuable even if the model is never deployed. Understanding how any model performs on your data teaches you a lot about your data. This, in turn, will inform which type of model and model-building technique will be advantageous to your business decisions. Don’t Be Misled by Generic Model Performance Indicators A canned model’s advertised performance can be deceptive. The shape of the data that the canned model learned from may be drastically different from the data in your specific business environment. For example, if 5% of the people in the model's sample work remotely, but your entire company is remote, then the impact and inferences drawn by the model about remote work are not likely to inform your decisions very well. When to be Skeptical of Model Performance Numbers Most providers of canned models are not eager to determine the specific performance of their model on your data because of the inherent weaknesses described above. So how do you sniff out performant models? How can you understand a good smelling model from a bad smelling one? The first reason to be skeptical lies in whether the model provider offers relative performance numbers. A relative performance value is a comparative one, and therefore failing to disclose relative performance should smell bad. Data scientists understand the importance of measuring performance. They know that it is crucial to understand performance prior to using a model’s outputs. So avoiding relative performance, the vendor is not being 100% transparent. The second reason to be skeptical concerns vendors who can't (or won't) explain which features are used in their model and the contribution that each feature makes to the prediction. It is very difficult to trust a model's outputs when the features and their effects lack explanation. So that would certainly smell bad. One Model published a whitepaper listing the questions you should ask every machine learning vendor. Focus on Relative Performance….or Else! There are risks that arise when using data without relative performance. The closest risk to the business is that faith in the model itself could diminish. This means that internal stakeholders would not realize “promised” or “implied” performance. Of course, failing to live up to these promises is a trust-killer for a predictive model. Employees themselves, and not just decision makers, can distrust models and object to decisions made with it. Even worse, employees could adjust their behavior in ways that circumvent the model in order to “prove it wrong”. But loss of trust by internal stakeholders is just the beginning. Legal, compliance, financial, and operational risk can increase when businesses fail to comply with laws, regulations, and policies. Therefore, it is appropriate for champions of machine learning to be very familiar with these risks and to ensure that they are mitigated when adopting artificial intelligence. Finally, it is important to identify who is accountable for poor decisions that are made with the assistance of a model. The act of naming an accountable individual can reduce the chances of negative outcomes, such as bias, illegality, or imprudence. How to Trust a Model A visually appealing model that delivers "interesting insights" is not necessarily trustworthy. After all, a model that has a hand in false or misleading insights is a total failure. At One Model, we feel that all content generated from predictive model outputs must link back to that model's performance metrics. An organization cannot consider itself engaged in ethical use of predictive data without this link. Canned and black box models are extremely difficult to understand, and even more difficult to predict how they respond to your specific set of data. There are cases where these types of models can be appropriate. But these cases are few and far between in the realm of people data in the human resources function. Instead, custom models offer a much higher level of transparency. Model developers and users understand their own data much better throughout the model building process. (This process is called Exploratory Data Analysis, and it is an extremely under-appreciated aspect of the field of machine learning.) At One Model, we spent a long time -- more than 5 years -- building One AI to make it easier for all types of human resources professionals build and deploy ethical custom models from their data, while ensuring model performance evaluation and model explainability. One AI includes robust, deep reporting functionality that provides clarity on which data was used to train models. It blends rich discovery with rapid creation and deployment. The result is the most transparent and ethical machine learning capability in any people analytics platform. Nothing about One AI is hidden or unknowable. And that's why you can trust it. Their Artificial Intelligence Still Needs Your Human Intelligence Models are created to inform us of patterns in systems. The HR community intends to use models on problem spaces involving people moving through and performing within organizations. So HR pros should be able to learn a lot from predictive models. But it is unwise to relinquish human intelligence to predictive models that are not understood. The ultimate value of models (and all people analytics) is to make better, faster, more data-informed talent decisions at all levels of the organization. Machine learning is a powerful tool, but it is not a solution to that problem.
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9 min read
Phil Schrader
People analytics teams tend to shy away from calculating revenue per employee. It’s a very macro number. On its own, a single revenue per employee calculation tells you very little. Even large differences in revenue per employee do not necessarily mean anything or, at least, are not particularly “actionable” when immediately discovered. If I’m being honest, I feel like we people analytics folks have collectively decided we’re just a little too nuanced in our thinking to risk spending time on a data point that can be explained away almost as easily as we can calculate it. But then again, it’s pretty easy to calculate. And, while it is a very macro number, you still ought to be attuned to how it’s changing. I check the weather, not the climate, before going out camping for the weekend, but that doesn’t mean it isn’t important to know if the climate is changing. So put aside your nuanced expertise with me and let’s do some basic calculations! How to calculate revenue per employee? The most simplistic view of this metric is to use this formula: The average revenue per employee = Revenue during period / Number of employees in that period Ok, so let’s say you get a number like $200,000. So what? It’s not necessarily good or bad. There are happy shareholders out there whose companies have a lower revenue per employee and unhappy shareholders whose revenue per employee is much higher. So next you want to look at how that is trending over time, like so. (Source: One Model Storyboard Visualization for revenue per employee using test database) Trending is probably the best way to look at this data. Ideally, the number trends up, right? But it might not always. Perhaps you are growing and investing heavily in building out teams that will deliver revenue in the future. Perhaps your employee mix is changing. You’re adding call center employees instead of research scientists. Perhaps perhaps perhaps. And so, even though we might be able to explain any changes away at this point, that’s exactly the value of making the simple calculation in the first place. The resulting thought process leads directly into asking really fantastic questions about your people strategy. Are we investing in people now for future revenue later? Are we running significantly leaner than we have in the past? Maybe too lean? Pulling this data together into a simple graph took me 5 minutes and 24 seconds. Find out how long these types of insights take YOUR team to generate with the the People Analytics Challenge. Variations to Consider on Revenue per employee by industry Thinking about direct vs indirect contributors Ok, let’s get back to our thoughts about call center employees vs research scientists. Both are valuable but you would expect an organization with more of the latter to have a higher revenue per employee. So in your own analysis, you can do a couple of things next. Segment by employee roles and do the same trend analysis. Again the face value number might be meaningless. “We make $10,000,000 per accountant.” The trend, however, IS interesting. “Say, we seem to need a lot more accountants per dollar of revenue than we used to. I wonder why that is?” You could switch to a harder calculation like revenue per dollar of compensation. (check out Mike West. Estimating Human capital ROI. p.102 of People Analytics for Dummies.) While you may not always be able to attribute revenue earned to specific departments, it will give you more insights if you monitor changes over time. If revenue per employee is dropping or increasing over time, it could indicate that adjustments need to be made on a departmental level. Switching to net vs gross If you do find some interesting insights as you trend out revenue per employee, you may want to check whether you are aligned with finance on the dollar amount that best aligns with the company's business strategy. If not, presenting this information to the C-level could be a mistake. You may find it relevant to look at net income or the amount left after all expenses. Here are the various levels of income to think about. Gross Revenue Sales discount = Net Revenue Cost of goods = Gross Profit Operating expenses = Operating Profit or EBIT Loan Interests = Profit before taxes (PBT) Taxes = Net Income If you’re losing money, Net Income could help you determine which areas of the business may be contributing to that more than others. Again, these revenue per metrics are more of an early warning system than a root cause analysis. Use them as a scanner to home in on more specific analyses. Once you get going, you can start to cut the data in more ways to see what stands out. Grouping by Seasonality Measuring this metric by quarter or by month may give you an idea of how the business cycle evolves. However, be sure to compare similar quarters YoY (year over year) or Months YoY to get a better idea of if things are improving or getting worse over time. Considering the Role of Employee Turnover These last few years have been tough. As HR leaders, we know that getting new employees can directly impact the productivity of the company. If you have had a lot of turnover, you may want to align dates to see how this has impacted your revenue per employee ratio. Rather than celebrating a spike in revenue per employee, you might raise the alarm that you are stretched much thinner than before. Group by Tenure If new employees cost more, then your most tenured employees must be worth the biggest bang for your buck, right? Breaking your data out by various cohorts will give you a better idea of how effectively you're building your talent. Do you want to see how One Model builds the insights you need in under 6 minutes? Request a Demo Today! How to find a Revenue per employee benchmark for your industry? Another upshot of this relatively simplistic metric. You can often estimate it for publicly traded companies. The easiest way to do this is to choose at least 3 to 6 public companies in your space. Because they are publicly traded, finding that information on the investor relations section of their site is much easier than trying to collect that same intel on private companies. In most cases, you can find that information over time as well. Once you have that, finding the estimated number of employees can be found on many database websites, or even on Linkedin. That said, revenue per employee by the industry for private companies can require a substantial amount of work. Look for investor information, and search for news articles on earnings. You Found Your Average Revenue per Employee and Benchmark - Now what? For starters, don’t panic (or celebrate). If you notice big differences between your organization and others. A competitor may use a much larger number of contingent workers who don’t appear in their employee counts. Or have a very different support model. Again this might feel like it devalues the comparison because so much just depends. That said, doing the calculation suddenly gets you thinking things like, “Hey has their people strategy changed significantly?” “Do they use more/less contingent staff?” These are great questions! How Can You Change Average Revenue per Employee? Now that you’re in a strategic mindset, start thinking about the levers you have at your disposal in order to adjust these numbers: Add Additional Hires focusing on their ability to contribute to your top and bottom lines. [Fun Game] Make a bet with yourself about how it will impact revenue per employee in the short and long term. See if your performance and engagement data trends align with the departmental trends you’re seeing. Are high-performing teams trending up in revenue per employee? If so, how might you quantify the value and find new ways to invest in these areas? Create some buzz around the metric. Tell people that, of course you know it’s a macro data point, but get others thinking about how it’s changing. Take a break from fighting over how to measure promotion rates and enjoy the landscape view. If paired with a high turnover rate, work to retain high-performing employees Look at tools that may increase the efficiency of your workforce Develop management to maximize performance in each of their departments.
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10 min read
Joe Grohovsky
During my daily discussions with One Model prospects and customers, two consistent themes emerge: A general lack of understanding of predictive modeling and a delay in considering its use until basic reporting and analytical challenges are resolved. These are understandable, and I can offer a suggestion to overcome both. My suggestion is based upon seeing successful One Model customers gain immediate insights from their data by leveraging the technology found in our One AI component. These insights include data relationships that can surface even before customers run their first predictive model. Deeper insights before predictive modeling? How? To begin, let’s rethink what you may consider to be a natural progression for your company and your People Analytics team. For years we’ve been told a traditional People Analytics Maturity Continuum has a building block approach that is something like this: The general concept of the traditional People Analytics maturity model is based upon the need to master a specific step before progressing forward. Supposedly, increased value can be derived when each step is mastered, and the accompanying complexity is mastered. While this may seem logical, it is largely inaccurate in the real world. The sad result is many organizations languish in the early stages and never truly advance. The result is diminished ROI and frustrated stakeholders. What should we be doing instead? The short answer is to drive greater value immediately when your people analytics project launches. Properly built data models will immediately allow for basic reporting and advanced analytics, as well as predictive modeling. I’ll share a brief explanation of two One Model deliverables to help you understand where I’m going with this. People Data Cloud™️ Core Workforce data is the first data source ingested by One Model into a customer's People Data Cloud. Although additional data sources will follow, our initial effort is focused on cleaning, validating, and modeling this Core Workforce data.This analytics-ready data is leveraged in their People Data Cloud instance. Once that has occurred storyboards are then created, reflecting a customer’s unique metrics for reporting and analytics. It is now that customers can and should begin leveraging One AI (Read more about People Data Cloud). Exploratory Data Analysis One AI provides pre-built predictive models for customers. The capability also exists for customers to build their own bespoke models, but most begin with a pre-built model like Attrition Risk. These pre-built models explore a customer's People Data Cloud to identify and select relevant data elements from which to understand relationships and build a forecast. The results of this selection and ranking process are presented in an Exploratory Data Analysis (EDA) report. What is exploratory data analysis, you ask? It is a report that provides immediate insights and understanding of data relationships even before a model is ever deployed. Consider the partial EDA report below reflecting an Attrition Risk model. We see that 85 different variables were considered. One AI EDA will suggest an initial list of variables relevant to this specific model, and we see it includes expected categories such as Performance, Role, and Age. This first collection of variables does not include Commute Time. But is Commute Time a factor in your ideal Attrition Risk model? If so, what should the acceptable time threshold be? Is that threshold valid across all roles and locations? One AI allows each customer to monitor and select relevant data variables to understand how they impact insights into your predictive model. Changing the People Analytics Maturity Model into a Continuum Now that we realize that the initial Core Workforce People Data Cloud can generate results not only for Reporting and Analytics but also for Predictive Modeling, we can consider a People Analytics Maturity Continuum like this: This model recognizes the fact that basic reporting and analytics can occur simultaneously after a proper data lake is presented. It also introduces the concept of Monitoring your data and Understanding how it relates to your business needs. These are the first steps in Predictive Modeling and can occur without a forecast being generated. The truth underlining my point is: Analytics professionals should first understand their data before building forecasts. Ignoring One AI Exploratory Data Analysis insights from this initial data set is a lost opportunity. This initial model can and should be enhanced with additional data sources as they become available, but there is significant value even without a predictive output. The same modeled data that drives basic reports can drive Machine Learning. The greater value of One AI is providing a statistical layer, not simply a Machine Learning output layer. The EDA report is a rich trove of statistical data correlations and insights that can be used to build data understanding, a monitoring culture, and the facilitation of qualitative questions. But the value doesn’t stop there. Integrated services that accompany One AI also provide value for all data consumers. These integrated services are reflected in storyboards and include: Forecasting Correlations Line of BestFit Significance Testing Anomaly Detection These integrated services are used to ask questions about your data that are more valid than what can be derived solely from traditional metrics and dimensions. For example, storyboards can reflect data relationships so even casual users can gain early insights. The scatterplot below is created with Core Workforce data and illustrates the relationship between Tenure and Salary. One AI integrated services not only renders this view but cautions that based upon the data used this result is unlikely to be statistically significant (refer to the comment under the chart title below). More detailed information is contained in the EDA report, but this summary provides the first step in Monitoring and Understanding this data relationship. Perhaps one of the questions that may arise from this monitoring involves understanding existing gender differences. This is easily answered with a few mouse clicks: This view begins to provide potential insight into gender differences involving Tenure and Salary, though the results are still not statistically significant. Analysts are thus guided toward discovering their collection of insights contained within their data. List reports can be used to reflect feature importance and directionality. In the above table report, both low and high Date of Birth values increase Attrition Risk. Does this mean younger and older workers are more likely to leave than middle-aged workers? Interesting relationships begin to appear, and One AI automatically reports on the strength of those relationships and correlations. Iterations will increase the strength of the forecast, especially when additional data sources can be added. Leveraging One AI's capability at project launch provides a higher initial ROI, an accelerated value curve, and better-informed data consumers. At One Model, you don’t need to be a data scientist to get started with predictive modeling. Contact One Model to learn more and see One AI in action. Customers - Would you like more info on EDA reports in One Model? Visit our product help site.
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7 min read
Nicholas Garbis
How do we measure the value of people analytics? Is your organization making better, more data-informed talent decisions today versus a year ago? This is the ultimate test of any people analytics (PA) program, initiative, team, COE, or department. If the answer is yes, the investments in PA continue and expand. If the answer is no, then PA budgets are questioned. So how can we demonstrate the value of people analytics? In our latest whitepaper, "Measuring the Value of People Analytics," we address this from the ground up, starting with the mission of people analytics and moving into the utilization of the content delivered by the PA team. With a more comprehensive view of the how PA creates value, you will be better positioned to build your business case for people analytics. Whether you are seeking initial, incremental, or transformational level investments, this value framework will help you to convince your organization to become fully invested in HR analytics. Tackling the ROI Conundrum The proposed ROI calculations that many vendors recommend for people analytics are not very good -- and some are downright laughable. This is one of the reasons I worked on this paper. Two common approaches: Estimated savings through efficiencies of system consolidation or process acceleration Estimated savings from the consolidation of systems or accelerating processes. Reduction in attrition or faster time-to-fill of job postings or other KPIs. The promise that PA technology will reduce turnover and putting a financial value on it ... then hiding when the 'Great Resignation' starts or saying 'it would have been worse!' Ugh! This is not honest or helpful. We can do better in declaring the value we propose to generate. This is one of the key points in the paper. This blog highlights some of the key elements of the whitepaper. You will definitely want to read the whole thing. Click here to get the full version. We Need to Address the Big “Why?” Why are we investing in people analytics? Is the deliverable we are committing to - the “return” on the investment - as simple as a bit of system and process savings and some hypothetical lift to a couple of KPIs? Mission of People Analytics: Drive better, faster, talent decisions at all levels of the organization. We are investing resources in people analytics to drive and accelerate this mission. The value of people analytics should be judged by the quality of talent decisions that are being made across the organization. We may not be able to get directly at measuring the quality of talent decisions (though we will address that in an upcoming paper), but we can use utilization as a proxy to get started. If our PA deliverables are being utilized, we can logically assume that the users are placing value on them. They are 'voting' for the content. If it was not valuable, they would ignore it. In the paper, we demonstrate how utilization can be used to calculate value with relative ease across your PA portfolio. Value Journey for People Analytics Looking at each 'analytics event' through a process sequence, a "value journey," we will see how critical PA content is in delivering value at scale. To impact talent decisions at all levels of the organization, we need to build a smooth and fast self-service cycle (left side) by focusing on: creating analytics mindset/culture, applying user-centered product design, and communicating effectively and applying sound change management. "We have data that can help here." The diagram below shows the target picture, where a user, encountering the talent elements of a business challenge thinks "We have data that can help here." This is the critical first step that ideally flows them into a set of high-quality PA products that can deliver the needed insights. Any business challenge can be divided into talent elements (staffing, skills, productivity, etc) and non-talent elements (market forces, supplier issues, etc). People analytics provides value through products and services that support understanding and solving for the talent elements of the challenge. To impact talent decisions at scale requires PA teams to deliver insight-generating self-service solutions. So now that we’ve covered that, how do we measure the value of people analytics at your company? Is there a formula we can use to make our PA investments more intentional? If so, how can we determine: where we should focus our efforts? What content or communications efforts are necessary to deliver the outcomes we expect? Another core assumption in people analytics is that your leaders’ time is a scarce and valuable resource. And we will use that assumption to anchor our value measurement approach. We assume that your organization’s leaders: Are selective about what they spend their time on. Choose to spend their time on things they consider valuable. See value in content if they engage with it regularly. Will rely on content that continues to inform better talent decisions over time. Download the paper to see the way we have calculated the value of a small PA portfolio based on the value-utilization framework. Further work is needed to articulate how to measure the change in talent decision quality more directly. We will be tackling that in future content -- so keep an eye out for it! Get the equation in our Measuring the Value of People Analytics Whitepaper Ready to see how upgrading your people's analytics solution will improve the value your team is bringing to the business?
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11 min read
Nicholas Garbis
The role of the Human Resources function is to ensure that the organization has the talent it needs to execute its strategies, making HR a strategic partner for the business. So if you’re an HR leader, your focus must always be on making the best talent decisions – best for the organization and best for the people in it. People analytics (PA) is the most important part of your HR strategy because the best decisions are always data-driven ones. Mission of HR: Deliver a sustainably high-performing workforce that is engaged in their work, having positive, inclusive experiences with the organization, its leaders, and their team. I think of HR strategy as having two pillars, each critical to the successful execution of an individual HR team’s mission. The first is the delivery and engagement/execution pillar, and it represents the HR organization’s systems, goals, metrics, processes, policies, procedures, and programs. Pillar 1: Delivery & Engagement Delivery Engagement/ Execution Talent Acquisition Employee Value Proposition (EVP) Employee Experience (Journey Design) Performance Management Compensation & Benefits Internal Communications Succession Planning Talent & Workforce Management Opinion Survey HR Operations & Technology etc. Learning & Development etc. Manager Effectiveness etc. But data is not information, and information is not knowledge. The best decisions involve all of these attributes. That’s why the second pillar of an HR strategy is decision support. People analytics is the engine that powers the decision support for talent. It consists of the systems that organize the HR data to generate insights, the products that enable the PA team to achieve scale, and the services the PA team will deliver directly to leaders. These elements will enable the organization to make the most optimal people decisions for the organization. Pillar 2: Decision Support via People Analytics Systems Products Services Data Warehouse Interactive Analytics Ad-Hoc Analytics Analytics / Visualization Storyboards & Dashboards Workforce Planning Organizational Network Analysis C-Suite/Board Reporting & Analysis Location Strategy HR Operations & Technology etc. Predictive Models etc. Market Analytics etc. Without people analytics, the human resource strategy won't be supported by sound decisions and can't be implemented. This will jeopardize the HR mission and risk the overall organizational strategy. On the floor, this can manifest itself as having the “wrong people in the wrong seats” or leaders making decisions that result in a sub-optimal or under-utilized workforce or introducing risk. Learn how to calculate the value of people analytics. Do you need a people analytics strategy? Yes, of course. Strategy involves making resource and prioritization decisions. All people analytics strategies must balance technology and consulting choices and recognize that there is no single strategy that's suitable for everyone. Some organizations need decision support tools that are quick and flexible. Others require robust and secure tools to support extremely complex decisions and are willing to sacrifice speed. And the incumbent capabilities and change readiness of each organization will vary. A sound people analytics strategy will support the ways in which your organization makes decisions. And yes, your people analytics strategy should be aligned to support the overall organization’s strategy and the HR mission. People Analytics Mission: Ensure that people decisions at all levels of the organization can be informed by quality data and insights, delivered through products and services that are ethical, easy to use and supported by effective communications and training. People analytics teams will vary in their strategies for technology, deliverables, operating model, internal collaborations, and communications. Your people analytics strategy should articulate the technologies, deliverables, operating models, and methods of communication that will enable the best talent decisions. These decisions will be made by central groups such as the HR leadership team, as well as HR and business leaders in every part of the organization. The value of people analytics is to be measured by the improvement in talent decisions. But how do you conceptualize that value, nevertheless measure it? The People Analytics Value Cycle The value of people analytics is the degree to which people data and insights are integrated into the organization’s talent decisions. People analytics deliverables that are underutilized such as unused models, reports, and dashboards all incur costs to maintain and they contribute to technical debt through decommissioning, reviewing, or redesigning. The people analytics team generates value for the organization every time a talent decision is made using data or actionable insights. Here are the steps that decision makers take to generate value. Seeing the opportunity to apply data to the decision Clarifying what questions will need to be answered Knowing where to access the data & analytics Generating insights from the data & analytics Making decision on action to take Implementing the action Following up to measure the impact of the action Delivering value from people analytics requires an understanding of the behaviors that you are trying to shape. People analytics technology can multiply the value created by the team. People analytics tools accelerate time to value People analytics technologies are often never seen by the end consumers of its decision support. Most users will never interact with the back-end technologies like data warehouses and predictive models. The users will work with innovative front-end solutions such as storyboards, dashboards, and reports that have been designed specifically for HR and business leaders. People analytics technologies need to accelerate the process of data being available and applied in talent decisions. Visual tools such as storyboards, dashboards, and planning tools that HR and business leaders will use in their talent decisions require the integration of many unique sources of data. The software platform and the visual design should give the PA team flexibility to create what is needed. The team needs to be responsive to the demand for new content and the ability to easily mine new insights. It may be tempting for HR IT teams and data engineers to build the data warehouse internally, but it is likely to take too long and cost too much. Plus, there’s the risk that a DIY data warehouse ends up being less flexible than a software-as-a-service platform. A SaaS-based solution like One Model delivers data integration, data warehousing, pre-built and custom storyboards, and predictive modeling tools, all in one package. SaaS solutions tend to cost less with a faster time to value, and include continuous innovation as well. Another key technology consideration is the visualization front-end which will be used by HR and business leaders. Sound visual design of the interface and its graphical components create wider accessibility and accelerate decisions by giving users who can generate insights in a moment’s notice. We recommend a people analytics technology roadmap that addresses these areas. Data sources. The upstream systems from which data must be integrated. Data processing. The way the data from these source systems will be extracted, transformed, and loaded, including derived data and metrics calculations. Content. Creation of effective visualizations, storyboards, dashboards, and reports. Predictive modeling. Clear prioritization of the predictive models to be explored and developed. Employ a product mindset More and more people analytics leaders are adopting a “product mindset” with respect to their deliverables. The product mindset appreciates that users have choices when they are seeking insights and that the PA deliverables need to be easy and insightful. A product mindset incorporates concepts such as portfolio management, road mapping, user research and feedback, benchmarks and metrics, deploying minimum viable products, and managing and communicating change. Adopting a product mindset will help ensure that the people analytics team is always delivering value to the organization. Choose your operating model There isn’t a perfect people analytics operating model for any particular type of organization. There is no right answer, but some approaches will be better and generate more value than others. The key is to design your team intentionally with a focus on value. The team structure, roles and responsibilities, and processes must align with the needs of the internal customers. The team should be composed of an appropriate mix of technical and consulting capabilities. Some teams may need more data engineers, others may need more visual storytellers. Make it happen Since every organization strives for better, data-driven support, people analytics is a critical facet of an effective human resource strategy. Talent decisions are the most important decisions any organization can make, and can help make HR a strategic partner to the overall business. People analytics is decision science for the HR function and is a key pillar of HR strategy. Making it happen means being able to communicate the value it will bring in order to get the investment and support you need. Start by calculating that value. Get the equation in our Measuring the Value of People Analytics Whitepaper Ready to see how upgrading your people's analytics solution will improve the value your team is bringing to the business?
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17 min read
Chris Butler
Workday vs SuccessFactors vs Oracle Ratings Based on Experience Integrating HR Tech for People Analytics This vendor-by-vendor comparison will be a living post and we will continue to update as we have time to collect thoughts on each vendor and as we complete integrations with new vendors. Not every source we work with will be listed here but we'll cover the major ones that we often work with. At One Model we get to see the data and structure from a load of HR systems, and beyond, basically anything that holds employee or person data is fair game as a core system to integrate for workforce analytics. After more than a decade of HR analytics integration architecture experience where the solution is directly integrating data from these systems into analytics and reporting solutions, we have a lot of experience to share. Below I'll share our experience with highlights from each system and how they align with creating a people analytics warehouse. Some are better than others from a data perspective and there's certainly some vendors that are yet to understand that access to data is already a core requirement of buyers looking at any new technology. Bookmark this blog, add your email to the subscription email list to the right, or follow me (Chris Butler) and One Model on LinkedIn to stay up to date. A Quick Note on HRIS Platform Ratings Ratings are provided as an anecdotal and unscientific evaluation of our experience in gaining access to, maintaining, and working with the data held in the associated systems. They are my opinions.] If you would like to make use of any of our integrations in a stand-alone capacity, we now offer a data warehouse only product where you utilize just our data pipeline and modelling engine to extract and transform data into a data warehouse hosted by One Model or your own data warehouse. We'll be releasing some more public details soon but you are a company that likes to roll your own analytics, visualizations, and just need some help with the data side of the house, we can certainly help. Contact Us Cloud HRIS Comparison Workday One Model rating - 2.5/5 Method - API for standard objects, built-in reporting for custom objects (via reporting-as-a-service, or "RaaS") The Good - Great documentation, Easy to enable API access and control of accessible fields, and Good data structures once you have access. The RaaS option does a good job but is limited. The Bad - Slow; Slow; Slow; No custom fields available in API, Geared towards providing a snapshot, number of parallel connections limited, constant tweaking required as new behaviors identified, Expert integration skills required; True incremental feeds require you to read and interpret a transaction log Workday Requires a Custom-Built People Analytics Integration Architecture Workday analytics embedded into the product is underwhelming and we're yet to see Prism Analytics make a dent in filling the needs that people analytics teams or HR analysts have beyond convenience analytics. So in the meantime, if you are serious about improving reporting and people analytics for Workday you're going to need to get the data out of there and into somewhere else. On the surface, Workday looks to have a great API, and the documentation available is excellent. However, the single biggest downfall is that the API is focused on providing a snapshot, which is fine for simple list reports but does not allow a people analytics team to deliver any worthwhile historical analysis. You don't get the bulk history output of other systems or the ability to cobble it together from complete effective-dated transactions across objects. To capture the complete history we had to build an intense process of programmatically retrieving data, evaluating, and running other API calls to build the full history that we need. If you want more detail take a look at my blog post on the subject The end of the snapshot workday edition. The complexity of the integration, therefore, is multiplied and the time taken suffers immensely due to the object-oriented architecture that requires you to load each object into memory in order to be able to retrieve it. A full destructive data extraction means you're looking at 8+ hours for a small-medium enterprise and expanding to a week if you're a giant. The problem is exacerbated by the number of parallel connections allowed to run at a fraction of the stated limit. A full historical API integration here is not for the faint of heart or skill, we have spent 12+ months enhancing and tweaking our integration with each release (weekly) to improve performance and solve data challenges. Our integration to give a sense of scale generates some 500+ tables that we bring together in our modelling engine in preparation for analytics. Beware of Oversimplifying the API Integration Out-of-the-box integration plugins are going to be focused on the snapshot version of data as well so if you don't have the integration resources available I wouldn't attempt an API integration. My advice is to stick with the built-in reporting tools to get off the ground. The RaaS tools do a good job of combining objects and running in a performant manner (better than the API). However, they will also be snapshot focused and as painful as it will be to build and run each timepoint you will at least be able to obtain a basic feed to build upon. You won't have the full change history for deeper analysis until you can create a larger integration, or can drop in One Model. Robert Goodman wrote a good blog a little while back looking at both the API and his decision to use RaaS at the time, take a read here. Workday API vs RaaS Regardless of the problems we see with the architecture, the API is decent and one of our favorite integrations to work with. It is, however, little wonder that with the data challenges we have seen and experienced, half of our customers are now Workday customers. One Model Integration Capabilities with Workday One Model consumes the Public Web Service API's for all standard objects and fields. One Model configures and manages the services for API extractions, customers need only to create and supply a permissioned account for the extraction. Custom objects and fields need to use a Raas (Report as a service) definition created by the customer in the Enterprise Interface Builder (EIB). The Report can then be transferred by SFTP or can be interacted with as an API itself. Figure 1: One Model's data extraction from Workday SuccessFactors One Model rating - 4/5 Method - API The Good - A dynamic API that includes all custom MDF data!! Runs relatively quickly; Comprehensive module coverage; The Bad - Several API endpoints that need to be combined to complete the data view; Can drop data without indication; At times confusing data structures 4 out of 5 is a pretty phenomenal rating in my book. I almost gave SuccessFactors a perfect 5 but there are still some missing pieces from the API libraries and we've experienced some dropped data at times that have required some adaptations in our integration. Overall, the collection of SF APIs is a thing of beauty for one specific reason: it is dynamic and can accommodate any of the Meta Data Framework (MDF) custom changes in its stride. This makes life incredibly easy when working across multiple different customers and means we can run a single integration against any customer and accurately retrieve all customizations without even thinking about them. Compared to Workday where the API is static in definition and only covers the standard objects this facet alone is just awesome. This dynamic nature though isn't without its complexities. It does mean you need to build an integration that can interrogate the API and iterate through each of its customizations. However, once it is complete it functions well and can adapt to changing configurations as a result. Prepare to Merge API Integrations for People Analytics Multiple API endpoints also require different integrations to be merged. This is a result of both upgrades in the APIs available in the case of the older SuccessFactors API and the OData API as well as providing an API to acquired parts of the platform (i.e. Learning from the Plateau acquisition). We're actually just happy there is now an API to retrieve learning data as this used to be a huge bug bear when I worked at SuccessFactors on the Workforce Analytics product. The only SF product I know of right now that doesn't have the ability to extract from an API is Recruiting Marketing (RMK) from the jobs2web acquisition, hopefully, this changes in the future. Full disclosure, I used to hate working with SuccessFactors data when we had to deal with flat files and RDFs, but with the API integration in place, we can be up and running with a new SuccessFactors customer in a few hours and be confident all customizations are present. Another option - Integration Center I haven't spoken here about the new Integration Center release from earlier last year as we haven't used it ourselves and only have anecdotal evidence from what we've read. It looks like you could get what you need using the Integration Center and deliver the output to your warehouse. You will obviously need to build each of the outputs for the integration which may take a lot of time but the data structure from what I can tell looks solid for staging into an analytics framework. There are likely a lot of tables to extract and maintain though, we currently run around 400+ tables for a SuccessFactors customer and model these into an analytics-ready model. If anyone has used the Integration Center in an analytics deployment please feel free to comment below or reach out and I would be happy to host your perspective here. One Model Integration Capabilities with SAP SuccessFactors One Model consumes the SF REST API's for all standard fields as well as all customized fields including any use of the MDF framework. One Model configures and manages the service for API extractions, customers need only to create and supply a permissioned account for the extraction. SF has built a great API that is able to provide all customizations as part of the native API feed. We do us more than one API though as the new OData API doesn't provide enough information and we have to use multiple endpoints in order to extract a complete data set. This is expertly handled by One Model software. Figure 2: One Model's data extraction from SuccessFactors Oracle HCM Cloud (Fusion) One Model rating - 2/5 Method - HCM Extracts functionality all other methods discounted from use The Good - HCM Extracts is reasonable once you have it set up. History and all fields available. Public documentation. The Bad - The user interface is incredibly slow and frustrating. Documentation has huge gaps from one stage to the next where experience is assumed. API is not functional from a people analytics perspective: missing fields, missing history, suitable only for point-to-point integrations. Reporting/BI Publisher if you can get it working is a maintenance burden for enhancements. HCM Extracts works well but the output is best delivered as an XML file. I think I lost a lot of hair and put on ten pounds (or was it ten kilos?!) working through a suitable extraction method for the HCM Cloud suite that was going to give us the right level of data granularity for proper historically accurate people analytics data. We tried every method of data extraction from the API to using BI Publisher reports and templates. I can see why people who are experienced in the Oracle domain stick with it for decades, the experience here is hard-won and akin to a level of magic. The barriers to entry for new players are just so high that even I as a software engineer, data expert, and with a career spent in HR data many times over, could not figure out how to get a piece of functionality working that in other systems would take a handful of clicks. Many Paths to HRIS System Integration In looking to build an extraction for people analytics you have a number of methods at your disposal. There's now an API and the built-in reporting could be a reasonable option for you if you have some experience with BI Publisher. There are also the HCM Extracts built for bulk extraction purposes. We quickly discounted the API as not yet being up to scratch for people analytics purposes since it lacks access to subject areas, and fields, and cannot provide the level of history and granularity that we need. I hope that the API can be improved in the future as it is generally our favorite method for extraction. We then spent days and probably weeks trying to get the built-in reporting and BI Publisher templates to work correctly and deliver us the data we're used to from our time using Oracles on-premise solutions (quite a good data structure). Alas, this was one of the most frustrating experiences of my life, it really says something when I had to go find a copy of MS Word 2006 in order to use a plugin that for some reason just wouldn't load in MS Word 2016, all to edit and build a template file to be uploaded, creating multiple manual touchpoints whenever a change is required. Why is life so difficult?? Even with a bunch of time lost to this endeavour our experience was that we could probably get all the data we needed using the reporting/BI publisher route but that it was going to be a maintenance nightmare if an extract had to change requiring an Oracle developer to make sure everything ran correctly. If you have experienced resources this may work for you still. We eventually settled on the HCM Extracts solution provided that while mind-numbingly frustrating to use the interface to build and extract will at least reliably provide access to the full data set and deliver it in an output that with some tooling can be ingested quite well. There are a number of options for how you can export the data and we would usually prefer a CSV style extraction but the hierarchical nature of the extraction process here means that XML becomes the preferred method unless you want to burn the best years of your life creating individual outputs for each object tediously by hand in a semi-responsive interface. We, therefore, figured it would be easier, and enhance maintainability if we built our own .xml parser for our data pipeline to ingest the data set. There are .xml to .csv parsers available (some for free) if you need to find one but my experience with them is they struggle with some files to deliver a clean output for ingestion. With an extract defined though there's a good number of options on how to deliver and schedule the output and reliability is good. We've only had a few issues since the upfront hard work was completed. Changing an extract as well is relatively straightforward if you want to add a field or object you can do so through the front-end interface in a single touchpoint. We do love Oracle data, and don't get me wrong - the construction and integrity are good and we have a repeatable solution for our customer base that we can deliver at will, but it was a harrowing trip of discovery that to me, explains why we see so few organizations from the Oracle ecosystem that are out there talking about their achievements. Don't make me go back, mommy! Want to Better Understand How One Model can Help You? Request a Demo Today. Other HRIS Comparisons Coming Soon ADP Workforce Now
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Featured
11 min read
Jamie Strnisha
One of the most common reporting challenges companies face is balancing headcount over time by adding and subtracting Hires and Terminations. How to Calculate Headcount? The process seems like it should be simple, especially when someone with a background in finance or accounting first looks at the issue. The misconception is that the company’s headcount will balance in much the same way money in a financial statement balances, where the analyst takes an initial amount of money the company has, and adds the money that came in for the month (e.g. customer sales, invoices) and subtracts the money that went out for the month (e.g. transportation, payroll cost, rent) and results in a final amount for the month, which then starts over the next month. If that’s how a financial statement is balanced, it seems that the same concept should be easily applied to balancing headcount reporting metrics. It might seem that a company should be able to use the following formula: Starting Headcount + Hires – Terminations = Ending Headcount And everything would balance and net out. Unfortunately, accounting does not seem to work out the same way in HR as it does in Finance. Rarely (if ever) does this simple formula work when counting people instead of money. There are a number of common reasons why this formula fails when applying it to the reconciliation of headcount reports over time: People that start at the beginning of the month are included in both starting headcount and hires for the time period, leading to some of the same people being counted twice. People that leave at the end of the month are included in both ending headcount and terminations for the time period, again leading to double counting. People that are on leave of absence may suddenly enter or exit headcount without hire or termination. The company may have restrictions on certain types of workers (e.g. Interns, Contractors) and exclude them from the headcount when they are in that category. If these workers move from an excluded category to one that is included in the headcount, or vice versa, they might suddenly appear or disappear from the headcount without hire or termination. The company may want to exclude certain Hire and Termination actions, such as acquisitions or divestitures, which again will cause an unbalanced headcount and a worker to suddenly appear or disappear from the headcount Fortunately, One Model can solve all of these issues in balancing headcount relatively easily by creating a new set of metrics specific to the company’s data that include populations that might not normally be counted, and exclude or include Hires and Terminations at the beginning and end of a time period. The New Metric: Reconciliation Headcount This Reconciliation Headcount Reporting Metric is effectively a more accurate mathematical equation that balances headcount to reflect these quirks in people data to match the financial statement approach to reconciliation. Each customer that works with One Model will have a slightly different version of a Reconciliation Headcount metric based on the methodology they use to count a Hire or Termination. An example formula for this metric may look like this: (Ending Headcount + Terminations on the Last Day of the Previous Time – Terminations on the Last Day of the Current Time Period) – (Starting Headcount + Hires – Terminations – Divestitures) When properly constructed, the new metric will correctly sum to 0, eliminating the problem HR sometimes has in justifying apparent irregularities in reconciling headcount. If the Metric does not equal 0, it means that there is at least one person or event in the data that does not have a requisite hire or termination to balance it out and that the company should investigate the record. One Model can also provide the company with a set of metrics that explain the difference between the events and populations that are included in the inputs for the new metric calculation and what the company would otherwise use for standard reporting on headcount, hires and terminations. One Model Application: Using the Reconciliation Headcount Metric Once the Reconciliation Headcount Metric is created, it can be used to monitor and understand data changes over time that might not be apparent in a less refined approach to reconciliation. The following is an example of the Headcount Reconciliation Metric for Company A across 4 months. If all factors that affect headcount are included in the reconciliation calculation, then each month the Headcount Reconciliation Metric would show as 0. In this example, it appears that the headcounts for November and February have no irregularities, but for December and January, instead of the expected 0, the results show 1 in December and 2 in January. Pinpointing the Discrepancy: Is it Even Possible? Without the Reconciliation Headcount Metric and One Model, it could be difficult to pinpoint the source of these discrepancies. In fact, it might not be possible at all, depending on how the data was reported. If an analyst was lucky enough to be using lists of individuals to perform the reconciliation and had the actual records for all relevant points in time (the beginning and end of each month), they might be able to figure out the specific people accounting for the differences in December and January by using vlookup formulas in Excel to locate each difference. Of course, this would make the entire reconciliation process very time consuming Reconciling headcount may not even be possible in all situations. In some cases, the analyst may be adding or subtracting data from past months’ reports that have already been aggregated. Using aggregated data would make the reconciliation process almost impossible, since the data in the source system may have changed since the reports were run, and the analyst would not be able to pinpoint the specific people whose situations are creating the discrepancies. One Model’s List Report Feature: Easy Identification of the Discrepancy in Headcount Reporting Metrics The One Model platform has a unique feature that eliminates all of these problems and makes the reconciliation process very easy. This feature is called List Reports. In One Model, a user can take a metric and then look within it to find the data points that are causing the discrepancies. In the example of Company A, where it appeared that there were discrepancies in the December and January headcount reports, the analyst creates a List Report that includes the Headcount Reconciliation Metric, Worker Number and Name of every individual accounted for in that period. Any individual whose status changed during the time period but was not properly accounted for in the reconciliation process would be flagged as a + or - in the Headcount Reconciliation column. The List Report can then be filtered to show only those individuals whose records are the cause of the apparent accounting error. In the example of Company A above, there was a net discrepancy of one for the month of December. By exporting the data and filtering out the 0s, only one record had a +1 in December: In only a couple simple steps, it was easy to determine that Joe Williams’s record is the source of the discrepancy in the headcount for December. Identifying the Reason for the Discrepancy and Rebalancing Headcount After identifying Joe Williams, the next question is why his record caused this discrepancy. Since it appears that his record caused an addition to headcount, it may make sense to first look at the data for hires and see if a new code was added to Company A’s HRIS that was not included originally. In the example for Company A, Joe entered Company A through an acquisition that was not coded as a hire. As a result, it now is apparent that the Headcount Reconciliation metric should be revised to include individuals who joined Company A through an acquisition. After Headcount Balances, What Next? Net Internal Change This Headcount Reconciliation Metric can now be used to better understand net internal change within Company A. In the example below, Company A’s Headcount Reconciliation Metric is broken out by Department. In disaggregated form, it’s easy to see that in December the company had 1 net move into Commercial and 1 net move out of HR. Even more helpful, it’s possible to see that while Headcount balanced at the overall level for November and February, there were actually movements across departments in those months. The fact that those movements netted to zero made them seem to vanish from the reconciliation metric, but One Model still makes it possible to identify this movement. One Model’s List Report Identifies the Individual Change Records Looking again at December, adding the Department field into the List Report reveals a department change for a different worker. In this situation, we see that Chris Jones moved from HR to Commercial in December. Using the Reconciliation Headcount Metric, makes it possible to look at internal movements and understand how the company’s headcount has changed internally over time. Difference between Typical Internal Movement Metrics and the Headcount Reconciliation Net Change While customers can traditionally use events like Transfers, Promotions, Demotions to pinpoint internal movement, these methods can often be deceiving. Very often customers do not have strict business processes about what is being counted in these movements and events in the HRIS are coded as Transfers when they’re technically a data correction. A Promotion may get coded as such when it really is a Transfer or Lateral move because the manager wants to send a positive message to an employee. While the net difference derived from the Headcount Reconciliation Metric doesn’t necessarily resolve all of those issues, it allows the analyst to see the specific internal net change across time. The examples above used months, but the time period could have been any (e.g. year, quarter, week). If you want to know more about One Model or Headcount Reconciliation, we’d be happy to talk to you. Personally, I love talking about people data and how to construct metrics to drive business decisions. Want to learn how your company can benefit from using One Model? Have questions on your team's specific challenges in balancing headcount and internal net movements? Learn more about the benefits of One Model and sign up for a demo.
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8 min read
Dennis Behrman
Brilliant talent decisions require superb data. But how do you know what decision power lies in your data? Test it for yourself! Our People Analytics Challenge can give you a sense for the state of your human resources data and your organization's ability to make great decisions. To find out just how prepared your organization is to make brilliant talent decisions with the data you have, download our handy worksheet here. People Data Cloud™, One Model’s leading people analytics platform, can transform how you make talent decisions by making the human resources and related enterprise data that you already have access. Unlocked and properly harnessed people analytics can be an incredibly valuable asset to your company. Understanding the People Analytics Challenge Where did we get the questions? Our very own Phil Schrader (One Model's Solution Architect), along with his peers across our business, spent decades in the human resources function applying data analysis to common HR decisions and solving talent challenges. Phil compiled the most common questions into this worksheet. He then used our proprietary People Data Cloud technology to produce the tables, charts, graphs, and reports to demonstrate how a true people analytics capability can help HR practitioners. For sh*ts and giggles, we also timed how long it took Phil to arrive at these answers. Here are some of those questions. Question #74: What is our new hire failure rate, by tenure and by department? Phil's time on this KPI: 4 minutes 45 seconds Finding out where your new hires fail, and at what point in their tenure, gives you a clear place to focus attention. Addressing new hire failures with this data reduces the average cost to hire and gives the organisation continuity and increased productivity. Here's the most important people metrics of all time. Question #1: What is our revenue by employee Phil's time on this KPI: 5 minutes 24 seconds Building a straightforward report like this doesn't take much time, but One Model also has a lot of pre-built views that interpret your data after ingestion and create the data visualization for you. Let's see how quickly Phil can get information like this. Are you ready to take the People’s Analytics Challenge? Download our whitepaper and put your team to the test. Follow the hashtag #peopleanalyticschallenge on LinkedIn and let us know if you can beat Phil! Question #38: What is ratio of managerial to non-managerial employees, and how does this vary by department? Phil's time on this KPI: 2 minutes 43 seconds It actually took Phil longer to take the screenshot I needed for this blog post than it did for him to pull the answer to this question. Now, let's pick a question that may require a little debate on how the metrics are built. This question will require Phil to meet with key players in the company to ensure the customizable calculations are exactly what we need to get the best insight. Question #25: What is the depth of our leadership pipeline within the company? Phil's time on this KPI: 44 minutes 56 seconds Benchmarks and succession planning in people analytics are not always measured the same from company to company. To make our timing as realistic as possible, we put together a quick meeting with our VP of Strategy and mapped out the best methodology for the three charts. If you'd like to better understand Succession Planning and see how the build was done, watch the video on this post. After we aligned on the variables that we needed to include and how to piece them together, connecting the dots did not take much time, about 15 minutes. Do you have a team of data scientists at your fingertips? All People Analytics Enterprise Solutions customers get a custom Blueprint and ongoing support to get the most out of their data. As the final part of this challenge, I want you to ask your team this question, “Can we trust the data?” If your team is working tirelessly in spreadsheets or SQL, they can probably tell you exactly how those metrics were calculated. However, if you were using some other HR analytics reporting tools, you’re trusting a black box. Moving to One Model not only means you can get to the analysis you need in record time, but you also have a fully transparent platform that is adjustable to meet the specific requirements of your organization. Are you Stumped? Get a Demo: Regardless of where your organization is on the maturity scale, this “challenge” can help you determine the selection of the right people analytics key performance indicators and analyses for your business context. Arguably any strong people analytics function should be able to answer these KPI questions, but success is found in focusing on the questions that matter and will drive business outcomes. Take the #PeoplesAnalyticsChallenge and let us know how fast you can pull that information in your organization.
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21 min read
Nicholas Garbis
Retailers are riding a supercharged shopping cart full of change that has accelerated due to the pandemic and exacerbated by a one-company megatrend: Amazon. Amazon has over 10,000 people … just in Kentucky! That’s more than many retailers' entire organization. Accordingly, workforce issues are some of the biggest strategic challenges that the rest of US retailers face today. People analytics, which aims to improve decisions involving employees, work, and business objectives, can deliver immediate impacts to retailers by bringing better data and insights to leaders at all levels of the organization who are making workforce decisions every day. Every retailer has sufficient velocity and scale to make them great candidates for the enormous value that can be captured through analytics, modeling, and insight generation. People Analytics (PA): The application of data and insights to improve business outcomes through better decision making regarding people, work, and business objectives. (Source: Explore the Power of People Analytics, Whiteman and Garbis, 2020) Retailers once blazed the early trails of people analytics. In the first wave, from 2005-2015, big retailers were at the forefront. Unfortunately, the retail industry has mostly been surpassed in their people analytics prowess by peers in industries such as technology, financial services, and pharmaceuticals over the past decade. Perhaps too many of those analytics leaders moved out of retail and into other industries? The pressure of today’s HR challenges in retail should inspire us to find the innovative spark once again. Retailers are in a great position to drive change in their organizations through the use of effective people analytics. Using people analytics technology, they can unlock significant value and show how workforce issues are understood and resolved. There’s really no choice but to apply the best decision-making practices possible toward solving the workforce challenges. The entire business model depends on it. How might data help us to better understand this issue? How can we use data and insights in deciding the actions we should take? Find out more by downloading our Retail Whitepaper If you’re a human resources leader in retail, you may be inspired to rise to these challenges by applying people analytics to get the most value from your workforce data. Amazon has been rolling over traditional retailers, capturing market share and exacerbating workforce challenges. Challenge #1: Workforce Retention Is Not Just A Stores Issue Anymore Retail has always had high turnover rates, but the truly eye-popping annual rates of 100%+ were limited to the store locations. These rates were treated as an accepted fact, a cost of doing business, and an operational challenge for management. In the past, only modest efforts were made in response to turnover, such as faster training to decrease time-to-productivity, recruitment automation to decrease open rates, and better benchmarking of prevailing local wages through labor market analysis. Now warehouses and distribution centers are exhibiting turnover rates that rival the stores. Turnover rates in DCs and warehouses historically ranged from 30-40% annually. Today, retailers see figures approaching 100% or more. Wow! In the meantime, store turnover has remained persistently elevated. In response to this challenge, retailers have adopted a “hole plugging” approach that involves ramping up recruiting resources to backfill departing employees. The analogy to a draining bathtub fits; the drain keeps opening wider, and stores keep trying to open the faucet further in response. Retail hourly workers are leaving for wage increases in many cases, but they are also leaving for concerns such as scheduling practices, paid time off policies, Covid and other safety protocols, and career growth opportunities. And the competition across big box retailers is being compounded by Amazon’s exploding demand for workers to fill its massive warehouse expansion. People Analytics to Consider for Workforce Retention: New Hire Failure Rate. This metric involves calculating the portion of hires that do not last for 30 or 60 days at a given location, district, region, or across the total chain. Analysts would then explore “hot spots” and address the causes to improve recruiting sources, selection and evaluation criteria, onboarding processes, etc. Drivers of Turnover. Advanced analytical methods can help to determine the key drivers of turnover for stores and warehouse locations as well as up through the organizational hierarchy. Cost of Turnover. This metric helps HR teams calculate the cost to replace an average store or warehouse worker. Analysts should provide this information to leaders to ensure that these costs can be used in informing turnover reduction strategies. Cluster Analysis. This type of analysis helps to determine if there are groups of similar locations that have significantly different turnover rates. HR retail teams can then conduct qualitative interviews to discover best practices that can be tested in other locations. Challenge #2: The Digital Talent Squeeze The competition for digital talent seems to be growing more fierce every day. Talented developers, software engineers, product managers, and data scientists are moving between a wide range of industries and in/out of start-ups. Most significantly, Amazon, Google, Apple, Facebook, and Microsoft are swooping up talent by the truckload, bidding up salaries and emptying the shelves of more cost-conscious retailers. The competition is so unrelenting that newly-hired digital talent is even being coaxed away between their offer acceptance and their start date. Wage compression is a significant concern as starting pay for highly-prized new hires approaches that of their more experienced peers and even their leaders. The impact to retailers extends beyond the costs and frustrations of hiring and losing of new hires. Constantly open positions impede progress on digital innovation that retailers desperately need to remain competitive, such as customer-facing solutions that meet changing shopping patterns and automation in the supply chain and stores. As retailers struggle to fill these digital roles, they are becoming more open to remote and location-flexible talent. This is providing them with a wider talent pool to recruit from, but retailers may find it difficult to manage these exceptions as they return to physical offices when the pandemic subsides. Retailers who push too aggressively on a return to the office could lose the talent that they worked so hard to secure. Or they may half-knowingly end up with a two-tier policy where remote work is only available to those with rarified skills. People Analytics to Consider for the Digital Talent Squeeze: Internal Mobility Rate. This metric involves calculating the rate at which the digital talent moves into different roles. Stagnant pockets of ‘hoarded’ talent should raise concerns since that talent will eventually find opportunities outside (rather than inside) the organization. Recruiting Funnel Analytics. HR retail teams should identify the phase of the recruiting process where the most-qualified talent voluntarily drops out of the candidate pool. Within this broader withdrawal rate analysis you can look at the rate of offers being declined. There’s also plenty of value in diagnosing where the speed of the recruiting process can be improved. Retention Surveys. Develop surveys that create a deeper understanding of the factors that keep critical digital talent in their roles and get specific data to help learn if your employee value proposition (EVP) is clear and compelling for talent in key roles. Challenge #3: Seasonal Staffing Models May Be Broken In retail, the holiday season accounts for about 20% of annual sales, but occur during a 10% slice of the calendar. For roughly five weeks, every aisle and sub-aisle gets clogged with stacks of TVs, toys, and gadgets. Every register light is blinking and joyful music plays in stores. This is shopping nirvana, and retailers are at the center of everyone’s life. But staffing up to deliver on this experience is becoming more and more difficult. Some retailers have quantified lost sales due to their inability to staff at necessary levels. The tight labor market is not just due to competition in the mall or across the parking lot. Changing shopping patterns have forced retail warehouses to hire incredible numbers of temporary workers for the holidays. Limited supply and high demand forces temporary worker wages to increase, and now its not uncommon for a temporary seasonal worker to be enticed to another retailer during the short holiday season. So the seasonal staffing model that has worked for so many years may have finally broken. It may be impossible (or cost prohibitive) to find and ramp up the number of workers that are needed in such a short period of time. Rather than increasing recruiting efforts or recruiting earlier, some retailers are experimenting with more durable, lasting relationships with temporary workers. An “occasional” or “intermittent” employee type could keep workers active in the HR system if they work a certain minimum number of shifts within a certain period of time. There are many advantages to “occasional employment” that address the challenge of holiday staffing. Retailers can benefit from decreased recruiting costs, reduced paperwork, faster onboarding and time-to-productivity, higher retention rates, and more experienced customer service. Naturally, there are some costs associated with keeping more active employees on the books. If an occasional employee works just one shift per month, then that employee will probably be less productive than a full-timer on a per-hour basis. However, when the holiday season arrives, that occasional employee will be better prepared and more reliable, reducing the overall staffing costs of the store. An effective “occasional employee” strategy may require a change to a more flexible shift bidding and selection system. It’s also worth considering whether incentives can be created to encourage more hours. In ride sharing apps like Uber, surge pricing is built into the system. Perhaps there are surge wage accelerators for certain shifts? People Analytics to Consider for Seasonal Staffing: Seasonal Staffing Rate. Retailers can calculate the increase in staffing, including both headcount and total hours worked) that each store or warehouse has experienced.Some locations will have more hours from seasonal workers than others. Focus on where the biggest opportunities for impacts are. Seasonal Staffing Rehire Rate This metric helps to determine how successful a retailer has been at re-recruiting seasonal workers. Ideally, rehires should be associated with lower recruiting and onboarding costs and higher retention rates. This measure informs efforts to sustain relationships with seasonal workers outside of peak periods. Seasonal Staffing Cost Analysis. Retailers can develop a comparative cost model for sustaining “occasional” workers with the current seasonal staffing model. This foundational measure can lead to a cost-effective and timely staffing model alternative. Download the Whitepaper to Share with Colleagues Challenge 4: Unionization A labor relations leader at a large global firm once said, “No site ever got a union that didn’t deserve one.” His point was that unionization is a direct result of management’s failure to provide to workers the things they value most. In retail, workers appear to value: Wages that can sustain a worker who is working full time Transparent and timely scheduling practices, including schedules being published with sufficient and consistent advance notice. Pay for shifts that are cut short due to low customer or warehouse volume Acceptable policies and practices for bathroom breaks Limits to scheduling beyond stated availability or short-notice extra hours; Participation in paid time-off policies; Career advancement opportunities It’s notable that most of these items are management practices and policies that have no direct additional costs. Recently, we’ve witnessed some high-profile unionizations, including one Amazon warehouse (out of 110 total) and eight Starbucks stores (out of 15,000 total). These unionizations have sent shock waves through retailer headquarters. Many more union drives are currently in progress across the industry. In response, retail leaders are looking back through their aging “union avoidance” playbooks. Many of these are leaked to the public and are somewhat unflattering for their brands. There is a positive side to the story. The desire to unionize indicates that workers want to keep their jobs and do not want to quit outright. Unionization, therefore, is an opportunity for the retailer and their workforce to align on their shared and individual values and desires. Unionization drives are also signals that there may be an issue with the quality of the front-line management, and possibly the broader company culture and policies. The retail industry is built on the idea of scale, where each store or warehouse is running the “same play” with minimal variation. Retailers are not well-equipped for managing an ever-widening range of policies across unionized and non-unionized locations. Unionization presents significant challenges that include logistical and communications activities, negotiations at each unionized site, management of a portfolio of agreements, and the possibility of creating new benefits plans, training programs, and so on. People Analytics to Consider Regarding Unionization: Site Stability Index. Retailers should create a “balanced scorecard” that covers all locations. The scorecard should include external information such as competitor openings, nearby union participation, local unemployment rates, and relevant legislative changes in that jurisdiction. Survey Analytics. Retailers should employ a continuous pulse survey strategy with appropriate sampling and rotating questions. This information can be correlated with turnover, mobility, leadership stability, manager performance, ombuds claims, and a sales or productivity versus plan report. Union / Non-Union Analytics. Retailers can learn from analytics that compare similar stores along union vs. non-union dimensions. Navigating complicated waters like this requires good information. Challenge #5: Making Data-Informed Workforce Decisions Beyond being a bit circular in the context of this paper, this last challenge should be considered very real and concerning. Every retailer with over 10 stores has the volume and velocity of people data in their systems to support data-informed workforce decisions of HR and business leaders. Each store and warehouse leader should have basic workforce data and analytics that are a couple of clicks away. Additionally, those leaders should be expected to use data and insights when making workforce decisions. It is not a minor undertaking to change the decision-making fabric of a large organization. There are multiple levels of the organization involved in this type of change, and naturally the right tools and support are essential. Starting at the top, the C-suite must set the expectation by integrating data into workforce decisions in ways that are visible to their teams. CHROs need to promote and hire HR retail leaders with analytical aptitude and curiosity. They need to drive change into their own function and groom HR business partners who truly shape the business. This is impossible without integrating people data and providing vehicles for sharing it. Organizations that have done this right have built a People Analytics Center of Excellence or similar sub-function within the Human Resources department. But the people analytics COE is not simply a reporting or HR tech team. The people analytics team is dedicated to driving better, more data-informed talent decisions at all levels of the organization through content, products, insights, and advanced analytics. Data is the foundation of any people analytics COE. It is crucial that data from multiple HR and non-HR data sources is integrated to create a high-quality data asset. Maturity in people analytics should be considered in organic, non-linear terms. You should not plan to perfect the data first, then proceed to reporting, then to analytics, as you may see in a Gartner maturity model. Instead, first gather as much data as you need to create the content and analytics that will generate the most decision-making value for the organization. Then repeat the process for additional decision domains. People Analytics Questions to Consider for Workforce Decisions: Which Data, Where, and How? Evaluate your company’s top priorities to determine what data is necessary to improve talent decisions. Locate both the HR and non-HR data required to inform those decisions. Explore how these multiple sources can be integrated. Determine which data sources are easily integrated and which integrations require heroic efforts. The Capabilities Ask how capable your HR function is in leveraging data for talent decisions. Ask if your business leaders are prepared to do the same. The Tools. Determine if a visualization and reporting platform is available to extend people analytics content across the entire organization. Is it flexible enough to accommodate our needs as they evolve? The retail industry is facing a dramatic inflexion point in its ability to make brilliant talent decisions that propel profit growth, reduce risk, and deliver an incredible employee experience in which people thrive. The key challenges of HR in the retail sector cannot be understated. The next era of people analytics for the retail industry is now – and you are here to lead it. We’re looking forward to exploring this with you. Would you like to learn more about people analytics obstacles in retail and how we can solve them? Sign up for a Demo:
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10 min read
Phil Schrader
Post 1: Sniffing for Bull***t. As a people analytics professional, you are now expected to make decisions about whether to use various predictive models. This is a surprisingly difficult question with important consequences for your employees and job applicants. In fact, I started drafting up a lovely little three section blog post around this topic before realizing that there was zero chance that I was going to be able to pack everything into a single post. There are simply no hard and fast rules you can follow to know if a model is good enough to use “in the wild.” There are too many considerations. To take an initial example, what are the consequences of being wrong? Are you predicting whether someone will click on an ad, or whether someone has cancer? In fact, even talking about model accuracy is multifaceted. Are you worried about detecting everyone who does have cancer-- even at the risk of false positives? Or are you more concerned about avoiding false positives? Side note: If you are a people analytics professional, you ought to become comfortable with the idea of precision and recall. Many people have produced explanations of these terms so we won’t go into it here. Here is one from “Towards Data Science”. So all that said, instead of a single, long post attempting to cover a respectable amount of this topic, we are going to put out a series of posts under that heading: Evaluating a predictive model: Good Smells and Bad Smells. And, since I’ve never met an analogy that I wasn’t willing to beat to death, we’ll use that smelly comparison to help you keep track of the level at which we are evaluating a model. For example, in this post we’re going to start way out at bull***t range. Sniffing for Bull***t As this comparison implies, you ought to be able to smell these sorts of problems from pretty far out. In fact, for these initial checks, you don’t even have to get close enough to sniff around at the details of the model. You’re simply going to ask the producers of the model (vendor or in-house team) a few questions about how they work to see if they are offering you potential bull***t. At One Model, we're always interested in sharing our thoughts on predictive modeling. One of these great chats are available on the other side of this form. Back to our scheduled programming. Remember that predictions are not real. Because predictive models generate data points, it is tempting to treat them like facts. But they are not facts. They are educated guesses. If you are not committed to testing them and reviewing the methodology behind them, then you are contenting yourself with bull***t. Technically speaking, by bull***t, I mean a scenario in which you are not actually concerned with whether the predictions you are putting out are right or wrong. For those of you looking for a more detailed theory of bull***t, I direct you to Harry G. Frankfurt. At One Model we strive to avoid giving our customers bull***t (yay us!) by producing models with transparency and tractability in mind. By transparency we mean that we are committed to showing you exactly how a model was produced, what type of algorithm it is, how it performs, how features were selected, and other decisions that were made to prepare and clean the data. By tractability we mean that the data is traceable and easy to wrangle and analyze. When you put these concepts together you end up with predictive models that you can trust with your career and the careers of your employees. If, for example, you produce an attrition model, transparency and tractability will mean that you are able to educate your data consumers on how accurate the model is. It will mean that you have a process set up to review the results of predictions over time and see if they are correct. It will mean that if you are challenged about why a certain employee was categorized as a high attrition risk, you will be able to explain what features were important in that prediction. And so on. To take a counter example, there’s an awful lot of machine learning going on in the talent acquisition space. Lots of products out there are promising to save your recruiters time by using machine learning to estimate whether candidates are a relatively good or a relatively bad match for a job. This way, you can make life easier for your recruiters by taking a big pile of candidates and automagically identifying the ones that are the best fit. I suspect that many of these offerings are bull***t. And here are a few questions you can ask the vendors to see if you catch a whiff (or perhaps an overwhelming aroma) of bull***t. The same sorts of questions would apply for other scenarios, including models produced by an in-house team. Hey, person offering me this model, do you test to see if these predictions are accurate? Initially I thought about making this question “How do you” rather than “Do you”. I think “Do you” is more to the point. Any hesitation or awkwardness here is a really bad smell. In the talent acquisition example above, the vendor should at least be able to say, “Of course, we did an initial train-test split on the data and we monitor the results over time to see if people we say are good matches ultimately get hired.” Now later on, we might devote a post in this series to self-fulfilling prophecies. Meaning in this case that you should be on alert for the fact that by promoting a candidate to the top of the resume stack, you are almost certainly going to increase the odds that they are hired and, thus, you are your model is shaping, rather than predicting the future. But we’re still out at bull***t range so let’s leave that aside. And so, having established that the producer of the model does in fact test their model for accuracy, the next logical question to ask is: So how good is this model? Remember that we are still sniffing for bull***t. The purpose of this question is not so much to hear whether a given model has .75 or .83 precision or recall, but just to test if the producers of the model are willing to talk about model performance with you. Perhaps they assured you at a high level that the model is really great and they test it all the time-- but if they don’t have any method of explaining model performance ready for you… well… then their model might be bull***t. What features are important in the model? / What type of algorithm is behind these predictions? These follow up questions are fun in the case of vendors. Oftentimes vendors want to talk up their machine learning capabilities with a sort of “secret sauce” argument. They don’t want to tell you how it works or the details behind it because it’s proprietary. And it’s proprietary because it’s AMAZING. But I would argue that this need not be the case and that their hesitation is another sign of bull***t. For example, I have a general understanding of how the original Page Rank algorithm behind Google Search works. Crawl the web and work out the number of pages that link to a given page as a sign of relevance. If those backlinks come from sites which themselves have large numbers of links, then they are worth more. In fact, Sergey Brin and Larry Page published a paper about it. This level of general explanation did not prevent Google from dominating the world of search. In other words, a lack of willingness to be transparent is a strong sign of bull***t. How do you re-examine your models? Having poked a bit at transparency, these last questions get into issues of tractability. You want to hear about the capabilities that the producers of the model have to re-examine the work they have done. Did they build a model a few years ago and now they just keep using it? Or do they make a habit of going back and testing other potential models. Do they save off all their work so that they could easily return to the exact dataset that was used to train a specific version of the model. Are they set up to iterate or are they simply offering a one-size fits all algorithm to you? Good smells here will be discussions about model deployment, maintenance and archiving. Streets and sewers type stuff as one of my analytics mentors likes to say. Bad smells will be high level vague assurances or -- my favorite -- simple appeals to how amazingly bright the team working on it is.If they do vaguely assure you that they are tuning things up “all the time” then you can hit them with this follow up question: Could you go back to a specific prediction you made a year ago and reproduce the exact data set and version of the algorithm behind it? This is a challenging question and even a team fully committed to transparency and tractability will probably hedge their answers a bit. That’s ok. The test here not just about whether they can do it, but whether they are even thinking about this sort of thing. Ideally it opens up a discussion about you they will support you, as the analytics professional responsible for deploying their model, when you get challenged about a particular prediction. It’s the type of question you need to ask now because it will likely be asked of you in the future. As we move forward in this blog series, we’ll get into more nuanced situations. For example, reviewing the features used in the predictions to see if they are diverse and make logical sense. Or checking to see if the type of estimator (algorithm) chosen makes sense for the type of data you provided. But if the model that you are evaluating fails the bull***t smell test outlined here, then it means that you’re not going to have the transparency and tractability necessary to pick up on those more nuanced smells. So do yourself a favor and do a test whiff from a ways away before you stick your nose any closer.
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9 min read
Chris Butler
A major shift has occurred in Human Resources over the past five years. The world went from a handful of companies experimenting with people analytics - early adopters - to thousands of companies investing in dedicated roles and teams to take on a new way of thinking about Human Resources. What can you learn from the early adopters? Every CHRO who has moved forward with people analytics has some secrets to success. These secrets will help you provide a better, faster way to more effectively deploy HR resources and build a successful organization. Better human resources strategy means better business You need to accept and measure change There will be change. That is the only constant. Change could come from any number of places like new technology, new customers, new leadership, new products, new competition, new market and strategies. You will get maximum benefit from your HR strategy if you accept the reality that change is the only constant - the only certainty is a world of uncertainty. If you want to survive in a world of uncertainty you need to have a process to constantly take in new information to understand changing reality and use this new information to adapt. You need a way to measure to see if your organization is changing in the way that you and your leadership team expect. Change is what people analytics is for. Hire specific talent for a meaningful business advantage The problem is that most HR strategies are far too general to develop any sustainable advantages: “We will hire great people.” Great idea - you and everyone else! You cannot do everything well all the time - as the cost to attract and retain top talent just gets more expensive so you have to choose. You have to choose specifically where you want your business advantage to be and then you have to figure out how to create this advantage through People. In this way we and others may realize that the people's perspective of the business is not necessarily based in “soft stuff”, “political correctness” and administrative minutia, but profound business insights that arise in conjunction with observation and reason. Basically there are two types of insight: that which is not based on any special observable reason and that which is backed by observable reason. In the case of the second type of view, an individual is motivated to examine an insight and investigate its relevance to his or her situation, needs and requirements. Actions are applied after seeing the reason why this insight is advantageous. Change with people, new action, is motivated by new insight; that insight is powered by people analytics. Don’t get led astray by “traditional” HR metrics it is not always clear how to relate HR actions to business impact, and so we settle to monitor activities as a measure of progress which is a traditional metric for success. Measuring progress as activities that have an unknown relationship to current business objectives leads HR into waste. HR is broken into multiple functional centers of excellence (Staffing, Benefits, Compensation, Labor Relations, Talent Management, Organization Design), each with different goals and activities, we end up with hundreds of metrics that do not align with each other and do not drive towards a unified goal. This results in efforts that either have no impact or work against each other, not to mention waste in the process of analytics itself. Because we have not previously devised a single HR metric that has a direct business impact that can be applied universally across organizations and sub organizations, we substitute simplistic measures that while a good intention, may not be a universally good idea, may conflict with other objectives and may not correlate in any way with measurable business impact. This results in the wrong efforts/objectives. Investing heavily in quantitative metrics doesn’t automatically give us solutions. Metrics can usually tell us what’s going wrong, usually not why. The more you invest in quantitative metrics, with a process for qualitative input, the more you end up drowning in a sea of non-actionable data. Create a culture of success Those leaders who want to create a healthy organization or “culture of success” are motivated (or should be motivated) to attain a genuine camaraderie with all people in an organization. When a group of people have a common vision, maintain a sense that they are all in it together, and have compassion for each other then there is nothing that cannot be accomplished. At this juncture, in addition to many great spiritual teachings of varied doctrines, we also have a foundation of great insights in science and engineering and access to those examples and methods. At the heart of it: we analyze people within an organization for benefit to that organization - and that is also the people. It must be both. Useful analysis helps us all understand current reality and take the right actions now to achieve optimal outcomes: an outcome of joint benefit to managers, employees, shareholders and possibly society. A continual reduction in tenacious organizational problems and continual reinforcement of a culture of success is the ultimate result of useful analysis. Disciplined action (as opposed to frantic thrashing) is the benefit of useful analysis. Our concept of a healthy organization is not something physical. Therefore the spread of a healthy culture depends on increasing the depth of understanding of the benefit of new actions to provide strong motivation to pursue those new actions. When we are able to reduce the defects in how we think about people in an organization a healthy culture will naturally increase. Thus, effecting positive transformations in organizations through observation and feedback situation by situation, subdivision by subdivision, manager by manager, and employee by employee is the method we will employ to effect the change we desire. Unlike manufactured goods it should be fairly obvious that culture is not a tangible entity, that it cannot be sold or bought in the marketplace or physically constructed. Watch out for HR constraints (budget, credibility, time) Most of the programs HR watches over have very large budgets. Labor costs are frequently 70% or more of revenue. Benefits may represent 30% or more of labor costs. On an absolute basis these costs increase over time as the employee base grows. Things get sideways when business plan projections get off track and the cost of labor grows faster than revenue or when revenue retracts. It is critical for CHROs to be able to identify—quickly, early, and accurately— whether a project or activity is worth pursuing, rejecting, continuing or dropping so it may protect its commitments and preserve resources for those programs that drive the most value. Besides the obvious constraint of budget, the other constraint is credibility. In order to influence, HR professionals need to hold on to and build on what little credibility they start with. As CHRO you will have to justify HR’s right to have a seat at the business table by demonstrating the business impact of your programs to a CEO, management team or business line head to whom you support. At some stage, you will all be called on to demonstrate progress. Finally, we are all constrained by time. Every minute spent on an activity that is doomed to fail is wasted. HR has historically relied on two categorical measures of progress: how much stuff they are doing and how much people like what they are doing. Yet unfortunately, both of these metrics are unreliable proxies of business impact and both of these lead us down the wrong path—building something that ultimately does not matter, has no impact on the business or worse, the wrong impact. People analytics can be hard First, there is a misconception around how successful earth shattering people analytics get built. The media loves stories of “wunderkind” nerds invading HR who are so smart they helped the moribund HR function (usually at some cool tech company) figure this problem out. The reality, however, rarely plays out quite as simply. Even the unveiling of the hiring algorithms at Google, in Laszlo Bock's words, was years in the making, built on the contributions of many and several incremental innovations (and failures). Second, the classic technology-centric Reporting or “Business Intelligence” approach front-loads some downstream business partner involvement during a “requirements-gathering phase” but leaves most of the HR business partner and business customer validation until after the reporting solution is released. There is a large “middle” when the Analytics function disengages from the ultimate intended users of these reports for months, maybe even a year, while they build and test their solution. Sometimes the solution is rolled out in HR first, just to be sure it is safe for humans before inflicting it on the rest of the organization. Imagine a few wild eyed HR people hiding in the bushes outside the office preparing to jump up on an unexpecting executive on his way into work one morning. During this time, it’s quite possible for the Analytics function to either build too much or be led astray from building anything remotely useful to the organization. Third, People are complex and messy. People are not structural engineering challenges that are within the abilities of an engineer to control precisely. People and organizations are not like machines or computers. There is always a certain degree of uncertainty about the effect of our actions on people and organizations. We try things based on an entirely plausible premise and they fail. Usually we had not factored in or considered the thing or things which made it fail. There are too many variables, too many possibilities and too much change occurring within and all around us. Is this not in some sense the beauty of life? Would you rather take this away? In human systems, failure is not the problem, the problem is failure to learn from the failure. If we want to improve HR we should shift our attention to how we can learn more quickly. These Secrets are the CHRO’s Real Guide to People Analytics People analytics gives CHROs a better, faster way to more effectively deploy HR resources and build successful organizations. People analytics enable better listening, learning, strategic focus, measurable business impact, and rigorous process.
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7 min read
Chris Butler
When I first started work with InfoHRM in the people analytics domain back in 2006, we were the only vendor in the space and had been for over a decade. The product was called Workforce Analytics and Planning and after its acquisition by SuccessFactors (2010) and SAP (2012) it's still called that today. So what's the difference? Why do we have Workforce Analytics, HR Analytics, and People Analytics and can they be used interchangeably? I have to give credit to Hip Rodriguez for the subject of the blog. He posted about People Analytics vs HR Analytics a couple weeks ago and I've followed the conversation around it. Hip's Linkedin post here. So what does the data say? Workforce vs HR vs People? Being an analytical person at heart, I turned to the data and analyzed job titles containing "HR, Workforce, People, Human" and "Analytics or Analyst". As you can see in the table below (truncated for space), the data isn't supportive of people analytics being the most popular. In fact, you have to get down to row 25 before you see a people analytics title. HR Analytics and Workforce Analytics related titles are the clear leaders here by volume. Keep in mind though that titles particularly for less senior roles can take time to adapt, especially for more rigid position structures in larger organizations. Likely, many of these junior roles have a more basic reporting focus than an analytics focus. So why then does it feel like People Analytics has become the dominant term for what we do? The Evolution of HR Analytics (and my opinion) I believe that it's not so much a difference between HR Analytics and People Analytics, but rather, an evolution in the term. Let’s Start with the Evolution of Workforce Analytics Early when we were delivering Workforce Analytics it was to only a handful of forward-thinking organizations that also had the budget to be able to take workforce reporting seriously. I specifically say reporting because mostly that's what it was: getting data in the hands of executives and directors weren't happening at scale so even basic data would blow people's minds. It's crazy how often the same basic data still blows people's minds 20 years later. There were not many teams running project focused analysis like there are today. For example: looking at drivers of turnover to trial different retention initiatives or how onboarding programs affect net promoter scores of recent hires. Workforce Analytics was for the most part aggregate reporting. The analysis of this was primarily driven by hardcore segmentation of this data looking for nuggets of gold by a handful of curious people. It was done at scale with large numbers and rarely focused on small populations. A Look at the Difference Between HR Analytics HR Analytics is by far and away the most common term and has lived alongside Workforce Analytics for a very long time now. It is a natural extension of the naming of the human resources department, you're in HR and looking at HR data from our Human Resources Information System (HRIS) you are therefore an HR Analyst. If we were to be more aligned with the term we would be analyzing the effectiveness and the efficiency of the HR function e.g. HR Staffing ratios and everything else that goes along with it. An HR Analyst in this fashion would be more aligned with Talent Acquisition Analyst roles that we see growing in the domain today. In my view, HR Analytics is really no different to Workforce Analytics and we will see these titles transition towards People Analytics over time. Why Evolve to People Analytics Then? I do not believe there is a significant difference between people analytics vs HR analytics vs workforce analytics in terms of the work that we do. The evolution of the terms, in my opinion, has been more about how we view people as individuals in our organizations as opposed to the large scale aggregate of a workforce or even worse to me as "human resources". We've recognized as a discipline that people need to be treated and respected as individuals, that we need to provide career development, and life support, and that it is important that people actually take vacation time. It is treating people as people and not numbers cranking out widgets. It is no coincidence that knowledge worker organizations have been the biggest adopters of people analytics, they have the most to gain especially in the tight labor market where choice and compensation are abundant. The care for workers must exist whereas many years ago it was a different story. I love the fact that we have people analytics teams who are going deep on how they promote a diverse workforce, on how they create career development opportunities. We even have one customer that integrates cafeteria data into their solution to help identify what people are enjoying eating. So is it just a branding change? Yes, and No. Our space has definitely matured, and our capabilities have grown. We've moved from basic reporting and access to data which is now table stakes, to project-based analysis with intent and hypotheses to prove or dispel. People Analytics is a more mature discipline than it ever was but effectively the same activities could roll up under either term. Impacting people's work lives through our analysis of data is ultimately our goal, having in mind that outcome is why we'll see further adoption of People Analytics as a term. We'll see job titles change to reflect this move over time. And I'm certainly not always right, and there are larger nuances between these terms applicable to some organizations. Heather Whiteman gives a good overview of a more nuanced definition here Interested in Learning More? So whether you call it HR Analytics or People Analytics, if you're new to this and want to understand what it can do for an organization, check out the eBook written by Heather White and Nicholas Garbis on Explore the Power of People Analytics for a further dive in this area. Download eBook Today
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3 min read
Nicholas Garbis
We wrote this paper because we believe that AI/ML has the potential to be a very valuable and powerful technology to support better talent decisions in organizations – and it also has the potential to be mishandled in ways that are unethical and can do harm to individuals and groups of employees. In this paper, we provide some process-thinking substance to the conversation that has too often been dominated by hyperbolic “AI/ML is great!” and “AI/ML will destroy us!” headlines. In the paper, you will find a set of Guiding Principles … And, most importantly, a set of Processes for Ethical ML Stewardship that we believe you should be discussing (immediately) within your organizations. Each of these processes (and sub-processes) is defined in the paper in plain, readable language to enable the widest possible readership. We believe we are at a delicate and critical point in time where AI/ML has been embedded into so many HR technology solutions without sufficient governance amongst the buying organizations. Vendors (like One Model) need to have their AI/ML solutions challenged to provide sufficient transparency into the AI/ML models – model features, performance measures, bias detection, review/refresh commitments, etc. One Model has built our “One AI” machine learning toolset to enable the processes that our customers can use to ensure ethical model design and outputs. To be clear, this paper is not a promotional piece about One Model, but it is absolutely intended to challenge the sellers and buyers of HR technology to get this right. Without the appropriate focus on ethics, AI/ML products and projects could become too risky for organizations and then summarily eliminated along with all the potential value for individuals and organizations. DOWNLOAD PAGE: https://www.onemodel.co/whitepapers/ethics-of-ai-ml-in-hr
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28 min read
Tony Ashton
[This article is taken from a presentation I delivered as part of a broader session on People Analytics for the Australian Human Resources Institute (AHRI) QLD Analytics Network on 7 October 2021. It uses the presentation slides and accompanying presentation script only slightly modified from the spoken word to fit the written form. thanks, Tony] This article focuses on setting up a people analytics capability in your organization and thinking about what the key challenges are and how to resolve those. Let’s start by talking about data-driven insights, people analytics is the focus. But more broadly, there is an untold number of articles and research papers on the importance of data-driven decision making and my bookshelf is full of these books and papers. And I'm sure yours are too (or your virtual bookshelves). Here is a nice example from Deloitte to set the scene. Of the organizations they surveyed 39% of those have a strong analytics culture, and 48% of those were significantly exceeding their business goals. Compared to those that didn't display a strong analytics culture, only 22% were significantly exceeding their goals. There's a double whammy in terms of the proportional impact that Analytics gives to an organization. But importantly, also here, there's this angle of culture. In the survey, most executives believe that they weren't really that insight-driven as an organization. So there is this challenge between the ability to pull data together, derive insights, and actually make decisions and make that a part of the framework for how business is done. So why is this important to HR? If you can't connect people to business outcomes, then you're really just doing stuff because you think it's a good idea. Doing stuff that you think is a good idea is ok, but it will only get you so far - being able to prove it is a good idea and measure your impact is another thing entirely. The importance of data-driven decision making for HR Using data helps you prioritize your strategies. You can't do everything, so you need to focus your resources on HR. Metrics and data help you do that and this helps you build that culture of data-driven decision making. If you think about the people space, people related decision making and HR processes are all underpinned by principles like merit, natural justice, fairness and transparency. Without a data-driven approach to this, you're very kind of at risk of replicating the same diversity issues that you see in many organizations: pay equity issues, how promotions and pay increases are awarded, or who should be hired. Some of these examples are on a macro scale, for example, your whole company's diversity profile, and some at the micro-level, but the same general principles apply. Data is important for setting strategy and for tactical decision making At the micro-scale let’s take a specific example of an individual hiring decision. We have selection criteria for hiring to ensure we get the right person for the role and use multi-source inputs to the process to base decisions on evidence and to avoid bias, nepotism, discrimination etc. At the micro-level, you heavily rely on good processes, training and company culture. Ideally, guiding these processes and strategies would be a great analytical understanding of your organization’s diversity profile, the skills and capabilities required for the next 2-3 years, market pay rates for similar roles, the complexity of the role, expected time to productivity… and on. Hiring strategies in the absence of this data are going to be much less effective than they would otherwise be. The reality is that HR has been somewhat late to the party around the use of data and people analytics. If we think about this from a simple business accountability perspective HR teams are custodians of lots of systems. Not many organizations have just one system. And even if you have just one, we still have to curate and care for that information. It's a rich asset to the organization. Putting data in the hands of managers is critical for creating a data-driven culture Let’s look at some research from the Annual HR Systems survey. This survey provides a rich set of longitudinal research and here I’ve highlighted some insights they developed regarding the differences between organizations that are data-driven compared to those that are less so. This construct is similar to the Deloitte research we talked about earlier. The bars on the left of the chart are the results for organizations that are described as not being data-driven, and bars on the right are those that are identified as being data-driven. As you would expect all segments on the right-hand side are higher than on the left, but by far the biggest difference and the thing that really stands out as being different is the deployment of information to managers, putting information in the hands of decision makers. I have circled this segment in red on the chart. So, this gives us something to think about in terms of what drives success. Success isn't necessarily having a great dashboard, success is determined by whether or not people are using data and making decisions with it. The “maturity” of people analytics There has been a lot written on this topic across the decades, there are more books and research papers than you can imagine. Just a few examples here. This is extremely well-trodden terrain and there is no shortage of great information to draw from. Facilitating the Utilization of HR Metrics – The Next HR Measurement Challenge; Irmer, Bernd E (Ph.D); Ellerby, Anastasia (MBA); Blannin, Heather, 2004 Early research on driving the adoption and use of people data In terms of the topic of adoption, this is a key theme for this discussion, and the focus is on the actual use of data in organizations. The image above is an extract from a paper published around 2004 by the InfoHRM company in partnership with the Corporate Leadership Council within the Corporate Executive Board (subsequently acquired by Gartner). This research identified the key phases of sophistication around the use of HR data for business impact. The phases were characterized as: getting your house in order by automating reporting and reducing the load from ad-hoc queries by introducing self-service starting to use more advanced metrics and multidimensional analysis, and then deploying more broadly into everyday decision-making and impacting business outcomes. Through a detailed survey and interview process companies self-identified into one of these categories regarding the maturity of their use of HR data. There was a big difference in what was required of companies in phase three and we will talk more about this. Facilitating the Utilization of HR Metrics – The Next HR Measurement Challenge; Irmer, Bernd E (Ph.D); Ellerby, Anastasia (MBA); Blannin, Heather, 2006 Two years later, the study was re-run and the framework was updated based on more findings and longitudinal data. There was an even stronger focus on understanding how to drive adoption and found that there was the dotted line after phase two was even more pronounced and that it was a really hard barrier for companies to jump across. The research provides a number of tactics and best practice advice to address this, but it was clear that having the technology to help scale, automate, improve quality etc. is necessary, but not sufficient for success and success takes good change management, cultural alignment and business impact orientation. It is the latter topics that also drove the creation of the additional phase, i.e. those companies that were truly having an impact on business outcomes through the use of HR data. This research was happening at a time when HR itself was heavily focused on the prevailing thought leadership of Dave Ulrich around HR and business alignment and other leading work by Mark Huselid, Brian Becker, Richard Beatty, John Boudreau, and others. A big part of being a business partner and a business driver was the use of data and evidence-based decision making. Maturity models enter the mainstream Interestingly, around a similar timeframe, Gartner was building its own model for how companies could be more data-driven, and the use of business analytics across an organization [the earliest reference to this I can find is from 2009]. Gartner’s framework described this in the form of a four-phase model describing increasing difficulty for companies to move from descriptive analytics up to being able to deliver prescriptive analytics for the highest value. Bersin & Associates (since acquired by Deloitte) published this model around 2010. As you can see lots of similarities to what has come before and presents a maturity scale of using people analytics starting from operational reporting through advanced reporting, advanced analytics and up to predictive analytics. Defining success While these models have helped companies and people analytics teams assess where they are and the opportunities to make more of a difference, I have a problem with all of these models. The problem is that they set up prescriptive or predictive analytics as the main destination everyone should be striving for and if you're not doing predictive analytics, then you're not really doing anything of worth and setting up an expectation that is hard to reach and not necessarily the right destination. Something I'd recommend considering is how you see success and what matters to your organization is the most important thing, how you get there is just a part of the journey. Build a sustainable capability and avoid the key person dependency risk So, what do we need to do? Many of you, myself included, may have had a role that could be characterized as “the Excel ninja” in your organization, or HR team. You are able to crunch through data, get data from lots of places, massage it, put it together, create some amazing reports and dashboards, and share them around. But then if someone wanted to see that data cut differently, that became a pile of work and maybe your weekend. This is all great for job security and feeling important and needed, but before long you get bored or burnt out, or both. And then you leave or move on to another role. You may have written some great handover notes, but there is an immense amount of tacit knowledge locked up in your brain and everyone likes to do things their own way, so the next person would invariably reinvent everything. In between times, it is probably hard to fill the role, because people with the right skills are scarce in HR. Basically, relying on the Excel Ninja isn’t a great idea for any company as at some point all their people's analytics capability is going to walk out the door and they have to start again. Data Scientists are amazing, but you need to build a broader people analytics team The lesson there is around building sustainable capability, not just relying on a single person. Now, get ready for a feeling of déjà vu. We are in a very similar position today with the role of the data scientist. Everyone wants to be at the top of the maturity scale right? So, how to get there, just hire a Data Scientist! But, you are actually creating a much worse problem than you had with the Excel Ninja. The data scientist is definitely a superhero and is able to do amazing things. But, as before, if you rely on only one person, you're at risk of not creating a sustainable capability for your organization. It is compounded here too, because 80% of the time the Data Scientist is cleaning and aligning datasets and curating predictive models. Most of the time this work is not repeatable and is designed for specific investigations, which can result in great insights, but pretty soon they get fed up and move on and you are left with a massive hole in your people analytics capability yet again. https://www2.deloitte.com/us/en/pages/human-capital/articles/people-analytics-and-workforce-outcomes.html Deloitte has been doing some nice work around evolving this thinking to be more focused on capability creation, as opposed to an escalating pathway of sophistication. Peter Howes webinar discussing this and other related topics: https://www.onemodel.co/events/peter-workforce-planning-webinar Reviewing all of this material I was reminded of a webinar I hosted in 2019 with Peter Howes. As many of you know, Peter is a giant in the industry. He was the founder of Infohrm and a pioneer in strategic HR, and HR systems, a speaker, educator, and author - a true thought leader in every sense of the term. Peter created this model in around 1980. His core principles are all still valid today and remain probably one of the best characterizations of people analytics done well that I have seen. Essentially, if your team is wrapped up in administrative tasks, you should aim to shift the mix to include professional and strategic activities for greater business impact. You still need to do operational and tactical reporting, that never goes away. Getting greater efficiency and automation for these activities frees you up to do work with greater business impact. The biggest challenges with People Analytics It is pretty clear that the challenges for adoption of people analytics have been around for a couple of decades now, and while technology has caught up with our desires, there is a lot more to success in harnessing technology and developing a sustainable people analytics capability. 1. Data is spread across multiple systems Even if your company has purchased an HRIS suite, you will still have issues pulling data together from across those different applications and invariably you will also have data in lots of different systems. You spend 80% of your time assembling data and probably no more than 5% of your time doing true insight generation. 2. Data is not trusted by leaders If someone doesn't like the message they are hearing from HR, they're going to attack the data. If you have any data quality issues, then that's going to show and it will undermine everything. Even if the inaccuracy is minor and doesn’t affect your message, it is an opening - a weakness. People will start generating their own data, and use different definitions, resulting in a lack of consistency and trust. 3. Analytical tools are not being adopted If your tools are too complex, then they won't be used. This is why many tools don't get used in most organizations, not just people analytics products. There are too many options, too many things to click, and that is a barrier to adoption. Focusing the solution on the real needs of the different users and personas is critical. More is not better in this case, focused insights and fewer options for the end-user is what will bring success. 4. Data security & privacy is really complex Obviously, in HR, security and privacy are critically important, and often a major reason why people data is not shared around organizations. Think back to the life of the Excel Ninja, they are probably generating hundreds of different spreadsheets and emailing them to managers. This is a lot of work, but it is also inherently risky. 5. High expectations for Data Science and AI/Machine Learning Machine Learning (ML) and Artificial Intelligence (AI) is seen as being too futuristic for most despite the incredible amount of hype. “How do we even get started?” is an all too common refrain. 6. Data and predictive models in HR apps are very “black box” Predictive models and even basic data transformation models are often locked in the head of your Excel Ninja, or in a black box from your software vendor. This means you have a lack of transparency in understanding if there are any quality issues in the movement of data and calculations, or if you have an inherent bias, or how reliable and trustworthy those models are. Back to where we began this discussion, if you are making decisions that impact people’s lives, you need to have good reliable evidence. Solving these challenges is necessary for success. So how do we actually do that? Let's talk through some tactics and ideas. Solving the People Analytics challenges STEP ONE - Bring your data together Naturally, bringing your data together is step one. Ideally, into a single data model, or if not, at least a repeatable process for merging your data together so you don't have hands involved in the process. This is really important, because if you have any manual processes you are again spending time on less value adding work taking you away from insight generation, and it's opening up opportunities for errors. STEP TWO - Create a set of key metrics and definitions Creating a set of metrics and a set of definitions is really important, because then you've got consistency. You can then drive reliability and quality through that process. STEP THREE - Deploy simple, guided storyboards/dashboards & data exploration tools Then with a set of defined metrics and storyboards (or dashboards, or whatever you call it) that are consistent and easily understood, you are able to start driving adoption. People get familiar with the frame you are presenting, the terms and the language, and the definitions. This brings a baseline of shared understanding and learning and the ability to then start adding to that through time. STEP FOUR - Wrap everything in role based security from the start In terms of security, you should think about security through the concept of user personas for which you construct roles, not thinking about security for individuals. Think about your Executives, GMs, People Leaders, HR Business Partners (HRBPs) etc. and what the different roles are, what data they need to see and then craft the security around the roles. This allows you to set your data free using security as a way of deploying content, not holding it back. Drowning in spreadsheets is often seen as a problem for data consistency, effort etc. but it is also a major security issue that can be avoided by taking this approach STEP FIVE - Leverage technology and skills to enable the use of ML/AI & predictive insights The technology issues around ML/AI are completely solvable. There is lots of technology available and it is not really a technology problem anymore. It is more an issue of capability and understanding. The key is to leverage technology in a scalable way and not fall into the key person dependency trap. STEP SIX - No magic allowed - make everything fully transparent & explainable This leads into the last point, which is don't allow the use of black magic and closed systems. Make sure that everything is explainable and understandable when it comes to metrics and predictive models or whatever kind of analysis that you're doing. Some practical examples of People Analytics in practice Let me share a couple of quick examples. Here is a storyboard that is structured around a specific topic and has the key questions your audience would be asking and these leading them through the data. So, it’s really easy to understand what's going on with layered complexity from the high level summary trends through to the details. Everything is interactive, you can click and drill. We are leading people through the topic pre-empting the questions that commonly arise when consuming this content. Below is an example using more of a classic KPI style Storyboard. Here you can assemble and browse through the KPIs from the simple to the more advanced, but the layout is consistent and easy to track from the big headline number through the trends and the detailed breakdowns. At any point, you can click and drill. One of the most important features here is this pervasive library of formulas, definitions and explanations. As important is the ability to drill into the details and see who the people are for this analysis (naturally all this is seamlessly controlled by role based security). The ability to drill down lets you validate the information, but also gets you into action. You are able to quickly dig into key employee segments, identify risks and target interventions. These are just a couple of these examples of what you can do to get started fairly simply, but quickly make a big difference in your organization. Building on the previous examples, in the scatterplot below we have added a correlation, which is normally something scary for the average non-statistician, but if you look at the text above the chart you can see an automatically generated written interpretation of the results using simple business language. Instead of just providing the numbers and expecting people to understand what a correlation coefficient is, or how to interpret significance, be explicit and explain whether something is significant or not – this goes a long way. Here is a zoomed in view so you can see this more clearly - the chart heading is in the form of a question and the text is directly answering this question. A Summary of Tactics to Build your People Analytics Capability The slide above summarizes some of the tactics we have covered, with a few additions to help you build a people analytics capability in your organization. If you don’t have the skills in HR, borrow from other disciplines, find the experts in the organization who can help you. Reach out to the broader people analytics community. There are lots of resources, networks and people ready to help. The People Analytics practice and network is bigger now than it has ever been. Also remember that it's not always just about the data, you're in HR, let's talk to people, be sure to check your findings, go around the organization and build your own network to better understand what's actually happening. Some final thoughts By way of some final thoughts. Focus on the questions that matter to your business, start with a small set of things that are repeatable and build trust. This will then give you time to do the more interesting stuff, find opportunities to drive success, and then market your successes. You can build a groundswell of people wanting to get analytics as opposed to you forcing it upon them. And again, it's about insight, not necessarily just about the data, but the actions you can take and the impact you can make. People Analytics is one of the hottest areas that organizations are looking to hire into internationally. The above framework is designed to help you put all this into practice. You need to deal with the job of data orchestration to get all of your data into one place and one logical construct. Focus on Storytelling, not just generating Dashboards. Blend Predictive Analytics into this and some think of it as an add-on. Answer the questions that matter If you are interested, One Model has heaps of assets that we can share with you. For example, contact us if you want some inspiration around the questions that matter. We have a great library of these and this is a really engaging way to talk to people in your organization about people analytics in a non-technical way. We also have an e-book titled "Explore the Power of People Analytics" that’s a great resource to get started.
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11 min read
Nicholas Garbis
Only humans would bother inventing something as complex as the concept of species. Attempting to organize every living thing around us into distinct buckets has been a massive and never-ending enterprise. We like categories and we love to argue about them! What's the Difference between People Analytics and Reporting? HR Reporting and People Analytics are intertwined concepts which are more valuable when they are clearly articulated for their distinct purpose and value to the organization. Both are necessary for the effective management of the workforce and the HR processes that are aiming to achieve efficiency and employee/manager experience. HR Reporting and People Analytics have been debated as being the same, as being different, as being parent-child and child-parent. So what’s all the fuss, and how can I offer some thinking that helps? Stirring up muddy water to make it become clear seems foolish, but here it goes. Consider the word origins of the two key words: “Report” is based on the word “port” which means to carry something from one place to another. Reports share information. “Analytics” is based on the word “analyze” which means to decompose and recombine something into something that increases understanding. Analytics facilitates insight. Both HR Reporting and People Analytics are built from a foundation of data that is generated by the multitude of systems-processes that exist within every organization. (See image below) Most systems are built to facilitate processes (eg, hiring) and generate data as a valuable by-product (eg, job open and closed dates). Other systems such as survey systems exist solely for the purpose of generating valuable data. What is HR Reporting? HR Reporting can be generalized based on the traits it most commonly displays (accepting that these lines can be blurry, just as some plants can behave in ways that are quite a bit more like animals). HR Reporting typically is ... Designed to provide information (versus insights) Simple in format, often as list or tables, possibly in a multi-tab spreadsheet Data is often from a single system (often, not always) Raw data, occasionally with calculations applied (ie, metrics) Rather fixed in structure and limited in terms of user interactivity Used in monitoring transactional activity (eg, list of currently open job postings) A source of data that is extracted for analysis in another tool (eg, Excel) Could be a part of a larger people analytics project HR Reporting is used by: Process and technology owners (eg, recruiting ops) HR functional leaders (eg, learning or talent management leaders) People leaders (eg, managers of teams) Executives (eg, C-Suite, CHRO, VPs, DEI leaders) HR Reporting is Valuable HR Reporting is an essential point of access to the data within a given system, enabling the owners of the related processes to retrieve data for review and analysis. I can’t think of a system that doesn’t provide at least some access to the underlying data via reporting. Reporting from these systems is typically organized into some set of pre-defined tabular views, each of them providing users with some options to filter the data to specific parts of the organization or process steps that they want to view (eg, course registrations for the Finance department). Metrics, which are calculations based on the raw data, may also appear in reports but will tend to be a summarization of the transactional data (eg, average time-to-fill for each recruiter). Trends of the data, perhaps displayed with different time periods in different columns, are also common and valuable. Human Resources Reporting is distinguished from “analytics” because analytics tends to be aimed more at generating insights rather than sharing information. Let’s look at People Analytics next. What is People Analytics? People Analytics is more complicated to define. To begin with, it can represent: category of deliverables (eg, interactive dashboards), a team within the HR organization (eg, PA COE), a set of activities (ie, consulting & advising), or a combination of all of these. For comparison with HR Reporting, which is a type of deliverable, we will focus on People Analytics as defined as a category of deliverables. Maturity Continuum? No, Sorry. HR Reporting and People Analytics do not belong on a maturity continuum, as they are both vital parts of running an organization well. Sure, if an organization has no People Analytics, you could confidently say they are less mature than another organization that does. You could even say that one organization’s People Analytics deliverables are more advanced (ie, mature) than another organization. The point is, you don’t move from one level (HR Reporting) to another level (People Analytics) -- you need to deliver both and do them well, even if we agree that People Analytics will create more value for the organization. Here’s a chart that may help orient the two: Notice the difference in the objectives of each: People Analytics will be focused on generating insights. In fact, some advanced analytics solutions will have insights directly within the solution, but most often the insights are expected to occur when the user views and interacts with the content in the deliverable. The value of People Analytics is more in the strategic realm, whereas HR Reporting generates more operational value focused on delivering information to keep the business running. Of course, there is some crossover, but generally, reporting helps with operational items such as efficiency, process monitoring and improvement, auditing, quality control, etc. Analytics is aimed at generating insights that will lead to decisions and actions. Analytics content is designed to facilitate valuable insights “at the speed of thought”, and in online settings this is achieved through interactive user experiences, issue highlighting, embedded insights using natural language. Analytics content facilitates hypothesis generation and testing simultaneously and is a learning and discovery vehicle for users. Deliverables: People Analytics Projects, Products, and Services Let’s outline People Analytics deliverables in terms of products, projects, and services. These are all aimed toward generating insights at scale that will drive the best quality, data-informed talent decisions. Systems and technology are not listed here because they are not deliverables, but enabling elements that help generate the deliverables. Products analytics content is most often distributed online via an analytics platform (like One Model), including metrics that may be sourced from multiple systems, and sometimes will have output from AI/ML predictive models. Dashboards / Storyboards -- an interactive collection of metrics with an explicit design goal of generating insights by or for the user. Some of this may be data science (AI/ML) results that are packaged for broader consumption. “Storyboards” are a variation designed, often with a question format, to elicit a story or path of thinking. Embedded Data Science -- AI/ML and modeling results that use HR data and are embedded within other products (eg, time-to-fill prediction that is consumed by recruiters directly within the recruiting system/ATS, or a restaffing projection rate within a project planning solution). HR Reporting -- while this is not ‘analytics’ in our working definition, it’s important to recognize that these deliverables are often part of the People Analytics team’s responsibility, so they are listed here so as to not be forgotten. Projects Deep-dive studies: covering a given topic, possibly testing a hypothesis, usually culminating in a presentation or delivered document which contains data, metrics, visualizations, written insights, and even conclusions and recommended action steps. These may include advanced analytics and data science methods. Experiments and explorations: digging into the data to understand relationships further, test hypotheses, generate mock-up content that may go into productions, etc. Services Evaluation: creating learning opportunities to elevate the analytics skills of partners (eg, HRBPs) in generic terms or specific to the People Analytics platform. Change Management: developing communications and user engagement plans to help drive adoption of tools and methods. Consulting: offering guidance on strategic decisions and programs that may be evaluated in response to insights generated through the PA team’s products and projects. From Data to Deliverables Let’s return to the diagram we shared in the previous section and expand on it a bit. The systems and processes that generate data are foundational to both. Data from the multiple systems is selectively extracted into a data layer where data from multiple systems is integrated. This layer can be a standalone data warehouse (eg, in Azure or Snowflake) or can be part of a solution. Metrics are calculated (eg, headcount, turnover rate) and dimensions are created (eg, organization unit, company tenure) by applying business rules against the data. Dashboards and Storyboards are designed and developed within a visualization layer (eg, Tableau) or within a People Analytics solution. Data Science will be done using data extracted from the warehouse into tools such as R or Python, or within the data science module in a People Analytics solution (only available in One Model at time of this article). Analytics Projects will be combining elements of all the underlying pieces in what will become a presentation (written and/or verbal), usually on a key topic of interest to leadership. Concluding Thoughts As demonstrated above, HR Reporting and People Analytics are intertwined concepts which are more valuable when they are clearly articulated for their distinct purpose and value to the organization. Both are necessary for the effective management of the workforce and the Human Resources processes that are aiming to achieve efficiency and employee/manager experience. You don’t need to have perfect reporting before you begin doing analytics. The two can mature in tandem and are often mutually reinforcing. A robust, integrated, and flexible data foundation is going to provide the greatest value by ensuring the analytics deliverables do not ‘hit a ceiling’ where the next tier of value becomes unachievable without going back to the architecture of the data warehouse. Think “value first.” Obsess about how your team can generate the most value at the fastest pace for your organization, not about the arcane differences between commonly used terms. To learn more about people analytics, download a free copy of the eBook Explore the Power of People Analytics (value $8.99 in paperback on Amazon) I co-authored with Heather Whiteman, PhD. Download My Free Copy
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2 min read
Chris Butler
One Model took home the Small Business Category of the Queensland Premier's Export Awards held last night at Brisbane City Hall. The award was presented by Queensland Premier and Minister for Trade, Hon Annastacia Palaszczuk MP and Minister for Employment, Small Business, Training and Skills Development, Hon Dianne Farmer MP. “We are delighted to receive this award given the quality of entrepreneurs and small business owners in Queensland,” One Model CEO, Chris Butler said. “It is a tribute to the exceptional team we have in Brisbane and the world leading people analytics product One Model has built.” “From our first client, One Model has been an export focussed business. With the profile boost this award gives us, we look forward to continuing to grow our export markets of the United States, Europe and Asia,” Mr Butler said. Following this win, One Model is now a finalist in the 59th Australian Export Awards to be held in Canberra on Thursday 25 November 2021. One Model was founded in Texas in 2015, by South-east Queensland locals Chris Butler, Matthew Wilton and David Wilson. One Model generates over 90% of its revenue from export markets, primarily the United States. One Model was also nominated in the Advanced Technologies Award Category. One Model would like to congratulate Shorthand for winning this award as well as our fellow finalists across both categories - Healthcare Logic, Tactiv (Advanced Technologies Category), iCoolSport, Oper8 Global, Ryan Aerospace and Solar Bollard Lighting (Small Business Category). The One Model team would like to thank Trade and Investment Queensland for their ongoing support. To learn more about One Model's innovative people analytics platform or our company's exports, please feel free to reach out to Bruce Chadburn at bruce.chadburn@onemodel.co. PICTURE - One Model Co-Founders Chris Butler, Matthew Wilton and David Wilson with Queensland Premier, Hon Annastacia Palaszczuk MP and the other award winners.
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15 min read
Chris Butler
The public sector is rapidly evolving, is your people analytics strategy fit for purpose and can it meet the increasing demands of a modern public sector? In this blog, we will highlight the unique challenges that public sector stakeholders face when implementing a people analytics strategy. In light of those challenges, we will then outline how to best design and implement a modern people analytics strategy in the public service. When it comes to people analytics, the public sector faces a number of unique challenges; The public sector is the largest and most complex workforce of any employer in Australia. A workforce that bridges everything from white collar professionals to front line staff and every police officer, teacher and social worker in between. Public sector workforces are geographically dispersed with operations across multiple capital cities in the case of the Commonwealth Government, or a mix of city and regional staff in the case of both state and federal governments. The public service operates a multitude of HR systems acquired over a long time, leading to challenges of data access and interoperability. Important public service HR data may also be held in manual non-automated spreadsheets prone to error and security risk. A complex industrial relations and entitlements framework, details of which are generally held in different datasets. Constant machinery of government (MoG) changes demand both organisational and technological agility by public servants to keep delivering key services (as well as the delivery of ongoing and accurate HR reporting). The public sector faces increased competition for talent, both within the public service and externally with the private sector. Citizen and political pressure for new services and methods of government service provision is at an all time high - so not only are your critical stakeholders your customers, they are your voters as well. Cyber security and accessibility issues that are unique to the public sector. This all comes under the pressure of constant cost constraints that require bureaucracies to do more with limited budgets. As a result - understanding and best utilising limited human capital resources is crucial for the public sector at both a state and federal level. Now that we have isolated the unique people analytics challenges of the public sector, how do HR professionals within the public service begin the process of implementing a people analytics strategy? 1. Data Orchestration “Bringing all of your HR data together.” The first stage of any successful people analytics programme is data orchestration, without having access to all of your relevant people data feeds in one place, it is almost impossible to develop a universal perspective of your workforce. Having a unified analytical environment is critical as it allows HR to; Develop a single source of truth for the data you hold on employees. Cross reference employee data within and between departments to adequately benchmark and compare workforces to drive team-level, department-level and public service wide insights. Establish targeted interventions and not one-size-fits-all solutions. For example, a contact centre is going to have very different metric results than your corporate groups like Finance or Legal. Blend data between systems to uncover previously hidden insights. Uncover issues such as underpayments that develop when different systems don’t communicate. Using people analytics to mitigate instances of underpayment is covered extensively in this blog. Provide a clean and organised HR data foundation from which to generate predictive insights. Have the capacity to export modelled data to an enterprise data warehouse or another analytical environment (PowerBI, Tableau etc). Allow HR via people analytics to support the Enterprise data mesh - covered in more detail in this blog post. People data orchestration in the public sector is complicated by the reliance on legacy systems, as well as the constant changes in structure driven by machinery of government reforms. Successful data orchestration can only be achieved through an intimate knowledge of the source HR systems and a demonstrated capacity to extract information from those systems and then model that information in a unified environment. This takes significant technology knowledge, such as bespoke API integrations for cloud based systems and proven experience working with on premise systems. It also requires subject matter expertise in the nuances of HR data. It can not be easily implemented without the right partners. Ideally, the end solution should be a fully flexible open analytics infrastructure to future proof the public sector and allow for the ingestion of data from new people data systems as they arise (such as new LMS or pulse survey products) while also facilitating the migration of data from legacy systems to more modern cloud based platforms. 2. Data Governance “Establishing the framework to manage your data.” Now that all of your data is in one place, it is important you develop a robust framework for how to manage that data - in our view this has two parts - data definition and data access. Data Definition Having consolidated multiple sources of data in one environment, the next step is metric definition, which is critical to being able to convert the disparate data sets that you have assembled into coherent, understandable language. It is all well and good to have your data in one place, but if you have 5 different definitions of what an FTE means from the five different systems you are aggregating then the benefits you receive from your data orchestration phase will be marginal. Comprehensive metric definitions with clear explanations are needed to ensure your data is properly orchestrated and organisation-wide stakeholders have confidence that data is standardised and can be trusted. Data Access HR data is some of the most complex and sensitive a government holds, so existing HR data management practices based on spreadsheets that can be easily distributed to non-approved stakeholders both inside and outside of your organisation are no longer fit for purpose. Since your people analytics data is coming from multiple systems you need to provide an overarching security framework that controls who gets access to what information and why. This framework must based on logical rules, aligned to broader departmental privacy policies and flexible enough to accommodate organisational change and to scale to your entire department or agency regardless of its size. Critically, there needs to be a high level of automation and scalability to use role based security as a mechanism for safely sharing data to decision makers. Today’s spreadsheet based world relies on limiting data sharing, which also limits effective data driven decision making. Finally, these role based security access frameworks need to be scalable so each new user or change in structure doesn’t require days of manual work from your team to ensure both access and compliance. 3. Secure People Analytics Distribution “Delivering people analytics content to your internal stakeholders.” The next step, once you have consolidated your data and established an appropriate data governance framework, is to present and distribute this data to your internal stakeholders. This is what we refer to as the distribution phase of your people analytics implementation. We established in the last section that for privacy and security reasons, different stakeholders require access to varying levels of information. The distribution phase goes one step further and places access within the prism of what individual stakeholders need in order to successfully do their jobs. For example, the information and insights necessary for a Departmental Secretary and a HR business partner to do their jobs are wildly different and therefore should be tailored to their particular needs. So, organisation wide metrics and reports in the case of the Departmental Secretary and team or individual level metrics for the HR BP or line manager. This is further complicated by disclosure requirements and reporting unique to the public service. This includes; Media requests regarding public servant pay and conditions Statutory reporting requirements for annual state of the public service reports Submissions to and appearances before parliamentary committees Disclosure to independent oversight inquiries or agencies As a result, public sector HR leaders are required to walk a tightrope of both breadth and specificity. So how do we recommend you do this? Offer a baseline of standardised metrics for the whole organisation. Tailor that baseline based on role-based access requirements, so stakeholders only see information that is relevant to drive data driven decision making. Deliver those insights at scale - the wider the stakeholder group consuming your outputs the better. Ensure those outputs are timely and relevant - daily or weekly updates are recommended. Be able to justify your insights and offer access to raw data, calculations and metric definitions. Continually educate your stakeholders about best practice people analytics. Increase reporting sophistication based on the people analytics maturity of your stakeholders - simple reporting for entry level stakeholders, more complicated predictive insights for the more advanced. To get the most out of your people analytics strategy you need to deliver two things; Role based access to the widest stakeholder group across your department, the wider the group of employees that have access to detailed datasets the easier it will be to deliver data driven decision making. Support your team with a change management programme to grow their analytical capability over the course of time. 4. Extracting Value from your Data “Using AI + Data Science to generate predictive insights.” Now we get to the fun part - using data science to supercharge your analysis and generate predictive insights. However, to quote the great theologian and people analytics pioneer - Spiderman - “With great power comes great responsibility.” Most data science work today is performed by a very small number of people using arcane knowledge and coding in technologies like R or Python. It is not scalable and rarely shared. The use of machine learning capabilities with people data requires a thoughtful approach that considers the following; Does your AI explain its decisions? Could the decisions your machine learning environment recommends withstand the scrutiny of a parliamentary committee? Do you adhere to ethical AI frameworks and decision making? What effort has been made to detect and remove bias? Does harnessing predictive insights require a data scientist or can it be used by everyday stakeholders within your department? Will your use of AI adhere to current or future standards, such as those recently proposed by the European Commission? To learn more about the European Commission proposal regarding new rules for AI, click here. In integrating the use of machine learning into your people analytics programme, you must ensure that models are transparent and can be explained to both your internal and external stakeholders. 5. Using People Analytics to Support Public Sector Reform “Public sector HR driving data-driven decision making.” A people analytics strategy does not exist in isolation, it is a crucial aspect of any departmental strategy. However, in speaking to our public sector HR colleagues - they often feel that their priorities are sidelined or they don’t have the resources to argue for their importance. A lot of this has to do with the absence of integrated datasets and outputs to justify HR prioritisation and investment. We see people analytics and the successful aggregation of disparate data sets as the way that HR can drive their people priorities forward. If HR can present an integrated and trusted dataset, that allows comparison and cross validation with data from other verticals including finance, community engagement, procurement and IT. This gives HR the capability to be central to overall decision making and support broader departmental corporate strategies from the ground up. We have written extensively about the importance of data driven decision making in HR and using people analytics to support enterprise strategy, this content can be found on our blog here - www.onemodel.co/blog Why you should invest in people analytics and what One Model can do to help. The framework of a successful public sector people analytics project outlined above is the capability that the One Model platform delivers. From data orchestration to predictive insights, One Model delivers a complete HR Analytics Capability. The better you understand your workforce, the more ambitious the reform agendas you can fulfil. One Model is set up to not only orchestrate your data to help the public service understand the challenges of today, but through our proprietary OneAI platform - to help you build the public service of the future. One Model’s public sector clients are some of our most innovative and pragmatic, we love working with them. At One Model, we are constantly engaging with the public sector about best practice people analytics - last year, our Chief Product Officer - Tony Ashton (https://www.linkedin.com/in/tony-ashton/) - himself a former Commonwealth HR public servant appeared on the NSW Public Service Commission’s The Spark podcast to discuss how the public sector can use people data to make better workforce decisions. That podcast can be found here. Let’s start a conversation If you work in a public service department or agency and are interested in learning more about how the One Model solution can help you get the most out of your workforce, my email is patrick.mcgrath@onemodel.co
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4 min read
Nicholas Garbis
Our team recently published a whitepaper which explains the "how and why" of our approach to getting data out of Workday. In it we share a lot of challenges and a heap of technical detail regarding our approach. There are also a couple of embedded videos within the paper (unless you print it!). We produced this whitepaper to share the knowledge and experiences we have gained working with our customers, many of whom have Workday as their core HCM. With these customers, we use our proprietary 'connectors' to extract the relevant data through Workday's APIs (adding in data from RaaS reports where needed). But that is just the beginning, because, while the extraction is critical, what comes out of it is essentially 'dull data' that lacks analytical value in its pre-modeled state. We don't stop there. One Model's unique expertise kicks in at this point, converting the volumes of data from Workday (and other HR and non-HR systems) it into what we like to call an "analytics-ready data asset". So, that begs the questions, "What exactly is an 'analytics-ready data asset'?" and "How does One Model create this data asset from Workday data?" So, here's a definition ... DEFINITION of an "Analytics-Ready Data Asset" A structured set of data, purpose built to support a variety of analytics deliverables, including: Metrics that are pre-calculated, can be updated centrally, and have relevant metadata Queries that can range from simple to complex Reports that contain data in table format (rows and columns) with calculations Dashboards and Storyboards that deliver data in compelling visuals that accelerate insights Data science such as predictive modeling, statistical significance testing, forecasts, etc. Integration of data from multiple sources (HR and non-HR) leveraging the effective-dated data structure Data feeds that can be set up to supply specific data to other systems (eg, data lakes) Security model that enables controls over who can see which parts of the organization AND which data fields they will see (some of them at summary, others at employee-level detail) One of the key elements of building such a data asset from Workday is the conversion of the source data into an effective-dated structure which will support views that trend over time (without losing data or creating conflicting data points). This is much more difficult than you'd expect, given that we are conditioned to think of HR data as representative of the employee lifecycle, and many systems of the past were architected with that in mind. This is not a knock on Workday -- not at all -- it's a great HCM solution that has transformed the HR tech industry with it's focus on manager and employee experience. They are not a huge success story on accident! However, delivering a great experience in a transactional HR system does not directly translate into an analytics capability that is powerful enough to support the people analytics needs of companies today (and for the future). To accelerate your people analytics journey, and to ensure you don't run out of runway, you need a solution like One Model to bring your Workday data to life. Download the whitepaper to get the full story. Go to www.onemodel.co/workday ABOUT ONE MODEL One Model’s industry-leading, enterprise-scale people analytics platform is a comprehensive solution for business and HR leaders that integrates data from HR systems with financial and operational data to deliver metrics, storyboard visuals, and predictive analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which One Model simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co
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1 min read
Chris Butler
One Model has announced its appointment to the Australian Government’s Digital Transformation Agency Cloud Marketplace, a digital sourcing arrangement of cloud computing offerings for Australian government. One Model’s globally recognised and award-winning People Analytics platform, is now available via the Cloud Marketplace to all Australian federal, state, and territory government agencies seeking to reimagine and accelerate their People Analytics journey. One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates, models and unifies data from the myriad of HR technology solutions through the out-of-the-box metric library, storyboard visuals, and advanced analytics using a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Many public sector departments and organisations around the world realised the power of One Model and selected One Model as their partner to success, including the Australian Department of Health, the Australian Civil Aviation Safety Authority (CASA), and Tabcorp to name just a few. The Cloud Marketplace can be accessed via the DTA’s BuyICT platform.
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6 min read
Nicholas Garbis
WATCH THE VIDEO! Conversation with our Chief Product Officer, Tony Ashton, on the topic of insight generation and he shows how One Model’s new insight function works. Insight Generation I believe that a key element of People Analytics should be on insight generation, reducing the time and cognitive load for HR and business leaders to generate insights that lead to actions. Many people analytics teams have made this a priority from a service offering, some of them even including "insights" in the naming of their team. With artificial intelligence, higher quality and faster insight generation can be driven across an organization. An organization with a mature people analytics capability should be judged on the frequency and quality of insight generation away from the center. Why I Stopped Liking Maturity Models Humor me for a moment while I share a very short rant and a confession. I have grown to despise the “maturity curves” that have been circulating through people analytics for over a decade. My confession is that I have not (yet!) been able to come up with a compelling replacement. My main issues? The focus is on data & technology deliverables, not on actions and outcomes. They are vague and imply that you proceed from one stage to the next, when in reality all of them can (and should) be constantly maturing and evolving without any of them ever being “done” or “perfect.” Too many times I have heard (mostly newer) people analytics leaders saying that they need to get their data and basic reporting right before they can consider any analytics. I personally don’t believe that to be true -- things will get easier, faster, and better with your analytics but you do not have to wait to make progress at any of the stages. Action Orientation For example, getting to “predictive” -- being able to foresee what is likely to happen -- is shown in many maturity models. It is easy to imagine, and you may have examples, where very mature predictive analytics deliverables have had little or no impact on the business. In my opinion, true maturity is not about the deliverable, but about the insights generated and the corresponding actions that are taken to drive business outcomes. Going further, getting to “prescriptive” means you have a level of embedded, artificial intelligence that is producing common language actions that should be considered. This would assume the “insight” component is completely handled by the AI which then proceeds into selecting or creating a recommended action. This is still quite aspirational for nearly all organizations, yet it is repeated often. Focus on Designing for Insight Generation at the “Edges” People analytics teams are typically centralized in a COE model, where expertise on workforce data, analytics, dashboard design, data science, insight generation, and data storytelling can be concentrated and developed. The COE is capable of generating insights for the CHRO and HR leadership team, but what about the rest of the organization? What about the HR leaders and managers farther out at the edges of the org chart? The COE needs to design and deliver content to the edges of the organization that enable them to generate insights without needing to directly engage the COE in the process. A storyboard or dashboard needs to be designed with specific intention to shorten the time between a user seeing the content and them having an accurate insight. A good design will increase the likelihood of a “lightbulb" moment. Humans and Machines Turning on “Lightbulbs” Together We need to ensure that the HR leaders and line managers are capable of generating insights from the people analytics deliverables (reports, dashboards, storyboards, etc). This will require some upskilling in data interpretation and data storytelling. With well-designed content, they will generate insights faster and with less effort. Human-generated insights will never be fully replaced. Instead, they will be augmented with machines in the form of AI and machine learning. With the augmentation of AI, the humans will get a boost and together the human-machine combination is a powerful force for insights and then actions. When we have augmentation of AI, we can stop trying to teach everyone statistical regression techniques which they will never use. The central PA team can manage the AI toolset and ensure it is delivering valid interpretations and then focus on enabling insight generation and storytelling by the humans, the HR leaders and line managers. One Model Lights Up Our Customers’ Data Visualizations One Model has just introduced a “lightbulb” feature that is automatically enabled on storyboard tiles that contain metrics that would benefit from forecasting or statistical significance tests. This is not just limited to the content our team creates, it is also automatically scanning the data within storyboards created by our customers. This is far more than basic language attached to a simple regression model. By integrating features of our One AI machine learning module into the user interface we are automatically interpreting the type & structure of the data in the visual and then selecting the appropriate statistical model for determining if there is a meaningful relationship which is described in easy-to-interpret language. Where a forecast is available it is based on an ARIMA model and all the relevant supporting data is just a click away. With this functionality built directly into the user interface, each time you navigate into the data, filtering or drilling into an organization structure, the calculations will automatically reassess the data and generate the interpretations for you. With automated insights generated through AI, One Model accelerates your people analytics journey, moving you from data to insights to actions. About One Model One Model’s industry-leading, enterprise-scale people analytics platform is a comprehensive solution for business and HR leaders that integrates data from HR systems with financial and operational data to deliver metrics, storyboard visuals, and predictive analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which One Model simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co One Model’s new Labor Market Intel product delivers external supply & demand data at an unmatched level of granularity and flexibility. The views in LMI help you to answer the questions you and your leaders need answers to with the added flexibility to create your own customized views. Learn more at www.onemodel.co/LMI
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3 min read
Nicholas Garbis
There are whole books written about Workforce Planning. I read them and enjoy them (maybe even more than I would like to admit). I will include a short list below for your reference. So, what can be added to the body of thought leadership on this topic? My former SWP colleague, Phil Mische and I got together (in person!) to discuss some elements of SWP and decided to create a video hitting on a handful of topics in rapid succession. We decided to call it "Lightning Round Learning." As background, Phil and I worked together on successfully designing and implementing SWP at scale at a global financial services firm. It was an intense experience but was the greatest test of everything that I had wanted SWP to be. Nothing is perfection, especially in year one of a multi-year journey, but it was world-class SWP and we each have an abundance of learnings to share. We listed out several topic ideas and then selected these in real-time, then hit each of them for a few minutes each: Operationalization of SWP Technology (data, tools, models) Granularity of Skills data Change Management Strategic v. Operational workforce planning There is way more depth on each of these elements -- we could easily have filled most of a day unloading our experiences -- and there are many other elements of SWP that we didn't cover. So, sit back and check out the video below. Then do these 2 things: Schedule time to chat with me on SWP, People Analytics, or One Model more generally. https://meetings.hubspot.com/nicholas-garbis Let me know what SWP elements you think we should cover in Part 2! You can message me here or post your comment to LinkedIn here. SCHEDULE TIME TO CHAT! Workforce Planning Book List: Agile Workforce Planning, by Adam Gibson Strategic Workforce Planning, by Ross Sparkman Strategic Workforce Planning: Guidance and Back-Up Plans, by Tracey Smith ... and one day I may write an SWP book to add to this list! .... :)
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14 min read
Chris Butler
If people analytics teams are going to control their own destiny they're going to need to need to support the enterprise data strategy. You see, the enterprise data landscape is changing and IT has heard its internal customers. You want to use your own tools, your own people, and apply your hard won domain knowledge in the way that you know is effective. Where IT used to fight against resources moving out of their direct control they have come to understand it's a battle not worth fighting and in facilitating subject matter experts to do their thing they allow business units to be effective and productive. Enter the Enterprise Data Architecture The movement of recent years is for IT to facilitate an enterprise data mesh into their architecture where domain expert teams can build, consume, and drive analysis of data in their own function...so long as you can adhere to some standards, and you can share your data across the enterprise. For a primer on this trend and the subject take read of this article Data Mesh - Rethinking Enterprise Data Architecture The diagram heading this blog shows a simplified view of a data mesh, we'll focus on the people analytics team's role in this framework. What is a Data Mesh? A data mesh is a shared interconnection of data sets that is accessible by different domain expert teams. Each domain team manages their data applying its specific knowledge to its construction so it is ready for analytics, insight, and sharing across the business. When data is built to a set of shared principles and standards across the business it becomes possible for any team to reach across to another domain and incorporate that data set into their own analysis and content. Take for example a people analytics team looking to analyze relationships between customer feedback and front-line employees' attributes and experience. Alternatively, a sales analytics team may be looking at the connection between learning and development courses and account executive performance, reaching across into the people analytics domain data set. Data Sharing becomes key in the data mesh architecture and it's why you've seen companies like Snowflake do so well and incumbents like AWS bring new features to market to create cross-data cluster sharing. There are two ways to share data across the enterprise: Cross Cluster / Data Warehouse sharing - each domain operates its own schemas or larger infrastructure for allowing other business units to access. AWS has an example here https://aws.amazon.com/redshift/features/data-sharing/ Feeding domain Analytics-Ready data into a centralized enterprise data architecture - This is more typical today and in particular is useful if the organization has a data lake strategy. Data lakes are generally unstructured and more of a data swamp, in order to be useful the data needs to be structured, so providing Analytics Ready data into either a data lake or data warehouse that adheres to common principles and concepts is a much more useable method of sharing value across data consumers. One Model was strategically built to support your HR data architecture. If you'd love to learn more, check out our people analytics enterprise products and our data mesh product. How can people analytics teams leverage and support the HR data architecture? The trend to the mesh is growing and you're going to be receiving support to build your people analytics practice in your own way. If you're still building the case for your own managed infrastructure then use these points for helping others see the light and how you are going to support their needs. Identify the enterprise data strategy I'm sure you've butted heads against this already but identify if the organization is supportive of a mesh architecture or you'll have to gear up to show your internal teams how you will give them what they need while taking away some of their problems. If they're running centralized or in a well-defined mesh, you will have different conversations to obtain or improve your autonomy. Supporting the enterprise data mesh strategy People analytics teams are going to be asked to contribute to the enterprise data strategy if you are not today. There are a number of key elements you'll need to be able to do this. Extract and orchestrate the feeds from your domain source systems. Individual systems will have their nuances that your team will understand that others in the enterprise won't. A good example is supervisor relationships that change over time and how they are stored and used in your HRIS. Produce and maintain clean feeds of Analytics-Ready data to the enterprise. This may be to a centralized data store or the sharing of your domain infrastructure across the business. Adhere to any centralized standards for data architecture, this may differ based on the tooling used to consume data. Data architected for consumption by Tableau is typically different (de-normalized) from a model architected for higher extensibility and maintenance (normalized) which would allow for additional data to be integrated and new analyses to be created without re-architecting your core data tables. You can still build your own nuanced data set and combinations for your domain purpose but certain parts of the feed may need to follow a common standard to enable easy interpretation and use across the enterprise. Define data, metrics, and attributes and their governance ideally down to the source and calculation level and document for your reference and for other business units to better understand and leverage your data. The larger your system landscape is the harder this will be to do manually. Connect with other domain teams to understand their data catalogues and how you may use them in your own processes. Why should people analytics care? This trend to the data mesh is ongoing, we've seen it for a number of years and heard how IT thinks about solving the HR data problem. The people analytics function is the domain expertise team for HR, our job is to deliver insight to the organization but we are the stewards of people data for our legacy, current, and future systems. To do our jobs properly we need to take a bigger picture view of how we manage this data for the greater good of the organization. In most cases, IT is happy to hand the problem off to someone else whether that's an internal team specialized in the domain or an external vendor who can facilitate How does One Model support the Data Mesh Architecture for HR It won't surprise you to hear but we know a lot about this subject because this is what we do. Our core purpose has been understanding and orchestrating people data across the HR Tech landscape and beyond. We built for a maturing customer that needed greater access to their data, the capability to use their own tools, and to feed their clean data to other destinations like the enterprise data infrastructure and to external vendors. I cover below a few ways in which we achieve this or you can watch the video at the end of the article. Fault Tolerant Data Extraction Off the shelf integration products and the front end tools in most HRIS systems don't cater for the data nuances, scale of extraction, or maintenance activities of the source system. Workday for example provides snapshot style data at a point in time and it's extraction capabilities quickly bog down for medium and large enterprises. The result is that it is very difficult to extract a full transactional history to support a people analytics program without arcane workarounds that give you inaccurate data feeds. We ultimately had to build a process to interrogate the Workday API about dozens of different behaviors, view the results and have the software run different extractions based on its results. Additionally most systems don't cater for Workday's weekly maintenance windows where integrations will go down. We've built integrations to overcome these native and nuance challenges for SuccessFactors, Oracle, and many other systems our customers work with. An example of a workday extraction task is below. Data Orchestration and Data Modelling Our superpower. We've built for the massive complexity that is understanding and orchestrating HR data to enable infinite extension while preserving maintainability. What's more it's transparent, customers can see how that data is processed and it's lineage and interact with the logic and data models. This is perfect for IT to understand what is being done with your data and to have confidence ultimately in the resulting Analytics-Ready Data Models. Data Destinations to the Enterprise or External Systems Your clean, connected data is in demand by other stakeholders. You need to be able to get it out and feed your stakeholders, in the process demonstrating your mastery of the people data domain. One Model facilitates this through our Data Destination's capability, which allows the creation and automated scheduling of data feeds to your people data consumers. Feeds can be created using the One Model UI in the same way as you may build a list report or an existing table and then just add it as a data destination. Host the Data Warehouse or Connect Directly to Ours We've always provided customers with the option to connect directly to our data warehouse to use their own tools like Tableau, Power BI, R, SAC, Informatica, etc. Our philosophy is one of openness and we want to meet customers where they are, so you can use the tools you need to get the job done. In addition to this a number of customers host their own AWS Redshift data warehouse that we connect to. There's capability to run data destinations to also feed to other warehouses or use external capability to sync data to other warehouses like Azure SQL, Google, Snowflake etc. A few examples Snowflake - https://community.snowflake.com/s/article/How-To-Migrate-Data-from-Amazon-Redshift-into-Snowflake Azure - https://docs.microsoft.com/en-us/azure/data-factory/connector-amazon-redshift Data Definitions and Governance With One Model all metric definitions are available for reference along with interactive explanations and drill through to the transactional detail. Data governance can be centralized with permission controls on who can edit or create their own strategic metrics which may differ from the organizational standard. HR Specific Content and Distribution We provide standard content tailored to the customers own data providing out of the box leverage for your data as you stand up your people analytics programs. Customers typically take these and create their own storyboards strategic to their needs. It's straightforward to create and distribute your own executive, HRBP, recruiting, or analysis project storyboards to a wide scale of users. All controlled by the most advanced role based security framework that ensures only the data permissioned can be seen by the user while virtually eliminating user maintenance with automated provisioning, role assignment, and contextual security logic where each user is linked to their own data point. Watch the two minute video of what One Model does
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10 min read
Phil Schrader
Thanks for stopping by the blog to check out our work on integrating Workday, Greenhouse, and Engagement Survey data. Along with a video walking through the exact insights you can get, we use this blog to dive into key considerations when combining HCM, recruiting, and engagement surveys. If you want to chat through any of the ideas here feel welcome to schedule a time on my calendar. I'd love to chat: Why We're Even Talking about Workday Greenhouse Integrations with Survey Data. We started noticing about a year ago. Ryan and I would get a cool new lead that came in from a really exciting company to talk to, often based on the West Coast, often in tech. During our initial conversation, they would talk about workforce growth, diversity, and engagement. Then we’d ask about their system mix, and they’d say, “Well, we switched to Workday a couple of years ago, but we use Greenhouse for recruiting, and we have Culture Amp for surveys (or Glint or Qualtrics).” Jump to video Ryan and I started joking about how this was happening all the time-- to the point where we’d sometimes try to autocomplete “Culture Amp” for the person after they mentioned Greenhouse. (This totally failed on a recent call so we’ll stop doing that now.) Over the winter and into the spring Ryan and I’d periodically throw some time on the calendar to talk about this batch of companies we kept running into. We’d talk about the type of storyboards and views we might put together to focus specifically on them. Then the conversation would drift over into our mutual interests like land, soil, gardening, and regenerative agriculture. Video: Insights from Greenhouse, Workday and Culture Amp Eventually we were able to get some initial versions of these ideas built out in a demo One Model site-- and felt really excited that the inspiration we were finding out among the trees (Ryan in Vancouver) and fields (me in Texas) fit really well with the story we wanted to tell about how organizations grow over time. For me personally it was just so satisfying to take the analytic side of my world and have it elevate, rather than reduce the more organic, intangible and relationship oriented lessons I learn as a parent, a cook, and a gardener. (I also play tons of Call of Duty so don’t go feeling like you have to be some sort of woodland saint to appreciate this stuff.) In the video above we introduce some of these ideas for looking at your workforce, anchoring around the idea of treating hiring cohorts as organizational growth rings. In other words, starting with data from Workday (or whatever core HR system) and grouping headcount by the year they joined the company. For example, everyone from what you might call “the hiring class of 2015”. Reviewing Your Growth Rings for Real Workday & Greenhouse BI When you lay the data out like that it’s just flat-out interesting to look at. It gives you (or me at least) a cool hybrid-style view. It makes me think of the way that people invariably slow down and pause to appreciate the growth rings you see on a cross-cut section of the tree. On one hand, you get a definite feeling of growth and movement and activity. On the other, you get a sobering perspective on long-time scales. You need this appreciation when thinking about how human beings cooperate together and change as they do the work of your organization. This second feeling is a great counterweight to the action-oriented, get-it-done-now energy that we also must bring to our work. As we looked at these growth rings, Ryan and I started to deepen our appreciation of how much human experience is represented in those layers. How much somebody who has been around for 5 or 10 years has seen and learned-- all the things about the organization that are usually intangible and difficult to measure. We thought that it was a humble and human perspective on what our analytic minds would call human capital, but what we could just call out as accumulated human experience. From the growth ring analogy, you can start to mix in other people analytics perspectives like diversity. You can see that maybe your current headcount is trending in a more diverse direction but you're going to see (and your newer hires might directly experience) a lagging effect where all that accumulated human experience takes longer to become more diverse. So much of it has already been accumulated in prior years. In fact, that gap might give you more appreciation for inclusion efforts in your workforce because you can start to visualize the gap between a diverse headcount and an organization that has grown, developed, and incorporated a diversity of experience. And then we thought, “This would be the perfect place to layer in engagement data from Glint or Culture Amp or other surveys because you could see both the engagement of your people but also get that visual sense of the engagement of all that accumulated human experience. Ryan and I felt like that really boils a lot of people analytics down into something pretty simple. If someone comes into (or logs onto) work to start the day, and they’ve got 5 or 10 or more years of experience with your company’s products, services, customers, culture, networks, systems, coworkers, etc. AND they’re engaged and eager to dive back into that work-- well then you can’t really go wrong with that. What more could you ask for? You can’t really artificially assemble that. You’ve got to grow it. If you pull together some thinking on how a resilient ecosystem handles disruption and then think about what a wild, disruptive period we’ve been going through, then you just get filled with this desire to grow a diverse, resilient workforce to match. And we also started seeing how the work that talent acquisition does can be informed by and elevated by this view. Recruiting is often seen as the fast-paced (time to fill), process-driven (time in status) side of HR. But now we have a view that emphasizes the long term consequences of that frenetic activity. And we have a view that guides us in our analysis of that data. Greenhouse and Survey Data Adds Insight from the Beginning to the End of your People's Journey. Greenhouse is both perfectly named and well designed for this type of thinking. Instead of leaving all that scorecard data (for example) behind at the point of hire, why not look back on past growth rings and ask-- what did we learn from the interview process that might help us predict if a certain candidate will really take root and become part of the deep-tissue of our organization? Did we focus too much on the immediate skills they would bring, when it turns out that communication and adaptability were the things that really mattered? And so, what resulted from all these great conversations was the beginning of some new views on people data-- woven together from Workday, Greenhouse, and Engagement Surveys. We’ve captured this thinking in the video above. Please check it out if you haven’t done so already. As a final note, think of all the questions you could answer with a Workday and Greenhouse integration with survey data like: Are our employees happy with their work-life balance? This took me less than an hour to bring the data together and build out some initial visuals. Are you asking all the right questions? Read about our People's Analytics Challenge! Don't let our communication stop here! It’s already been rewarding for me personally-- and I hope that there are many more conversations to come that grow these ideas further. If you’ve got some of those next ideas or if you’ve got some questions about the views we put together-- grab some time to chat with me here:
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11 min read
Chris Butler
This week One Model was delighted to participate with an elite group of our industry peers in the HR Tech Alliances, Virtual Collaboration Zone - Best New People Analytics Solution competition. I'm excited to share some detail on what the judges saw to justify the outcome. This wasn't an empty competition either and had some significant companies in the field. The overall scores were as below: 1st - One Model - 4.28 2nd - activ8 intelligence 4.06 3rd - Visier - 3.93 Given how proud I am of our team for winning this award, I thought I would share our presentation. Before I do that, I would like to acknowledge how far the pure play people analytics space has come in recent time. As an industry, this is something that we should celebrate as we continue on a path of innovation to deliver better products and better outcomes for our clients. People analytics is an exciting place to be as 2020 comes to its (merciful) conclusion! We'll take a quick tour through the highlights of our presentation and demonstration. Who are we? One Model provides its customers with an end to end people analytics platform that we describe as an infrastructure. We call it an infrastructure, because from the ground up - One Model is built to make everything we do open and accessible to our most important stakeholder - you the customer. Everything from our data models to our content catalogues, right down to the underlying data warehouse is transparent and accessible. One Model is not a black box. Over the last five years, we have been guided by the principle that because of One Model’s transparency and flexibility - our customers should feel as if this is a product that they built themselves. Our History For those of you who are unfamiliar with the history of One Model, the core of our team is derived from workforce analytics pioneers InfoHRM. InfoHRM was acquired by Successfactors in 2010 and subsequently SAP in 2012. During our extraordinary ride from humble Australian business to integral part of one of the world’s largest software companies - our team learned that while our solution gave low maturity users what they needed in terms of the what, why and how of measuring their workforce. Our solution remained an inflexible tool that customers outgrew as their own capabilities increased. With an increased sophistication, customers were asking new and more complicated questions and the solution simply couldn't evolve with them. Five years later and sadly, this is what we continue to see from other vendors in our space. Meeting our customers where they are on their people analytics journey and supporting them through their evolution is fundamental to the One Model platform. Be Open; Be Flexible; Don't put a ceiling on your customers capabilities. One Model takes care of the hard work of building a people analytics infrastructure. We built One Model to take care of both low maturity users, who need simple and supported content to understand the power of people analytics. At the same time, we need to deliver an experience that customers grow into and higher maturity users can leverage world-leading One AI data science and statistical engine. Furthermore, if they want to use their own tools or external data science teams - their people analytics platform should enable this - not stand in its way. One Model’s Three Pillar People Analytics Philosophy Pillar 1: Data Orchestration People data is useless if you can’t get access to it. Data orchestration is critical to a successful people analytics program. At One Model - Data Orchestration is our SUPERPOWER! Many thousands of hours have been invested by our team in bespoke integrations that overcome the native challenges of HR Tech vendors and provide full, historic and transactional extracts ready for analytics. This isn’t easy. Actually, it’s terrifyingly hard. Let’s use Workday as an example; To put it mildly, the data from their reporting engine and the basic API used to download these reports is terrible. It's merely a snapshot that doesn't provide the transactional detail required for analytics. It's also impossible to sync history as it changes over time - an important feature given the nature of HR data. You have to go to the full API to manage a complete load for analytics. We are 25,000 hours in and we're still working on changes! To power our data orchestration, we built our own Integrated Development Environment (IDE) for managing the enormous complexity of people data and to house our data modelling tools. Data quality and validation dashboards ensure we identify and continue to monitor data over time for correction. Data destinations allow us to feed data out to other places, many of our customers use this to feed data to other vendors or push data to other business units (like finance) to keep other business units up to date. Unlike garden variety Superpowers (like flying), our data orchestration capability did not develop by serendipity or luck. It developed and continues to develop by the hard work and superior skills of our team. Pillar 2: Data Presentation Most other vendors in our space exist here. They don't provide open and flexible toolsets for Data Orchestration or Value Extraction / Data Science. When we started One Model, we hadn't planned on a visualization engine at all. We thought we could leverage a Tableau, Looker, or Birst OEM embedded in our solution. After much evaluation, we just couldn't deliver the experience and capability that analyzing and reporting on HR data requires. Generic BI tools aren't able to deliver the right calculations, with the right views across time, in a fashion that allows wide distribution according to the intense security and privacy needs of HR. We had to build our own. Ultimately our vertical integration allows unique user security modelling, integration of One AI into the frontend UI, all while not limiting us to the vagaries of someone else's product. Our implicit understanding of how HR reports, analyzes, and distributes data required us to build a HR specific data visualization tool for One Model. Pillar 3: Data Science / Value Extraction - One AI I like to describe the third pillar of our people analytics philosophy as our 'Value Extraction' layer. This layer is vertically integrated on top of our data models, it allows us to apply automated machine learning, advanced statistical modelling, and to augment and extend our data with external capabilities like commute time calculations. Predictive capabilities were our first target and we needed to build unique models at scale for any customer, regardless of their data shape, size, or quality. A one size fits all algorithm that most other vendors in the HR space provide wasn't going to cut it. Enter automated machine learning - Our One AI capability will look across the entire data scope for a customer, it will introduce external context data, select it's own features, train potentially hundreds of models and permutations of those models and select the best fit. It provides a detailed explanation of the decisions it made, enough to keep any data scientist happy. The best of all these models can be scheduled and repeated so every month it could be set to re-learn, re-train, and provide an entirely different model as a fit to your changing workforce. This unbelievable capability doesn't lock out an experienced team, but invites them in should they wish to pull their own levers and make their own decisions. The One AI engine is now being brought to bear in a real time fashion in our UI tacking forecasting, bayesian what if analyses, bias detection, anomaly detection, and insight detection. We have barely scratched the surface of the capability and our vertical integration with a clean, consistent data model allows these advanced tools to work to deliver the best outcomes to customers. Labor Market Intelligence One Model has the world’s best understanding of your internal HR data set; we do wonderful things with the data you already have - but we were missing the context of the external labor market and how that impacted our customer's workforces. As a result, we have developed a proprietary Labor Market Intelligence (LMI) tool. LMI is being released in January 2021 as a standalone product providing labor market analytics to our customers. LMI retains the functionality that you love about our people analytics platform - the ability to flexibly navigate data, build your own storyboard content, and drill through to granular detail. Importantly what LMI will allow for One Model enterprise customers is the ability to link external market data to internal people data. Delivering outcomes like identifying persons paid lower than the market rate in their region, identifying employees in roles at risk of poaching due to high market demand and turnover, and helping you understand if your talent are leaving for promotions, or lateral moves. Collaboration with the HR Tech Ecosystem Finally, One Model understands the power of collaboration in the HR Tech ecosystem. We are already working with leading consultancies like Deloitte and are embedded in HCM vendors helping consume and make sense of their own data to deliver people analytics and extract value for their customers. At the end of the day, our vision is to understand the entire HR Tech ecosystem at the data layer, to help customers realize their investment in these systems, and to provide a data insurance policy as they transition between systems. Analytics is a by-product of this vision and thankfully it also pays the bills ;)
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3 min read
Nicholas Garbis
As part of a recent People Analytics course from the Future Workplace, Nicholas Garbis joined forces with course leader Heather Whiteman, PhD to co-author an eBook on People Analytics called "Explore the Power of People Analytics: A Guide for Business and HR Leaders". While the book was specifically aimed at a general HR and business leader audience, we quickly found that a number of well-accomplished People Analytics leaders were getting value out of it as well. Whereas some of the HR and business leaders may be entering this content for the first time, the more mature people analytics leaders are always searching for that same introductory content that can help them to increase understanding and adoption of their team's work. We are here to accelerate you people analytics journey. As titled, the aim of the eBook is to "explore" the topic of People Analytics. In terms of a journey, this is a guidebook that highlights various "points of interest" that make the journey interesting and worth pursuing. Download the eBook (.pdf) Explore the Power of People Analytics We hope this eBook sparks ideas for how you can apply people analytics in your organization and makes it more accessible for your teams. We invite you to start building greater capability in this area so you can take advantage of the opportunities people analytics makes possible. Paperback edition is available on Amazon.com. About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co. One Model’s new Labor Market Intel product delivers external supply & demand data at an unmatched level of granularity and flexibility. The views in LMI help you to answer the questions you and your leaders need answers to with the added flexibility to create your own customized views. Learn more at www.onemodel.co/LMI.
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6 min read
Joe Grohovsky
As a result of my blogs and customer conversations, I receive a variety of interesting comments and feedback from my contacts in the People Analytics space. A common topic is that different stakeholder groups within a People Analytics project have vastly different ideas as to what is acceptable in a People Analytics tool. This often leads to disappointment, failed initiatives and wasted budget. Examples provided are that LOB (Line of Business) and general HR professionals tend to be attracted to and satisfied with "Convenience Analytics". Convenience Analytics is a term referring to simplistic, easy to digest metrics or reports. They are typically generated without much effort, often by the source system, but are limited in breadth, depth, and growth possibilities. The appeal of Convenience Analytics may be their low-cost of entry and their non-threatening nature to the decision makers that use them, but they are extremely inflexible. Significant challenges occur when Convenience Analytics are deployed to an organization expecting deep insights, growth of use-cases, or the addition of new data sources. The People Analytic and HRIS (Human Resource Information System) professionals supporting these Convenience Analytics projects ultimately suffer from a lack of long-term data quality and a capability to drive future insight that is uniquely strategic to their organization and not a pre-canned report. One Model recognizes that a properly constructed People Analytics infrastructure has a system agnostic HR Data Strategy, and this has driven our industry leading Data Orchestration capabilities. Data Orchestration is a process that takes siloed data from multiple locations, combines it, and makes it available for data analysis. One Model breaks Data Orchestration into 4 activities/phases: Data Ingestion – This phase is the process of removing data from source systems and delivering it into One Model. We take a flexible approach and accommodate strategies ranging from API extraction, to file based transfer over SFTP, to manual uploading of data through the One Model interface. Data Modeling – After data is ingested, it is combined into a single, interconnected data model that supports a broad range of analytics. Taken together with the ingestion phase these activities constitute ETL (extract, transform and load) activities. This results in what is recognized as a fact and dimension star schema style of data model. Data Quality – This phase is driven by rules and logic. As a result, quality issues in source data begin to surface. These issues are captured and resolved during this time. Data Destinations – This phase is the scheduling of data exports out of the One Model system and delivering them to SFTP sites, Amazon S3 buckets, and/or other destinations. This reflects the vision of our company; not to be the ultimate destination for your data but a data asset existing amid your analytics infrastructure feeding downstream system and tools. Data Accessibility is a noteworthy benefit of One Model's Data Orchestration process. A customer is not restricted to accessing their data only through our query engine. Access is also provided to your orchestrated data directly in the data warehouse hosted on AWS. This allows the usage of your own tools like Tableau, Looker, Qlik, etc. for presentation purposes. Additional benefits include being able to run your own integrations or internal application development against a clean, comprehensive data set to solve challenges specific to your organization. Let us look at two of the most popular HRIS systems and some of the data orchestration advantages One Model offers. Workday - Workday uses point-in-time (snapshot) based reporting. This snapshot reporting is recognized as being limited and brittle in accommodating backdated changes and other HR analytic scenarios. External data is difficult to connect with and pulling and maintaining snapshots from Workday is a pain. One Model avoids all issues with snapshot reporting by rebuilding a data schema that is effective dated and transactional in nature. The result is a dataset perfect for delivering accurate, flexible reporting and analytics. We support both full and incremental refreshes of data from Workday. SAP SuccessFactors - One Model has pre-built data processing logic that can be used to transform data from various SuccessFactors objects into a well-organized, effective-dated structure that supports a wide range of analytic use cases. The SAP SuccessFactors API allows us to identify customizations in your SuccessFactors configuration -- and our data model readily supports the inclusion of those custom fields in the resulting data model. We support both full and incremental refreshes of data from the SuccessFactors API. One Model has perfected data orchestration so well that we are often included in searches for integration partners. Our tailored solution enables the accurate transfers of large files of complex data from existing tools such as an ATS into new, replacement tools. This creates tremendous possibilities for efficiency in migrations and adoption of new technology. If you are interested in receiving full value from your People Analytics investment, please click here to reach out to One Model to schedule an in-depth discussion. Listed below are links to various articles that provide further insight into this topic. The end Snapshot Reporting for People Analytics The need to build Structural Views of SAP SuccessFactors Data People Analytics for SAP SuccessFactors Using People Analytics to support system migration About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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3 min read
Nicholas Garbis
Yes, it's 'Whiteboard Time' again! In this blog post, we are sharing a video recording on the topic of modeling future diversity levels using a basic example explained in a whiteboard learning session. We will also include a simple, downloadable tool in Excel (link below). The video starts with a quick look at a Diversity storyboard from One Model's demo environment where sample data has been set up for sharing design ideas with current and prospective customers. This is aimed at a broad audience of HR leaders and managers who would automatically see just their own areas of responsibility (with ability to filter further). This structure is a 'storyboard' in that it uses clearly stated questions followed by relevant metrics in a set of 'tiles' intentionally designed to shorten the time from question to insight. VIDEO: click below to launch the video. Beneath the video you will see the download link for the basic diversity modeling worksheet. PROJECTION MODEL: click below to download the Excel-based tool that is referenced in the video. It includes some basic instructions as well. Please reach out with any feedback or suggestions for this topic area -- and to let us know of other topics you would like to see us covering in a future session. About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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11 min read
Joe Grohovsky
Most of my One Model work involves chatting with People Analytics professionals discussing how our technology enables them to perform their role more effectively. One Model is widely acknowledged for our superior ability to orchestrate and present customer’s people metrics, as well as leveraging Artificial Intelligence/Machine Learning for predictive modeling purposes. My customer interactions always result in excited conversations around our data ingestion and modeling, and how a customer can leverage the flexibility of our many presentation options. However, when it comes to further exploring the benefits of Artificial Intelligence, enthusiasm levels often diminish, and customers become hesitant to explore how this valuable technology can immediately benefit their organization. One Model customer contacts tend to be HR professionals. My sense is they view Artificial Intelligence/Machine Learning as very cool, but aspirational for both them and their organization. This is highlighted during implementations as we plan their launch and roll-out timelines; the use of predictive models is typically pushed out to later phases. This results in a delayed adoption of an extraordinarily valuable tool. Machine Learning is a subset of Artificial Intelligence and is the ability for algorithms to discern patterns within data sets. It elevates decision-support functions to an advanced level and as such can provide previously unrecognized insights. When used with employee data there is understandable sensitivity because people's lives and careers risk being affected. HR professionals can successfully use Machine Learning to address a variety of topics that impact an array of areas throughout their company. Examples would include: Attrition Risk – impact at the organizational level Promotability – impact at the employee level Candidate Matching – impact outside the organization Exploratory Data Analysis - quickly build robust understandings of any dataset/problem With this basic understanding, let us explore three possible reasons why the deployment of Machine Learning is delayed, and how One Model works to increase a customer’s comfort level and accelerate its usage. #1: Machine Learning is undervalued For many of us, change is hard. There are plenty of stories in business, sports, or government illustrating a refusal to use decision-support methods to rise above gut-instinct judgments. The reluctance or inability to use fact-based evidence to sway an opinion makes this the toughest category to overcome. #2: Machine Learning is misunderstood For many of us, numbers and math are frightening. Typically, relating possibility and probability to a prediction does not go beyond guessing at the weather for this weekend’s picnic. Traditional metrics such as employee turnover or gender mix are simple and comfortable. Grasping how dozens of data elements from thousands of employees can interact to lead or mislead a prediction is an unfamiliar experience for many HR professionals that they would prefer to avoid. #3: Machine Learning is intimidating This may be the most prevalent reason, albeit subliminal. Admitting a weakness to colleagues, your boss, or even yourself is not easily done. Intimidation may arise from several sources. The first occurs from the general lack of understanding referenced earlier, accompanied by a fear of liability due to data bias or unsupported conclusions. Often, some organizations with data scientists on staff may pressure HR to transfer the responsibility for People Analytics predictions to these scientists to be handled internally with Python or R. This sort of internal project never ends well for HR; it is a buy/build situation akin to IT departments wanting to build their own People Analytics data warehouse with a BI front-end. Interestingly, when a customer’s data science team is exposed to One Model’s Machine Learning capabilities, they usually become some of our biggest advocates. During my customer conversations, I avoid dwelling on their reluctance and simply explain how One Model’s One AI component intrinsically addresses Machine Learning within our value proposition. Customers do not need familiarity with predictive modeling to enjoy these benefits. Additionally, I explain how One AI protects our customers by providing complete transparency in how training data is selected, results are generated, how any models are making decisions, validating the strength of resulting prediction, and thorough flexibility to modify every data run to fit within each customer’s own data ethics. This transparency and flexibility provide protection against data bias and generally bad data science. Customers simply apply an understanding of their business requirements to One AI’s predictions and adjust if necessary. Below is a brief explanation of a few relevant components of One Model's Machine Learning strategy and the benefits they provide. Selection of Training Data After a prediction objective is defined, the next step is to identify and collect the relevant data points that will be used to teach One AI how to predict future or unseen data points. This can be performed manually, automatically, or a combination of both. One AI offers automatic feature selection using algorithms to decide which features are statistically significant and worth training upon. This shrinks the data set and reduces noise. The context of fairness is critical, and it is at this point that One AI starts to measure and report on data bias. One measurement of group fairness that One AI supports is Disparate Impact. Disparate Impact refers to practices that adversely affect one group of people of a protected characteristic more than another, even if a group does not overtly discriminate (i.e. their policies may be neutral). Disparate Impact is a simple measure of group fairness and does not consider sample sizes, instead focusing purely on outcomes. These limitations work well with attempting to prevent bias from getting into Machine Learning. It is ethically imperative to measure, report and prevent bias from making its way into Machine Learning. This Disparate Impact reporting is integrated into One AI along with methods to address the identified bias. One AI allows users to measure group fairness in many ways and on many characteristics at once, making it easy to make informed, ethical decisions. Promotability predictions could serve as an example. If an organizations historic promotion data is collected for training purposes, the data set may reflect a bias toward Caucasian males who graduated from certain universities. Potential bias toward gender and race may be obvious, but there may also be a hidden bias toward these certain universities, or away from other universities that typically target different genders or race. An example of how hidden bias affected Amazon can be found here. One AI can identify bias and help users remove bias from data using the latest research. It is important to One Model that our users not only be informed of bias but can also act upon these learnings. Generation of Results After a predictive model is run, One AI still takes steps that ensure the predictions are as meaningful as possible. It is important to note that One AI does all the “heavy lifting”; our customers need only provide oversight as it applies to their specific business. Any required modifications or changes are easily handled. An example can be found in an Attrition Risk model. After running this model our Exploratory Data Analysis (EDA) report provides an overview of all variables considered for the model and identifies which were accepted, which were rejected, and why. A common reason for rejection is that of a “cheating” variable. This is when there is too close of a one-to-one relationship between the target and identified variable. If “Severance Pay” is rejected as a cheating variable, we likely will agree because logically anyone receiving a severance package would be leaving the company. However, if “Commute Time 60+” is rejected as a cheating variable, we may push back and decide to include this because commuting over an hour is something the organization can control. It is an easy modification to override the original exclusion and re-run the model. One Model customers who are more comfortable with predictive modeling may even choose to dive deeper into the model itself. A report on each predictive run shows which model type was used, Dataset ID’s, Dimensionality Reduction status, etc. One Model’s flexibility allows a customer to change these with a mouse click should they want to explore different models. Please remember that this is not a requirement at all and simply a reflection of the available transparency and flexibility for those customers preferring this level of involvement. My favorite component of our results summary reporting is how One AI ranks the variables impacting the model. Feature Importance is listed in descending order of importance to the result. In our Attrition Risk model above, the results summary report would provide a prioritized list of items to be aware of in your attempt to reduce attrition. Strength of Prediction It is important to remember that Machine Learning generates predictions, not statements of fact. We must realize that sometimes appropriate data is just not available to generate meaningful predictions and these models would not be trustworthy. Measuring and reporting the strength of predictions is a solid step in developing a data-driven culture. There are several ways to evaluate model performance; many are reflected in the graphic below. One Model automatically generates multiple variations to help provide a broad view and ensure that a user has the data they feel comfortable evaluating. Both “precision” and “recall” are measured and displayed. Precision measures the proportion of positive identifications (people who terminate in the future) the model correctly identified. Put another way when the model said someone would terminate, how often was it correct? Recall reflects the proportion of actual positives (people who terminate in the future) that were correctly identified by the model. Put another way, of all the people that actually terminated - how many did the model correctly identify. Precision & recall are just one of the many metrics that One AI supports. If you or your team is more familiar with another method for measuring performance, we most likely already support it. One Model is glad to work with your team in refining your algorithms to build strong predictive models and ensure you have the confidence to interpret the results. Summary Machine Learning and Data Science are extremely valuable tools that should be a welcome conversation topic and an important part of project roll-out plans. People Analytic professionals owe it to their companies to incorporate these tools into their decision-support capabilities even if they do not have access to internal data scientists. Care should be taken to ensure all predictive models are transparent, free from bias, and can be proven so by your analytics vendor. Want to Learn More? Contact One Model and learn how we can put leading-edge technology in your hands and accelerate your People Analytic initiatives.
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9 min read
Chris Butler
Following our blog last month about how systems issues can open the door to staff underpayment, a number of our stakeholders have asked if we might be able to go deeper into how a people analytics solution and specifically, how One Model can solve this problem. We are nothing if not obliging here at One Model, so here we go! We thought we would answer this question by articulating the most common system-derived problems associated with people data and how One Model and an integrated people analytics plan can help resolve these issues. PROBLEM NUMBER ONE - PEOPLE DATA IS STORED IN MULTIPLE NON-INTEGRATED SYSTEMS As discussed previously, our experience is that most large organisations have at least 7 systems in which they store people data. In some larger organisations - that number can be more than 20! Data silos present a major risk to HR governance. Silos create the risk that information may be different between systems or updated in one system and then not updated in others. If information in one non-integrated system is wrong or out of date, it becomes very hard - firstly to isolate the issue and remediate it and secondly, if the error was made months or years in the past to understand which system controls the correct information. At One Model, we are consistently helping our customers create a single source of truth for their people data. Blending data together across siloed systems provides a great opportunity for HR to cross-validate the data in those systems before it becomes an issue. Blended data quickly isolates instances of data discrepancy - allowing HR to not only resolve individual data issues, but to uncover systemic problems of data accuracy. Often when people are working across multiple systems they will take shortcuts to updating and managing data; this is particularly prevalent when data duplication is involved. If it isn’t clear which system has priority and data doesn’t automatically update in other systems - human error is an inevitable outcome. With One Model, you can decide which systems represent the most accurate information for particular data and merge all data along these backbone elements resulting in greater trust and confidence. The data integration process that is core to the One Model platform can, in effect, create a single source of truth for your people data. This presentation by George Colvin at PAFOW Sydney neatly shows how the One Model platform was used by Tabcorp to manage people data silo issues. PROBLEM NUMBER TWO - LIMITED ACCESS TO DATA IN OLD AND NON-SUPPORTED SYSTEMS Further to the issue of data spread across multiple systems, our experience tells us that not only are most large organisations running multiple people data systems - at least one of those systems will be running software that is either out-of-date or no longer supported by the vendor. So even if you do wish to integrate data between systems, you may be unable. It is always best if you can identify data issues in real time to minimise exposure and scope of impact, but this isn’t always possible and you may have to dig into historical transactional data to figure out the scale of the issue and how it impacts employees and the company. If that wasn’t challenging enough - most companies when changing or upgrading systems for reasons of cost and complexity end up not migrating all of their historical data. This means that you are paying for the maintenance of your old systems or to manage an offline archived database. Furthermore, when you need to access that historical data, running queries is incredibly difficult. This is compounded when you need to blend the historical data with your current system. It is, to put it mildly, a pain in the neck! One Model’s cloud data warehouse can hold all of your historical data and shield your company from system upgrades by managing the data migration to your new system, or housing your historical data and providing seamless blending with the data in your current active systems. If you are interested in this topic and how One Model can help - have a read of this blog that covers in more detail how One Model can mitigate the challenges associated with system migration. PROBLEM NUMBER THREE - ACCESS TO KEY HR DATA IS LIMITED TO THE CENTRAL HR FUNCTION. Either as a result of technology, security, privacy and/or process, HR data in many large organisations is only accessible by the central HR department. As a result, individual line managers don’t have the autonomy or capability to isolate and resolve people data issues before they develop. Data discrepancies are more likely to be identified by the people closest to the real-world events reflected in the transactional system. Managers and HR Business Partners are your first line of defence in identifying data issues, as well as any other HR issue. Of course, line managers need good people analytics to make better decisions and drive strategy, but a byproduct of empowering managers to oversee this information is that they are able to provide feedback on the veracity of the data and quickly resolve data accuracy issues. Sharing data widely requires a comprehensive and thoughtful approach to data sensitivity, security, and privacy. One Model has the most advanced people analytics permissions and role based security framework in the world to help your company deploy and adopt data-driven decision making. PROBLEM NUMBER FOUR - EVEN IF I RESOLVE A HISTORICAL UNDERPAYMENT, HOW DO I ENSURE THIS DOESN’T HAPPEN AGAIN? One of the consistent pieces of feedback we received from the initial blog was that many stakeholders were comfortable that once an issue had been identified they would be able to resolve it - either internally or with the support of an external consulting firm. However, those stakeholders were concerned about their ability to uncover other instances of underpayment in their business or ensure that future incidents did not occur. There is no silver bullet to this problem, however, our view is that a combination of the following factors can ensure organisations mitigate these risks; integrated people data - having a one-stop single source of truth for your people data is crucial. access to historical data - to understand when and how issues developed is also very important. empowerment of line managers to isolate and resolve issues - managers are your first line of defence in understanding and resolving these issues and you need to enable them to fix problems before they develop. People analytics and the One Model product give organisations the tools to resolve all of these problems. If you are interested in continuing this conversation, please get in touch. PROBLEM NUMBER FIVE - A COMPLEX INDUSTRIAL RELATIONS SYSTEM AND A LACK OF PEOPLE HR RESOURCES Previously, most back office processes had a lot of in-built checks and balances. There were processes to cross-check work between team members, ensure transactions totaled up and reconciled correctly and supervisors who would double-check and approve changes. Over the last 20 years large enterprises have been accelerating ERP adoption, in order to realise ROI from that investment, many back office jobs in payroll and other functions were removed with organisations and management expecting that the systems would always get it right. Compounding this and despite many attempts over the years to simplify the industrial relations system, the reality is that managing employee remuneration is incredibly complex. This complexity means that the likelihood of making payroll system configuration, interpretation or processing mistakes is high. So what to do? Of course you need expertise in your team, or be able to access professional advice as needed (particularly for smaller companies). In addition, successful companies are investing in people analytics to support their team and trawl through the large volumes of data to find exceptions, look for anomalies, and track down problems. Our view at One Model is that organisations need to develop metrics to identify and detect issues early. It's what our platform does. We have developed data quality metrics to deal with the following scenarios; Process errors Data inconsistency Transactions contrary to business rules Human error A combination of quality metrics, system integrations, and staff empowered to isolate and resolve issues before they become problems are key to minimising the chances of an underpayments scandal at your business. Thanks for reading. If you have any questions or would like to discuss how One Model can help your business navigate these challenges, please click the button below to schedule a demo or conversation.
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7 min read
Tony Ashton
Following recent media reports that another significant Australian institution has been involved in an underpayments scandal, we thought it appropriate to write a blog about the technical and systemic risk of underpayments for large organisations with disparate HR and payroll systems. While some underpayments are deliberate actions by employers, our experience from over twenty years of working with people data for large organisations is that the overwhelming majority of staff underpayments are driven not by malice. In fact, most HR and payroll professionals care deeply about the people in their organisations. Our experience is that the complexity of the industrial relations system coupled with technology and process mistakes are much larger drivers of ongoing mispayment of employees than deliberate intent. That is why HR and payroll teams need support to prevent these issues from developing. It is important to note that the intent of the mispayment is irrelevant to regulators, your customers, the media, and most importantly - your employees. Whether your organisation, the HR team, or the specific line managers involved intentionally or unintentionally underpaid their staff doesn’t justify the underpayment or limit the risk of such a scandal to your organisation. This is a serious issue and the reputation and employee satisfaction risks to any organisation of an underpayments scandal are immense. Worst still, as in the case of the Made Establishment restaurant chain, if the underpayments are systemic and ongoing, they can become existential for that business and lead to collapse and liquidation. Ensuring your employee payments system works and integrates into other parts of your HR technology ecosystem is crucial to the success of your business. So, how can a minor systems or process issue manifest itself into a broader underpayments scandal? Let’s take an employee who was recently promoted. Information as to their performance and resulting promotion is held in their employer’s learning and development and HR systems. However, a process oversight means that although that individual is listed in a higher pay band in one HR system, their promotion either hasn’t translated into the payroll platform or the information was entered into the payroll system manually and a mistake was made during this process. Let’s also assume that the increased salary is about $2,500 a year - so $50 a week, and it comes during a period of change in that organisation’s payroll practices (they moved from monthly to fortnightly pay cycles) so the employee doesn’t readily notice the absence of their increased salary. If you are an organisation of 2,500 employees and this situation occurred to just 5% of your workforce during a yearly performance review, by the end of the second year your underpayments would total almost one million dollars. In large organisations these relatively minor employee changes occur thousands if not tens of thousands of times a year. Extrapolate that out over multiple years and what initially appears as a minor reporting mistake can quickly become an underpayment scandal involving millions of dollars. In the example given above, a $100 per pay cycle oversight becomes a one million dollar problem. This example is deliberately simple in order to illustrate how quickly these minor issues can multiply. In reality, the employee remuneration frameworks in most large organisations are infinitely more complex and individualised, making it much harder to identify and isolate problems before they spiral into a major scandal. This is compounded when organisations are using outdated software or an amalgamation of different non-integrated IT systems to manage this process. Why should you invest in preemptively isolating and resolving issues of underpayment? Firstly, paying your staff their correct salary is not only the right thing to do, it is the law. Secondly, underpayment and wage theft scandals cause untold damage to an organisation's reputation. Thirdly, underpayment is expensive - not only in wage repayment - but in potential fines from regulators and fees to external consulting/accounting/law firms to understand and resolve your underpayments issues. Underpayment at best is expensive, time consuming, and distracting to your organisation. At worst, it could kill your organisation. Finally, to your employees - your most important assets - underpayments represent a crucial failure in the mutual obligation you both have to do the best for one another. How can an organisation expect the best out of its staff if it can’t pay them properly? How can I use people analytics to anticipate and resolve underpayments issues before they become major scandals? Our experience is that large organisations have at a minimum seven different systems in which they store employee information. In larger and group structured organisations that total can be much higher - in some cases more than twenty! Many of the high profile instances of underpayment, especially those where the organisation has self-reported, indicate that the organisation was unaware of the issue until it was too late. This further indicates that they didn’t have the internal capability to understand what was occurring with their people data and aggregate it in a meaningful way. Part of the reason we created One Model is to fix this exact problem. One Model enables our customers to aggregate their disparate people data into one system (hence the name - One Model), so that they can more comprehensively understand their organisation and avoid systemic issues like underpayments. The ability to compare data in different systems and flag any discrepancies is a core feature of the One Model platform and a feature that is required to avoid the technology and process issues outlined above. Every conversation we have with a customer or prospective customer begins in the same place - organisations are worried about the quality and accessibility of their people data. Sadly, this is often used as an excuse not to invest in people analytics. However, if we have learned anything from these underpayment scandals, it is that you need to take control of your systems, processes, and data. Implementing a people analytics capability helps you achieve this. Internally, we often talk about the importance of One Model aggregating your data to become a single source of truth for all of the people data in your organisation. It's essential to trust the information that is presented to you and make confident decisions based on accurate information. Underpayments are something that your organisation needs to get right and we think accurate people analytics is one of the tools that you can use to get it right. One Model wants to work with enterprises to make sure that these issues are discovered and resolved before they turn into existential threats to your business. If you would like to continue this conversation and learn how One Model can help, or have any feedback on this blog, please comment below or click here to schedule a chat.
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2 min read
Nicholas Garbis
On July 28, 2020, Nicholas Garbis of One Model and Cary Sparrow of Greenwich.HR delivered a press conference from the floor of the old Minneapolis Grain Exchange to discuss emerging trends in the US labor markets based on daily job posting data in the Covid Job Impacts website. Below is an excerpt of some of the main points, followed by a link to the video of the event. "Next Friday, August 7th, the Bureau of Labor Statistics will release the July jobs report. (Link to latest release here.) While there’s an ongoing debate regarding what will be in the July report, our recent analysis demonstrates that despite the dramatic increases in Covid-19 infections in many states over the month of July, the positive trends in the job market in many industries have continued through today. As a quick summary: The June jobs report was quite positive -- with a gain of 4.8 million jobs driven by big gains in Retail and Hospitality, the unemployment rate dropped 2.2% to 11.1%. Moving into July, the same two industries continued their upward trend -- and they were joined by others: Healthcare, Wholesale, and Construction. Our projection is that next week’s jobs report will be very strong -- much stronger than what you might expect given the gloom and doom in the news. Getting more specific, we project an increase in employment of about 6.0 million, which will drop the unemployment rate to around 9%, possibly below 9%. While we are optimistic for the July numbers, we are very cautious about what will happen in August. There is certainly continued momentum, but a change in state policies could easily reverse the gains." Cary and Nicholas proceeded to highlight various industries' results and then answer questions from the online audience. Be sure to sign up for our One Model blog and follow us on LinkedIn to learn about the next Labor Markets Press Conference!
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2 min read
Stacia Damron
One Model recently took part in HR.com's "Inspire 2020" virtual symposium. Nicholas Garbis, One Model's VP of People Analytics Strategy, led the audience through a presentation titled "People Analytics for the COVID Reset." He shared some updates on the impacts of Covid on job markets and the various ways people analytics teams are supporting their businesses with data and analytics in this time of need. His presentation also includes some guidance on 'designing your workforce with intentionality' that lays out several steps that people analytics and workforce planning leaders should be considering at this time. Fortunately, we have been able to provide a link to the video below. Check it out and let us know what you think! Click play below: Enjoy the presentation above? Subscribe using the form below for related content from Nicholas and other members of our team covering people analytics, Covid-19, and other topics of interest to HR professionals. About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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10 min read
Nicholas Garbis
SUMMARY: June was great at 4.8 million new jobs. July will be over 6 million. The June 2020 employment report from the Bureau of Labor of Statistics showed an increase of 4.8 million jobs. It was welcomed as good news, but asterisks were quickly added based on the recently surging Covid infection numbers. July, it is feared, will shown softening in the market. Contrary to many, our data indicates that July jobs report will be better than June, likely exceeding 6 million new jobs. Our analysis is based on the positive signs we have been tracking in the Hospitality and Retail industries over the past several weeks through our Covid Job Impacts site, where we show new job postings on a daily basis by industry, state, and job family. Within the site’s commentary we have been commenting on the progress in these two industries specifically over the past several weeks. The BLS highlighted Hospitality and Retail in their June comments. These industries made up ~60% of the new jobs (2.8 million of the 4.8 million) while they make up ~20% of the total employment. Both were severely impacted in the early stages of the pandemic downturn and are now working their way back toward normal staffing activities. We see Hospitality and Retail combining to create over 4 million new jobs in July, as their job listing activity continues to surge. Most other industries are also showing increased hiring activity, so we estimate they will contribute another 2 million jobs. The August jobs numbers are a bit more difficult to estimate at this point. As states pause and reverse their opening plans, market uncertainties will drive job listings downward though to what extent and how quickly. At the early stages of the pandemic, companies’ reaction times were a bit slower. Perhaps now they have built up more rapid reflexes. Job Listings as a Leading Indicator A company’s decision to advertise a job opening is a clear indicator of their business outlook. If they were not confident at some level, they would delay or cancel the decision to hire. This is true for new, growth hiring as well as backfilling of current positions vacated by resignations or illness. The aggregate of these decisions to hire, as seen in the new job listings data on our Covid Job Impacts site, is therefore a clear and leading indicator for the economy overall. The site provides views of new job postings by industry, state, and job family, indexed to the job listing levels of March 1st, providing a high-resolution lens on the impacts of Covid-19 on the labor market and overall economy. Some views of the Covid Job Impacts site are below as reference: Fig. 1: Overall view of new job listings across all US industries. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Fig. 2: Industry view of new job listings across all US industries. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Trends in Hospitality and Retail industries New job listings in Hospitality hit a low in mid-April at around -80% verus March 1st , and have been on a steady path upward since. This is big progress, albeit far below pre-pandemic levels and still around -45% off versus March 1st. In Retail, the drop was also significant in March, regaining some ground in April and May, then demonstrating strength in June. It is the only industry that, even if for just a moment, has crossed into the positive terrain, exceeding the new job postings figures from March 1st on June 21. Fig. 3: Hospitality and Retail industry trends show continuing improvement. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) BLS summary of Hospitality and Retail in June In the June BLS report, Hospitality and Retail combined to create 2.84 million of the 4.8 million increase in employment. This is roughly 60% of the added jobs coming from two industry sectors that comprise about 20% of the workforce (roughly 10% each). From the June BLS report: Estimating the July Figures for Hospitality and Retail Emerging alongside this good news from BLS are escalating concerns regarding the employment impacts of states’ policy responses to recently increasing infection rates. These actions will certainly have a downward pressure on job creation across industries, Hospitality and Retail notwithstanding. However, the trend data for new job listings for Hospitality and Retail indicates that they will further increase employment in the month of July, at least during the period which will be covered in the next BLS report (which for a single week, generally the week including 12th day of the month). To demonstrate this, we are showing the Hospitality and Retail industry job listing trends along with timing windows to support our estimates for the July report. Fig. 4: View of Hospitality and Retail industries and June BLS reporting week. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) The very positive results from this particular week in June (Fig. 4) would not be from new job listings within that specific week, since job listings take some time to fill and for a new hire to begin working (and therefore be captured in the BLS data). While higher paying jobs can require a few months or more to fill, the jobs in Hospitality and Retail that were so significant in the June the report are relatively lower paying, so we would expect that they are requiring only a few weeks to fill. We have added a box to indicate the period of job listing activity that we assume to comprise most of the new jobs in the June report (Fig. 5, Box A). Fig. 5: Hospitality and Retail industries, with Box A indicating period aligned to June BLS data. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Looking at a similar time window of job listing activity that will correspond to the employment levels on July 12th (Fig. 6, Box B) provides two key observations: the job listings that will be related to new jobs in that week have, for the most part, already been created, and many of them filled; and the volume of job listings in the time period that will be reflected in the July report are considerably higher than the levels that drove the very large job numbers in the June report. Fig. 6: Hospitality and Retail industries, with Box B indicating period aligned to July BLS data. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Our estimate of 4 million jobs in Hospitality and Retail in the July BLS report is based on the analysis of the job listing volumes in these two industries, focusing on Box A (June report) versus Box B (July report). We can see far greater volumes in the later period. Where Hospitality gained 2.1 million new jobs in June based on the period in Box A, we estimate that the new figures will be around 3 million. Similarly, where Retail created 740,000 new jobs in June, the increased job listings figures in Box B versus Box A lead us to estimate that this industry will create over 1 million new jobs in the July report. Fig. 7: Hospitality and Retail industries, with average lines inserted in Boxes A and B. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) The next few weeks will be critical to watch As state policies regarding Covid-19 are adjusted over the next couple of weeks, we will be closely watching the changes in businesses’ hiring plans as seen through their job listing activities. A closer look at state-by-state results on the Covid Job Impacts site will provide a leading indicator and a way to gauge the August BLS report well before it arrives. About One Model, Inc. One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co
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6 min read
Jamie Strnisha
Over the years I have worked with several operational and strategic (analytic) reporting tools. I have found challenges with both types of reporting tools. Most tools I have worked with focus on solving only one of these reporting challenges, either operational or strategic. Fortunately, One Model’s flexibility and openness in the data model allows us to solve both for our customers. The Challenges: One of the biggest challenges in an operational reporting tool is working with hierarchical (i.e. structured) data. It is extremely challenging to build out the structural relationship of data, such as the Region to Country to State to Work Location relationship. Even though the data relationships exist in the base system, it is almost impossible to use those relationships in reporting and visualization. Even if the relationships can be built out, the structure is typically only available as different columns and there is no way to connect the hierarchical relationship for effective visualization. While these relationships are often defined in strategic reporting tools like SAP SuccessFactors Workforce Analytics, such tools are limited by the data brought in and structured. If a customer has an operational reporting need, they do not have an easy way to bring that data in and use the pre-built structural relationships that exist in the data. (Side note: One Model alleviates this issue and allows customers to bring in any data or data source relatively easily.) One often significant challenge, especially with SAP SuccessFactors Workforce Analytics, is that most of the data is limited to data stored in SAP SuccessFactors. For obvious reasons, this can be frustrating for your team. Perhaps you want to use the data modeled and structured in SAP SuccessFactors to connect with other non-SAP SuccessFactors data sources (e.g. Survey, Facilities, Finance). One Model can make that happen. Overview of SAP SuccessFactors Data Objects Available in the Employee Central API One Model typically sources data from SAP SuccessFactors Employee Central via the OData API. SAP SuccessFactors makes three types of data available in the API: Employee Objects. Personal and employment details for employees, referred to as Person Objects and Employment Objects. Foundation Objects. Organization, pay, and job structure details. Metadata Framework (MDF) Objects. When the standard delivered foundation objects do not meet requirements, existing foundation objects are migrated to the MDF framework (becoming generic objects in the process). New MDF objects are also available. While data from the Employee Objects are critical for reporting, the focus of this blog is the structural relationships defined in the Foundation and Metadata Framework (MDF) Objects, as discussed in more detail below. Foundation and Metadata Framework (MDF) Objects Foundation and Metadata Framework Objects are used to set up data that can be shared across the entire company, such as job codes, departments, or business units. SAP SuccessFactors’ Foundation Objects can be used to populate data at the employee level. For example, if a job code is assigned to an employee, that employee’s record is then populated with all information based on the attributes of the job code. Starting with the November 2014 release, Foundation Objects were migrated to the Metadata Framework (MDF). Source: SAP SuccessFactors Employee Central OData API: Reference Guide Associations in Foundation Objects and Structural Dimensions SAP SuccessFactors uses Associations to define relationships between Foundation Object. One Model can use these defined Associations to build a hierarchical structure. One model will use the data and defined relationships to build a structural dimension, maybe something as simple as: FOGeozone > FOLocationGroup > FOLocation That structural dimension will then allow a user to navigate and filter on the defined relationship. Example: Below is a chart that shows 3 distinct regions (FOGeozone). A user can hover over any of the region labels and find a hyperlink. When the user clicks on the Americas hyperlink, it will drill down to reveal the Countries (FOLocationGroup) below, which in this case includes a breakout of the USA and Canada. Parent-Child Associations in Foundation Objects and Structural Dimensions SAP SuccessFactors also allows for building a Parent-Child Association in the Foundation Object. This relationship can also be translated in One Model. For example, if a larger department is divided into sub-departments, a parent-child association can be created against the department object. One Model can use the relationship defined in the following area FOBusinessUnit > FODivision > FODepartment to define the higher levels of the structure and then use the Parent-Child Relationship within the Department to create desired visualization and filtering experiences for the end user. This behavior can be replicated and created for any of the Foundation Objects where an Association in SAP SuccessFactors has been configured by the customer: Cost Center Department Division Business Unit Legal Entity Legal Entity Local Job Function Pay Group Job Classification Job Classification Local Foundation and Meta Data Framework Object across SAP SuccessFactors and Non-SAP SuccessFactors These structural relationships can be used for reporting across SAP SuccessFactors data, including Recruiting, as well as non-SAP SuccessFactors data. The linking keys will be the IDs used in the Foundation Objects or the employee identifier. If you have questions about how this may work for your organization, we would be happy to chat and share more information. Find success with SuccessFactors. Click here to watch our recorded webinar.
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10 min read
Joe Grohovsky
To help understand why some People Analytics professionals are more successful than others I undertook a worldwide request for insight. I have long held the opinion that 3 basic core competencies were prevalent in successful People Analytics professionals; but to generate a complete profile, I wanted accompanying information on their professional backgrounds, career aspirations, and the organizations who gave them their first People Analytics role. The core competencies referred to are: Close familiarity with the organizations needs and culture Strong people skills An open mind Ultimately my request would suggest the importance of 2 additional factors: Familiarity with data and HR (context) An identified focus (definition of success) Respondent Profile Most respondents were currently working within HR when they assumed their role, though their specific task at the time is unknown. HR was and continues to be the organizational home for most People Analytic roles. Almost half indicated their first People Analytics role emerged gradually from a previous role rather than being specifically created. It was an even split between the first role being a Team of One or not. Two thirds had no specific career path in mind and the same portion feel their careers’ next step will remain within HR. However almost 100% envision People Analytics (PA) being part of their future career, in or out of HR. The greatest self-reported strengths attributable to receiving the People Analytics role were familiarity with data and HR, with technology and math skills also being significant. Lessons Learned If we use SUCCESS and EMBRACING RESULTS separately for scoring, there are 3 areas where lessons can be learned in building our profile: Employee background Availability of People Analytics resources Identification of a specific business problem These lessons are inter-related, but they raise two new questions that are not fully answerable from these results. We can discuss these in our recommendations, but the questions are: Can core competencies overcome deficiencies in the ideal profile? Can a People Analytics role that fails to influence an organization be considered a success? Employee Background No link could be identified between a specific background attribute and success. However, there is a definite link between their background and having their results being embraced. Those respondents who did not have results embraced heavily attributed data familiarity as a strength but had no reported HR strength. Perhaps this was a contextual issue pointing to a weakness in understanding what is important to the company, the correct perspective on HR data, or poor people skills (core competencies). Availability of People Analytic Resources Resource availability seemed to have no impact on success. Slightly more than half of successful respondents were given specific tools, but 40% of successful respondents were provided no team, budget, tools, or other resources. This seems to be another area suggesting the need for core competencies. An open mind may allow the focus to remain on the problem to be solved instead of viewing it from the perspective of an available solution to be used. People skills can empower a professional to leverage resources from other areas of the organization. Identification of a Specific Business Problem Unsuccessful roles usually lacked an identified business problem to address. Stated another way, there was no stated focus. It is my sense that defining focus is the biggest improvement opportunity for both organizations new to People Analytics as well as those who have been practicing for a while. We have already drawn a link between an employee’s background and results not being embraced. Almost none of those situations had a specific business problem to address, and neither were they considered successful. In addition to pre-identifying business problems, many organizations do find value in exploring data to uncover unknown areas for improvement (focus) and following the insights provided. Predictive modeling is a common example of this in People Analytics. In these circumstances business value is found in both historic metrics such as turnover as well as predictive metrics such as attrition risk. Conclusions If we construct a candidate profile of a successful People Analytics professional whose work was embraced, they would be working within HR and have a well-rounded familiarity with HR, data, technology, and math. Their employer provides a clear definition of success by defining a problem on which to focus. Core competencies they possess allow them to overcome the dearth of any resource need as well as the ability to deftly convey their insights back to their organization in an effective, appreciable manner. It is important to note that these core competencies could possibly exist within a single individual or be spread amongst a team. In initiatives that were not embraced, there are several identifiable trouble spots to address. The most visible is the lack of focus/defined business problem. It is not uncommon to expect data to tell you where to focus, but perhaps this is a distinct skillset beyond the stated core competencies. Another concern is highlighted by unembraced initiatives involving People Analytic professionals who reported strength in data familiarity but no strength in HR. Core competencies may provide the people skills to appropriately share insights. However, the nuance of people data and the HR process seems to be lacking in this subset. This possibly points to the need for some HR functional context or guidance on conveying their message. To summarize, ingredients for a successful People Analytics professional producing results that will be embraced by the organization seem to be: 1) Presence of the stated core competencies Close familiarity with the organizations needs and culture Strong people skills An open mind 2) Familiarity with data and HR (context) 3) An identified focus (definition of success) Recommendations The lack of core competencies in an individual does not necessarily doom a People Analytics initiative, or that individual’s participation in it. This situation can be overcome by using formal or informal teams to ensure each skill set is available. It is also advisable to ensure proper context is in place. This involves more than simply examining how the defined business problem is impacting the organization. The People Analytics professional(s) involved may not have a full awareness of the nuances and breadth of the HR function itself. Perhaps an “HR 101” course could be used to explain the relevance of Recruiting, Learning, Total Rewards, Performance, etc. and why those employee processes and data are unique. An alternative to this could be ensuring an HR expert closely reviews all results before they are shared with the business. Perhaps the most significant recommendation is having a definition of success: an identified business problem was a strong component of successful initiatives. There is also a place for exploring your data to find areas of improvement. Caution should be used, and this is where strong people skills will come into play; without a defined focus, the People Analytics professional will have found a problem that was previously unidentified. Calling attention to it and providing suggestions on its resolution can be interpreted as criticizing an organizational leader and telling them how to do their job. The two questions raised but unanswerable by the provided insights were: Can core competencies overcome deficiencies in the ideal profile? Can a People Analytics role that fails to influence an organization be considered a success? Core competencies are true skills and reflect an ability to get things done. This ability powers People Analytics professionals to find resource alternatives, ideal communication techniques, and relevant focus topics. It is my opinion these competencies do a tremendous job of overcoming any inherent shortcomings in a defined role. We must not settle for simply being right but also strive to be effective. People Analytics cannot be successful when results are unembraced by the organization. The goal of any decision support role is to empower better decision making and provide our data-consumers with relevant insights in a meaningful way. Effective People Analytic professionals base their insights on trustworthy data and irrefutable metrics. This is especially relevant with the burgeoning use of artificial intelligence and predictive modeling. People Analytic professionals would do well to remain skeptical of any predictive model that is not fully transparent, cannot be explained, and is verifiably void of hidden bias. Insight Purpose & Process My insight request occurred as a survey shared among social media and industry websites so as broad an audience as possible could be captured. Participants responded from all global regions and the intent was to create a snapshot in time reflecting circumstances when they undertook their first People Analytics role. These circumstances were then compared with both their success in that role and whether their organization embraced their results. The quest was not driven by simple curiosity but a desire to help identify a replicable profile. My work In the People Analytics technology space involves helping my customers succeed in their role and build a practiced embraced by their organization. This resulting profile will be shared with my customers and used to identify areas where I can help them improve. Where are you in your People Analytics Career or Journey? One Model can provide guidance around all the above profile ingredients, and create a path for you to establish yourself as a People Analytics leader as you move forward. Step 1: One Model can help you define your organization's critical metrics and understand how to present them to various layers of decision makers. Step 2: Our team of data engineers can solve your problem of HR data portability and quickly integrate all relevant customer data sources into one platform. Step 3: Our Machine Learning/Artificial Intelligence platform will equip you with a suite of easy-to-use predictive pipelines and data extensions that allow your organization to build, understand, and predict workforce behaviors. If you would like further information on this study or to learn more about One Model, please reach out to me at: Joe Grohovsky | joe.grohovsky@onemodel.co About One Model: One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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31 min read
Chris Butler
The first in a series of posts tackling the individual nuances we see with HR technology systems and the steps we take in overcoming their native challenges to deliver a comprehensive people analytics for SuccessFactors program. Download the White Paper on Delivering People Analytics from SAP SuccessFactorsQuick Links A long history with SuccessFactors Embedded Analytics won't cut it, you have to get the data out World leading API for extraction Time to extract data Full Initial Load Incremental Loads Modelling Data Both SuccessFactors and External SF Data Modelling Analytics Ready Fact Tables Synthetic Events Core SuccessFactors Modules MDF Objects Snowflake Schema Inheritance Metrics - Calculations - Analytics Delivered Reporting and Analytics Content Creating and Sharing your own Analytics Content Using your own Analytical Tools Feed Data to External Vendors What About People Analytics Embedded? What About SAP Analytics Cloud? What About SuccessFactors Workforce Analytics? The One Model Solution for SAP SuccessFactors A long history with SuccessFactors I'm starting with SuccessFactors because we have a lot of history with SuccessFactors. SF acquired Infohrm where many of our team worked back in 2010 and the subsequent acquisition by SAP in 2012. I personally built and led a team in the America's region delivering the workforce analytics and planning products to customers and ensuring their success. I left SAP in 2014 to found One Model. Many of One Model's team members were in my team or leading other global regions and, of course, we were lucky enough to bring on a complete world-leading product team from SAP after they made the product and engineering teams redundant in 2019 (perfect timing for us! Thanks SAP they're doing a phenomenal job!). So let's dive in and explore SuccessFactors data for people analytics and reporting. Embedded Analytics won't cut it, you have to get the data out. It's no secret that all vendors in the core HR technology space espouse a fully integrated suite of applications and that they all fall short to varying degrees. The SF product set has grown both organically and via acquisition, so you immediately have (even now) a disconnected architecture underneath that has been linked together where needed by software enhancements sitting above. Add in the MDF framework with an almost unlimited ability to customize and you quickly have a complexity monster that wasn't designed for delivering nuanced analytics. We describe the embedded reporting and analytics solutions as 'convenience analytics' since they are good for basic numbers and operational list reporting but fall short in providing even basic analytics like trending over time. The new embedded people analytics from SF is an example where the data set and capability is very limited. To deliver reporting and analytics that go beyond simple lists and metrics (and to do anything resembling data science), you will need to get that data out of SF and into another solution. World leading API for data extraction One Model has built integrations to all the major HRIS systems and without a doubt SuccessFactors has the best API architecture for getting data out to support an analytics program. Deep, granular data with effective dated history is key to maintaining an analytics data store. It still has its issues, of course, but it has been built with incremental updates in mind and importantly can cater for the MDF frameworks huge customizability. The MDF inclusion is massive. It means that you can use the API to extract all custom objects and that the API flexes dynamically to suit each customer. As part of our extraction, we simply interrogate the API for available objects and work through each one to extract the full data set. It's simply awesome. We recently plugged into a huge SuccessFactors customer of around 150,000 employees and pulled more than 4,000 tables out of the API into our warehouse. The initial full load took about a week, so it was obviously a huge data set, but incremental loads can then be used for ongoing updates. Some smaller organizations have run in a matter of minutes but clearly the API can support small through to enormous organizations, something other vendors (cough, cough ... Workday) should aspire to. To give you a comparison on level of effort we've spent on the One Model API connectors, approximately 600 hours has been spent on SuccessFactors versus more than 12,000 hours on our Workday connector. Keep in mind that we have more stringent criteria for our integrations than most organizations including fault tolerance, maintenance period traversal, increased data granularity, etc., that go beyond what most individual organizations would have the ability to build on their own. The point is, the hours we've invested show the huge contrast between the SF and Workday architectures as relates to data access. Time to Extract data Obviously, the time needed to extract the data depends on the size of the organization but I’ll give you some examples of both small and huge below. Figure 1: Data extraction from SAP SuccessFactors using APIs Full Initial Loads In the first run we want everything that is available -- a complete historical dataset including the MDF framework. This is the most intense data pull and can vary from 20 minutes for a small organization of less than 1,000 employees to several days for a large organization above 100,000 employees. Luckily, this typically only needs to be done once during initial construction of the data warehouse, but there are times where you may need to run a replacement destructive load if there are major changes to the schema, the extraction, or for some reason your synchronization gets out of alignment. API’s can behave strangely sometimes with random errors, sometimes missing records either due to the API itself or the transmission just losing data, so keep this process handy and build to be repeatable in case you need to run again in the future. The One Model connectors provide an infrastructure to manage these issues. If we're only looking for a subset of the data or want to restrict the fields, modules, or subject areas extracted, we can tell the connector which data elements to target. Figure 2: Configuring the connector to SF in One Model platform Incremental Updates With the initial run complete we can switch the extraction to incremental updates and schedule them on a regular basis. One approach we like to take when pulling incrementals is to take not just the changes since the last run but also take a few extra time periods. For example, if you are running a daily update you might take the last two to three days worth of data in case there were any previous transmission issues, this redundancy helps to ensure accuracy. Typically we run our incremental updates on a daily basis, but you want to run more often than this you should first need to consider: How long your incremental update takes to run. SF is pretty quick, but large orgs will see longer times, sometimes stretching into multiple hours How long it takes your downstream processes to run an update any data If there’s a performance impact to updating data more regularly, typically if you have a level of caching in your analytics architecture this will be blown away with the update to start over again. Impact on users if data changes during the day. Yes, there can be resistance to data updating closer to real-time. Sometimes it's better to educate users that the data will be static and updated overnight. Whether or not the source objects support incremental updates. Not all can, and with SF there’s a number of tables we need to pull in a full load fashion, particularly in the recruiting modules. Modelling data both SuccessFactors and External Okay, we have our SF data and of course we have probably just as much data from other systems that we're going to need to integrate together. SF is not the easiest data set to model, as each module operates with its own nuances that, if you're not experienced with, will send you into a trial and error cycle. We can actually see a lot of the challenges the SF data can cause by looking at the failures the SF team themselves have experienced in providing cross-module reporting over the years. There have been issues with duplicates, incorrect sub domain schemas, and customer confusion as to where you should be sourcing data from. A good example is pulling from employee profile versus employee central. The SAP on premise data architecture is beautiful in comparison (yes really, and look out soon for a similar post detailing our approach to SAP on premise). Modeling the SF Data At this point we're modelling (transforming) the raw source data from SF into analytics-ready data models that we materialize into the warehouse as a set of fact and dimension tables. We like to keep a reasonable level of normalization between the tables to aid in the integration of new, future data sources and for easier maintenance of the data set. Typically, we normalize by subject area and usually around the same timescale. This can be difficult to build, so we've developed our own approaches to complete the time splicing and collapsing of records to condense the data set down to where changes occurred. The effort is worth it though, as the result is a full transactional history that allows the most flexibility when creating calculations and metrics, eliminating the need to go back and build a new version of a data set to support every new calculation (something I see regularly with enterprise BI teams). This is another example of where our team's decades of experience in modelling data for people analytics really comes to the fore. During the modelling process there's often a number of intermediate/transient tables required to merge data sets and accommodate modules that have different time contexts to each other, but at the end of the day we end up materializing them all into a single analytics-ready schema (we call it our One schema) of tables. Some of what you would see is outlined below. Analytics Ready Fact Tables One.Employee - all employee effective dated attributes One.Employee_Event - all employee events, equivalent to action/reason events (e.g. Hire, Termination, Transfer, Supervisor change, etc.). Usually you'll need to synthetically create some events where they don't exist as action/reason combinations. For example, many customers have promotions that aren't captured in the system as a transaction but are logically generated where a pay grade occurs alongside a transfer or any similar combination of logic. One.Requisitions - all Requisition's and events One.Applications - all application events One.Performance_Reviews - all performance review events ... the list goes on Dimension Tables One.dim_age - age breakout dimension with levelling One.dim_gender - gender breakout dimension typically a single level One.organizational_unit - The multi-level organization structure … we could go on forever, here's a sample below of fields Figure 3: Examples of tables and fields created in the One Model data schema Synthetic Events A core HRIS rarely captures all events that need to be reported on, either because the system wasn't configured to capture it or the event classification is a mix of logic that doesn't fit into the system itself. These are perfect examples of why you need to get data out of the system to be able to handle unsupported or custom calculations and metrics. A frequently recurring example is promotions, where an action/reason code wasn't used or doesn't fit and for reporting a logic test needs to be used (e.g. a change in pay grade + a numeric increase in salary). We would implement this test in the data model itself to create a synthetic event in our Employee_Events model. It would then be seen as a distinct event just like the system-sourced events. In this fashion you can overcome some of the native limitations of the source system and tailor your reporting and analytics to how the business actually functions. Core SuccessFactors Modules Employee Central - Aligns with our Employee, Employee Event tables and typically includes about 100+ dimensions as they're built out. The dimension contents usually come from the foundation objects, picklist reference tables, an MDF object, or just the contents of the field if usable. This is the core of the analytics build and virtually all other modules and data sets will tie back to the core for reference. Recruiting - Aligns with our Applications, Application_Event, and Candidates fact tables covering the primary reporting metrics and then their associated dimensional tables. Succession - Aligns with Successor and associated dimensions Performance - Performance Reviews (all form types) and associated dimensions Learning - Learning Events, Courses, Participants Goals - Goals, Goal_Events MDF objects MDF objects are generally built into the HRIS to handle additional custom data points that support various HR processes. Typically we’ll see them incorporated into one of the main fact tables aligning with the date context of the subject fact table (e.g. employee attributes in One.Employee). Where the data isn’t relevant to an existing subject, or just doesn’t align with the time context, it may be better to put the data into its own fact table. Usually the attribute or ID would be held in the fact table and we would create a dimension table to display the breakout of the data in the MDF object. For example, you might have an MDF object for capturing whether an employee works from home. Captured would be the person ID, date, and the value associated (e.g. ‘Works from Home’ or ‘Works from Office’). The attribute would be integrated into our Employee fact table with the effective date and typically a dimension table would also be created to show the values allowing the aggregate population to be broken out by these values in reporting and analysis. With the potential for a company to have thousands of MDF objects, this can massively increase the size, complexity, and maintenance of the build. Best to be careful here as the time context of different custom objects needs to be handled appropriately or you risk impacting other metrics as you calculate across domains. Inheritance of a snowflake schema Not to be confused with Snowflake the database, a snowflake schema creates table linkages between tables that may take several steps to join to an outer fact or dimension table. An example is that of how we link a dimension like Application Source (i.e., where a person was hired from) to a core employee metric like Headcount or Termination Rate which has been sourced from our core Employee and Employee Event Tables. An example of this is below, where to break out Termination Rate by Application Source and Age we would need to connect the tables below as shown: Figure 4: Example of connecting terminations to application source This style of data architecture allows for a massive scale of data to be interconnected in a fashion that enables easier maintenance and the ability to change pieces of the data model without impacting the rest of the data set. This is somewhat opposite of what is typically created for consumption with solutions like Tableau which operate easiest with de-normalized tables (i.e., giant tables mashed together) which come at the cost of maintenance and flexibility. Where one of our customers wants to use Tableau or similar solution we typically add a few de-normalized tables built from our snowflake architecture that gives them the best of both worlds. Our calculation engine is built specifically to be able to handle these multi-step or matrix relationships so you don’t have to worry about how the connections are made once it’s part of the One Model data model. Metrics - Calculations - Analytics When we get to this point, the hardest work is actually done. If you've made it this far, it is now relatively straight forward to build the metrics you need for reporting and analytics. Our data models are built to do this easily and on the fly so there isn't a need for building pre-calculated tables like you might have to do in Tableau or other BI tools. The dynamic, on the fly nature of the One Model calculation engine means we can create new metrics or edit existing ones and be immediately using them without having to generate or process any new calculation tables. Creating / Editing Metrics Figure 5: Example of creating and editing metrics in One Model Delivered Reporting and Analytics Content With an interconnected data model and a catalogue of pre-defined metrics, it is straight forward to create, share and consume analytics content. We provide our customers with a broad range of pre-configured Storyboard content on top of their SuccessFactors data. A Storyboard library page allows a quick view of all subject areas and allow click through to the deeper subject specific Storyboards beneath. This content is comprehensive covering off the common subject areas for analytics and reporting such as workforce profile, talent acquisition, turnover, diversity, etc. There is also the ability to create dashboards for monitoring data quality, performing data validations, and viewing usage statistics to help manage the analytics platform. Figure 6: Sample of standard Storyboard content in One Model Creating and Sharing your own Analytics Content Every one of our customers adds to the pre-configured content that we provide them, creating their own metrics and storyboards to tell their organization's people story, to support their HR, business leaders, and managers, and to save their people analytics team time by reducing ad-hoc requests for basic data. Our customers make the solution their own which is the whole point of providing a flexible solution not tied to the limitations of the underlying source system. Content in One Model is typically shared with users by publishing a storyboard and selecting which roles will have access and whether they can edit or just view the storyboard itself. There's a number of other options for distributing data and content including: Embedding One Model Storyboards within the SuccessFactors application itself Embedding One Model Storyboards within Sharepoint, Confluence, or any other website/intranet (e.g. the way we have used frames within this site: https://covidjobimpacts.greenwich.hr/#) Pushing data out to other data warehouses (what we call a "data destination") on a scheduled basis, something that works well for feeding other tools like Tableau, PowerBI, SAP Analytics Cloud, and data lakes. Sharing Storyboards Embedding Storyboards Example of embedded storyboard COVID Job Impacts site - https://covidjobimpacts.greenwich.hr/# Figures 7, 8, 9: Storyboard sharing and embedding Using your own Analytical Tools We want to ensure you never hit a ceiling on what you can achieve or limit the value you can extract from your data. If you wish to use your own tools to analyse or report on your data, we believe you should have the power to do so. We provide two distinct methods for doing this: Direct Connection to the One Model Data Warehouse. We can authorize specific power users to access the data warehouse directly and read/write all the raw and modeled tables in the warehouse. If you want to use Tableau or PowerBI in this way, you are free to do so. You can write your own queries with SQL or extract directly from the warehouse in your data science programs such as Python or R. The choice is yours. At this point, it is essentially your warehouse as if you created it yourself, we have just helped to orchestrate the data. Data Destinations. If you need to feed data to an enterprise data warehouse, data lake, or other data store, then our data destinations functionality can send the selected data out on a scheduled basis. This is often used to integrate HR data into an enterprise data strategy or to power an investment in Tableau Server or other where teams want the HR data in these systems but don't want to build and run the complex set of APIs and data orchestration steps described above. In both of these scenarios, you're consuming data from the data model we've painstakingly built, reaping the productivity benefits by saving your technical team from having to do the data modelling. This also addresses a perennial issue for HR where the IT data engineering teams are often too busy to devote time to understanding the HR systems sufficiently to deliver what is needed for analytics and reporting success. Feed data to external vendors Another use for the data destinations described above is to provide data to external vendors, or internal business teams with the data they need to deliver their services. Many of our customers now push data out to these vendors rather than have IT or consultants build custom integrations for the purpose. We, of course, will have the complete data view, so you can provide more data than you did in the past when just sourcing from the HRIS system alone. A good example of this is providing employee listening/survey tools with a comprehensive data feed allowing greater analysis of your survey results. Another use case we've also facilitated is supporting the migration between systems using our integrations and data models as the intermediate step to stage data for the new system while also supporting continuity of historical and new data. (Reference this other blog on the topic: https://www.onemodel.co/blog/using-people-analytics-to-support-system-migration-and-innovation-adoption) Scheduled Data Destinations Figure 10: Example of data destinations in One Model What About People Analytics Embedded? This solution from SF is great for what we call 'convenience analytics' where you can access simple numbers, low complexity analytics and operational list reports. These would provide basic data aggregation and simple rates at a point in time without any historical trending. In reality, this solution is transactional reporting with a fancier user interface. Critically, the solution falls down in its inability to provide the below items: Trending across time (an analytics must have) Limited data coverage from SF modules (no access to data from some core areas including learning and payroll) Challenges joining data together and complexity for users in building queries No ability to introduce and integrate external data sources No ability to create anything of true strategic value to your organization. What About SAP Analytics Cloud? SAC has shown some great promise in being able to directly access the data held in SF and start to link to some external source systems to create the data integrations you need for a solid people analytics practice. The reality, however, is the capability of the product is still severely limited and doesn't provide enough capacity to restructure the data and create the right level of linkages and transformations required to be considered analytics-ready. As it is today, the SAC application is little more than a basic visualization tool and I can't fathom why an organization would take this path rather than something like Tableau or PowerBI which are far more capable visualization products. SAP Analytics Cloud has not yet become the replacement for the Workforce Analytics (WFA) product as it was once positioned. The hardest parts of delivering a robust people analytics software has always been the ongoing maintenance and development of your organizational data. The SF WFA's service model provided this with an expert team on call (if you have the budget) to work with you. With SAC, they have not even come close to the existing WFA offering, let alone something better. The content packages haven't arrived with any depth and trying to build a comprehensive people analytics suite yourself in SAC is going to be a struggle, perhaps even more than building it on your own in a more generic platform. What About SuccessFactors Workforce Analytics? Obviously, our team spent a lot of time with SuccessFactors' WFA product even predating the SF acquisition. The WFA product was a market and intellectual pioneer in the people analytics field back in the day and many members of our team were there, helping hundreds of organizations on their earliest forays into people analytics. The WFA solution has aged and SF has made little to no product improvements over the last five years. It is, however, still the recommended solution for SF customers that want trending and other analytics features that are relatively basic at this point. Several years ago, we started One Model because the SF WFA product wasn't able to keep pace with how organizations were maturing in their people analytics needs and the tool was severely limiting their ability to work the way they needed to. It was a black box where a services team (my team) had to deliver any changes and present that data through the limited lens the product could provide, all for a fee of course. Organizations quickly outgrew and matured beyond these limitations to the point I felt compelled to tackle the problem in a different fashion. One Model has become the solution we always wanted to help our customers become successful and to grow and mature their people analytics capability with data from SAP SuccessFactors and other systems. We provide the integrations, the analytical content, the data science, the transparency, scalability, and configurability that our customers always wished we could provide with SF WFA. We built our business model to have no additional services cost, we keep all aspects of our data model open to the customer, and our speed and delivery experience means there's no limit to which modules or data sets you wish to integrate. The One Model Solution for SAP SuccessFactors Direct API Integration to SuccessFactors Unlimited data sources Daily data refresh frequency Unlimited users Purpose built data models for SAP and SF No additional services costs People analytics metrics catalogue Create your own metrics and analytics Curated storyboard library for SuccessFactors Operational reporting Embed and share storyboards HR's most advanced predictive modelling suite Access all areas with a transparent architecture Use your own tools e.g. Tableau, PowerBI, SAC Take a tour in the video below We are happy to discuss your SuccessFactors needs.
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5 min read
Nicholas Garbis
Two weeks ago, we released a new publicly available Covid Job Impact website to share daily data and insights on US job listings by industry, job family/role, and region/state. As we are looking at this data each day, we have watched some hopeful signs of recovery slipping away over the past week. This observation arrives on the same day that news reports are calling out reductions in the unemployment rate (see Forbes, "The Headlines are Dead Wrong - Unemployment Dropped 16% to 21M"). The total number of people on unemployment insurance has fallen from 24.9M to 21.1M in the past week according to the US Dept. of Labor reporting. A chart of this data is include in our site. So it's true, but it's in the rear view mirror and we want to look through the windshield. For quick reference, our site tracks US job listings with all figures being indexed to March 1st (ie, March 1 = 0%). Since job listings are decisions by businesses to hire, we believe this is reflective of their prospective outlook. And since job listings precede actual hiring by ~3 months, we see this data as a leading indicator of unemployment. Slipping Away? While we saw job listings up through mid-March, they quickly tumbled by ~50%, then proceed to bounce around a bit until they seemed to be holding steady in mid-May at around -27%. But over the past week (the last several dots) things have slipped down below -43% (see image). Together, Apart, and Together Again Another trend we spotted early on was that all industries fell together in mid-March, then began to diverge as different industries returned to hiring at different rates. While all industries were still down below March 1 levels, the range was wide, with some around -20% and others closer to -50%. In the most recent week, however, we see them all bending down together again. We think this is signaling a negative business outlook across multiple industries. (See image below with the periods marked out.) School's Out for Summer? In the midst of this, we can also see that the Hospitality industry, which fell fastest and stayed down the longest, has started to creep back up a bit. It appears that the Hospitality industry is expressing some confidence, perhaps betting that families will begin travelling now that stay-at-home orders are being lifted and the school year is wrapping up. The exception, viewable on the site, is the state of Nevada which continues to show Hospitality job listings down around -95% compared to March 1st. So, Will Unemployment Continue to Improve? As we see unemployment insurance claims dropping a bit, we believe a good portion is people returning to work from temporary unemployment (furloughs, temporary layoffs). These 'hires' will not require job listings. (Some of this was discussed with our guest, former Chief Economist of GE and Deutsche Bank, Marco Annunziata on our recent webinar.) We believe the reduction in job listings is indicative of a broad-based decrease in the business outlook and that unemployment may drop a bit more as temporary layoffs/furloughs end, but will remain stubbornly high for at least the next couple of months. If job postings begin to bend upward again toward -25%, there would be good reason to anticipate some material decreases in the unemployment numbers. One thing for certain: we will be watching this play out daily on the site. Keep an eye out for additional insights on the Covid Job Impacts site: https://covidjobimpacts.greenwich.hr -ng About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co or email us at info@onemodel.co
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7 min read
Nicholas Garbis
Our objective in this series is to offer our team’s expertise to the public during this crisis in the same way that Walmart delivers trailers of water to natural disasters. The “water” we have to share is our expertise in workforce strategy, HR processes, data orchestration, and people analytics. This is a follow-up to our first COVID-related blog post People Analytics for measuring the impact of Coronavirus (COVID-19), in which we laid out a set of critical “Level 1” questions that organizations should be able to answer during the onset of this pandemic. Later in this blog, you will find information regarding a very quick-and-dirty tool we have developed. Before we get there, however, let’s step back for a moment to assess the broader topography of today’s situation. We are in a massive global pandemic that has already demonstrated its exponential potential. Organizations will have a variety of people analytics to help them make data-driven decisions regarding their workforce and their overall operations. We have not experienced anything quite like this, so we should expect it will require great empathy and creativity, a willingness to lead, and the agility to test, fail, and learn. If we consider breaking this out into phases that organizations may pass through over the next several months, we can start to anticipate the shifting needs for people analytics. These phases will not come with clear markers, so the only way to know where you are is to step back at regular intervals to reevaluate the situation and reallocate your efforts. We have developed a quick tool for the current phase. The tool we assembled is focused around the first phase of the crisis -- where we are right now. It is built to answer some basic questions, acknowledging the unfortunate reality that our HR systems are unlikely to possess the information we need. As a result, a process will be needed to capture and consolidate the needed information. In these situations, it’s critical to seek out only the most critical data elements (ie, “KISS”). A simple dataset, updated daily, has the potential to provide business and HR leaders with the “situational awareness” they need to make decisions quickly based on facts. The “COVID-19 Workforce Tracking” tool that we developed is a free Excel-based tool that can be used by organizations of any size, including those with limited people analytics resources. It aims at answering most of the questions laid out in our previous blog. One example is a mid-size medical device firm without a people analytics team. They are already getting started with the tool, standing up a daily update process with ~4 representatives from various parts of the business making updates into a shared worksheet. One person then ensures the dashboard is validated and then it goes to the CHRO for review with business leaders. It’s worth restating that our objective here is to get something out to the maximum number of people in the shortest amount of time. Hence, this quick solution in Excel. Our team obviously has an ability to stand up these metrics and a series of new ones in our One Model platform where we have robust data handling and visualization, but moving data to our clouds will require information security reviews by most organizations which takes time. (We have opted to deliver the “water” now and come back with sandwiches real soon.) The first iteration of this tool (v1.0) can be accessed at the bottom of this blog. You will be asked for your email so we can communicate when any new versions are released. Here’s a view of the dashboard: And here is a view of the dataset. It is a series of HRIS data fields (not all shown here) combined with a collection of 8 data fields (in yellow) which should be updated daily: Below are a few notes on the tool: Manage sensitive data according to the applicable laws and your company policies. This is built in Excel so it can be used quickly by the widest range of users. The data collection template is intentionally simple to ensure it can be sustained as a daily process, ideally by a single point of contact. A slim set of ~8 questions, combined with some basic employee data will enable you to answers to nearly all of the critical questions. The basic employee data will be taken as a baseline/snapshot. Terminations will be tagged (not deleted) and hires would be added to the list. The dashboard will provide a set of key metrics with ability to filter the results several ways. You and your teams can and should modify it to meet your needs. Future enhancements being considered (your input on this is welcome!) Macro to import the real-time statistics from the global COVID-19 tracking data as managed by Johns Hopkins University. Access to cloud-based solution within One Model technology to enable benchmarking across companies, reduce version control issues, and enable advanced modeling and forecasting through our One AI machine learning/AI platform. Below is the link to the latest version. As updates are made, the latest version will always be posted within this blog. Current Version: 1.0 To download the tool, please click the link below. About One Model One Model delivers our customers with a people analytics infrastructure that provides all the tools necessary to directly connect to source technologies and deliver the reporting, analysis, and ultimately prediction of the workforce and it's behaviors. Use our leading out-of-the-box integrations, metrics, analytics, dashboards, and domain expert content, or create your own as you need to including the ability to use your own tooling like Tableau, Power BI, R, Python as you need. We provide a full platform for building, sustaining, and maturing a people analytics function delivering more structure information, measurement, and accountability from your team. Learn more at onemodel.co.
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5 min read
Tony Ashton
In the last One Model product update post I talked about our new user experience and hinted at some exciting new developments on the horizon. In this post I want to share some more information on those future designs. Thanks again to our customers for sharing their time and collaborating with us on our UX developments and everyone in One Model, but I want to make a special mention of the powerhouse behind One Model’s product designs - Nicole Li - a true UX unicorn! While the new user experience showcases Nicole’s incredible work, the new stuff is where things get super exciting. This content is being shared to provide an insight into future product developments planned by One Model. This should not be interpreted as a commitment to deliver any particular functionality or to any defined timeline and may be changed at any point by One Model at its discretion. Purchasing decisions should be made based on current product capability only. Having said this, we are super excited to share what we are working on and actively engage with you regarding product innovation and the future of people analytics. At the risk of overusing some classic cliches; startups run on pizza and business runs on PowerPointTM (well slides anyway). The slides phenomenon has been prevalent for the last couple of decades and when I ask almost any company how they share information with managers, executives, boards, or in general meetings the answer over 90% of the time is “slides”. Storyboards & Slides When we recently announced One Model’s new Storyboard capability we hinted at a broader vision and here is part of that vision starting to unfold. The new Storyboards will have two modes, one where you have a fairly traditional tile based layout and the other where you are in presentation mode. Online interactive use of One Mode is growing rapidly, but much of the content from One Model still ends up in a presentation at some point, so we want to reduce the effort to create and maintain this content. Storyboards are then acting as both a modern interactive storytelling dashboard and interactive slide based presentation without the need for any rework. This will save a massive amount of time and also pre-positions the content for the most common destination to meet the consumers where they are. So, how does this work? The Storyboard view lets you arrange tiles on a forever vertically scrolling space with control over layout, size etc. You can then flip to Slides view to get an auto-arranged presentation with one tile per slide and controls to optimize the display for presenting to a group of people. Within the Slides view you can manage layout, decide which tiles to show or hide from the presentation, combine slides together etc. To help you create a narrative for your presentation you can open the outline view and craft the flow of your story. Being able to present online from within the One Model platform is powerful and provides you the ability to interact with the data in real-time to really engage with your audience. And, “yes”, to the question you are starting to form in your mind… you will be able to export this to PowerPointTM to blend with other presentations you are creating offline :) Telling a Story Using Narrative Insights Having assembled a compelling set of data isn’t sufficient to drive action. You also need to engage your audience and the best way to do this is through storytelling. The next major feature to our Storyboards vision is the ability to add a narrative to any tile that describes what is going on in straight-forward business language. Initially this capability will include information from One Model’s metric library and your own narrative, but over time we will incorporate insights powered by the One Ai machine learning platform. Captions can be rearranged as elements within a tile, or a separate, linked, tile with controllable positioning and layout depending on how you want to arrange your storyboard. The Storyboards vision is incredibly exciting and customers we have engaged in the design thinking behind these innovations can’t wait to get their hands on this new capability. Neither can we! Stay tuned for more information as the roadmap unfolds. This article has been primarily concerned with the developing technology of Storyboards, but I also want to let you know that One Model has a vast library of content to help you tell the story of how people drive impact in your business. We’ll write some more on this soon, but reach out if you want to learn more about our metric catalogue and ever-growing library of topic storyboards. When combined with OneAi, our Machine Learning platform, you can generate automated insights, future forecasts and identify key risks to answer the most pressing business questions you have today.
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8 min read
Chris Butler
With the continued growth of the Coronavirus pandemic our leaders are going to be asking for regular updates on our employees health and our business’s productivity. This is not going to be a flash in the pan event either. The path back to normal will be long and gradual which means we need to approach data collection, reporting, and analysis with an emphasis on repeatability. To that end there’s a number of questions that HR teams are going to need to answer in order to provide a status of and show the progression of the businesses adaptation to these challenges and how our workforce is coping. What questions are we going to need to answer? What % of our workforce can be switched to work remotely if needed? What % has already shifted to working remotely due to COVID-19? What is the trend as we ramp this ability? What % of our workforce is currently not working due to COVID-19? How are infection rates trending in the countries/states/provinces where we have employees? What is the trend in our employee infection rates and how do they compare to the relevant country/state/province? What is the risk level of our workforce in a given area based on the age distribution and other relevant factors? Do we have any locations that are significantly impacted by COVID-related absences? What is the average duration of employees being unavailable due to COVID-19 - illness or other? What % of our infected workforce has recovered and returned to work? What % of our temporarily remote workforce has returned to working on location? What is our current productive capacity %? How long are impacted employees non productive for? How much productive capacity have we lost? So, what data do we need and how do we organize it to address the questions above? Download our Covid-19 Tracking Worksheet This is where things get tricky and HR needs to be collecting additional data beyond what they have today. This is likely going to need to come from manager input with HR acting as the central collation point. Ideally this information can be captured and held within your HRIS, but most likely this is going to start out as a spreadsheet as your HRIS may not have the required fields for what we need to measure beyond traditional absence & availability information. My view is that shortly we're going to be asking managers to provide information to HR when their employees move into quarantine, infection, and start/stop work (when remote) because of illness. This may or may not also be in association with an HRIS event recording absence or similar. As this data is collated you'll want to make sure you can collect a few key data points as per the below Ability to work from home Currently working from home Date employee stopped working Date employee returned to work Date employee in a Quarantine status Date employee in a Infected status Date employee cleared of Quarantine/Infected status This data can then be merged with the following HRIS information Location information: Country, State/Province, City/Location (for site-level metrics and comparison to global/national statistics) Personal information: age, gender (optional - for risk assessment and forecasting, data would not include name of employee ID number) Employment: employee type (regular, temp/FTC, contractor), Full/Part time A combination of this event-related data alongside the HRIS data will create the ability to track over time the status of our workforce so we can report and analyze the trend and impact on the business. Some of these data points can be inferred from your existing systems It’s going to be a challenging job to collect and keep collated the above data so if you have already or can get data from some additional systems like facilities and IT access you can infer some of these data points. Below are some examples of business logic some of the organizations we have been talking to are using. Ability to work from home = Have access to a VPN, Have a laptop. Currently working from home = Are accessing the VPN, have not badge swiped into an office/facility. Not working due to infection = A leave of absence record with no recent vpn access or badge swipe. How would we present this information? Note: the above is mock data Workforce Composition & Employee Health Overall metrics on the current workforce showing the total population, working status, remote working rates, and infection rates. We also want to show this trending over time so we have an idea of the growth and ultimately the recession of infection rates. Key Metrics Headcount, Headcount % - Quarantine, Infected, Recovered statuses Currently Working % Working from Home % High Risk Populations Comparison against daily statistics by country/state/province produced by health organizations will enable you to compare your infection trend to the prevailing trend in the relevant geographical area. If the area is seeing an acceleration of cases, you should anticipate similar risks for your workforce. If the area has hit an inflection point and is leveling off, the risk to that part of your workforce should be on the wane as well. Beyond geographic risk, age is the biggest factor on the impact to the employee and we're going to see longer infection periods and mortality rates for older employees than we will for other populations. Obviously any steps the organization can take to protect higher risk populations should be fought for. Key Metrics Regional Infection Rates (where available) Active, Quarantine, Infected, Recovered by Age, Location, Department Productive Status The absence of employees will reduce the productivity of your workforce, potentially impacting customers and creating financial risks such as over-ordering of supplies and raw materials, over-estimating orders and revenues, or committing to delivery dates that cannot be achieved because of workforce impacts beyond the view of the manufacturing location’s management. Questions we need to answer include how many of our employees are currently working, whether from home or their normal location? How many hours have we lost due to infection-related absences? While productivity isn't a major concern when lives are at stake, many of the actions we may take in protecting our employees will show up in either infection rates or in absences and we should be measuring to see what was effective and what wasn't. Key Metrics Currently Working % - Active, Quarantine, Infected Ability to work from home % Lost productive hours (key here are the dates for when people stop and return to work) Duration of Quarantine and Infection. As we plan for how our employees are impacted we need to be analyzing how long our quarantined and infected employees are unable to work. Many employees may still be able to work from home while under quarantine and or infection but there will be periods when they are infected and unwell that they won’t be able to work while when their symptoms are significant or while they are recovering. Questions we need to answer include how long is the infection period for our workforce? What is our forecast for our currently infected employees being back to work? Key Metrics Days in Quarantine Days from Infection to Recovered Number of Reinfections, Reinfection Rate Extension opportunities The above metrics and questions are a bare bones set of pertinent information that you could provide to leaders even if you don’t have fully integrated HR systems. Of course, there are many more attributes available that leaders may want to view. These will be specific to each business’ strategy so my advice is to include a number of common HRIS fields into the data collection process when you build your initial data set so you can segment later as needed. Suggested other data elements to consider adding Succession – can we tap into the successors for persons in critical roles? How impacted are these successors? Critical roles – prioritize remote work arrangements or advise early preventive quarantine measures for specific roles in certain areas Skills data – capture potential temporary backfills for employees that are unable to work Set-up expense tracking – spending to facilitate remote working capabilities Re-infection rates – tracking of persons previously cleared
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4 min read
Chris Butler
The SuccessFactors Workforce Analytics (platform pictured above) is soon to be sunset. If you haven't heard already, the SuccessFactors Workforce Analytics and Planning teams were made redundant yesterday. Product, Support, and Engineering teams for the platform (pictured above) have been given notice leaving a handful of services to maintain existing customer deployments. A lot of talented friends and pioneers in people analytics are now looking for new jobs. If and when formal word comes out of SAP, I am sure it will be along the lines of "Workforce Analytics (WFA) is not dead but moving to SAP Analytics Cloud (SAC)" with no specific timeline or plan for doing so let alone whether equivalent capability will be available (it won't be). Luckily, if you're up and running on WFA you've done all the hard work to get there. Your data is flowing and your business logic is defined. I'm here to offer all WFA customers a transition to One Model with no cost and a promise you'll be up and running with a more capable solution in a matter of days. Simply switch your existing data feeds to One Model, provide us your WFA data specification, and we'll do the rest. Literally - we'll have you up and running in a matter of days. And we can do more in more in a single day than SuccessFactors Workforce Analytics used to be able to provide in six weeks. What's awesome about One Model: Experience an all-inclusive platform: access all your data with no limits, no modules, and no implementation fees. Leverage our experience, models, and content catalogues. Don't deal with extra charges: no paid services for building metrics, dimensions, and building new modules. Daily data refreshes. Get a real HR Data Strategy built for the future of people analytics that will fully support your evolving technology landscape. Gain full access to the data warehouse, data modeling, and full exposure for user transparency. Plug in your own tools like Tableau, Excel, SAC. Truly system agnostic. Access automated machine learning to build custom predictive models relevant to you. Use the worlds most advanced Role Based Security and overcome the challenges you currently have providing secure data views to the right users. Embed within SuccessFactors using a SF extension built by one of our partners. Embed within Portals like Sharepoint, and Confluence. Feed external systems and vendors with clean, consolidated data and use us as part of any system migration to maintain history and configure data for the new system. Way too much more to list here... The Offer: Switch to One Model with no implementation fee. Redirect your feeds. Provide your Workforce Analytics data specification. Receive a people analytics infrastructure and toolkit built to support your growth in maturity and capability. Bonus: One Model will match the SF WFA subscription price if our subscription is higher. HR Analytics should flow as a by-product of how you manage your people data. "This is the way data will be managed." "OneModel’s approach is significantly different from the rest of the pack. It understands the dynamic nature of organizations and provides monitoring and maintenance capacity for the inevitable moment in which a data model ceases to be effective." - John Sumser, HR Examiner About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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3 min read
Nicholas Garbis
Here at One Model, we have some great news regarding our team! Nicholas Garbis has joined us as VP of People Analytics Strategy, bringing over a decade of significant global experience in people analytics and strategic workforce planning roles at firms including Allianz, General Electric, and Target. We like to let our people speak for themselves, so we'll turn it over to Nicholas now ... Why I am joining One Model? As a leader of People Analytics and Strategic Workforce Planning teams over the past ~15 years I have seen (and been part of) the transition of this work from fringe concept to a core requirement of the human resources function. The early leaders of teams like mine are now entering their second and third roles in different firms, leaving their imprint on their former organizations. At the same time, there are now many CHROs that have the knowledge and appetite for analytics and planning, and they are also moving between firms and building new teams. The days of conferences where presentations covered theories and frameworks with minor variations are in the past. We are definitely in people analytics/planning 2.0 now -- and it’s great to be a part of it. “The future is already here -- it’s just not very evenly distributed.” -- William Gibson. Having moved between a few large firms, and consulted into several more, I can assure you that Gibson was right. Going from one company to another might look like going back in time technologically and/or strategically, or it might be like achieving warp speed. As a strategist and practitioner, one needs to assess the situation as it stands and chart a course that has the best chance for creating value for the organization. Then it requires a constant balancing of priorities of team size and skills, new talent processes, and securing the needed technology. It’s not easy work, and the choices are never simple, but dare I say, it’s “fun”? Maybe “fulfilling” is a better word. I am joining One Model because I believe that technology is key to unlocking the next wave of value in people analytics and planning. I believe I can draw upon my own experiences and synthesize the experiences of others to help shape this 2.0 world. I have seen where the technology constraints can slow things down and create immense manual work, and I have seen where technology creates speed and scale. (Trust me, the latter situation is preferred!) Data is the strategic asset of the future, and this includes human capital data! As organizations continue to set up new people analytics teams, others will continue to progress into more advanced analytics and data science. Capturing the fullest value possible from human capital data will require technology that meets organizations where they are: for some it will be foundational data, metrics and dashboards, and for others it will be a robust data analytics platform with AI and machine learning already built in. One Model does both and will continue to lead the way. This is why I am joining the One Model team. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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4 min read
Josh Lemoine
Software companies today aren't exactly selling the idea of "lovingly crafting you some software that's unique and meaningful to you". There's a lot more talk about best practices, consistency, and automation. It's cool for software capabilities to be generated by robots now. And that's cool when it comes to things like making predictions. One Model is a leader in that space with One AI. This post isn't about machine learning though. It's about modeling your company's people data . The people at One Model work with you to produce a people data model that best suits your company's needs. It's like having your own master brewer help guide you through the immense complexity that we see with people data. Why does One Model take this hands-on approach? Because the people employed at your company are unique and your company itself is unique. Organizations differ not only in structure and culture but also in the combinations of systems they use to find and manage their employees. When you consider all of this together, it's a lot of uniqueness. The uniqueness of your company and its employees is also your competitive advantage. Why then would you want the exact same thing as other companies when it comes to your people analytics? The core goal of One Model is to deliver YOUR organization's "one model" for people data. A Data Engineer builds your data model in One Model. The Data Engineer working with you will have actual conversations with you about your business rules and logic and translates that information into data transformation scripting. One Model does not perform this work manually because of technical limitations or an immature product. It's actually kind of the opposite. Affectionately known as "Pipeo", One Model's data modeling framework is a major factor in allowing One Model to scale while still using a hands-on approach. Advantages of Pipeo include the following: It's fast. Templates and the "One Model" standard models are used as the starting point. This gets your data live in One Model very quickly, allowing for validation and subsequent logic changes to begin early on in the implementation process. It's extremely flexible. Anything you can write in SQL can be achieved in Pipeo. This allows One Model to deliver things outside the realm of creating a standard data model. We've created a data orchestration and integrated development environment with all the flexibility of a solution you may have built internally. It's transparent. You the customer can look at your Pipeo. You can even modify your Pipeo if you're comfortable doing so. The logic does not reside in a black box. It facilitates accuracy. Press a validation button, get a list of errors. Correct, validate, and repeat. The scripting does not need to be run to highlight syntax issues. OMG is it efficient. What used to take us six weeks at our previous companies and roles we can deliver in a matter of hours. Content templates help but when you really need to push the boundaries being able to do so quickly and with expertise at hand lets you do more faster. It's fun to say Pipeo. You can even use it as a verb. Example: I pipeoed up a few new dimensions for you. The role the Data Engineer plays isn't a substitute for working with a dedicated Customer Success person from One Model. It's in addition to it. Customer Success plays a key role in the process as well. The Customer Success people at One Model bring many years of industry experience to the table and they know their way around people data. They play a heavy role in providing guidance and thought leadership as well as making sure everything you're looking for is delivered accurately. Customer Success will support you throughout your time with One Model, not just during implementation. If you'd like to sample some of the "craft people analytics" that One Model has on tap, please reach out for a demo. We'll pour you a pint right from the source, because canned just doesn't taste as good.
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11 min read
Chris Butler
About ten years ago, as the pace of HR technology migration to the cloud started to heat up, I started to have a lot more conversations with organizations that were struggling with the challenges of planning for system migration and what to do with the data from their old systems post-migration. This became such a common conversation that it formed part of the reason for One Model coming into existence. Indeed much of the initial thought noodling was around how to support the great cloud migration that was and still is underway. In fact, I don't think this migration is ever going to end as new innovation and technology becomes available in the HR space. The pace of adoption is increasing and more money is being made than ever by the large systems implementation firms (Accenture, Deloitte, Cognizant, Rizing etc). Even what may be considered as a small migration between two like systems can cost huge amounts of money and time to complete. One of the core challenges of people analytics has always been the breadth and complexity of the data set and how to manage and maintain this data over time. Do this well, though, and what you have is a complete view of the data across systems that is connected and evolving with your system landscape. Why then are we not thinking in a larger context about this data infrastructure to be able to support our organizations adoption of innovation? After all, we have a perfect data store, toolset, and view of our data to facilitate migration. The perfect people analytics infrastructure has implemented an HR Data Strategy that disconnects the concept of data ownership from the transactional system of choice. This has been an evolving conversation but my core view is that as organizations increase their analytical capability, they will have in place a data strategy that supports the ability to choose any transactional system to manage their operations. Being able to quickly move between systems and manage legacy data with new data is key to adopting innovation and organizations that do this best will reap the benefits. Let's take a look at a real example, but note that I am ignoring the soft skill components of how to tackle data structure mapping and the conversations required to identify business logic, etc., as this still needs human input in a larger system migration. Using People Analytics for System Migration Recently we were able to deploy our people analytics infrastructure with a customer to specifically support the migration of data from Taleo Business Edition to Workday's Recruiting module. While this isn't our core focus as a people analytics company, we recently completed one of the last functional pieces we needed to accomplish this, so I was excited to see what we could do. Keep in mind that the below steps and process we worked through would be the same from your own infrastructure but One Model has some additional data management features that grease the wheels. To support system migration we needed to be able to Extract from the source system (Taleo Business Edition) including irregular data (resume files) Understand the source and model to an intermediate common data model Validate all source data (metrics, quality, etc) Model the intermediate model to the destination target model Push to the destination (Workday) Extract from the destination and validate the data as correct or otherwise Infinitely and automatically repeat the above as the project requires. Business logic to transform and align data from the source to target can be undertaken at both steps 2 and 4 depending on the requirement for the transformation. Below is the high level view of the flow for this project. In more detail The Source There were 132 Tables from Taleo Business Edition that form the source data set extracted from the API plus a separate the collection of resume attachments retrieved via a python program. Luckily we already understood this source and had modeled them. Model and Transform We already had models for Taleo so the majority of effort here is in catering for the business logic to go from one system to another and any customer specific logic that needs to be built. This was our first time building towards a workday target schema so the bulk of time was spent here but this point to point model is now basically a template for re-use. The below shows some of the actual data model transformations taking place and the intermediate and output tables that are being created in the process. Validation and Data Quality Obviously, we need to view the data for completeness and quality. A few dashboards give us the views we need to do so. Analytics provides an ability to measure data and a window to drill through to validate that the numbers are accurate and as expected. If the system is still in use, filtering by time allows new data to be viewed or exported to provide incremental updates. Data Quality is further addressed looking for each of the data scenarios that need to handled, these include items like missing values, and consistency checks across fields Evaluate, Adjust, Repeat It should be immediately apparent if there are problems with the data by viewing the dashboards and scenario lists. If data needs to be corrected at the source you do so and run a new extraction. Logic or data fills can be catered for in the transformation/modelling layers including bulk updates to fill any gaps or correct erroneous scenarios. As an automated process, you are not re-doing these tasks with every run - the manual effort is made once and infinitely repeated. Load to the Target System It's easy enough to take a table created here and download it as a file for loading into the target system but ideally you want to automate this step and push to the system's load facilities. In this fashion you can automate the entire process and replace or add to the data set that is in your new system even while the legacy application is still functioning and building data. On the cutover day you run a final process and you're done. Validate the Target System Data Of course, you need to validate the new system is correctly loaded and functioning so round-tripping the data back to the people analytics system will give you that oversight and the same data quality elements can be run against the new system. From here you can merge your legacy and new data sets and provide a continuous timeline for your reporting and analytics across systems as if they were always one and the same. Level of Effort We spent around 16-20 hours of technical time (excluding some soft skills time) to run the entire process to completion which included Building the required logic, target to destination models for the first time Multiple changes to the destination requirements as the external implementation consultant changed their requirements Dozens of end to end runs as data changed at the source and the destination load was validated Building a python program to extract resume files from TBE, this is now a repeatable program in our augmentations library. That's not a lot of time, and we could now do the above much faster as the repeatable pieces are in place to move from Taleo Business Edition to Workday's Recruiting module. The same process can be followed for any system. The Outcome? "Colliers chose One Model as our data integration partner for the implementation of Workday Recruiting. They built out a tailored solution that would enable us to safely, securely and accurately transfer large files of complex data from our existing ATS to our new tool. They were highly flexible in their approach and very personable to deal with – accommodating a number of twists and turns in our project plan. I wouldn’t hesitate to engage them on future projects or to recommend them to other firms seeking a professional, yet friendly team of experts in data management." - Kerris Hougardy Adopting new Innovation We've used the same methods to power new vendors that customers have on-boarded. In short order, a comprehensive cross-system data set can be built and automatically pushed to the vendor enabling their service. Meanwhile the data from your old system is still held in the people analytics framework enabling you to merge the sets for historical reporting. If you can more easily adopt new technology and move between technologies you mitigate the risks and costs of 'vendor lock-in'. I like to think of this outcome as creating an insurance policy for bad fit technology. If you know you can stand up a new technology quickly, then you can use it while you need it and move to something that fits better in the future without loss of your data history then you will be more likely to be able to test and adopt new innovation. Being able to choose the right technology at the right time is crucial for advancing our use of technology and ideally creating greater impact for our organization and employees. Our Advice for Organizations Planning for an HR System Migration Get a handle and view across your data first -- if you are already reporting and delivering analytics on these systems you have a much better handle on the data and it's quality than if you didn't. The data is often not as bad as you expect it to be and cleaning up with repeatable logic is much better than infrequently extracting and running manual cleansing routines. You could save a huge amount of time in the migration process and use more internal resources to do what you are paying an external implementation consultant to deliver. Focus more time on the differences between the systems and what you need to cater for to align the data to the new system. A properly constructed people analytics infrastructure is a system agnostic HR Data Strategy and is able to deliver more than just insight to your people. We need to think about our people data differently and take ownership for it external to the transactional vendor, when we do so we realize a level of value, flexibility and ability to adopt innovation that will drive the next phase of people analytics results while supporting HR and the business in improving the employee experience. About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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1 min read
Stacia Damron
One Model welcomes Bruce Chadburn as the new APAC Region Sales Leader. Bruce has a long history of successful sales in the HR domain and particularly with people analytics platforms, having sold the Infohrm/SuccessFactors Workforce Analytics product for many years across the APAC region. Bruce’s most recent position was with Infor Global selling their ERP suite of software, where he led a strong team and achieved top global Sales Executive three years running. “It’s great to once again be working with the One Model team and reconnecting with the people, departments, and businesses that we helped be successful for so many years. I’m looking forward to doing it all over again.“, says Bruce. Bruce will take a Sales Leadership position at One Model primarily responsible for the APAC region but will lend his wealth of experience to the growing global team. “I’m excited to once again work with Bruce in the People Analytics domain, his knowledge, experience, and personability allows him to be able to anticipate our customers needs and deliver exactly what they want. His experience will be of enormous benefit as we scale our global sales team and processes” said Chris Butler CEO at One Model. If you would like a conversation about your people analytics needs, feel free to contact Bruce using the info below. Bruce Chadburn bruce.cadburn@onemodel.com.au +61 (0)434128866
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4 min read
Stacia Damron
This summer, One Model opens new Data Center in Sydney, Australia. It's been a busy period for One Model, especially for our growing Australia office. If you can scroll past this gorgeous teaser photo without getting sidetracked and planning a vacation, we are going to provide some updates on what exactly the team has been up to. To begin with, the team has just opened a new state-of-the-art, enterprise grade infrastructure in its Sydney, Australia AWS hosted Data Center. The Australian infrastructure, which meets strict security standards, joins One Model’s fabric of existing infrastructure in the United States and Europe, all of which are designed to provide a local, robust, secure, and high-performance environment for its customers’ people and business data. This is our first data center in Australia. The data center opening comes shortly after the acquisition of our newest Melbourne-based, customer. Our newest customer, an Australian wagering and one of the world’s largest gaming companies, selected One Model as the company of choice for their people analytics platform in Q2 of 2019. Our team is thrilled to be a foundational element to their employee experience strategy and we plan to provide a number of key benefits including improved insight into our people, increased efficiency, and strategic value to key stakeholders. Our people analytics infrastructure's fast speed of deployment will help this new customer shift away from a reliance on legacy ways of working and technologies. “With an Australian founding team and a sizeable part of the One Model Engineering and Product Management teams being based in Brisbane, the team’s local knowledge and proximity represents a unique opportunity for customers in the Asia Pacific region. It allows One Model to be an active part of our global product innovation compared to traditional analytics software vendors.” says Tony Ashton, Chief Product Officer for One Model. These additional data centers play a crucial role in the company’s ability to better serve its current and future Australian and Asia Pacific region customers, as well as ensuring business continuity as the company continues to grow within the Australian market. Earlier this year AWS received PROTECTED and IRAP certification ensuring security compliance for working with the Australian Government and large enterprise. “The opening of this new data center is inline with One Model’s commitment to expand where our customers need us and to provide local infrastructure and personnel for data security and delivery of support services. An additional data center is already planned for delivery in Canada to support our Canadian customers in Q4 of 2019”, says Chris Butler, One Model CEO. One Model looks forward to welcoming additional internationally-based companies into it's family of customers as we continue to expand to serve these additional markets. In Australia? Want to meet the One Model team in person? Join us for the annual Australian HR Institute (AHRI) Convention in Brisbane this September 16-19th, where we'll be exhibiting at stand #64. The exhibition hall is open to visitors free of charge. Let us know if you plan to stop by! About One Model One Model delivers our customers with a people analytics infrastructure that provides all the tools necessary to directly connect to source technologies and deliver the reporting, analysis, and ultimately prediction of the workforce and it's behaviors. Use our leading out-of-the-box integrations, metrics, analytics, dashboards, and domain expert content, or create your own as you need to including the ability to use your own tooling like Tableau, Power BI, R, Python as you need. We provide a full platform for building, sustaining, and maturing a people analytics function delivering more structure information, measurement, and accountability from your team. Learn more at onemodel.co.
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10 min read
Tony Ashton
Here at One Model, we are incredibly excited to have Tony Ashton join us from SAP SuccessFactors as our first Chief Product Officer and there is no better way to introduce the company’s first Chief Product Officer than for Tony to share his thoughts directly below. Why One Model? I'm incredibly excited to join the One Model team as the Chief Product Officer. I'm writing this blog to share my enthusiasm for One Model and also a bit about my background to hopefully serve as a guide to how we drive product innovation going forward. So why One Model? Simply put, One Model is doing the most exciting, innovative work in the people analytics space today. People Analytics is one of the most complex analytical domains due to the variety and complexity of the data. Even in systems purporting to provide a complete suite of integrated HR solutions the underlying data models for all of the different functional areas remain varied and complicated, if not impenetrable. Just think of the underlying data models within the core HRIS or Recruiting, Performance, Succession, Payroll, Benefits, Learning et.al. Then overlay concepts like date effectiveness, position management, multiple occupancy, changing organizational structures! I could go on. The One Model team has decades of experience in dealing with this specialized HR data domain and is the best company in the world at transforming all this data into one unified data model. No other company is going as deep and innovating as fast as One Model on the data modelling side and this is essential for success in People Analytics. Good clean data is critical for data science and most data scientists have to spend over 80% of their time assembling, organising and cleansing data. One Model solves this problem and provides scalability for data science. Beyond this, One Model is also leading innovation in the areas of Artificial Intelligence and Machine Learning in the People Analytics space. AI & ML are massively over-hyped, particularly when applied to the Human Resources domain. In large part this is because most data science in HR is based on one-off projects and not built to scale. The One AI platform One Model has built - it isn’t a generic toolkit, it is purpose built for developing insights for People Analytics. The introduction of Artificial Intelligence, Machine Learning & Robotics are now starting to drive real change and this has an incredible impact on how work gets done in business today. Analytics provides understanding and through the use of advanced technologies like One AI you are able to model the future, build alternate scenarios, understand the things that are driving change and take control of the future of work. I’ll talk more about “Why One Model?” at the end of the blog, but now want to turn to how I see the world of people analytics product management. Customer centricity and deep understanding. When building products you always need to think from the outside-in to understand the real problems you are trying to solve for your customers and this is my philosophy. The contemporary term for this is the "jobs-to-be-done theory", which has been around for a while and basically says you should focus on the task someone is trying to perform, or the outcome they are striving for and then design your solution to help achieve that outcome. When you say it out loud it is incredibly obvious, but then most of the best ideas are. Here's a great quick primer for you that's also a fun read: https://hbr.org/ideacast/2016/12/the-jobs-to-be-done-theory-of-innovation. (I have hired many donuts in my time - this will make sense when you read the article 🙂 ) I'm excited by ideas. Big ideas and concepts are important. I studied philosophy and history (with a focus on the history of innovation) at university and this passion nicely intersected with my business life when I read Clayton Christensen's quintessential book "The Innovator’s Dilemma". This book was ground-breaking in 1997 and the concept of the “Innovator’s Dilemma” is now part of popular parlance, but the principles are still impacting business today, so I recommend you have a read if you haven't already been there. Going further back, I was also influenced by Thomas Khun's seminal book "The Structure of Scientific Revolutions", where he coined the phrase "paradigm change" before it was hijacked for pop-psychology purposes. We are in a time of revolutionary change right now and understanding this is critical for success. Making something great. A mantra I borrowed from my good friend Philip Haine when we would work on new product designs together was this phrase he would often use: "What would be amazing?". You can run a detailed design thinking process and generate lots of ideas and this is a great structured way to involve people in designing a solution based on empathy, etc. but standing back and thinking about a problem from the perspective of the person at the center and simply asking “what would be amazing” for them is an awesome way to cut through and quickly generate truly ground-breaking solutions. Another mentor along the way was Dmitri Krakovsky, who would always ask this simple question of any project: "Is it great?". Mid-way through a product development cycle, if you sit back and ask yourself "is it great?" and it isn't then you should seriously think about what you are working on and why. Applied Technology & Innovation. Building on the 'making something great' discussion above, the best technology becomes seamlessly integrated with your work/life and genuinely helps you get things done. It should also be cool and fun to use. Have a think about what apps you like to use. Some I use everyday include Pocket, Flipboard, Slack, Evernote, Dropbox, Google Maps. What do all these apps have in common? They have a focus, they do what they do incredibly well and don't try to be something they aren't. Why isn't enterprise software like this? Why are all analytics products just like using a big spreadsheet, or so complicated you need a degree in statistics? Some products look pretty and appear simple to use, but often when you dig into it they just don’t deliver the goods. It doesn't need to be this way. The Art & Practice of People Analytics. When I found myself working in People Analytics I felt I found my calling. Building my skills along the way I recall a seminal event was when I attended an HR technology conference and saw an amazing presentation by Peter Howes on multidimensional analysis in HR using what was ground-breaking technology at the time. I then went to one of his workshops on how to measure the Return on Investment (ROI) of HR Interventions. Not long after I met Peter's business partner Anastasia Ellerby through a public sector project measuring and benchmarking the effectiveness of the Human Resources function. Through this project I started using their company's products as a customer. The company was Infohrm. With the help of serendipity I started working for Infohrm and the company built the most impactful workforce analytics and workforce planning products, practice and community in the world - it was cool. This then continued through two acquisitions, first by SuccessFactors and then by SAP. What always keep me engaged was working on the cutting edge of innovation in the field, working with companies all over the world and working with a great team of passionate people. We managed to build some innovative stuff, but I found myself in a 90,000+ employee company and it was becoming increasingly more difficult to deliver focused people analytics and planning innovation for customers and I wanted to get back to my passion. Everything I have discussed above dovetails perfectly with what One Model is all about. We are passionately creating the world’s most amazing technology specifically designed to help you deliver people analytics insights that accelerate decision making and drive positive outcomes for your business and a workforce planning capability that helps you plan, forecast and built a talent strategy for today and tomorrow. I’ve now written a much longer blog that originally intended, but hopefully it shows how enthusiastic I am for this domain and for this new role. The One Model team is incredible and the product is awesome. We have shared history and shared values stemming from the original Infohrm company and we do whatever it takes to make our customers successful. I'm grateful for the opportunity and super excited by the innovations we are cooking up and can't wait to share these with you soon. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. Learn more at onemodel.co. About One AI - Trailblazer Trailblazer is One Model's newest way of helping leaders incorporate workforce analytics and distill big data into every HR decision for recommendations that are smarter, faster, and more efficient. The Trailblazer program does this by giving HR teams access to the only openly configurable, HR-focused, automated machine learning engine in the world: One AI. Introductory Offer: Try Trailblazer out for a month - $1,000 Visit onemodel.co/trailblazer-program to learn more.
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3 min read
Stacia Damron
The One Model team is excited to announce that Tony Ashton has moved from Vice President of Product Management at SAP SuccessFactors to be the Chief Product Officer at One Model. One Model is an Austin-based HR technology company, with offices in the United States, United Kingdom, and Australia. Tony will join our Brisbane, Australia office, which headquarters our rapidly growing engineering team. With over seventeen years of experience leading the people analytics product team at SAP SuccessFactors and before that, Infohrm (acquired by SuccessFactors), Tony brings a wealth of product leadership experience to the quickly-growing HR technology startup. “One Model is doing the most exciting, innovative work in the people analytics space today,” asserts Ashton. “No other company in the world is going as deep or innovating as fast as One Model in HR data modeling and the application of machine learning and artificial intelligence to the field of people analytics.” As One Model’s Chief Product Officer, Tony will play an instrumental role in driving One Model’s product innovation strategy and bringing the company's vision to life across our People Analytics Infrastructure, One AI, and Trailblazer offerings. “This strategic hire will support One Model as it continues to remain a market leader in product innovation, development, and people analytics strategy on a global scale,” says Stacia Damron, Senior Marketing Manager. “Scaling our team is the next step; the right hires will be instrumental in the creation and evolution of our offerings, and in our commitment in the alignment of those offerings with both current and future customers needs.” One Model CEO, Chris Butler, is thrilled with this addition to the team. “Tony is without doubt the highest calibre and most experienced product leader in the people analytics domain. I am incredibly excited about the capability that Tony brings to drive our product forward and focus on the success of our customers" says Butler. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. Learn more at onemodel.co. About One AI Tony is instrumental in leading the One AI team. Making HR machine learning transparent and accessible to all is a key differentiator between One Model and other People Analytics tools on the Market. Tony's passion for building the community is unprecidented. Learn more about One AI.
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3 min read
Stacia Damron
One Model is pleased to be a sponsor for the upcoming Bersin/ Deloitte IMPACT 2019 conference this April 15-17 in Phoenix, Arizona. IMPACT, which is now celebrating its twelfth year as an industry-leading conference, attracts over 600+ HR leaders eager to explore innovative technologies, trends, and strategies for today's rapidly evolving HR environment. There, One Model will be joining Deloitte in their Innovation Lab, where team members will be running demos of one of Deloitte's newest workforce solutions, which is powered by One Model technology. Demos can also be viewed throughout the conference in the Camelback Room. One Model, provides people analytics solutions for companies that want to accelerate their time to value and want the flexibility, control, and options to support their continued growth. “HR leaders attend IMPACT for inspiration and education on the industry’s most innovative new ideas and technologies,” says Stacia Damron, One Model Senior Marketing Manager. “We are looking forward to connecting with companies and pairing them with the right resources to achieve the best return on their organizational performance.” Powerful enough for Deloitte's needs, flexible enough for your own We are proud to partner with Deloitte to help power their analytics capabilities and continue to create a seamless customer experience for people analytics teams on a global scale. One Model, which won this past year’s HRExaminer Watchlist Award for AI Companies in HR Technology, “provides a complete set of tools for designing and building your own nuanced analytics, predictions, and applications,” proclaims John Sumser of HRExaminer. The company’s partnership with Deloitte’s Workforce Insights helps further One Model’s mission of delivering this complete set of tools to customers and highlights its commitment to doing so on a global scale. Ready to scale your people analytics program? Attending IMPACT 2019? Stop by the Deloitte Innovation Lab to speak with the One Model team, or join us in the Camelback room to listen to a live demo and ask any questions you may have. About One Model One Model, the leader in people analytics infrastructure provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. Learn more at onemodel.co.
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13 min read
Phil Schrader
As the people analytics leader in your organization, you are responsible for transforming people data into a unique competitive advantage. You need to figure out what it is about your workforce that makes your business grow, and you need to get leaders across the organization on board with using data to make better employee outcomes happen. How will you do that in 2019? Will you waste another year attempting to methodically climb up the 4 or 5 stages of the traditional analytics maturity model? You know the one. It goes from operational reporting in the lower left, up through a few intermediate stages, and then in the far distant upper right, culminates with predictive models and optimization. Here’s the Bersin & Associates one for reference or flip open your copy of Competing on Analytics (2007) for another (p. 8). The problem with this model is that on the surface it appears to be perfect common sense while in reality, it is hopelessly naive. It requires you to undertake the most far-reaching and logistically challenging efforts first. Then in the magical future, you will have this perfect environment in which to figure out what is actually important. If this were an actual roadmap for an actual road it would say, “Step 1: Begin constructing four-lane highway. … Step 4: Figure out where the highway should go.” It is the exact opposite of the way we have learned to think about business in the last decade. Agile. The Lean Startup. Etc. In fact it is such a perfect inverse of what you should be doing that we can literally turn this maturity model 180 degrees onto its head and discover an extremely compelling way to approach people analytics. Here is the new model. Notice the axes. This is a pragmatic view. We are now building impact (y axis) in the context of increasing logistical complexity (x axis). Impact grows as more people are using data to achieve the people outcomes that matter. But, as more and more people engage with the data your logistical burden grows as well. These burdens will manifest themselves in the form of system integrations, data validation rules, metric definitions, and a desire for more frequent data refreshes. From this practical perspective, operational data no longer seems like a great place to start. It’s desirable because it’s the point at which many people in the organization will be engaging with data, but it will require an enormous logistical effort to support. This is a good time to dispense with the notion that operational data is somehow inferior to other forms of data. That it’s the place to start because it’s so simplistic. Actually, your business runs operationally. Amazon’s operational data, for example, instructs a picker in a warehouse to go and fetch a particular package from the shelves at a particular moment in time. That’s just a row of operational data. But it occurs at the end of a sophisticated analytics process that often results in you getting a package on the very same day you ordered it. Operational data is data at the point of impact. Predictive data also looks quite different from this perspective. It’s a wonderful starting point because it is very manageable logistically. And don’t be put off by the fact that I’ve labeled its impact as lower. Remember that impact in this model is a function of the number of people using your data. The impact of your initial predictive models will be felt in a relatively small circle of people around you, but it’s that group of people that will form your most critical allies as you seek to build your analytics program. For starters, it’s your boss and the executive team. Sometime around Valentines Day they will no doubt start to ask, “Hey, how’s the roadmap coming along?” In the old model, you would have to say, “Oh well you know it’s difficult because it’s HR data and we need to get it right first.” Then you’d both nod knowingly and head off to LinkedIn to read more articles about HR winning a seat at the table. But this year you will say, “It’s going great! We’ve run a few hundred predictive models and discovered that we can predict {insert Turnover, Promotion, Quality of Hire, etc} with a decent degree of recall and precision. As a next step, we’re figuring out how to organize this data more effectively so we can slice and dice it in more ways. After that we will start seeking out other data sets to improve our models and make a plan for distributing this data to our people leaders.” Ah. Wouldn’t that feel nice to say? Next, you begin taking steps to better organize your data and add new data sets. This takes more logistical effort so you will engage your next group of allies: HR system owners and IT managers. Because they are not fools, they will be a little skeptical at first. Specifically, they’re going to ask you what data you need and why it’s worth going after. If you’re operating under the old model, you won’t really know. You might say, “All of it.” They won’t like that answer. Or maybe you’ll be tempted to get some list of predefined KPIs from an article or book. That’s safer, but you can’t really build a uniquely differentiating capability for your organization that way. You’re just copying what other people thought was important. If you adopt our upside down model, on the other hand, you’ll have a perfectly good answer for the system owners and IT folks. You’ll say, “I’ve run a few hundred models and we know that this manageable list has the data elements that are the most valuable. These data points help us predict X. I’d like to focus on those. “Amen,” they’ll say. How’s that for a first two months of 2019? You’re showing progress to your execs. Your internal partners are on board. You are building momentum. The more allies you win, the more logistical complexity you can take on. At this stage people have reason to believe in you and share resources with you. As you move up the new maturity model with your IT allies, you’ll start to build analytic data sets. Now you’re looking for trends and exploring various slices. Now is the time for an executive dashboard or two. Now is the time to start demonstrating that your predictive models are actually predictive. These dashboards are focused. They’re not a grab bag of KPIs. They might simply show the number of people last month who left the company and whether or not they were predicted by the model. Maybe you cut it by role and salary band. The point is not to see everything. The point is to see what matters. Your execs will gladly take three pieces of meaningful data once per month over a dozen cuts of overview data once a day. Remember to manage your logistical commitment. You need to get the data right about once a month. Not daily. Not “real time.” Finally, you’re ready to get your operational data right. In the old world this meant something vague like being able to measure everything and having all the data validated and other unrealistic things. In the new world it means delivering operational data at the point of impact. In the old world you’d say, “Hey HRBP or line manager, here are all these reports you can run for all this stuff.” And they would either ignore them or find legitimate faults with them. In the new world, you say, “Hey HRBP or line manager, we’ve figured out how to predict X. We know that X is (good | bad) for your operations. We’ve rolled out some executive dashboards to track trends around X. Based on all that, we’ve invested in technology and process to get this data delivered to you as well. X can be many things. Maybe it’s a list of entry-level employees likely to promote two steps based upon factors identified in the model. Maybe it’s a list of key employees at high risk of termination based. Maybe it’s a ranking of employee shifts with a higher risk of a safety incident. Whatever it is for your business, you will be ready to roll it out far and wide because you’ve proven the value of data and you’ve pragmatically built a network of allies who believe in what you are doing. And the reason you’ll be in that position is because you turned your tired old analytics maturity model on it’s head and acted the way an agile business leader is supposed to act. Yeah but… Ok Phil, you say, that’s a nice story but it’s impossible. We can’t START with prediction. That’s too advanced. Back when these maturity models were first developed, I’d say that was true. The accessibility of data science has changed a lot in ten years. We are all more accustomed to talking about models and predictive results. More to the point, as the product evangelist at One Model I can tell you with first-hand confidence that you can, in fact, start with prediction. One Model’s One AI product offering ingests sets of data and runs them through a set of data processing steps, producing predictive models and diagnostic output. Here’s the gory details on all that. Scroll past the image and I’ll explain. Basically there’s a bunch of time consuming work that data scientists have to do in order to generate a model. This may include things like taking a column and separating the data into multiple new columns (One Hot Encoding) or devising a strategy to deal with missing data elements, or checking for cheater columns (a column like “Severance Pay” might be really good at predicting terminations, for example). There’s likely several ways to prepare a data set for modeling. After all that, a data scientist must choose from a range of predictive model types, each of which can be run with various different parameters in place. This all adds up to scrubbing, rescrubbing, running and re-running things over and over again. If you are like me, you don’t have the skill set to do all of that effectively. And you likely don’t have a data scientist loitering around waiting to grind through all of that for you. That’s why in the past this sort of thing was left at the end of the roadmap-- waiting for the worthy few. But I bet you are pretty good at piecing data sets together in Excel. I bet you’ve handled a vlookup or two on your way to becoming a people analytics manager. Well… all we actually need to do is manually construct a data set with a bunch of columns that you think might be relevant to predicting whatever outcome you are looking for. Then we feed the data into One AI. It cycles through all the gnarly stuff in the image above and gives you some detailed output on what it found. This includes an analysis of all the columns you fed in and also, of course, the model itself. You don’t need to be able to do all the stuff in that image. You just need to be able to read and digest the results. And of course, we can help with that. Now, the initial model may not have great precision and recall. In other words, it might not be that predictive but you’ll discover a lot about the quality and power of your existing data. This exercise allows you to scout ahead, actually mapping out where your roadmap should go. If the initial data you got your hands on doesn’t actually predict anything meaningful in terms of unique, differentiating employee outcomes-- then it’s damn good you didn’t discover that after three years of road building. That would be like one of those failed bridges to nowhere. Don’t do that. Don’t make the next phase of your career look like this. Welcome to 2019. We’ve dramatically lowered the costs of exploring the predictive value of your data through machine learning. Get your hands on some data. Feed it into One AI. If it’s predictive, use those results to build your coalition. If the initial results are not overly predictive, scape together some more data or try a new question. Iterate. Be agile. Be smart. Sometimes you have to stand on your head for a better view. How can I follow Phil's advice and get started? About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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5 min read
Josh Lemoine
2019 Goals: With it being the dawn of a new year, a lot of us are setting goals for ourselves. This year, I set two goals: To write and publish a blog post To run a marathon As the father of two young children, I'm always looking for ways to maximize time management. As I ran on the treadmill recently, a bizarre idea came to me in between thoughts of "why do I do this to myself?" and "this sucks". I might be able to accomplish the first goal and get a start on the second at the same time. See, on my very first run 6 years ago, I brought my phone and tracked the run using a fitness tracker app. Since then, I never quit running and I never stopped tracking every single run using the same app. I have literally burned 296,827 calories building this data set... ...and this data deserves better than living in an app on my phone. As a Data Engineer, I feel ashamed to have been treating my exciting (to me) and certainly hard-earned data this way. What if I loaded the data into One Model and performed some analysis on it? If it worked, it would provide an excellent use case for just how flexible One Model is. It would also give me a leg up (running pun intended) on marathon training. One Model is flexible! One Model is a People Analytics platform. That said, it's REALLY flexible and very well positioned as the definition of "People Data" becomes more broad. The companies we work with are becoming increasingly creative in the types of data they're loading. And they're increasing their ROI by doing so. One Model is NOT a black box that you load HRIS and/or ATS data into that then spits out some generic reports or dashboards. The flexible technology platform coupled with a team of people with a massive amount of experience working with People Data is a big part of what differentiates One Model from other options. Would One Model be flexible enough to allow for analyzing running data in it? Yes. Not only was it flexible enough, but the data was loaded, modeled, and visualized without using any database tools. Everything you're about to see was done through the One Model front end. One Model has invested substantially over the past year in building a data scripting framework and it's accessible within the UI. This is a really exciting feature that customers will increasingly be able to utilize in the coming year. Years ago, as a customer of a People Analytics provider, I would have given my right arm for something like this. That said, as a One Model customer you also get access to a team of experts to model your data for you. What did I take away and what should you take away from this? Along with gaining a better understanding of my running, this exercise has gotten me more excited about running. Is "excited about running" even a thing? I plan to start capturing and analyzing more complete running data in 2019 with the use of a smart watch. I'll also be posting runs more consistently on social media (Strava). It'll be interesting to watch the changes as I train for a marathon. Aside from running though, it has given me some fresh perspective on what's possible in One Model. This will surely carry over into the work I do on a daily basis. Hopefully you can take something away from it as well. If you're already using One Model you might want to think about whether you have other data sources that can be tied to your more traditional People Data. If you're not using One Model yet but have an interesting use case related to People Analytics, One Model might be just the ticket for you. Without further ado, here's my running data in One Model: "Cool - this is all really exciting. How can I get started?" Did the above excite you? Could One Model help you with your New Year's resolution? I can't guarantee it'll help you burn any calories, but you could be up and running with your own predictive analytics during Q1 of 2019. One Model's Trailblazer quick-start program allows you to get started with predictive analytics now. Want to learn more? About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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10 min read
Stacia Damron
Wouldn't it be incredible to predict the future? Let's ask 63-year-old Joan Ginther. She's arguably one of the luckiest women in the world. This Texas woman defied odds to win million-dollar lottery payouts via scratch cards not once, not twice, but four times over the past decade. Her first lottery win landed her $5.4 million, followed by $2 million, $3 million, and then a whopping $10 million jackpot over the summer of 2010. Mathematicians calculate the odds of this happening as one in eighteen septillion. Theoretically, this should only happen once in a quadrillion years. So how did this woman manage to pull it off? Was it luck? I'd certainly argue yes. Was it skill? Maybe. She did purchase all four scratch off cards at the same mini mart. Most interestingly, did it have something to do with the fact that Joan was a mathematics professor with a PhD in statistics from Stanford University? Quite possibly. We'll never know for sure what Joan's secret was, but the Texas Lottery Commission didn't (and still doesn't) suspect any foul play. Somehow, Joan (pictured to the left) predicted it was the right time and right place to buy a scratch off ticket. All we know for sure is that she's exceptionally lucky. And loaded. Most of us have a hard enough time predicting traffic on our morning commute. We can, however, make some insightful predictions for people analytics teams by running people data through predictive models. So, what is HR predictive analytics? Most specifically - predictive analytics use modeling, or a form of artificial intelligence that uses data mining and probability, to forecast or estimate specific outcomes. Each predictive model is comprised of a set of predictors (variables) in the data that influence future results. When the data set is processed by the program, it creates a statistical model based on the given data set. Translation? Predictive analytics allow us to predict the future based on historical outcomes. Let's discuss predictive analytics in HR examples. So predictive analytics can help HR professionals and business leaders make better decisions, but how? Maybe a company wants to learn where they're sourcing their best sales reps so they know where to turn to hire more top-notch employees. First, they must determine whether their "best" reps have measurable qualities. For the sake of this post, let's say they sell twice as much as the average sales reps. Perhaps all the best reps share several qualities such as referral source (like Indeed), a similar skill (fluency in Spanish listed on their resume) or personality trait (from personality tests conducted during the job interview). A predictive model would weigh all this data and compare it against the outcome: the superior sales quotas being hit. The model references the exploratory data analysis used to find correlations across all your data sources. This allows a company to run job candidates' resumes through the model in an effort to predict their future success in that role. Sounds great right? Now - here are the problems to consider: 1) Predictive models can only predict the future based on historical data. If you don't have enough data, that could be a problem. Download Ethics of AI Whitepaper. 2) Even if you do have enough data, that can still be a problem. Amazon, for example, recently scrapped its resume software (which evaluated resumes of current/previous employees to help screen potential ones) because it discovered the algorithm was biased towards men in engineering roles over women, which disqualified candidates that listed any women's organizations on their resume. (And it's not Amazon's fault. It's the data; historically, most men had been in those roles.) Kudos to them for scrapping that. That's why it's so important to use a human capital predictive analysis tool that is transparent and customized to your data vs. another big-box company in your industry. Check out One Model's One AI. HR predictive analysis is helpful, but it's also a process. Are there more applications? What HR-related problems does it solve? Predictive analysis applications in people analytics are vast. The right predictive models can help you solve anything from recruiting challenges to retention/employee attrition questions, to absenteeism, promotions and management, and even hr demand forecasting. The sky's the limit if you have the right tools and support. Time for a people analytics infrastructure reboot Sure - a people analytics infrastructure reboot isn't as exciting as winning the lottery and buying a yacht, but it's really, really helpful in solving questions large corporations struggle with daily. If you haven't used predictive modeling to solve a burning business problem, this might be a great place for your people analytics team to dive in. For One Model Customers - We recommend you push a couple of buttons and start with an exploratory data analysis. More and more companies are beginning to incorporate machine learning technology into their stack, and there's so much value that can be derived. If you're not sure where to get started, just keep it simple and bite off one piece of the puzzle at a time with One Model. One Model is built to turn your general HR team into people data scientists, no advanced degrees required. One Model provides the people analytics infrastructure - aka - it provides a platform for you to import your workforce data from all sources, transform it into one analytics-ready asset, and build predictive models to help you solve business challenges. Our customers are creating customized models and you can too. It's not as intimidating as you might think. It's super easy to get started: One Model will work with you to pull your people data out of any source that's giving you trouble (for example, your Greenhouse ATS, Workday, or Birst). We'll export it, clean it up, and put it all in the same place. It takes just a few weeks. From there, you can glean some insights from it. To learn more about One Model's capabilities (or to ask us any questions about how we create our predictive models), click the button below and a team member will reach out to answer all of your questions! Let's Talk More About Predictive Analytics for HR. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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13 min read
Stacia Damron
What's machine learning? Is it artificial intelligence? Deep learning? Is it black magic, or better yet, just a phrase the industry's marketing folks say to pique your interest? The answer? Let's crack it open. What is it? Machine learning is an application of artificial intelligence (AI) that uses statistical techniques to give computer systems the ability to automatically learn and steadily improve their performance from their experience with the data - all without being explicitly programmed to do so. Think of it this way: it's a program that's automatically learning and adjusting its actions without any help or assistance from humans. Cool, right? How is it used in data analytics? Machine learning is used to create complex models and algorithms that predict specific outcomes. Thus, it's coined as predictive analytics. The predictive models it creates allow the end users (data scientists, engineers, researchers, or analysts) to "produce reliable, repeatable decisions and results" that reveal otherwise "hidden insights through learning historical relationships and trends in the data." [1] Here's what artificial intelligence (AI) and machine learning are not: 1) Glorified statistics. Sure - both statistics and machine learning address the question "how do we learn from data?" In its most basic definition, "Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation." [2] Statistics is a field of mathematics that addresses sample, population, and hypothesis to understand and interpret data. Machine learning, on the other hand, allows computers to act and make data-driven decisions without being directly programmed to carry out a specific task. It involves predictions and supervised/unsupervised learning. Above, supervised learning is explained with apples. Supervised machine learning is when a program is trained on a pre-defined dataset. It's provided with example inputs (the data) and their desired outputs (results), and the computer's goal is to analyze these to learn the rule that maps these inputs to outputs. It can then apply it's knowledge to the learning algorithm to adjust and improve its future predictions about output values. In the graphic above, you provide a data set that teaches the program, "these are apples. this is what apples look like." The desired output in this case is knowing and recognizing an apple. The program learns from this data, and next time, it will be able to identify apples on it's own. Viola! - it has officially been trained. A real world example of supervised learning is predicting a car sale price based on a given dataset of previous auto sales data for that make, model, and condition in that area. Above: unsupervised learning, explained by some tasty fruit. Unsupervised learning, on the other hand, is when a program automatically recognizes patterns or relationships in a given dataset. The algorithm is essentially on its own finding structure in its input, as it's not provided with classifications or labels ahead of time. Above, the raw data is represented with a selection of fruit. In it goes, where the algorithm finds structure in the data (it notices there are some apples, some bananas, and some oddly shaped oranges). It processes this information and clusters these into groups to be classified. The output is shown above as sorted fruits in neatly defined groups: one for apples, one for bananas, and one for the oranges. Unsupervised learning helps: make inferences regarding the data, which; classify hidden structures within the previously unlabeled data. Since unsupervised learning helps discover and classify hidden patterns in the dataset, a solid example would be a program grouping a variety of documents (the documents are the dataset) by subject with no prior knowledge or training. To summarize: while machine learning certainly utilizes statistics, it's a different way of addressing and solving a problem. It's not some magical version of stats that's going to suddenly provide all the answers. On that note... 2) It's not magic that will solve any problem with any data set with 100% accuracy. Machine learning algorithms can only analyze the data they're provided. For example, a machine learning system trained on a company's current customer data might be limited in that it's only able to predict the needs of new customers that are already in the data, eliminating another type of customer demographic that's not present in the data it was trained on. It can also take over any intrinsic biases that lie in the current data. Machine learning isn't perfect. Take Google for example. The tech giant famously struggled with this in 2015, when its Google photo software exhibited signs of accidental algorithmic racism. It made headlines when the machine learning algorithm mistakenly tagged people of certain ethnicities as gorillas. The company took immediate action and removed all gorilla-based learnings from the training data, and the algorithm was modified. Google Photos will no longer tag any image as a gorilla, chimpanzee, or monkey - including the actual animals. While machine learning can make some extremely helpful and enriching business predictions, it's not always going to make accurate predictions. Machine learning is just that - constantly learning. 3) Marketing buzzwords. At this point, journalists are saying "AI" is on it's way to becoming the meaningless, intangible tech-industry equivalent of "all natural." Yes - there are absolutely some companies that claim to have an AI component when they actually do not, just to hype up their product (and shame on them!). But for every one company that's throwing the term loosely around, there's a few more that just don't know any better. Thus AI isn't well defined. As a result, any piece of software that employs a convolutional neural network, deep learning system, etc. is being marketed as “powered by artificial intelligence." Here's some questions you can ask to evaluate if a company truly is has an AI strategy: a) Is the company using machine learning? Artificial intelligence technology uses machine learning. Can they tell you what machine learning algorithms they're using? If you ask a rep this question and you're met with a blank stare, that's a red flag. b) Ask about the data. What data are you using to train your algorithms? Is there enough of it? According to this source, around 5,000 training examples are necessary to begin generating results. 10 million training examples are needed to achieve human-level performance. Also, ask about a company's claim to reliably produce a certain result. How do they generate that number? How do they prevent overfitting errors? c) Get to know the technology and company itself. Was this technology developed in-house? What was the company doing before? Were they always an AI company specializing in predictive, or were they riding on the bandwagon of whatever was cool and trendy before? No one's an expert in something for a few years back, and then all of a sudden an expert in something totally different that's hot right now. Who founded the company, and where does their industry expertise lie? Learn about the current leadership. If you stick with the check-list above to vet AI technology, you'll be able to dig up some answers pretty quickly - and you'll look pretty freakin' savvy while you're doing it. So, how is machine learning being used in the HR space? Well-informed leaders in the people analytics space are embracing AI and budgeting for the resources to incorporate machine learning technology into their HR strategies for the long-term. Machine learning technology can create a variety of predictive models that help companies gain insights and solve challenges in the following areas: Recruiting - Where are you sourcing your best candidates from? Know where your high performers are coming from and get insights into the KPIs their resumes or career histories have in common. Retention & Employee Attrition - Predictive analytics use a company's historical data to determine potential attrition risks prior to their occurrence, giving leadership otherwise unknown insights and an opportunity to take preventative actions. Absenteeism - The Bureau of Labor and Statistics says that in 2017, the average number of days an employee missed annually was 2.8 days. It doesn't seem like a lot, but if your company has 1,000 employees, then that amounts to 2,800 days per year. According to Circadian, unscheduled absenteeism costs roughly $2,650 each year for salaried employees. That adds up to a whopping 7.42 million a year in absenteeism costs. That's a huge incentive to find a solution. Predictive models can help identify patterns and trends in why employees are absent. Would they have been able to complete their assignments as scheduled if they were able to work from home? Are there are lot of absences under a particular manager? Or is a particular department under a high level of stress? The answers may lie in the data. Promotions and Management - What are some inputs in the company datasets that indicate a higher likelihood of minorities receiving promotions or opportunities? How can we encourage more women to apply for or join X department? Predictive models can analyze the data and provide helpful insights into why. People Spend - Predictive models can forecast the effects of any type of spend toward future workforce productivity, whether that's hiring more employees, increasing training and educational opportunities, or implementing new systems. What that means for today's people analytics leaders More and more companies are beginning to benefit from incorporating machine learning technology that supports their long-term strategy. If you're evaluating different tools to solve your people analytics challenges, add One Model to your list of companies to your list. One Model provides people analytics infrastructure - aka - it provides a platform for you to import your workforce data and build predictive models to help you solve business challenges such as the ones listed above (and many more). Our customers can create customized models or use our out-of-the-box integrations. To learn more about One Model's capabilities (or to ask us any questions about our machine learning algorithms and how we create our predictive models), click the button below and a team member will reach out to answer all of your questions. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. [1] "Machine Learning: What it is and why it matters". www.sas.com. Retrieved 2016-03-29. [2] Dodge, Y. (2006) The Oxford Dictionary of Statistical Terms, Oxford University Press. ISBN 0-19-920613-9
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8 min read
Stacia Damron
It’s a great time to be in management, right? According to a Harvard Business Review survey, we live in a world where trust is at an all time-low; 58 percent of respondents admitted to trusting strangers more than their own boss. Meanwhile, Uber’s giving an average of 5.5 million rides a day. (The average Uber driver rating is 4.8/5 stars, by the way.) 5.5 million people are trusting a complete stranger to get them the airport, but not their own managers. Workplace Trust Trust promotes confidence in the company’s future. A high level of trust encourages employees to work more effectively, engage with their work and peers, and allows them to be more productive overall. One could say it's both a cause and effect of a company's culture. Every day, we make decisions (consciously or unconsciously) based on the trust we have in each other. Each and every one of those decisions either encourages or discourages trust. So where did the workplace trust supposedly go? How should companies and managers work to build more than trust? How are we, as people analytics professionals, working to measure, track, and improve workplace satisfaction altogether? This article doesn't unlock a magical answer, but here are some good KPIs to keep on your radar: Absenteeism Rate Employees who are present, on-time, and hitting their goals and deadlines are going to be more engaged, satisfied employees. Those who aren’t…might not be singing the company's praises. Monitoring absenteeism and cross referencing with other KPIs is a good place to start. Employee Turnover Rate According to Office Vibe, only 12 percent of employees leave an organization for more money. On the other hand, 89 percent of bosses believe their employees quit because they want more money. Hmm. Is the company conducting exit surveys? Tracking why employees are leaving is vital, in addition to measuring additional metrics such as turnaround under specific managers, departments, or within specific minority groups. Is there a pattern in turnover? Perhaps a specific department, manager, or trigger event is responsible? Do you have predictive models that can help you internalize your data and answer the big questions? Employee Net Promoter Score (graph above) The infamous Net Promoter Score, which was originally a customer service tool, was later used internally on employees instead of customers. The Employee Net Promoter Score (eNPS) measures the likelihood of whether an employee would be willing to recommend your company as a great place to work, (get this - according to research - 59% of employees wouldn’t recommend theirs) and whether they would recommend the products or services your company creates. If you haven't yet started, track your eNPS. Then you can filter the data through a platform where you can see patterns and trends that could have affected the results. (Quick, shameless plug: you can measure the results and track and monitor changes to these in One Model’s people analytics platform to measure company-wide trust-related trends, and to view correlations with other key data and metrics.) Training When your car runs out of gas, do you fill up the tank, or leave it on the side of the road? Unless you’re from Dubai (and if you are, please send me the Maserati instead - we can work out the delivery instructions in the comments thread), then no, it’s not normal for people to do that. Same with employees. Training for a new employee can cost upwards of 20% of an employee’s annual salary. It’s better to engage your employees ahead of time than have to constantly rehire new ones. Employees who are actively choosing to participate in optional company-sponsored training and education programs (and allowed to pursue outside continued education) have been proven to be more invested in both their role and the company, feel more valued, and maintain a high level of loyalty and trust for their workplace. They have a higher likelihood of having a high eNPS score, and fuel company growth through positive word of mouth to their community (and network of prospective employees). The Summary For everyone out there that's not a rideshare driver, there's still hope. Yes, it takes extra time digging into the data, and yes, it requires a platform that can help you make sense of the KPIs you're tracking. But not all is lost. If you're digging into your workforce analytics data - have you considered building predictive models? They can shed light on things like the following: 1) Attrition Rates: Predict how many of your employees are going to leave within the next six or twelve months (based on maybe 30+ factors like manager turnover, whether or not they applied for jobs internally and were rejected, commute time, training attendance and participation, etc., etc., etc.). 2) Manager Toxicity Levels: Is there a lot of turnover under a particular department or manager? Is there high female turnover under a particular male executive? Shed light on what's going on. 3) Recruitment and Hiring: Are you recruitment strategies sound? Furthermore - are you hiring the right people for the job? Where are your best, high-performing sales representatives sourced from? Do you have data to backup your assumptions? One Model provides people analytics infrastructure - we provide a platform for you to import your workforce data and build predictive models such as the ones listed above (and so, so, so many more). Whether that means creating customized models or going with our out-of-the-box integrations - you get the whole shebang. We can take data from any source, clean and normalize it, and use it to create these models for you. Then, we provide a means to view your data in these models with nice, simple visualization tools. (Example: think, all three of your last (or future) HRIS systems - all that data - cleaned and normalized from ALL of those systems - living in one place, in clear visuals.) Want to add data from more sources and see how it affects that model? No problem. The awesome thing is that once a model is built with your data in One Model - you don't have to rework everything and start from scratch if you want to add another source. It can be added right on in. Painless. Maybe I'm biased because of all the cool initiatives I see our team's data scientists and engineers working on, but I have to admit - I'd give One Model a five star rating. That's more than I can say for some of my Ubers. If you'd like to talk to a team member, check us out. We won't force you into a demo; ask us whatever questions you'd like. About One Model One Model's people analytics software provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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3 min read
Chris Butler
Earlier this year i joined one of The Learning Forum's workforce analytics peer groups and i wanted to share my experience in attendence and why i came away thinking these groups are a great idea and should be considered by every PA practitioner. There are a number of groups that you can take a look at joining including Insight 222, and The Conference Board, but Brian Hackett from The Learning Forum had asked me earlier this year to come and present to their group about what we were doing at One Model. We had come up in their conversations and peer group emails where members had been asking about different technologies to help them in their building their HR analytics capabilities. The Learning Forum is a group of mostly Fortune 2000 companies with a sizable proportion being Fortune 500 organizations, of course i accepted. Our presentation went well and we had some great questions from the group around how we would tackle existing challenges today and where the platform is heading for their future projects. A great session for us but the real value i took away was in staying for the rest of the day to be a fly on the wall for how the group worked and what they shared with each other. Brian had tabled on the agenda some pre-scheduled discussions on what the attendees were interested in learning about and discussing with their peers. The agenda was attendee curated so all subjects were relevant to the audience and provided some structure and productivity to the event. Following was time for members to be able to present on any recent projects, and work they had been conducting in their teams and any valuable insights, outcomes, and advice they could share with the group. This was awesome to sit in on and listen to how others in our space are working, what their challenges are, how they fared, and to do so in an environment of open confidential sharing. It's the spirit of confidentiality and sharing between peers that i felt most made this group able to help and learn from each other that you just don't receive from a run of the mill conference. Practitioner's were here to share, to learn, and openly seek advice from their more experienced colleagues. Presentations ranged from experience using different vendors, to cobbled together projects using spit, glue, and anything else hands could be laid on. I found the cobbled together solutions to be the most innovative, even where a company of the practitioner's size has significant resources the insight's came from innovative thinking and making use of tools that every company has access to. It's these projects of working smart not hard that make me smile the most, and the best part is that it could be shared in a fashion, and truthfulness that couldn't have occurred at a conference, or a public linkedin post. Peer forums provide an educational opportunity that you won't get elsewhere, i highly recommend for all people analytics practitioners. Thanks Brian Hackett at The Learning Forum for letting me present and learn about how your members are learning from each other.
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6 min read
Stacia Damron
It’s sounds ridiculous, but it’s true. According to the New York Times, 4.2% of women held CEO roles in America’s 500 largest companies. Out of those same 500 companies, 4.5% of the CEO’s were named David.* While shocking, unfortunately, it’s not incredibly surprising. Especially when a whopping 41% of companies say they’re “too busy” to deploy diversity initiatives. But for every company out there that’s “too busy”, there are plenty of others fighting to get it right. Take Google, for example. In 2016, Google’s tech staff (specifically tech roles - not company-wide roles) was 1% Black, 2% Hispanic, and 17% women. They announced a plan to invest 150 million in workforce initiatives. The tech staff is now 2.5% Black and 3.5% Hispanic/Latinx, and 24.5% female, according to their 2018 diversity report. So what does that mean? It means that even the brightest and most innovative companies have their work cut out for them in regards to improving diversity. Change doesn’t happen overnight. Diversity breeds innovation; a diverse talent pool leads to diverse ideas. Get this; a Forbes article touts that transitioning a single-gender office to a team equally comprised of men and women would translate to 41% in additional revenue. “Metrics” (which is just a fancy word for data btw) don’t lie. It’s important to set, track, and monitor workforce diversity goals - especially when we have more tools than ever at our disposal to do so. Over the past few years, here at One Model, we've seen a huge push for placing a priority on monitoring diversity metrics. In 2016, a Fortune 100 financial services organization, Company X (name anonymized) selected One Model’s platform to measure and monitor company-wide trends in diversity data and metrics. As their people analytics and workforce planning solution, One Model allowed them to not only better report on their data - but also more easily track and monitor changes, determine key KPIs, and see how improvements they’re making internally are affecting the data. More Accurate Data = Better Reporting. During Company X's transition from SAP to Workday, they used One Model to retrieve and migrate survey data. This platform allowed them to combine and normalize the data from several sources, enabling the team to report off of it as one source. The successful migration provided the HR team with the recovered data and prevented the team from having to redeploy the survey, allowing them to more accurately reflect their current diversity metrics and progression towards goals. This was a win. Here’s the challenge: When pulled together, the data referenced above indicated that out of several thousand employee responses, a number of employees failed to select or identify with one of the given race selections. This represented a sizeable portion of the employees. One Model’s software helped them identify this number. Once they realized this, they realized they had an opportunity to setup other processes internally. They did just that - which helped identify 95% of the employees who fell within that group, obtaining vital missing data that raised the percentage of diversity within the organization. Determining Key KPIs and Measuring Improvements Furthermore, Company X used the One Model platform to identify and reward the departments that successfully hit their recruitment-based diversity goals. This allowed the team to survey these departments and identify the hiring trends and best practices that led to these improved diversity metrics. By identifying specific process and KPI’s surrounding these diversity metrics, departments that successfully met their goals could share recruiting tactics and best practices to ensure appropriate actions were taken to maximize diversity throughout the whole of the recruiting pipeline. Company X is currently implementing these processes and working towards replicating a similar outcome amongst other departments in need of workforce diversity improvement. Tracking and Monitoring Changes Last but not least, Company X wanted more visibility into why females had a lesser presence in managerial roles within the organization. While, male to female promotions were equal. (This past year, 32 people were promoted. 55% of promotions (16 people) were women), there were significantly more males than females in managerial roles. Upon reviewing the data, they learned that out of the company’s requisitions, females applicants only made it to certain stages within the interview process (namely, an in-person interview) 50% of the time. Half the time, the only applicants that made it to a particular stage were male. They determined a hypothesis surrounding a particular KPI - that if more females made it to this particular stage, the odds were higher that more females would fill these roles. Company X set a goal that they wanted a female candidate make it to a manager interview stage 80% of the time. They are testing different methods on how best to achieve this, and with One Model's help, they are able to measure the effectiveness of those methods. By providing this visibility, One Model’s platform is currently helping them monitor their progress towards this goal, and allows them to see the affect - the direct impact on numbers of M/F managers in real-time. Company X is one of the many companies that has realized and embraced the importance of diversity in workforce planning. We’re confident they’ll eventually hit their goals, and we’re proud to be a part of the solution helping them do so. Is your company ramping up it’s People Analytics Program or diving into workforce diversity initiatives? One Model can help you better view and report on the data associated with your diversity goals. Here are just a few of the top metrics companies are currently focusing on: Recruitment Metrics Representation Metrics, such as: Minorities / URMs Veterans Women IWDs Staffing/Placement Metrics Transaction Metrics Training Metrics, such as: Penetration of diversity-related training, general training participation rates, and demographics of talent pipeline Advancement Metrics External Diversity Metrics Culture / Workplace Climate Metrics *based on 2016 NYT data. Want to see what One Model can do for you? Scheduled some time to chat with a One Model team member. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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3 min read
Stacia Damron
Today, at The HR Technology Conference and Exposition, HRExaminer unveiled its 2019 Watchlist - "The Most Interesting AI Vendors in HR Technology." One Model is one of thirteen companies named, narrowed down from a list of over 200 intelligence tools, only 70 of which were invited to provide a demo. One Model was featured alongside several notable vendors including Google, IBM, Workday, and Kronos. The Criteria HRExaminer, an independent analyst of HRTechnology and intelligence tools, selected two winners across five distinct categories: AI as a Platform Data Workbench Microservices Embedded AI First Suite One Model was named as one of two featured companies in HRExaminer's Data Workbench Category and commended for its management of disparate data from disparate sources - specifically the platform's robust Analytics Integration. “Each of the companies on our 2019 Watchlist is demonstrating the best example of a unique value proposition. While we are in the early stages of the next wave of technology, they individually and collectively point the way," said John Sumser, HRExaminer’s founder and Principal Analyst. "Congratulations are in order for the work that they do. The award is simply a recognition of their excellence." Sumser goes on to state, “There are two main paths to analytics literacy and working processes in today’s market. The first is templated toolkits for specific purposes that can give employers a quick start and repeatable/benchmarkable processes. One Model represents the alternative: a complete set of tools for designing and building your own nuanced analytics, predictions and applications.” One Model is currently exhibiting at The Technology Conference and Exposition in Vegas, September 11th-13th. Attendees can visit booth #851 for more information. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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4 min read
Stacia Damron
One Model is keen on ensuring our customers have an exceptional experience interacting with both our software and team alike. That experience begins the moment we meet. Often, the moment that relationship begins is on our website. One Model's platform helps HR and People Analytics teams simplify the messiest of their workforce data, strewn over multiple systems. Our software makes life easier - and our website needs to reflect that simplicity. It needs to be straightforward, easy to navigate, and provide helpful resources and tools to help you continue to grow your people analytics functions. For months, we have been diligently working to create a site that betters your experience - a place that provides you with tools and resources to support you in your data-wrangling journey. Well, now it's official - at the end of Q2, we launched it! The new site has clearly defined solutions for companies looking to scale their people analytics capabilities at all levels - regardless of company size, including resources to get started for evolving teams, and strategies to leverage for more mature people analytics programs. Namely - our new website will more effectively serve those seeking more information regarding people analytics platforms and data warehousing solutions. One Model helps HR departments better support their people analytics team. The new website contains more materials, including white papers, customer testimonials, videos, and data-sheets. Our blog authors helpful tips, relevant articles, best practices, and useful insights for today's data-driven HR professionals and data scientists. The new website includes: Updated navigation better aligns customers with our offerings and core capabilities, reduces the number of user clicks to navigate the website, and directs users to relevant, meaningful content and solutions. List of integrations and partnerships enable users to easily identify integrations that can add value with their current software or platforms. Updated Blog enables users to quickly find applicable, informative content and industry news regarding workforce analytics, data warehouse management, data science techniques, and people analytics programs. More options to connect with the team via numerous information request forms. Additionally, they include more form variation, allowing users to submit requests for quotes, demos, or discussions. Supplementary materials to aid in decision making provide more materials to view, including white papers, customer testimonials, videos, and data-sheets. Career Opportunities showcase open roles and allow job-seekers to apply directly via that page. As our company continues to grow and expand within the US and UK markets, our new website will better represent One Model as we continue to set the bar for excellence in HR data warehouse management and people analytics team solutions. Visit onemodel.co for a comprehensive breakdown of our workforce data solutions. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Its newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data.
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8 min read
Phil Schrader
Last week I was doodling some recruiting graphs in my notebook, with an eye toward building out some new recruiting efficiency dashboards. I was thinking about how requisitions age over time and I got an idea for a cool stacked graph that counts up how many requisitions you have open each month and breaks them out into age buckets. Maybe some supporting breakouts like recruiter, some summary metrics, etc. Something like this: Phil's Beautifully Hand-illustrated Cholesterol Graph (above) This would be an awesome view. At a glance I could see whether my total req load was growing and I could see if I’m starting to get a build up of really old reqs clogging the system. This last part is why I was thinking of calling it the Requisition Cholesterol Graph. (That said, my teammate Josh says he hates that name. There is a comment option below… back me up here!) But then I got to thinking, how am I actually going to build that? What would the data look like? Think about it: Given: I have my list of requisitions and I know the open date and close date for each of them. Problem #1: I want to calculate the number of open reqs I have at the end of each time period. Time periods might be years, quarters, months, or days. So I need some logic to figure out if the req is open during each of those time periods. If you’re an Excel ninja then you might start thinking about making a ton of columns and using some conditional formulas. Or… maybe you figure you can create some sort of pancake stacks of rows by dragging a clever formula down the sheet… Also if you are an Excel ninja… High Five! Being an Excel ninja is cool! But this would be pretty insane to do in Excel. And it would be really manual. You’d probably wind up with a static report based on quarters or something and the first person you show it to will ask if they can group it by months instead. #%^#!!! If you’re a full on Business Intelligence hotshot or python / R wiz, then you might work out some tricky joins to inflate the data set to include a record or a script count a value for each time the reqs open date is before or within a given period, etc. Do able. But then… Problem #2: Now you have your overall count of reqs open in each period. Alls you have to do now is group the requisitions by age and you’re… oh… shoot. The age grouping of the requisitions changes as time goes on! For example, let’s say you created a requisition on January 1, 2017. It’s still open. You should count the requisition in your open req count for January 2017 and you’d also count it in your open req count for June 2018 (because it’s still open). Figuring all that out was problem #1. But now you want to group your requisitions by age ranges. So back in January 2017, the req would count in your 0 - 3 months old grouping. Now it’s in your > 1 year grouping. The grouping changes dynamically over time. Ugh. This is another layer of logic to control for. Now you’re going to have a very wild Excel sheet or even more clever scripting logic. Or you’re just going to give up on the whole vision, calculate the average days open across all your reqs, and call it a day. $Time_Context is on my side (Gets a little technical) But I didn’t have to give up. It turns out that all this dynamic grouping stuff just gets handled in the One Model data structure and query logic -- thanks to a wonderful little parameter called $Time_Context (and no doubt a lot of elegant supporting programming by the engineering team). When I ran into $Time_Context while studying how we do Org Tenure I got pretty excited and ran over to Josh and yelled, “Is this what I think it is!?” (via Slack). He confirmed for me that yes, it was what I hoped it was. I already knew that the data model could handle Problem #1 using some conditional logic around effective and end dates. When you run a query across multiple time periods in One Model, the system can consider a date range and automatically tally up accurate end of period (or start of period) counts bases on those date ranges. If you have a requisition that was opened in January 2017 and you want to calculate the number of reqs you have open at the end of every month, One Model will cycle through the end of each month, check to see if the req was opened before then and is not yet closed, and add it to the totals. We use this for all sorts of stuff, particularly headcount calculations using effective dates and end dates. So problem one was no problem, but I expected this. What I didn’t expect and what made me Slack for joy was how easily I could also deal with Problem #2. Turns out I could build a data model and stick $Time_Context in the join to my age dimension. Then One Model would just handle the rest for me. If you’ve gotten involved in the database side of analytics before, then you’re probably acquainted with terms like fact and dimension tables. If you haven’t, just think vlookups in Excel. So, rather than doing a typical join or vlookup, One Model allows you to insert a time context parameter into the join. This basically means, “Hey One Model, when you calculate which age bucket to put this req in, imagine yourself back in time in whatever time context you are adding up at that moment. If you’re doing the math for January 2017, then figure out how old the req was back then, not how old is is now. When you get to February 2017, do the same thing.” And thus, Problem #2 becomes no problem. As the query goes along counting up your metric by time period, it looks up the relevant requisition age grouping and pulls in the correct value as of that particular moment in time. So, with our example above, it goes along and says, “Ok I’m imagining that it’s January 2017. I’ll count this requisition as being open in this period of time and I’ll group it under the 0 - 3 month old range.” Later it gets to June 2018 and it says, “Ok… dang that req is STILL open. I’ll include it in the counts for this month again and let’s see… ok it’s now over a year old.” This, my friends, is what computers are for! We use this trick all the time, particularly for organization and position tenure calculations. TL;DR In short, One Model can make the graph that I was dreaming of-- no problem. It just handles all the time complexity for me. Here’s the result in all it’s majestic, stacked column glory: So now at a glance I can tell if my overall requisition load is increasing. And I can see down at the bottom that I’m starting to develop some gunky buildup of old requisitions (orange). If I wanted to, I could also adjust the colors to make the bottom tiers look an ugly gunky brown like in the posters in your doctors office. Hmmm… maybe Josh has a point about the name... And because One Model can handle queries like this on the fly, I can explore these results in more detail without having to rework the data. I can filter or break the data out to see which recruiters or departments have the worst recruiting cholesterol. I can drill in and see which particular reqs are stuck in the system. And, if you hung on for this whole read, then you are awesome too. Kick back and enjoy some Rolling Stones: https://www.youtube.com/watch?v=wbMWdIjArg0.
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6 min read
Chris Butler
A few weeks ago I gave a presentation at the Talent Strategy Institute’s Future of Work conference (now PAFOW) in San Francisco about how I see the long term relationship between data and HR Technology. Essentially, I was talking through my thought process and development that I could no longer ignore and had to go start a company to chase down it’s long term vision. So here it is. My conviction is that we need to (and we will) look at the relationship between our data and our technology differently. That essentially the two will be split. We will choose technology to manage our data and our workflows as we need it. We will replace that technology as often as our strategy and our business needs change. Those that know my team, know that we have a long history of working with HR data. We started at Infohrm many years ago which was ultimately acquired by SuccessFactors and shortly after SAP. Professionally this was fantastic, worlds opened up and we were talking to many more organizations and the challenges they were facing across their technology landscape. How to achieve data portability. Over time I was thinking through the challenges our customers faced, a large one of which was how to help grease the wheels for the huge on-premise to cloud transition that was underway and subsequently the individual system migrations we were witnessing across the HR landscape. The pace of innovation in HR was not slowing down. Over the years hundreds of new companies were appearing (and disappearing) in the HR Tech space. It was clear that innovation was everywhere and many companies would love to be able to adopt or at least try out this innovation but couldn’t. They were being hampered by political, budgetary, and other technology landscape changes that made any change a huge undertaking. System migration was on the rise. As companies adopted the larger technology suites, they realized that modules were not performing as they should, and there were still gaps in functionality that they had to fill elsewhere. The promise of the suite was letting them down and continues to let them down to this day. This failure, combined with the pace of innovation meant the landscape was under continuous flux. Fragmentation was stifling innovation and analytical maturity. The big reason to move to a suite was to eliminate fragmentation, but even within the suites the modules themselves were fragmented and we as analytics practitioners without a method for managing this change only continued to add to this. We could adopt new innovation but we couldn’t make full use of it across our landscape. Ultimately this slows down how fast we can adopt innovation and downstream how we improve our analytical maturity. All HR Technology is temporary. The realization I started to come to is that all of the technology we were implementing and spending millions of dollars on was ultimately temporary. That we would continue to be in a cycle of change to facilitate our changing workflows and make use of new innovation to support our businesses. This is important so let me state it again. All HR technology is temporary. We’re missing a true HR data strategy. The mistake we were making is thinking about our technologies and our workflows as being our strategy for data management. This was the problem. If we as organizations could put in place a strategy and a framework that allowed us to disconnect our data from our managing technology and planned for obsolescence then we could achieve data portability. We need to understand the data at its fundamental concepts. If we know enough to understand the current technology and we know enough about the future technology then we can create a pathway between the two. We can facilitate and grease the migration of systems. In order to do this effectively and at scale you had to develop an intermediate context of the data. This becomes the thoroughfare. This is too advanced a concept for organizations to wrap their minds around. This is a powerful concept in essence and seems obvious, but trying to find customers for this was going to be near impossible. We would have to find companies in the short window of evaluating a system change to convince them they needed to look at the problem differently. Analytics is a natural extension. With the intermediate thoroughfare and context of each of these systems you have a perfect structure for delivering analytics from the data and powering downstream use cases. We could deliver data to vendors that needed it to supply a service to the organization. We could return data from these services and integrate into data strategy. We could write this data back to those core source systems. We could extend the data outside of these systems from sources that an organization typically could not access and make use of on their own. Wrap all this up in the burgeoning advanced analytics and machine learning capabilities and you had a truly powerful platform. We regain choice in the technology we use. In this vision, data is effectively separate from our technology and we regain the initiative back from our vendors in who and how we choose to manage our data. An insurance policy for technology. With freedom to move and to adopt new innovation we effectively buy ourselves an insurance policy in how we purchase and make use of products. We can test; we can prove; we can make the most of the best of breed and innovation that has been growing in our space. If we don’t like we can turn it off or migrate-- without losing any data history and minimizing switching costs. This is a long term view of how our relationship to data and our vendors will change. It is going to take time for this view to become mainstream, but it will. The efficiencies and pace that it provides to change the direction of our operations will deliver huge gains in how we work with our people and our supporting vendors. There’s still challenges to making this happen. Vendors young and old need to provide open access to your data (after all it’s your data). The situation is improving but there’s still some laggards. The innovative customers at One Model bought us for our data and analytical capabilities today, but they know and recognize that we’re building them a platform for their future. We’ve been working with system integrators and HR transformation groups to deliver on the above promise. The pieces are here, they’re being deployed, now we need to make the most of them.
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9 min read
Phil Schrader
We’re back with another installment of our One Model Difference series. On the heels of our One AI announcement, how could we not take this opportunity to highlight it as a One Model difference maker? In preparation for the One AI launch, I caught up with Taylor from our data science team and got an updated tour of how it all works. I’m going to try to do that justice here. The best analogy I can think of is that this thing is like a steam engine for data science. It takes many tedious, manual steps and let’s the machine do the work instead. It's not wizardry. It's not a black box system where you have to point at the results, shrug, and say, “It’s magic.” This transparent approach is a difference in its own right, and I’ll cover that in a future installment. For now though, describing it as some form of data wizardry simply would not do it justice. I think it’s more exciting to see it as a giant, ambitious piece of industrial data machinery. Let me explain. You know the story of John Henry, right? John Henry is an African-American folk hero who, according to legend, challenged a steam-powered hammer in a race to drill holes to make a railroad tunnel. It’s a romantic, heart-breaking story. Literally. It ends with John Henry’s heart exploding from the effort of trying to keep pace. If you need a quick refresher, Bruce Springsteen can fill you in here. (Pause while you use this excuse to listen to an amazing Bruce Springsteen song at work.) Data science is quite a bit easier than swinging a 30 pound hammer all day, but I think the comparison is worthwhile. Quite simply, you will not be able to keep pace with One AI. Your heart won’t explode, but you’ll be buried under an exponentially growing number of possibilities to try out. This is particularly true with people data. The best answer is hiding somewhere in a giant space defined by the data you feed into the model multiplied by the number of techniques you might try out multiplied by (this is the sneaky one) the number of different ways you might prepare your data. Oh, and that’s just to predict one target. There’s lots of targets you might want to predict in HR! So you wind up with something like tedious work to the fourth power and you simply should not do it all by hand. All data science is tedious. The first factor, deciding what data to feed in, is something we’re all familiar with from stats class. Maybe you’ve been assigned a regression problem and you need to figure out which factors to include. You know that a smaller number of factors will probably lead to a more robust model, and you need to tinker with them to get the ones that give you the most bang for your buck. This is a pretty well known problem, and most statistical software will help you with this. This phase might be a little extra tricky to manage over time in your people analytics program, because you’ll likely bring in new data sets and have to retest the new combinations of factors. Still, this is doable. Hammer away. Of course, One AI will also cycle through all your dimensional data for you. Automatically. And if you add factors to the data set, it will consider those factors too. But what if you didn’t already know what technique to use? Maybe you are trying to predict which employees will leave the company. This is a classification problem. Data science is a rapidly evolving field. There are LOTS of ways to try to classify things. Maybe you decide to try a random forest. Maybe you decide to try neural nets using Tensorflow. Now you’re going to start to lose ground fast. For each technique you want to try out, you’ve got to cycle through all the different data you might select for that model and evaluate the performance. And you might start cycling through different time frames. Does this model predict attrition using one year of data but becomes less accurate with two years…? And so on. Meanwhile, One AI will automatically test different types of models and techniques, over different time periods, while trying out different combinations of variables and evaluating the outcomes. In comparison, you’ll start to fall behind pretty rapidly. But there’s more... Now things get kind of meta. HR data can be really problematic for data science. There is a bunch of manual work you need to do to prepare any data set to yield results. This is the standard stuff like weeding out bad columns, weeding out biased predictors, and trying to reduce the dimensionality of your variables. But this is HR DATA. The data sets are tiny and lopsided even after you clean them up. So you might have to start tinkering with them to get them into a form that will work well with techniques like random forests, neural nets, etc. If you’re savvy, you might try doing some adaptive synthetic sampling (making smaller companies appear larger) or principal component analysis. (I’m not savvy, I’m just typing what Taylor said.) So now you’re cycling through different ways of preparing the data, to feed into different types of models, to test out different combinations of predictors. You’ve got tedious work to the third power now. Meanwhile, One AI systematically hunts through these possibilities as well. Synthetic sampling was a dead end. No problem. On to the next technique and on through all the combinations to test that follow. This is not brute force per se-- that actually would introduce new problems around overfitting. The model generation and testing can actually be organized to explore problem spaces in an intelligent way. But from a human vs. machine perspective, yeah, this thing has more horsepower than you do. And it will keep working the models over, month after month. This is steam powered data science. Not magic. Just mechanical beauty. And now that we have this machine for HR machine learning. We can point that three-phase cycle at different outcomes that we want to predict. Want to predict terminations? Of course you do. That’s what everyone wants to predict. But what if in the future you want to predict quality of hire based upon a set of pre-hire characteristics. One AI will hunt though different ways to stage that data, through different predictive techniques for each of those potential data sets, and through different combinations of predictors to feed into each of those models…and so on and so on. You can’t replicate this with human powered data science alone. And you shouldn’t want to. There’s no reason to try to prove a John Henry point here. Rather than tediously cycling through models, your data science team can think about new data to feed into the machine, can help interpret the results and how they might be applied, or can devise their own, wild one-off models to try because they won’t have to worry about exhaustively searching through every other option. This might turn out similar to human-computer partnership in chess. (https://www.bloomreach.com/en/blog/2014/12/centaur-chess-brings-best-humans-machines.html) One AI certainly supports this blended, cooperative approach. Each part of the prediction pipeline can be separated and used on its own. Depending on where you are at in your own data science program, you might take advantage of different One AI components. If you just want your data cleaned, we can give you that. Or, if you already have the data set up the way you want it, we can save you time by running a set of state of the art classifiers on it, etc. The goal is to have the cleaning/preprocessing/upsamping/training/etc pieces all broken out so you can use them individually or in concert. In this way, One AI can deliver value whatever the size and complexity of your data science team, as opposed to an all-or-nothing scenario. In that regard, our human vs. machine comparison starts to break down. One AI is here to work with you. Imagine what John Henry could have done if they’d just given him the keys to the steam engine? Book some time on Phil's calendar below to get your HR data-related questions answered. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Our newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data. Notable customers include Squarespace, PureStorage, HomeAway, and Sleep Number.
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5 min read
Stacia Damron
How did Spring cleaning become a thing, and why do we do it? It’s officially March. Daylight savings has us up an hour earlier, the weather’s teasing us by thinking about getting warmer, and most of us are envious of the students enjoying spring break on a beach somewhere. Supposedly, this odd combination of things gets us in the mood to clean house. But there’s research to back it up: according to the experts, the warm weather and extra light are responsible for giving us the additional boost of energy. What is it about cleaning that gets us so excited? Is it the fresh smell of mopped floors? Is is the sigh of relief when you can actually park your car in the garage instead of using it for storage? Or is it the look of shock on your significant other’s face when they realize their 10-year-old socks (the ones with the huge holes) are gone for good? It's kind of weird. Now, before we get too far in - I hope you didn’t get really excited about reading some “spot-free window cleaning tips” or “how to declutter your closet in 12 easy steps.” After all, 1) this is a software blog, and 2) I haven’t mastered either of those things. Spring cleaning is a way to refresh and reset. It feels GOOD to declutter. This is the premise here. Most people associate Spring cleaning with their home - but what if we went into Spring with that same excitement at work as well? What if we wanted to share that same, cathartic feeling with our teams and coworkers? You can! One Model can help you Spring clean your people analytics data and provide your team with access to more insights within your current workforce analytics data. We’re the experts at pulling data from as many as 40 or so sources. We can place it on a single platform (that will automatically refresh and update), allowing your team can see how it all interacts together - in one place. Say goodbye to the days of exporting data and poking around with Vlookups in excel, only to have to manually create the same report over and over again. Using the One Model platform to manage your HR data is akin to having someone come in and untangle 200 feet of Christmas lights (but instead of lights, it’s untangling data from your workforce analytics systems). And when you use our platform, you won't have to untangle it again. How awesome is that? A work-related spring cleaning is even more satisfying than a spring cleaning at home. Honestly, it is. You’re not going to get a promotion from organizing your cookware cabinet. However, at work, you might be considered for one if you detangle your data and save your team hours of their valuable time and resources on preparing data for analyzation. So, if you suddenly get the itch to clean something - I urge you and your HR team to commit to participating in a workforce data spring cleaning. Call it a day, and contact One Model to sort out your data organization problem for you. Same satisfaction, less scrubbing - I promise. Then, go home and turn your Roomba on, knowing you just conquered spring cleaning on both frontiers. Book a demo. Or just book some time to get your HR data-related questions answered. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Our newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data. Notable customers include Squarespace, PureStorage, HomeAway, and Sleep Number.
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6 min read
Phil Schrader
There will be over 400 HR product and service providers in the expo hall at HR Tech in September. A typical company makes use of 8 - 11 of these tools, some as many as 30. And that is wonderful. I love working in HR Technology. Companies are increasingly free to mix and match different solutions to deliver the employee experience that is right for them. New products come to market all the time. And the entrepreneurs behind these products are pretty consistently driven by a desire to make work better for employees. All that innovation leads to data fragmentation. Better for employees that don't work in HR Operations and People Analytics, that is. Because all that innovation leads to data fragmentation. In your organization, you might recruit candidates using SmartRecruiters in some countries and iCIMS in others. You might do candidate assessments in Criteria Corp and Weirdly. Those candidates might get hired into Workday, have their performance reviews in Reflektive and share their own feedback through Glint surveys. This would not be in the least bit surprising. And it also wouldn't be surprising if your internal systems landscape changed significantly within the next 12 months. The pace of innovation in this space is not slowing down. And the all-in-one suite vendors can’t keep pace with 400 best of breed tools. So if you want to adopt new technology and benefit from all this innovation, you will have to deal with data fragmentation. How do you adopt new innovation without losing your history? What if the new technology isn’t a fit? Can you try something else without having a gaping hole in your analytics and reporting? How will you align your data to figure out if the system is even working? This is where One Model fits in to the mix. We're going to call this One Model Difference your Data Insurance Policy. One Model pulls together all the data from your HR systems and related tools, then organizes and connects this data as if it all came from a single source. This means you can transition between technology products without losing your data. This empowers you to choose which technology fits your business without suffering a data or transition penalty. I remember chatting about this with Chris back at HR Tech last year. At the time I was working at SmartRecruiters and I remember thinking... Here we are, all these vendors making our pitches and talking about all the great results you're going to get if you go with our product. And here's Chris literally standing in the middle of it all with One Model. And if you sign up with One Model, you'll be able to validate all these results for yourself because you can look across systems. For example, you could look at your time to hire for the last 5 years and see if it changed after you implemented a new ATS. If you switched out your HRIS, you could still look backwards in time from new system to old and get a single view of your HR performance. You could line up results from different survey vendors. You'd literally have "one model," and your choice of technology on top of that would be optional. That's a powerful thought. A few months later, here I am getting settled in at One Model. I'm getting behind the scenes, seeing how how all this really comes together. And yeah, it looks just as good from the inside as it did from the outside. I've known Chris for a while, so it's not like I was worried he was BS-ing me. But, given all the new vendors competing for your attention, you'd be nuts if you haven't become a little skeptical about claims like data-insurance-policy-that-makes-it-so-you-can-transition-between-products-without-losing-your-data. So here are a couple practical reasons to believe, beyond the whole cleaning up and aligning your data stuff we covered previously. First off, One Model is... are you ready... single tenant. Your data lives in its own separate database from everyone else's data. It's your data. If you want to have direct database access into the data warehouse that we've built for you, you can have it. Heck, if you want to host One Model in your own instance of AWS, you can do that. We're not taking your data and sticking it into some rigid multi-tenant setup at arms length from you. That would not be data insurance. That would be data hostage-taking. Second, One Model doesn't charge per data source. That would be like one of those insurance policies where everything is out-of-network. With One Model, your systems are in-network. If you add a new system and you want the data in One Model, we'll add the data to One Model. If we don't have a connector, we'll build one. One of our clients has data from 40 systems in One Model. 40 systems. In one single model. In its own database. With no fees per data source. So go wild at HR Tech this fall. It is in Vegas after all. Add all the solutions that are right for your employees. And tell all your new vendors you'll be able to hold them accountable for all those bold ROI-supporting metrics they’re claiming. Because you can put all your data into One Model for all your people analytics. You can see for yourself. And if you swap that vendor out later, you’ll take all your data with you. Just don't wait until then to reach out to us at One Model. We love talking shop. And if you happen to like what you see with One Model, we can have your data loaded well before you get to Vegas. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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3 min read
Stacia Damron
The One Model team is pleased to announce its official launch of One AI. The new tool integrates cutting-edge machine learning capabilities into the current One Model platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data. One Model’s core platform enables its customers to import multiple data sources into one, extensible, cloud-based platform. Organizations are then able to take full control of their people and business data, gaining increased visibility and spotting trends in the data that otherwise, would remain unnoticed. Machine Learning Insights like HR Professionals Have Never Seen Before One AI delivers a suite of out-of-the-box predictive models and data extensions, allowing organizations to understand and predict employee behavior like never before. One AI extends upon the current One Model platform capabilities, so now HR Professionals can access machine learning insights alongside their current people analytics data and dashboards. Additionally, the solution is open to allow customers and their partners to create and run their own predictive models or code within the One Model platform, enabling true support for an internal data science function. “One AI is a huge leap into the future of workforce analytics,” says Chris Butler, CEO of One Model. “By applying One Model's full understanding of HR data, our machine learning algorithms can learn from all of a customer’s data and predict on any target that our customers select.” The new tool offers faster insights: it can create a turnover risk predictive model in minutes, consuming data from across the organization, cleaned, structured, and tested through dozens of ML models and thousands of hyperparameters. It utilizes these to create a unique, accurate model that can provide explanations and identify levers for reducing an individual employees risk of turnover. This ability to explain and identify change levers is a cutting-edge capability. It allows One AI to choose a high accuracy model that’s otherwise unintelligible and explain it’s choices to our users. “The launch of One AI will have a huge impact on current and future customers alike.” says Stacia Damron, One Model’s Senior Marketing Manager. “One AI’s ability to successfully incorporate machine learning insights into an organization’s people analytics strategy is significant. It means it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results. By creating more precise models, and augmenting internal capabilities, an organization can better identify cost-saving opportunities and mitigate risk.” The One Model team looks forward to sharing more information about One AI with this year’s People Analytics World Conference attendees in London on April 11-12. Stop by the One Model booth if you would like to connect and learn more. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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20 min read
Chris Butler
We received a lot of interest from Part 1 of this blog post so if you haven't read it yet head over for a summary view of our observations in Part 1. In Part 2 I'm going to give you a brief walkthrough of setting up and running a turnover risk prediction in AWS' machine learning. At the end of this post, I have some further observations about improving tweaking and improving the performance of the base offering and additionally why we chose to move away from these toolsets and develop our own approach. AWS Machine Learning https://aws.amazon.com/aml/ Step 1 - Sign up for an account If you don't have an AWS account, you can sign up through the above link. Please check with your IT department for guidance on using AWS and what data you can upload to their cloud. You may need authorization or to anonymize your data prior to loading. Cost A quick exploration of expected cost so you know what to expect. Current pricing is below. $0.42c per hour for model processing $0.10c per thousand predictions In my experience for a 5,000 employee company, this results in the below 10 minutes processing per model = $0.07c 5,000 predictions = $0.50c $0.57c per model and set of predictions run. I typically will create historical backtests generating a model each month for at least the last two years so I can gauge expected performance and track any wild divergence in model behavior. So let's call it $15 to run a full test (optional). Step 2 - Prepare your Data We'll need a flat .csv file that we can load, it's best to include a header row otherwise you will need to name your columns later in the interface which is just painful. The data you include will be all the data features we want to process and a field that shows our target that we are trying to predict, in this case, terminated that I have highlighted in yellow below. The data i use in my file is generally the active headcount of current employees and the last 1-2 years of terminations. The actives have a 0 for terminated and the terminated records have a 1. For a 5,000 person company with a 12% turnover rate that means I should have 5,000 active (0) records and around 1,200 terminated (1) records. The data features used are important and as you create different models or try to improve performance you'll likely spend a good chunk of time adding, removing, or cleaning up the data in these features. A couple guiding points you'll want to do as you build your file You can't have null values, it will drop the record if there's an empty value in a column. Instead, replace any nulls either with a placeholder (the ? you can see above) or depending on the data field you may want to insert the median value for the column. The reason being is any placeholder will be treated as a distinct value and used in pattern detection, the median instead will treat the record as no different from other median records. If you can create a range, it's often useful to do so at this step especially if you are writing SQL to extract as it will then be repeatable on each data extraction (although there are options to do this in the aws UI later). I will often use both the actual value and the range itself as individual data features i.e. Tenure (years) we would have the number of years say 3 as a column and the range 3-<5 years as a column as well. One will be treated as a continuous numeric value while the other as a categorical grouping. I like to include hierarchical structures in the data features like department, or supervisor relationships, you don't need the whole tree, the top parts of the structure are often redundant but the middle to leaf levels are quite important. You can spend days building features and creating calculations, my general approach is to start with a basic set of features and expand as I can lay my hands on more data or have time to merge in a new data set. You can then at least test how a basic set of features performs which for some organizations can perform extremely well. Adding features can reduce performance and cause overfitting so having a baseline to compare with is always good. Step 3 - Create a Datasource and ML Model The wizards make the process of creating a datasource and a model ridiculously easy. Select "Datasource and ML model" from the "Create new" menu on the Machine Learning dashboard. You'll need to load your data file into S3 (AWS file storage system) and from there you can provide it's location to the wizard and give the source a name. You will likely have a number of datasources created over time so make the name descriptive so you can tell them apart. You'll notice some information about providing a schema file. I do prefer to provide a schema file (see documentation here) as it means i can skip the next step of creating a schema for the file but if you have included a header row in your file you can tell the wizard to use the first row as the column names. You still, however, will need to provide a data type for column so the engine know how to treat the data. You have a choice of Binary - use this where there are only two possible states, our target status of terminated is either a 0 or 1 so it's a binary. Can also be used for other binary types e.g. true/false, yes/no, etc Categorical - perfect for any of the attribute or dimension style of fields i.e gender, age range, tenure range, department, country, etc. This is the most common selection I use. Numeric - any number will automatically be assigned this value but you will want to check it is applied properly to a numeric range i.e. age is correct as a numeric and will be treated as a discrete series but if you leave a department number as a numeric this is going to be worthless (change it to categorical) Text - you really shouldn't have a set of text values for this type of scenario so ignore for now and use categorical if in doubt. If you hit continue from here you'll get an error that you haven't selected a target so go ahead and select the column that you used for your terminated status then hit continue. You'll need to do the same for your person identifier (usually an employee id) on the next screen. The next Review screen will give some info on the number of types etc but there's nothing else to do here but hit continue and move to our model selections. Name your model (usually I'll match the datasource name with a -model or similar to the name). The same with the evaluation. Your biggest decision here is to use the default training and evaluation settings or to use the custom. With the custom you change the amount of training and evaluation data, the regularization type, the number of passes the engine should run over your data to detect patterns and the size of the model itself. For the most part, I've had the most success using the default settings, don't get into the custom settings until you are really trying to fine tune results as you can spend a lot of time here and have mixed results. So select default and move on. You can see the default settings on the review screen, we're going to have a training/evaluation split of 70/30, it will run 10 passes over the data looking for patterns and apply a regularization method (helps to reduce the number of patterns and avoid overfitting). Hit create, grab a coffee, and in a few minutes, you'll have a data source, a predictive model, and an evaluation demonstrating it's performance. Refresh your screen until the model shows as completed. Once complete you can click on the data source id and go explore some of the data source information, I like to view the correlations of each data feature to our target which helps to decide if I should remove features or change them in some fashion. The big piece of info though is the Evaluation result which in the above tells us that the Area Under the Curve (AUC) was 0.944 which as the next screenshot tells you is extremely good (suspiciously good). Click on the result and you'll see the performance metrics Yes you'll want to explore performance The above information set is pretty impressive, if we set our probability score threshold at 0.5 which is the point where a score above will be predicted as a termination and a score below will be predicted as active then we end up with 90% of our guesses being accurate. You can see the other metrics associated here for false prediction rates and you can play around with the sliders to adjust the trade-off score to different levels. Now, this looks awesome but keep in mind this is an evaluation set of historical data and I had spent a fair amount of time selecting and constructing data features to get to this point. In real life the model didn't perform this well, success was more like 70-75% of guesses being correct which is still great but not as good as what you'll see in the evaluation. My guess here is I still have some overfitting occurring in the model. If your evaluation performs poorly you'll want to go look at the info provided, you may have rows or columns being dropped from the data source (explore the data source id), your features may not be relevant, or some other problem has occurred. If your results are too good AUC = 1.0 then you likely have included a perfect predictor in the data features without realising i.e. an employment status or a placeholder department when somebody terminates or is about to terminate, check for something like this and remove. Step 4 - Generate Predictions When ready to generate some real-life predictions you can go ahead and click the "Generate Batch Predictions". You'll need to load a file to S3 for your predictions, this file will be the same as your input file but you will remove the terminated column (our target column) so it will only be slightly different. The contents will be for the people you wish to predict on, usually the current active headcount or if you are testing historically the active headcount at x point in time (if you do test historically your model obviously needs to be generated using data from x-1 day point in time). Use the "My data source is in S3, and I need to create a datasource" go through the same prompts as you did for your training data source and once finished processing you'll have a predictions file to download. This file gives you each person, their prediction value, and the probability score associated. You can load this into your own database or just view in excel however you may wish to consume. Observations and Tweaking suggestions Data Sources Start with a basic set of features and expand over time so you can evaluate how the new data is affecting your models. Some targets and models for organizations respond better to simple models and others need a lot more data features to find predictive patterns. Review the correlations of your attributes from the data source information after the source is created and processed. These will help you decide if a feature is useful and most importantly if you have a feature that is suspiciously predictive that you may wish to remove so that you don't pollute the model. If you are going to continue to experiment and iterate then definitely create .schema file it will save a bunch of time in avoiding setting UI options and make generating new source/models very fast. Try creating some features combining different fields you think may have some relation to each other e.g. Age-Tenure, 30-35_3-<5 yrs as an example of joining two ranges together. The ML will pick up some patterns like this but I've found creating some of these can help. The amount of data I describe early in the post is a little controversial i.e. using the current active headcount and historical terminations. Many data scientists will have issue here for one reason or another. For these people know that yes i have tested a number of different methods of balancing the data set, of oversampling data, and generally constructing to overcome different problems and through testing found in this example case of turnover the changes haven't reliably produced better real-life results. So my advice for people starting out is to just use a simple data set, and allow the toolset to do it's thing, then evaluation what you are seeing by applying your predictions back to your actual turnover. The amount of termination history can impact how a model performs, if behaviors change and you have a long history of terminations then the model may not adjust fast enough to cater for these new behaviors, it does help sometimes to shorten the amount of history you use if you have changing workforce behaviours. I was additionally creating new models every month for this reason as well. Models Always use the default to start with while you figure out the datasource and features being used. No point playing around with advanced settings when you can extract the most gains from adding or altering data features early on. If you suspect overfitting and you've looked at all your features for anything suspicious then try a higher level of regularization in the advanced settings, you should still be able to leave the other settings at their default. I've not had Evaluations Use them as an indicator that the model is doing its job and not perfectly fitting and not severely underfitting the data. In general aim for a AUC between 0.75 and 0.95 and you will generally do well. Adjust the score threshold to focus on precision if you want to reduce the number of people predicted as going to terminate (see next section). Using Predictions Generally, I'll take my predictions output and ignore the binary terminated/active column and just use the probability score column. With this I can create my own risk categories where I can bucket people into Low, Medium, High Risk categories. The high risk people may be only the top 100 or so people that I have a high confidence are at risk. Particularly if you are going to focus on a group of people you probably want to focus on a smaller group to start with. If creating your own risk buckets i will plot out these scores and the actual results and decide which scores fit into each buckets. To do this you need to test historically to see how the model performs and to help guide your decision. Watch the model and it's results over time, don't do anything about the results just yet but try to understand how it is performing and if you can be confident in what it is predicting. MOST IMPORTANTLY - if you have enough confidence to start putting retention strategies in place with these people at risk, you must record this action. The action or lack of action needs to feed back into the model as it may affect behaviors and it's absence from the model will pollute its accuracy over time. I generally describe this as my back to the future theory of turnover risk, if you take an action and the model doesnt know about it you are effectively changing the past and destroying it's prediction of the future. Why we didn't use these tools ourselves The toolsets available from AWS, Google, Azure are fantastic easy entry points to start using your data in a predictive fashion. For One Model though they did not provide enough levers to pull when data or workforce behaviors don't fit into the out of the box view from these simplified toolsets. We needed a solution that would allow us to roll into any customer, evaluation all data for that customer, test through thousand of models, and build the most effective predictive model for any target. What's more, we wanted to open this capability to our customers whether they wanted to create their own models in a few clicks or if they had their own data science team and they wished to run their own predictive or statistical models in our infrastructure. We couldn't achieve these objectives and we had to build our own approach that gave us this flexibility. One AI the new name for our augmentations is the result, and I obviously am biased but it is truly amazing. One AI is a collection of advanced calculations (feature engineering), data extensions (commute time, stock price, social data, etc), and the application of our automated machine learning frameworks. It can concurrently test thousands of models and select the most accurate model for the target and the customer's data set. One problem it may choose a basic decision tree, for the next it will decide a neural network works best, and it's able to do this in minutes. The customer though still has the ability to adjust, customize, and put their own stamp on the models in use. One of the biggest drawbacks of the black box methods though is that you have very little explanation as to why a prediction is made, this meant we couldn't provide our customers with the reasons why a person was at risk or what to do about it. In One AI we've built an explanation and prescriptive action facility to be able to show for each person the reasons why their prediction was made and what the biggest levers are to change this prediction. We'll be officially announcing One AI shortly and making available collateral on our website in the meantime if you would like to talk about our framework sooner please contact us or
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7 min read
Phil Schrader
People often ask us, "What makes One Model different?" Well...there's a lot we could show and tell. We've decided to respond with a series of blog posts covering each and every reason. Read on for more! You can't do this with Tableau. One Model features the most advanced role-based security system of any analytics application. It has to. People data is often the most complex and most sensitive data in an organization. Through 15 years of experience working with People Analytics teams and knowing how they wish to provide access, we built a security methodology that caters for all scenarios and fills the complex gaps that other vendors ignore. One Model allows administrators to define custom security groups and designate fine-grained application permissions to each: Can these users create dashboards or just view them? Can they even filter dashboards? Can they drill down to detail? Can they use the Explore tool? Can they build their own metrics? Can they access a metric in one population but not access it in another without changing roles? At the data layer, One Model group permissions control access at both the column level (which data elements a user can see) and the row level (which records of the data element a user can access): Can they see a given department’s data? Do they have access to compensation metrics? Can they cut those metrics by Grade or Position? If they drill in to that data, can they see person-level detail? Better still, One Model security roles understand each user’s relationship to the data. Who reports to whom, for example. That means you could grant all the leaders in your organization drill-down access to their own team members with a single contextual data rule that follows them through the organization as their data changes. Done. Zero maintenance required. Multiple roles merging together to provide the exact level of access for each user regardless of whether they're a HRBP, executive, or director with complex reporting lines. This is not something you can achieve with tableau, qlik, or any other vendor in our space. They come close but they don't understand the relationship between a user and the data itself, which results in constant role security maintenance -- if the desired access can be achieved at all. Why it matters Most teams have self-service is part or goal of their People Analytics Roadmap. If you want to deliver self-service with HR data, you’ll need to effectively and sustainably manage fine-grained sets of permissions like the ones described above. Here’s a look at what is possible with the right role based security capabilities. Let’s say that you’ve developed an integrated recruiting effectiveness dashboard. Your business leaders, recruiting managers, and HRBPs all have access to this dashboard. Based on aggregate data, your business leader can see that the new candidate assessment is, in fact, doing a great job predicting high performing new hires across the company. She drills into her own team’s details and scans through a few examples. This builds her confidence both in the assessment tool and in the dashboard. She’s likely to come back and use other dashboards in the future. The recruiting manager, looking at the same dashboard, is excited by the overall results, but wants to see if this assessment result is having a negative impact on protected groups of candidates in the hiring process. Given her role, you’ve given her access to aggregate slices of demographic data. She uses dashboard filters to cut the data by gender, age, and ethnicity without having to request a one-off ad-hoc report. She’s ready when the topic comes up in a meeting later that day. She thanks you the next time you see her. The division’s HRBP has similar ideas but her security clearance is more complex. Because her division is split across countries and, due to local laws in one country, she's not allowed to view performance ratings, or conduct age and gender analyses, which are seamlessly unavailable for this population. With this limitation in place, she wants to take things a step further in the One Model Explore tool and analyse a recent change to recruiting practices. She combines assessment results and termination data along with her most recent employee survey results. The results are so interesting that she reaches out to you. “Hey, my termination rates are down. We think we’re making better hires based on this new assessment tool, and employee satisfaction is up as well. These are all good signs, but can you figure out which results are driving the others?” After a cursory analysis, the next step is to prove there is a correlation and quantify its impact with the built-in OneAI machine learning suite. Awesome. Isn’t this scenario why your company funded the program in the first place? Without advanced role-based permissions? Well, you probably know that story already. It starts with a generic, one-size fits all dashboard. The plot thickens with the arrival of ad hoc reporting requests and vlookups. And the story ends with… well… more ad hoc reporting and vlookups. If this is something that excites you, let's talk. Click the button below to schedule some time with a One Model team member. We can answer any specific questions you may have, or just chat about role-based permissions (if that's what you're into). About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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4 min read
Stacia Damron
Find our team in a city near you, and stop by in person to learn more about our workforce analytics solutions. February 9, 2018 - Austin, TX - The One Model team recently returned from the People Analytics and Future of Work (PAFOW) in San Francisco, where we participated as a key sponsor and speaker. There, our CEO, Chris Butler, was invited to announce a preview of our latest feature: One AI. (Above) One Model CEO, Chris Butler, announces One Model's newest tool: One AI, at PAFOW in San Francisco. One AI is a huge leap into the future of workforce analytics. Finally - there's a tool that makes machine learning readily accessible to HR professionals . By applying One Model's full understanding of HR data, our machine learning algorithms can draw a parallel, predicting any target that our customers select. For example, this means a turnover risk predictive model can be created in minutes; consuming data from across the organization, cleaned, structured, and tested through dozens of ML models and thousands of hyperparameters to select a unique, accurate model that can provide explanations and identify levers for reducing an individual employees risk of turnover. Our Next Stop: London The One Model team will be showcasing One AI at the People Analytics World Conference in London this April. We invite HR professionals, people analytics experts, and partners to join. Come find the One Model team and learn more about our workforce analytics software for HR professionals and data scientists. If you'd like an opportunity to meet the team in person and learn more, we'll be attending the following events later this year: People Analytics Conference - London, England - April 11-12, 2018 HR Technology Conference and Expo - Vegas, NV - September 11-13th, 2018 More events, TBD. “As One Model continues to expand our client base in the U.S. and abroad, we’re looking forward to participating in more international HR, data science, and AI events,” says One Model’s Senior Marketing Manager, Stacia Damron. “Both domestic and international trade shows have helped us showcase our workforce analytics solution to a broader, more diverse audience, and they offer us an opportunity to foster and maintain valuable relationships with clients and partners alike." About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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11 min read
Chris Butler
“We scoffed when you predicted he would leave, six weeks later he was gone. Never in a million years would I have said he would leave ” — One Model AWS ML Test Customer Prediction is becoming a Commodity I've been meaning to write this post for a couple of years now after first testing AWS machine learning tools for use with our customer's data sets. Prediction is becoming commoditized by highly available and inexpensive tools like AWS Machine Learning, Google's Cloud Machine Learning Engine, and Microsoft's Azure ML platform. It is now easy to take advantage of machine learning at a ridiculously low cost to the point that anyone can pick it up and start using the toolsets. For HR this means any analyst can cobble together a data set, build a predictive model, and generate predictions without a data science team and no advanced knowledge required. Further below I give a rundown on how to create your own attrition risk model and predictions using Amazon's machine learning service but first, I'll discuss some of the observations we've had in using the service. When everything works well Right out of the gates I had a good experience with using these toolsets, I loaded a fairly simple data set of about 20 employee attributes (known as data features) and ran through the available UI wizard creating a predictive model. Even before generating a set of predictions the data source and model created provide some interesting information to look at, correlations, and a test set of predictions to see how well the model was expected to perform. You can see in the images above an example of the data correlations to target (termination), and the performance of the model itself in a test evaluation. An encouraging first step, and so far I've spent about $0.05c in processing time. Loading a data file of employees that I wanted to run a prediction on and a couple minutes later we have a probability score and prediction for each person in the organization. The performance wasn't quite as good as the evaluation test but it was still quite significant, I ran this test on a historical dataset (data as at one year ago) and could check the real-life performance of the model using actual terminations since that time. It wasn't bad, around 65% of people the model predicted as a 1 (terminated) ended up leaving the organization. This was on a data set that had a historical termination rate of ~20%. With some minor tweaking adding additional data features, removing some others that looked problematic and running the models and predictions monthly to incorporate new hires we pushed the performance up to an average of 75% over the following 12 months. That means 75% of the people the machine said would leave, did so in the next 12 months. Not bad at all. For one of our customer tests, we found 65 high performing employees that were at risk of leaving. That's a turnover cost equivalent to at least $6,000,000 and this was on the first run only two weeks after they started our subscription with us. In fact, if they could save even one of those persons from leaving they would have well and truly paid for our subscription cost let alone the $15 it cost me to run the model. I mocked a dashboard on our demo site that was similar to that delivered to the customer below. Since testing with other real world data sets I have the below observations about where the AWS tools work well. Works really well on higher turnover organizations, you simply have more patterns, and more data to work with, and a statistically greater chance of someone leaving. Turnover greater than 15% you could expect good performance. Simple feature sets work well with high turnover organizations i.e. employment attributes, performance, etc. I would however always add in more calculated features though to see if they correlate e.g. Time since last promotion/transfer/position change, Supervisor changes, Peer terminations etc. The less turnover you have the more important these additional data features are. A model generated across the whole company's data worked just as well as a model generated across a subset i.e. sales, engineering. Great, for the most part we could generate a single model and use against the whole organization. Ignore the categorical prediction 1 vs 0 and instead use the probability score to create your own predictions or buckets, i've found it easier to look at and bucket populations into risk categories using this method and obtain populations with probability values that we can focus on. This is particularly useful when we want to bucket say the top 600 or the top 12% of our population to match our historic turnover. I found the best test of performance before applying to current data was to run one model every month for a historical period say the last two to three years (24-36 monthly models), load the results into a database and be able to see how the models perform over time. It allows you to take a wider view of the models performance. When everything falls apart Well not quite but when it doesn't perform as well as you might expect, conversely to the above i've run tests on organizations where i haven't seen the same stellar outcomes. Or where the model works really well for a period of time but then dives off a cliff with no explanation as you can see in the below image. This is an example where we had a model that was perfoming really well until the turnover behaviour changed completely and was no longer predictable with the data we had feeding the model. This could happen with any model but we had particular issue trying to overcome with limited set of levers we could pull in AWS. You can see that the new behaviours were being identified but it took time to re-learn and regain it's performance. A note on the metrics use below; I like to use Termination Rate - Annualized as a measure of performance because typically we run and make these predictions monthly so the populations in each bucket are changing as new hires are made, terminations leave, and peoples attributes change which may make them change risk categories. This is the reason why you will see rates exceeding 100% as the denominator population is being refreshed with new people in the risk bucket i.e. Termination Rate - Annualized: High Risk = Terminations: High Risk / Average Headcount: High Risk (annualized then of course) Generally I've seen lower performance working with organizations who have low turnover (<8%) or are just relatively small. There just were not enough reliable patterns in the data to be able to obtain the same level of gains that we see in higher turnover, larger organizations. You can increase performance by adding more features that may show additional patterns but in the testing we did we could only get so far with the data available. However while we had lower performance we still saw turnover rates (terminations/average headcount) of high risk populations around the 40-60% mark which is still significantly better than the average turnover and provides a population to go and work with so the effort is not wasted. To counter some of this you can use the probability scores to create risk buckets where you can then focus on precision of the prediction sacrificing recall (number of terminations captured). In this way you can be quite confident about a population even though it will be a smaller subset of the terminated population. Ultimately we didn't use these tools in a production capacity because we needed to overcome a different set of challenges that individual organizations don't have to deal with i.e. how to deliver at scale for any customer, with any size (even small companies) and shape data set, to do so regularly, and always be at the highest level of accuracy. The automated tools available just couldn't meet our requirements and i'll discuss some of those reasons below, so we had to build our own Machine Learning for HR which we will release some content around soon. In People Analytics the most common use case of prediction is still turnover as it represents a huge cost to the business and data is for the most part readily available. Next we will spin up a model in AWS and generate some predictions. Stay Tuned for Part 2 If you would like to talk about our framework sooner please contact us or
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7 min read
Chris Butler
The biggest change in people analytics that surprised me in 2017 wasn't any new leap in technology or shiny new object. For me, it was the growth in interest and uptake by smaller organizations. Traditionally this space has been reserved for companies that had statistically significant populations and budget's to match them. They could hire a team to build and grow HR analytics and had discretionary budget to spend on tool-sets to assist them. A few years ago you rarely would have seen a company with less than 5,000 employees spending resources on these initiatives. The last couple years and this year in particular however we've seen a substantial increase in appetite from companies with less than 1,000 employees. In fact, the smallest company I spoke to in 2017 was barely over 100 employees. These companies are not just kicking tires either, they are purchasing technology, hiring people analysts, and making out sized gains in capability when compared to their larger peers. Budget is being procured and goals are being set that would shame many large organizations. Did you know that 35% of our new customers in 2017 came from organizations with less than 1000 employees. What are they doing? Basically the same activities as larger organizations. They are gathering and making sense of all their people data, delivering reporting/analytics to their business users, and moving into advanced analytics across the hire to retire spectrum. Goals are lofty, and without a significant organizational burden they are able to move fast. Time from analysis to decision to action is on the order of days if not hours. System complexity is a major challenge. Smaller companies still struggle with the same challenges however, often the system complexity is the same or sometimes more complex than larger organizations. We have observed that often smaller companies have collected a number of systems to help make life easier but generally they're sourced from a rainbow of vendors and often are the bright new shiny applications that don't have the maturity to provide the level of data detail and access that a more established vendor may provide. At this size it is also much easier to transition to a different product or spin up a new technology which while collecting some great data can make life much more difficult to merge these systems together for analysis. Overall i think many organizations at this scale have much richer data than many of their larger peers but it is more fragmented. How can you possibly find statistical significance with a small population? From my conversations with these companies this is a known factor in how they conduct analysis and interpret their findings. It's not a showstopper but just another data point to be kept in mind. We personally had to adapt some of our machine learning prediction functionality to be able to cater for smaller companies. A predictive attrition model for example generally works better the more terminations you have, with a small population of terminations you typically won't do so well. "For smaller organizations we now employ a method of synthetically creating data that is not the same as, but is representative of the original data set - essentially making a 500 person company look like it's a 50,000 person company." One Model has had great success in employing this to very large enterprises as well to enhance the behaviors and patterns seen in the data. There are options to overcome the smaller data set challenges. Most people we know in this space don't care that they only have 500 people, because our software allows us to deliver value to their organization irregardless. Is it a passing fad for smaller organizations? I don't know yet, but I don't think so. We have to keep in mind the number of smaller organizations is orders of magnitude larger than larger organizations and it really is only the most forward thinking of these companies that are undertaking these activitites. Typically (but not always) it's the companies that are growing, doing well, and apt to hiring people who are interested in using data to support decisions (think technology, and bio-tech). This is not every smaller company, but my belief is that the entry point for HR analytics is becoming earlier, and earlier in an organization's growth curve. What does it mean for us in larger organizations and the space in general? Increased demand for HR analytics skillsets from smaller companies (more choice in who you work for). Conversely, if you need to hire for your people analytics practice don't discount people who have worked for smaller companies - you may find some great candidates in this pool. Any human capital competitive advantage you have in being a larger company is being assumed by your smaller competitors. An increase in the number of vendors supplying technology in the space, small companies are typically the entry point for new startups. With availability of technology targeted at the systems smaller companies use, expect the adoption rate and therefore the effects of the first two points to increase. We're going to see different examples for the use of people analytics at smaller scale companies, these will be interesting and learnings may even apply to smaller business units within large organizations. We're giving away free 30-min consultations to help companies take charge of their HR people analytics and data in 2018. Would like like to learn how we can help you take your people analytics and workforce data to the next level? Take advantage! Click here, or click on button below to schedule a complimentary consultation. One of our team members will get in touch with you and speak with you one-on-one to address any specific challenges your company might have. Cheers to a new year, Chris Butler One Model, CEO
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6 min read
Chris Butler
On behalf of the One Model team I am excited to announce on our third anniversary of founding that we have secured an amazing seed round investment of $3.7M to take our people data platform to the next level for our customers. 2017 has been an incredible year of growth for us and has shown that our approach and value proposition resonates strongly with customers. So much so that we are yet to lose a single customer (0% churn) which is just about unheard of for a SaaS company that is now three years old. Our vision hasn't changed, we believe that in order to fully deliver on the value to be found in people data organizations need One Model to connect and understand the data held in the dozens of systems they use to manage the workforce today. Effectively we become a secondary system of record free of the constraints that transactional systems (HRIS, ATS, Talent Management, Payroll) suffer from. The last three years have been spent building out our core data platform, to connect and accept data from any source, to understand all the behaviors between data sets, and deliver our bespoke reporting and analytics platform. With this powerful framework in place we can add in more of the high value use cases that ordinary organizations would never be able to achieve on their own. Extending data with external sources, advanced algorithmic calculations, your own custom R/Python programs, and our incredible new automated machine learning tools. All running within our data pipeline and managed by HR. We're incredibly excited about our immediate future and this investment gives us the resources to chase it down. Chris Butler (press release below) One Model Secures $3.7 Million in Funding to Fuel Growth in HR Data and Analytics Software Market Austin, Texas, November 1, 2017 – One Model, the people data strategy platform, announced the closing of $3.7 million in Series Seed funding from The Geekdom Fund, Otter Consulting, Techstars, and Lontra Ventures. The One Model team will leverage this additional funding to fuel its international growth strategy, accelerate enterprise adoption for its products, and to further develop its leading HR data and analytics platform. “Getting to know the One Model team over the past couple of years made it an easy decision for us to want to lead this round. From the beginning, the team has been able to address major enterprise needs with their powerful HR data analytics platform driving data insights from machine learning, delivering this to customers flexibly while making implementation easy,” voiced Don Douglas, Managing Director of The Geekdom Fund. “One Model's people analytics infrastructure has changed how a number of organizations plan, execute, and evaluate their HR strategies and we are excited to support the proliferation of this game changing platform.” “The HR departments of multinationals see the value proposition that One Model brings to their infrastructure, evidenced by the rapid growth One Model has experienced. The implementation time and dollars saved are enormous,” states Mike Wohl, the Investment Manager of Otter Consulting. “The future looks very bright for One Model and all of the companies that utilize their offering.” The Austin, Texas-based startup is uniquely positioned to address a key pain point within the HR industry and is primed for growth. The company’s platform removes the heavy lifting out of extracting, cleansing, modelling, and delivering analytics from your workforce data. “One Model sits at the center of all people data held by an organization. As such, we’re in a unique position to understand, extend, and deliver organizations with transformative value from this data. Our vision is that every company will need what amounts to a secondary system of record that connects together all of their disparate people systems and provides a level of insight that no transactional system can achieve on it’s own. We’re only beginning to scratch the surface of what is possible with the level of HR system interaction we are now achieving, and this investment allows us to double down on our approach” says Chris Butler, CEO of One Model. Founded in late 2014, the company has rapidly grown to support HR data and analytics needs of customers in over 156 cities around the world. This additional round of funding continues to authenticate the universal need for improved HR data and analytics management, and to validate One Model’s decision to assume a leadership role in addressing these data challenges head-on. “One Model leads the charge as the HR industry embraces analytics to improve career satisfaction, retention, and equity. The team is comprised of true industry experts who understand the nuances of enterprise software and the power machine learning. One Model’s robust pipeline of enterprise and channel customers will transform the lives of millions of professionals across the globe,” according to Andrea Kalmans, Lontra Ventures. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. Request a demo today at http://www.onemodel.co. About The Geekdom Fund The Geekdom Fund is a venture capital fund that invests in early stage IT startups in San Antonio, South Texas and beyond. It is managed by Riverwalk Capital, LLC. About Lontra Ventures Lontra Ventures is an Austin, Texas based entrepreneurial consultancy that specializes in life science consulting, and technology for high-growth companies. About Otter Consulting Otter Consulting, LLC operates as a venture capital firm. The company, which is headquartered in Florida, provides early stage venture capital financing services. About Techstars Techstars Ventures is the venture capital arm of Techstars. Techstars Ventures has $265M under management and is currently investing in their third fund ($150M). Alongside the VC and Angel communities, they co-invest in companies built by Techstars accelerator companies and alumni. For questions, please contact Stacia Damron, Senior Marketing Manager at stacia.damron@onemodel.co.
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13 min read
Chris Butler
The vast majority of organizations and People Analytics vendors use snapshots for extracting data from Workday because this is really the only choice they've been given to access the data. But snapshots are problematic for several reasons: They are inaccurate - You will typically miss out on the changes occurring between snapshots. This makes it impossible to track data/attribute changes in between, to pro-rate, and create analysis any deeper than the snapshot's time context. They are inflexible - An object or time context has already been applied to the data, which you can't change without replacing the entire data set with a new context. They don't allow for changes - If data is corrected or changed in history, you need to replace the entire data set, urggh. External data is difficult to connect - Without effective dating, joining in any external data means you have to assume a connection point and apply that time context's values to the external data set. This compounds the inaccuracy problem if you end up having to snapshot the external data as well. A pain in the #$% - To pull snapshots from Workday now, you need to create the report for each snapshot period that you need to provide. Three years of data with a month-end snapshot means 36 reports to build and maintain. With our background in working with raw data directly from HR systems, this approach wasn't going to cut it—it couldn't deliver the accuracy that should be the basis of an HR data strategy. The answer isn’t just buying another big data tool, because this same challenges will still exist. Instead, you need to enhance your existing structure and fundamentally rebuild your data architecture to truly solve these issues. We do just that, we extract all employee and object data, analyze the data as it flows and generate additional requests to the Workday API that work through the history of each object. Data is materialized into a schema close to the original but has additional effective-dated transactional records that you just wouldn't see in a snapshot-based schema. This becomes our raw data input into One Model, delivered to your own warehouses to be used any way you wish. The resulting dataset is perfect for delivering accurate, flexible reporting and analytics. The final structure is actually closer to what you would see with a traditional relational schema used by the HRIS sold by SAP, Oracle, PeopleSoft etc. Say what you will about the interfaces of these systems, but for the most part, the way they manage data is better suited for reporting and analytics. Now don't get me wrong, this should be a low priority decision point for Workday when selecting an HRIS. Don't compromise the value of a good transactional fit of an HRIS for your business in an attempt to solve for the reporting and analytics capability because ultimately you will be disappointed. Choose the HRIS system that fits how your business operates, solving for the reporting and analytics needs in another solution, as needed. Time to get a little more technical. What I'm going to discuss below is the original availability format of data in comparison to the approach we take at One Model. Object-Oriented - The Why of the Snapshot Okay, so we all know that Workday employs an Object-Oriented approach to storing data, which is impressively effective for its transactional use case. It's also quite good at being able to store the historical states of the object. You can see what I mean by taking a look at the API references below: The above means the history itself is there, but the native format for access is a snapshot at a specific point in time. We need to find a way of accessing this history and making the data useful for more advanced reporting and analytics. Time Context In providing a point in time, we are applying a time context to the data at the point of extraction. This context is then static and will never change unless you replace the data set with a different time context. Snapshot extractions are simply a collection of records with a time context applied. Often when extracting for analytics, companies will take a snapshot at the end of each month for each person or object. We get a result set similar to the below: The above is a simple approach but will miss out on the changes that occur between snapshot, because they're effectively hidden and ignored. When connecting external data sets that are properly effective- dated, you will need to make a decision on which snapshot is accurate to report against, but you simply don't have enough information available to make this connection correct. This snapshot is an inaccurate representation of what is really occurring in the data set, and it's terrible for pro-rating calculations to departments or cost centers and even something as basic as an average headcount is severely limited. Close enough is not good enough. If you are not starting out with a basis of accuracy, then everything you do downstream has the potential to be compromised. Remove the Context of Time There's a better way to represent data for reporting and analytics. Connect transactional events into a timeline Extract the details associated with the events Collapse the record set to provide an effective-dated set of records. The above distills down the number of records to only that which is needed and matches transactional and other object changes which means you can join to the data set at the correct point in time rather than approximating. Time Becomes a Flexible Concept This change requires that you apply a time context at query time, providing infinite flexibility for aligning data with different time constructs like the below Calendar Fiscal Pay periods Weeks Any time construct you can think of It's a simple enough join to create the linkage left outer join timeperiods tp on tp.date between employee.effective_date and employee.end_date We are joining at the day level here, which gives us the most flexibility and accuracy but will absolutely explode the number of records used in calculations into the millions and potentially billions of intersections. For us at One Model, accuracy is a worthwhile trade-off and the volume of data can be dealt with using clever query construction and, of course, some heavy compute power. We recently moved to a Graphics Processing Unit (GPU)-powered database because, really, why would you have dozens of compute cores when you can have thousands? (And, as a side note, it also allows us to run R and Python directly in the warehouse #realtimedatascience). More on this in a future post but for a quick comparison, take a look at the Mythbusters demonstration What About Other Objects? We also apply the same approach to the related objects within Workday so that we're building a historical effective-dated representation over time. Not all objects support this, so there are some alternative methods for building history. Retroactive Changes? Data changes and corrections occur all the time, as we regularly see volumes of changes being most active in the last six months and can occur several years in the past. Snapshots often ignore these changes unless you replace the complete data set for each load. The smarter way is to identify changes and replace only the data that is affected (i.e., replace all historical data for a person who has had a retroactive change). This approach facilitates a changes-only feed and can get you close to a near-real-time data set. I say "close to near-real time" because the Workday API is quite slow, so speed will differ depending on the number of changes occurring. Okay, So How Do You Accomplish This Magic? We have built our own integration software specifically for Workday that accomplishes all of the above. It follows this sequence: Extracts all object data and for each of them it... Evaluates the data flow and identifies where additional requests are needed to extract historical data at a different time context, then... Merges these records, collapses them, and effective-dates each record. We now have an effective-dated historical extract of each object sourced from the Workday API. This is considered the raw input source into One Model, and it is highly normalized and enormous in its scope, as most customers have 300+ tables extracted. The pattern in the below image is a representation of each object coming through; you can individually select the object slice itself The One Model modeling and calculation engines take over to make sense of the highly normalized schema, connect in any other data sources available, and deliver a cohesive data warehouse built specifically for HR data. 6. Data is available in our toolsets or you have the option to plug in your own software like Tableau, PowerBI, Qlik, SAS, etc. 7. One Model is up and running in a few days. To accomplish all of the above, all we need is a set of authorized API credentials with access provided to the objects you'd like us to access. 8. With the data model constructed, the storyboards, dashboards, and querying capabilities are immediately available. Examples: Flexibility - The Biggest Advantage You Now Have We now have virtually all data extracted from Workday in a historically accurate transaction-based format that is perfect for integrating additional data sources or generating an output with any desired time context (even convert back to snapshots, if required). Successful reporting and analytics with Workday starts with having a data strategy for overcoming the inherent limitations of the native architecture that is just not built for this purpose. We're HR data and People Analytics experts and we do this all day long. If you would like to take a look, please feel free to contact us or book some time to talk directly below. Learn more about One Model's Workday Integration Book a Demo
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12 min read
Mike West
This is continued from a "Gloves Off Friday Post" by Mike West on Linkedin Pulse here: How the Best Company Award is Wrong How The Best Companies To Work For Are Ranked. Different newspapers, magazines and institutes have different methodologies to rank order companies, however the thing they have in common is that a large portion of the rating is based on an employee survey. Below is what it states on the Fortune website: "To identify the 100 Best Companies to Work For, each year Fortune partners with Great Place to Work to conduct the most extensive employee survey in corporate America. Two-thirds of a company’s survey score is based on the results of the Trust Index Employee Survey, which is sent to a random sample of employees from each company. This survey asks questions related to employees’ attitudes about management’s credibility, overall job satisfaction, and camaraderie. The other third is based on responses to the Culture Audit, which includes detailed questions about pay and benefit programs and a series of open-ended questions about hiring practices, methods of internal communication, training, recognition programs, and diversity efforts. Glass Door also bases its rating on survey questions. "To determine the best places to work, Glassdoor looks at company reviews provided by employees between November 13, 2013 and November 2, 2014, in which individuals are asked to consider and rate such factors as overall satisfaction, CEO leadership, career opportunities, compensation, and work-life balance." To the credit of the rating agencies, asking employees at different employers the same questions seems like the fairest and most scientifically valid way to compare employers to each other. How to Game it. Knowing surveys are the most substantial part of the ranking, the key to gaming Best Company to Work For Awards is to know the natural distribution of attitude by employee characteristics and use this information to increase the % of employee surveyed in segments that have higher positive attitude than average, while decreasing the sample rate in those with lower positive attitude than average. Relatively unsophisticated rating agencies, such as local newspapers, could easily be exploited in this manner. On the other hand some rating agencies, like Fortune and the "Great Company to Work For Institute" will reply to this with, "we use a random sample". Unscrupulous HR bosses do not be deterred. While using a "random sample" seems like a great way to prevent manipulation, this too can be beat. The unscrupulous HR boss could beat random sampling by proportionally manipulating the quantity of email addresses from different segments he/she provided to the rating agency (determined by an understanding of the natural response of varied segments) and/or he/she could just filter which emails allowed to pass through the email server. To be clear - I am making a point - I am not suggesting a company should cheat, however if a company wanted to do so this would be how they could do so without directly standing behind employees shoulders while they fill out the survey or offering to throw pizza parties for the groups with the highest results. By the way, you better believe that some companies and managers DO do things like that. I am not aware of a specific circumstance where a company has deliberately manipulated a survey process, however I am aware of circumstances where companies have benefited indirectly without the knowledge of the rating agency. Here is the Problem. How positively people respond to questions at their employer varies reliably by certain employee characteristics and these characteristics are not uniformly sampled across all employers. Characteristics that may statistically matter extend from natural demographic distributions (age, gender, ethnicity..), to natural job type distributions (professional, skilled labor and unskilled labor), to natural geographic distributions, to other characteristics we may not even typically record. I left out the most important. The characteristic that I have found to consistently vary between segments by substantial margin, unrelated to the actual quality of the company, is Tenure Group. Company Tenure is calculated something like this (Current Date - Start Date) and is usually grouped something like this (0-1 Year, 1.1 to 3 Years, 3.1 to 5 years, 5.1 years to 10, 10+ Years). What it Looks Like. A typical tenure group pattern looks something like this: Typical Employee Engagement Pattern by time in job It is worth mentioning that Tenure Distribution is at least partly driving geographic and industry differences. You can see this if you consider that the labor market characteristics of geographies and industries have a relationship with the proportional distribution of Company Tenures. Faster growing local economies and industries have lower overall tenure so these populations would also have proportionally more people in low tenure groups. In the graph below note the growth characteristics of our leading industries. Think about the growth characteristics of the industries to the right. See it? PWC 2015 Report PWC 2015 Report Does it Matter That Much? You might say, "Come on!, How much could this problem really matter? Actually a lot! The phenomenon can be observed in rare cases when the #1 Company Award unexplainably flips away from a company in one year and returns to them in a future year. What explains the difference is decreased hiring rate, relative to other nearby companies on the list. Statista This is a little far fetched but the other thing you could do to game the award is to hire a large number of people right before the time of year of the awards and/or right before you apply for the award the first time. I can't say anybody does that to win awards of this nature intentionally, or anyone ever would, but some benefit from a massive growth rate that ensures this will happen for them whether they try to do it or not. Should We Care? "Google has been on the list for 10 years with this being its seventh time at No. 1, thanks to sparking the imagination of its talented and highly compensated workers, and by adding perks to an already dizzying array of freebies ." The first reason we should care is that the companies that win these awards receive substantial press and as a result receive a remarkable increase in the amount of job applicants. Think something akin to a million new applications to Google. If this is coupled with an increased ability to filter job applicant pools to identify high quality candidates then these #1 picked employers have a substantial competitive advantage in ability to select the most highly qualified workers. Further, these employers have a PR gains from which to take key talent away from other companies and keep their own key talent. Another reason we should care is that many organizations try to imitate the "Best Practices" of the companies that are highly ranked on these lists. The companies that want to be like them may unfortunately be imitating characteristics that have no actual relationship to what makes a great company to work for, or the reported survey results and therefore arbitrary. Recall, correlation does not imply causality. Trying to imitate all of the practices of the purported great companies may result in investments that generate no return and simultaneously decrease margin, thus making it more difficult for the imitating company to compete in the future. This could provide substantial advantages to companies that can make the top of the list AND afford to give up a small portion of big margin to spend above average on salary and unusual employee perks. I have written extensively about how Best Practices lead us astray in a prior blog post : 7 Reasons Best Practices Are Not Best For You. What can be done about it? For starters the newspapers, magazines and rating agencies could sample survey responses in tenure groups to ensure an apples to apples comparison. Instead of a random sample this would be called a "Stratified Random Sample". If they really wanted to step up their game they could also just put all of the data into a single multiple regression model, to isolate a company effect from tenure effects and any other variables that may skew results, be they demographic, job related, geography or whatever. This sounds complicated but actually any undergraduate statistics major or any graduate behavioral level science major could run this analysis. Now, as I state repeatedly, I am not suggesting anyone should really try to game the Best Company to Work For Awards, however I can understand why you would want to up your game to truly improve employee engagement and be a truly great employer. The best way to do so is to look across data sets and use the engagement data in increasingly better ways to get better at actually moving engagement. Survey providers are good at managing the process of constructing a good survey and collecting data but provide a very limited view of the data and no survey providers work with their survey data, plus your other data sources to provide a single longitudinal view of the truth. No survey provider maintains ongoing relationship with your sources that adjust with underlying structural changes automatically and that you can query in real time. I know of some employers who can look at survey data in this way, however they cannot do so while maintaining employee confidentiality as a survey provider would. The world is now in luck - One Model can take data from a survey provider (or tool) and join it into a single view of the truth with other employee related data, allowing a longitudinal view, update automatically, and most importantly, can do this while maintain employee confidentiality just a survey provider would (by not allowing you to report data below a sample size threshold) :-) If this interests you, let us know and we would be happy to provide you with a demo so you can see for yourself what sort of new advantages this can give you! ---------------------------------------------------------------------------------------- This is a continuation of a "Gloves Off Friday" post : How the Best Company Award is Wrong More like it: Why Josh Bersin is Wrong About Embedded Analytics? The Most Dangerous Technology in HR Today What Your HR Technology Sales Rep Doesn't Want You To Know ---------------------------------------------------------------------------------------- Who is Mike West? Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, and PeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology startups. Mike is currently the VP of Product Strategy for One Model -the first cloud data warehouse platform designed for People Analytics. Connect with Mike West on Linkedin
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You may not think your company is yet doing People Analytics but here is one way your employer is already doing so, poorly. Employers routinely use Credit Scores to screen candidates. “47% of employers conduct credit checks to screen potential new potential hires” (Society of Human Resource Management) That is to say, they use a score designed to measure credit default risk and apply it to employment screening on some dubious premise that this may be predictive of success, good judgment or good moral character in the context of employment. What is a credit score? A credit score is a numerical expression of an analysis of a person's credit files, to represent the creditworthiness of the person. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. A credit score is based on a history of financial transactions sourced from credit bureaus. A credit score applies a mathematical "algorithm" to a profile and history of transactions to categorize a loan candidate so that financial institutions can make a better decision about whether or not to loan someone money, and/or to make a better decision about how to price a loan to loan money profitably, with regard to risk.A bank may offer to lend a low credit score candidate a loan but at a higher interest rate. This allows the bank to maintain a certain level of profitability from different risk segments or deny working with certain risk segments all together. Why do employers use credit scores to screen employees? In employment the credit score is presumably used for a similar high level purpose as banks (control risk). In the case of employment you are trying to reduce the risk of a "bad" hire or stated inversely, reduce the risk of not selecting a good hire. Employers have more applicants than available positions and they limited time to consider each candidate, so they want some quick means to make a decision. Or sometimes we make a decision but apply additional rigor through screening devices to prove to ourselves we made the right decision. Directly from a credit reporting firm’s website : Credit Scores can help employers “make decisions quickly and easily when deciding on potential candidates” (TransUnion) To shed additional light on this practice, I bring up a conversation at a recent U.S. Senate hearing… “What is the evidence that there is strong correlation between accessing an applicant's credit history and eventually problems of loss to the employer?” Senator Rosenbaum (Oregon) “We don’t have any research to show any statistical correlation between what is in somebody’s credit report and their job performance or their likelihood to commit fraud.” Erick Rosenberg (TransUnion) Here are some other facts to consider about using Credit Scores for employment screening: - 1 in 4 credit reports have been found to have an error - 1 in 20 have been found to have serious errors - Other issues : “52% of all debt on credit reports stems from medical expenses” CFPB 25% error rate? If you thought your core HR data has problems, maybe it doesn't sound so bad now? Scores are being used as a reflection of "character" but if the medical debt statistic is correct, in half or more cases credit scores may be low because of uncontrollable circumstances - how is that reflective of character? Imagine that as a result of chance circumstances you are in dire need of money and you are also as a result refused a means of obtaining money? Does this practice make any sense? It's predictive but you don't need any math at all to predict this outcome - it's a self fulfilling prophecy - self fulfilling prophecies are convenient if you sell predictions. How do the credit reporting agencies and employers defending this practice? The "all else equal" claim. That is that "all else equal" this is a better way of making a decision than nothing. Is it really? If we can find evidence of errors, systematic bias and NO evidence that there is any relationship with job performance - is this really a better way of making a decision than a coin flip? Or why not do the work to find something else not equal that has less errors, less systematic bias or more evidence of relationship to job performance? Why not find a better way of making things not equal? We have always done it this way or this is how others are doing it. Come on, is this really a good reason? Is this how to make good business decisions? This is not how any great business decisions are made, ever. It seems like a "plausible method" of making decisions by logic or rational argument. Here are some theories that are at least as plausible as the theory that credit reports may be good predictors of performance … Maybe because we really have no idea how to screen a candidate for characteristics that relate to performance without doing this work directly … Maybe because we want to find a way to systematically discriminate against populations that come from impoverished communities, thus discriminating against a high percentage of minorities, without doing so directly… Or we don't delve into the details to see how this math may or may not play out and we don't care. Maybe because we want to eliminate candidates who have high medical expenses and would cost our health plans money, without doing so directly… (This actually seems like the most mathematically plausible scenario to me) I ask again, why are you using credit scores to screen job applicants? How about this, why don't we actually do the work to see what factors drive performance in our organization and/or isolate with data how we can increase the probability of high performance, regardless of starting individual characteristics. To me this is a better way of making decisions and a better way to run a business. Thank you to John Oliver (yes, John Oliver the comedian) for highlighting these issues in a clear, emotionally charged and entertaining way on his show : Last Week Tonight (HBO) – Be advised - wear headphones - there is some language in this that may be offensive and not safe for a work environment. I would argue the practice of using credit reports as screening devices is equally offensive and unsafe. I wish that each employer would put as much time researching the practice of using credit reports as employment screening devices as an HBO comedian did. Seems like it would be even more useful to us to know these things than it is for him, is it not?
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Mike West
“Half the money I spend on advertising is wasted; the trouble is, I don't know which half.” Henry Ford, Lord Lever, John Wanamaker... People often ask, “Does People Analytics work?” These are smart people, who genuinely would like a straight answer to an honest question. I will sometimes start a conversation about People Analytics with something like this: did you know that in the United States alone, companies currently spend about 7 trillion on employees in Payroll*? My experience has been that how, where, when and why money is spent matters and results will vary on how well or poorly money is invested. Unfortunately having worked in HR for over 15 years I can tell you that more often than not decisions affecting how people are : a.) coincidental b.) arbitrary, c.) biased or d.) political (by this I mean rooted in conflict – the expression of personal or group advantages and disadvantages). None of these imply a reason that is good for doing business, or people or the economy. (*It is probably over 10 trillion when you include the cost of Benefits and other Perks) Before you can say whether or not People Analytics’ works, you need to know what its job is. We can gather from the name, People Analytics is the application of analysis to people. O.k. but what’s the goal of People Analytics? How can you say whether it works if you don’t know what it’s supposed to achieve? In prior People Analytics Q/A posts I attempted a definition - I was looking for a unique combination of words to represent the essence of a complex concept simply without missing what is different about it. This is what I came up with:People Analytics is the systematic application of behavioral science and statistics to Human Resource Management to achieve probability derived business advantages. I chose these words deliberately but admittedly a little too deliberately to roll off the tongue in casual conversation. So let’s take a step back and talk about it. More simply put, People Analytics is the application of analysis to people in a business. For the sake of discussion we are going to put "people in a business" in the realm of Human Resources. To understand People Analytics lets first understand what is the job of HR? Most people think of HR as a series of low-level administrative activities: record-keeping, recruiting, benefits administration or as the legislation compliance arm of the government that sits in your organization. There is an element of truth to this. No organization could exist for very long without some attention to those things, however you think this is all HR is you simply have not been exposed to HR in a successful, large, modern organization. The need for a dedicated HR function, historically, and for any given organization, occurs when we start to go from something small – where you can know everybody and everything that is going on in an organization - to something big – when it is no longer possible for one person to know everybody and everything that is going on. If at some point no one person can see clearly what is actually going on the organization will grow into chaos. The reaction to chaos now and forever will be, “My God we need to get control over this.” e.g. Ultimately successful organizations eventually arrive at, "We need a Human Resource function". At its essence, HR is about controlling the chaos of organization growth. Control doesn’t sound very exciting. A more exciting way of framing it is, if your investors wishes comes true (growth), then Human Resource Management is your reward. HR is a necessity as a consequence of growth, and growth is the primary way businesses derive value for shareholders over the long run, then it might be accurate to describe HR as both a consequence of growth and a cause. Yes, just as eggs and chickens go, so goes HR. It is a great mystery, however in the case Kentucky Fried Chicken I will abdicate that I think the fried chicken came first, and then HR came along later. The idea that having an HR function may be a good thing for an organization begins with the need to create efficient processes to expand the business and eventually evolves into a more expansive appreciation for what intelligent employees and management expect out of an organization. Tending to The Employment Relationship The main thing everybody in an organization has in common is that we are all getting paid– that makes it a job - otherwise we probably wouldn’t be together 8 hours per day, 5 days per week, 50 (+/-2) weeks per year or whatever you do. You may like each other but you probably don’t like each other that much! The other thing we all have in common is that we are at this job voluntarily – beyond acknowledgement that the dissolution of direct slavery is a good thing – we can all thankfully acknowledge labor markets can sometimes be competitive and therefore opportunities for us abound. There are two sides to the equation and both sides make choices. The relationship between an employee and an employer is a rich interaction that can be understood through many different lenses (Psychology, Sociology, Social Psychology, Labor Economics, Anthropology, etc.) however these interactions boil down to decisions and consequences. Decisions are at the crux of our interaction. The leadership of an organization can truly decide to treat people however they want to but they don’t get to decide if people come or go as a consequence – the people decide this. The people also decide the level of creativity and effort they are willing to exert on behalf of an organization. In the short term, none of these interactions may appear to matter - in the long-term it is clear they always do. Achieving clarity in decisions to achieve desired outcomes is the entire point of management. HR wants a seat at the business table, but the truth is they were silent participants all along. Strategic Human Resource Management After we get the basic blocking and tackling of organization under control modern Human Resource Management extends into the macro-concerns of the organization regarding structure, quality of talent, culture, values, matching resources to future needs and other longer-term people issues related to the organization’s plans – we call this group of activities Strategic Human Resource Management. Strategic HRM gives direction on how to build the foundation for strategic advantage by creating an effective organizational structure and design, employee value proposition, systems thinking problem diagnosis, and preparing an organization for a changing landscape, which include new competition, downturns and mergers & acquisitions. Sustainability, diversity, corporate social responsibility, culture and communication also fit within the ambit of Strategic HRM by reflecting chosen organizational values and their expression in business decision-making. (loose credit to the Society of Human Resource Management for this) If that two paragraph description of Strategic HRM sounds like something straight out of a textbook, that is because it probably was. You and I both wish I would have a better mental firewall. Basically, I’m saying that if you are doing it right the purpose of HR is not really about specific activities or compliance; it is about enabling business growth and as you do that it about designing an organization for competitive advantage. Good HR should help extend the life of organizations by helping them extend the reach of what they do or grow better at what they do over time. Here is a brutal fact : over the long run most organizations fail If over the long run most organizations fail – are you getting a clear picture of how good of a job we are do with this strategic HRM stuff? Then again, maybe it is not HR’s fault, maybe HR had great things to say that were not heard. What is the goal of People Analytics? In 3 words : do HR better. In more words: help leaders and employees make better decisions together – reinforcing organization based competitive advantages - which result in sustained organization growth over time. What is the job of People Analytics? People Analytics provides a means to see and explain what is going on inside of an organization. People Analytics provides a framework to give HR’s disjointed practices a reason, coherence and direction. People Analytics also gives HR a more powerful language to communicate with others. Does People Analytics Work? Here is an interesting 18 minute macro way of answering this question in the form of a Ted Talk : The Surprising math of Cities and Corporations : by Geoffrey West Or you can formulate your own answer in less time than that thinking about the following questions… What is it worth to you, to find out what characteristics in a manager lead to a statistically better performing software engineering or sales team? What is the ROI of using math and science to identify a poor manager, that un-checked could influence the organization to do things that result in devastating class action litigation against your company? How much more likely is a person to be motivated to react to criticism of their management style when presented with data that “We asked the same questions of all managers and you are in the bottom 25th percentile on these measures – please explain”? If you believe that the best hiring criteria for success in your organization is intelligence, represented by an Ivy League education, believing they are more successful, and that is not actually true, what is that worth to you? How much additional do you pay for a graduate of an Ivy League Education x number of employees over time? Further, what additional costs to business performance about being wrong about what makes people successful in an organization? Not to mention, what possible business drags and penalties will accumulate when your organization grows less statistically representative of its community as a result of a faulty premise? What is it worth to you to discover that a many million-dollar Benefit program actually does not relate at all to retain employees when that was the primary reason cited for implementing that program? What is it worth to you, to discover that a 10 million dollar benefits program now, will result in a 100 million benefits program later if your organization continues to grow on the same track? What is it worth to you to avoid the consequence to commitment and morale of offering a benefit and not being able to deliver on it, or slowly taking it back over time? Does the current gain in morale from implementing this program, exceed the future consequences of retracting it? What is the ratio of value to the organization? What is the ROI for a hospital to have a 12.5% 1st Year Tenure Attrition Rate versus 25%? What is the difference in patient satisfaction or outcomes? What is the potential cost of Nurse mistakes? What is the reduction in probability of mistakes by reducing the 1st year turnover rate? … Based on my experience with the question above and real organization data, the question “Does People Analytics Work?” is an absurd one. Would you dream of asking a mathematician, “Does math work?” or asking a scientist, “Does science work?”, or an engineer, “Does engineering work?” or a doctor, “Does medicine work?” Someday I hope we will move past this question for People Analytics. ---------------------------------------------------------------------------------------- #PAQA = People Analytics Question and Answer Series What is People Analytics? What is not People Analytics? Why People Analytics? What is the history of People Analytics? What are the key questions of People Analytics? What is the actual work of People Analytics? What are the alternatives to People Analytics? What is the technology of People Analytics? Feel free to suggest ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, and PeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model. One Model takes data from numerous systems and organizes it so that you can measure, predict and influence workforce behavior to effect change. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin
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# 1 Reason people leave google: to connect with their personal calling to change the world. Ironically, it is also the number one reason people join Google. To be fair, I'm a data guy and I don't have access to their data anymore - so consider these an educated guess. It's great that I haven't seen the data in a long time (fuzzy) because I can speculate without assertions I am sharing secrets. Most importantly you can read the things that Laszlo Bock has to say about what attracts, motivates and retains employees directly from him. I do not believe I am off message at all. Across a number of People data projects we get a big theme : you best attract, engage and retain people by connecting with their passion. The role of the company is to leverage assets to provide support to help people materialize dreams because some of those crazy bets are going to someday hit. Get out of the way Google's head of Engineering, Alan Eustace, once said, "One thing is certain about the next big idea, I probably won't have it." (I paraphrase from memory). They work hard to attract, engage and retain talented employees for this reason. They pay more than lip service to these ideas - they invest big in them. As a result of this effort they have much lower average overall turnover than most companies, and really really low key employee turnover, however even Google can't keep them all. Keeping them all might not even be the right goal (for them, the company or for the world). Here is a human story I worked with the now CEO of Omada Health, Sean Duffy, years ago in the Google People Analytics group. We were analysts. I recall the moment when was accepted to Harvard Medical. He had excitement on his face, I asked him what was going on and he showed me the letter. Looking in his eyes I could see it and said, "I'd bet a million dollars you take it". I was right and I was wrong. He went, but soon thereafter he met up with some other guys and they decided to start a company together. They have been working on it for over 5 years, they have iterated, they raised a lot of money from VCs, and I think they have something. Most importantly I believe they have a unique business model. If I understand their business correctly they don't take money for services up front. They get paid by changing population health outcomes, or if not that precisely, at least there is some kind of reconciliation based on outcomes. They can make money from interested insurance companies, large employers and now, it sounds, from Medicaid/Medicare. This might just be the future of medicine. Historically Google has hired people who were overqualified for their roles : a.) because Google is attractive enough that they can, and b.) because they were betting on the future of this person with the company, not a temporary functional skill-set. The result of that is that you end up with too many individuals in small roles who are chasing too few big roles and you get a jammed up talent funnel. If you are stuck in small thinking, there is no solution. You choose winners and losers. You acknowledge that some of your good people are going to go, and that's o.k. You wish them well and try to maintain good relations through a strong alumni network. However, Google doesn't think small. The most interesting thing for me is to watch the big business changes at Google that I believe may directly stem from people data. One word, many letters : Alphabet. What other company would do this? Crazy? No way. The beauty of Alphabet is many fold. By splintering into many parts Google reaps the following benefits: 1.) Increasing the number of available internal paths to big roles, 2.) clarification of decision making, and 3.) clarification of impact - on an entity level but on an individual level. Opportunity can expand infinitely if you think big. Opportunity + Talent + Resources = an economic engine for the future. Don't take my word, here is what Google Chief Finance Officer said himself : “This belief was the impetus for our organizational structure, which enhances focus on opportunities within Google and across Alphabet, while also pushing our leadership to extend the frontier that we are addressing,” said Alphabet’s chief finance officer, Ruth Porat, in the company’s last earnings call." This quote is taken from the The Atlantic's, "Alphabet, Jigsaw, and the Puzzle of Google's New Brand". Stop a minute and think about this. Google's Chief Finance Officer is talking about organization structure and leadership opportunities as a core component of business strategy. Yes, that just happened. No doubt about it Google's Management team is different. They are also smart, they mean business AND they get people. This is The_New_HR. Say what you will about Google, I believe they have this people growth driver thing pretty much wrapped up if other companies don't get on it fast. Alphabet will allow Google to take apart any industry they want to. Consider this fair warning.
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I spent four days in San Francisco visiting People Analytics professionals and attending Al Adamsen's Talent Strategy Institute : People Analytics and Future of Work Conference. I am deeply grateful for Al's work, that of the conference speakers, and those people working in the space that were willing to allow me to come by, drink their coffee and chat. Here is what I learned. Sometime last year a Deloitte report indicated that growth in People Analytics had plateaued or was slowing, however, at the conference this week Josh Bersin @ Deloitte suggested that over night we have moved into the "Tornado" on Geoffrey Moore's Crossing the Chasm diagram. Basically, I will paraphrase Josh by saying the long winter is over, the birds are chirping but the sleeping giants have awoken. Another way of putting this is that is that People Analytics has begun the "Tipping Point" of exponential growth and/or that we have started to pass through the infamous "Hockey Stick" point of inflection on a growth curve. Believe me when I say the VC's ears are perking up. Two years ago, when we presented our ideas we got blank stares. It was like presenting at the Elk's Lodge, with less alcohol. Still, they had a point; "who is going to buy it?" We are in a better market for entrepreneurs, but seeing this now everyone else is rushing in too so we all experience something like a tornado until winners and losers are sorted out. If you talk to startup entrepreneurs you get a similar picture, although if you get behind the scenes most will admit some degree of frustration with the market - they are having difficulty connecting with buyers and budgets despite that they have made substantial advances in technology and its possible uses within HR. People Analytics Technologists look at the strange HR customer market in utter disbelief, mirroring how the HR customer looks back. These people need us now more than ever, why aren't they beating down a path to our door? To be realistic, in our winter analogy, I remind, we are still only in February, the Texas Blue Bonnets don't peak until April. Maybe we can go on a walk together then. Regardless of where we are on the curve, the original view of "slowing" or "plateau" was clearly incorrect. The argument digresses into methodology. The Deloitte report suggested growth in People Analytics related titles (on job boards) + their consulting revenue was not what would be expected on an exponential growth path, however this vastly underestimated internal job growth, restructuring and refocusing of staff. It is not a small thing - that may have masked at least a 10x year over year magnitude global increase in investment in People Analytics, in one year. Simultaneously, we see growth at PWC, Hay, KPMG, McKinsey and the rest. This is the nature of this space, we have no sense of the pie or each share. People Analytics related job titles in my network indicate anything less than 2x annual growth estimates are far far far off. 2x growth per year is a more than safe bet. For example, my network of People Analytics friends have expanded by 1000 people in just over the last quarter. We are in a once in a lifetime shift in HR akin to the emergence of Finance from Accounting or Marketing from Sales. There was a time they did not exist - you can't remember it. It is big trend, it will have broad impact on the world, potentially every human being, and we are apart of it. Part of the problem is that to get an accurate estimate of growth we must count roles that can be classified under many different titles (People Analytics, HR Analytics, Workforce Analytics, Workforce Insights, Workforce Intelligence, Talent Analytics). These were all represented at the conference. Additionally, we should consider I/O Psychologists, which may or may not go into a formal People Analytics job title or function but relate to the same underlying trend. I have heard I/O Psychologists are the fastest growing job in the country, with an expected annual growth rate of 53%. I/O Psychologists are 1 out of 4 types of people you will find in these pop up People Analytics groups. When I talk about People Analytics I/O Psychologists put their hands on their hips and roll their eyes at me, "What about us, we have been doing this work in organizations for a long time?". Meanwhile they meet by themselves at their conference, SIOP. If you are not an I/O Psychologist, you probably never heard of it. :-) Love you I/O guys and gals - I just have to take little jabs because I didn't get my phD - it is my own insecurity. We also may or may not be counting folks who sit in an HRIS/HR Technology role that have received a new calling. For many, the focus is shifting from an underlying purpose of organizational HR efficiency to organizational HR effectiveness. The emphasis is changing from "some data, any data", to "how do we get data from here to there?", to the question "What can we do with this data?" We learned that Chevron is well into a multi-year effort to train a distributed network of over 200 HR people on how to support business decisions with data - some of these were previously HRIS analyst positions. None of these people were technically "People Analysts", in a People Analytics usage, and many will never carry this actual title. With no standard professional framework for the function materializing yet (like say Finance or Marketing) it is all very confusing, particularly to those on the outside. Another part of what is going on is technology fragmentation. We see a host of new services and products from names we have never heard before AND we see extensions of old services and products from all the names we have heard before. I think IBM has 12.5 different products relevant to our space. I don't even think people who work for IBM know all their People Analytics related products. :-) In effort to focus on the solution and not the technology, technologists are pretty much all using words now like, "insights", "actionable", "business outcomes", "tell stories with data", etc. such that without looking carefully at the details it is difficult to know what anyone is really doing, why they are different, and why you would go with one over another. Technologists are selling to people that have day jobs and People Analytics typically has never before been one of them - so those who have something to sell are on precarious path. CHROs are inclined to hire and wait. Let's let the new guy (or gal) sort this problem out. The demographic change in HR is exactly what demographic change is to presidential politics. Change takes a while to get going, plays out over decade, not always in the ways we expect, but demographics are the most powerful force for change on earth. Similarly change in the People Analytics space has primarily been in repurposed headcount and where it comes from - The_New_HR pitted against the huddled old HR masses whose only remaining options are to join, resist or spout utter non-sense. In our political skit : Jeb Bush appears overwhelmed with disbelief, Hillary Clinton reminds "I was like you once too", and Donald Trump fights politically correctness with factual incorrectness. Sarah Palin is not in the race but hangs around to entertain us with poetry. With Millennials joining together with GenX in disgust of all that is established, boomers gather in fear, and there either is or should be tear gas everywhere. Sarah Palin, I can write poetry too. Much like the US Republican Presidential debates, it is a crowded field with differing views and plenty of interesting arguments. It is also a complete mess. Indeed, isn't this is the way the whole world changes? Fits and starts, a little discomfort, and then one day you wake up and think, "20 years ago what exactly did we do without the internet?" Millennials, by definition, have no idea. Many of us have already tired of email and Facebook.The Millennials response, "come on everyone, we agree, but let's keep moving." Wasn't this true of every generation before them, just with different technology backdrops? Note the background images of the atomic bombs in our parents generation. I believe I caught the tail end of hiding under the desks bit at school - never mind. Maybe Snapchat and Twitter is not really all that bad after all, not negating the atomic power new ways to communicate have. Absent shared mindspace (among executives, HR and People Analysts) for a dedicated budget the go to pragmatic solutions for HR is: 1.) Hire more people (go figure), 2.) training (go figure) and 3.) try to leverage existing technology until we can figure out our roadmap and make the business case for what else we need to do this thing (go figure). Lately I have had no fight in me for "Gloves Off Friday Posts" - apparently needing to recover from my brutal loss to Excel a few weeks ago. What is the current #1 business tool for analysis in the world? Excel. This is not a scientific assessment, but at the conference the number of negative references to "we had to do this in Excel" or we are "trying to get out of Excel", were something akin to counting "ums" when I speak. "Um, there goes another one." :-) All of the successful examples of People Analytics provided at the conference that I heard were stories of the journey out of Excel. The conference should have been named: "People Analytics And Your Future At Work After Excel". In the field of People Analytics, at the moment, success is found via, "we spent x years and x dollars to get on a scalable technology architecture for People Analytics workflow". In one example provided : 2 years and 3.5 Million dollars: I appreciate the transparency. The stories of this journey provided just at this conference include: Intuit (Michelle Deneau), RackSpace/Tesoro Corp (Robert Lanning), McGraw Hill (Antony Ebelle-Ebanda), Gap (Anthony Walter), GE (Heather Whiteman),... but this is only a small sample. Nobody disagreed. Nobody was shocked. The story is the same everywhere - there are many more interesting things we can do, but there is little way around the journey - wrapping arms around the data is a part of the journey. Confront the brutal facts. It should be mentioned the folks that presented are just the heros, the survivors. I appreciate Tauseef Rahman for pulling me aside and descretely mentioning the problem of "Survivor Bias" - e.g. we do not hear from the failures so we don't know if they might have done the exact same things and got a different result. Survivor bias may be a problem, but unless you go looking around for skeletons you will probably never know for sure. At the moment we have no real measure of success for our field. Success is not measured in terms of speed or cost of implementing a data warehouse and reporting suite, relative to peers. That said there are real differences. Absent any other clear measure of success this might do as a proxy in the beginning. I'm anxious to see what happens with Robert Lanning in his new role at Tesoro - he has a particularly good track record. It is not that Excel and other ad-hoc tools are not useful, it is just that they don't scale with demand after you actually show people what insights it is possible for them to get in our space. Beware of this - they never knew before how powerful this HR Analytics stuff really was - all bets are off when executives finally see it. Also at the conference, someone reminded us of one of Amit Mohindra's (Apple) clever laws, which I paraphrase as "upon exposure, demand for People Analytics related insights increases exponentially" My personal experience is the same. If you believe us, pay careful attention to what this implies. If you do not create a scalable analytical architecture at the outset to meet demand in an equation like T+1 = demand^2, you will fail if you cannot increase resource^2. The math suggests this will probably occur about 1.5-2 years into the role. No worries, opportunities in the space will abound for some time so you will get a second or third chance to get it right at a clean employer. My learning parallels what Ian O'Keefe (next gen Google People Analytics) says, "we must work three problems simultaneously : efficiency, effectiveness, and user experience". Some people are better at managing expectations than I was while working in HR, but in my experience the examples provided indicate this scale problem is a competency for technology, not people. People are for asking good questions, constructing ways of approaching those questions, finding insights, telling stories, consulting, making decisions, etc. --- technology is simply for scale. These are two different sides of the coin that must be managed simultaneously (and well). You can manage expectations with "no", but we are not equally exposed to executives that understand this word - let the conflicts ensue. In my biased, not humbly stated, opinion you must use technology to take basic reporting off the table, but you must simultaneously create means to expose that data to a variety of downstream systems to support a complete analytical workflow : statistical applications for data scientists (R, SPSS, Python), niche people analytics applications to augment (varied), and data innovative visualization applications used by other functions of your businesses and executives (Tableau, Domo...). If your data doesn't join sources AND port to these environments efficiently, I don't care how good it is now, you are going to get stuck somewhere. If you are addressing the problem with people your people needs/costs are going to need to expand exponentially with demand. If you do not have an exponential budget (e.g. think Google) and don't have a scalable architecture for repeatability and change, then you have real trouble. Other troubles. If you can't get data into advanced statistical models you will bore and be eventually replaced. If you can't communicate in tomorrow's visual frameworks you will be dismissed. There is no application that is simultaneously best at all desired functions, or after achieving that mountain will be able to maintain a lead for long, so consider carefully how each technology extends and connects with the other technologies, what technology providers are most facilitative of partnerships, and what the support model looks like. Who is going to do the work to support changes, when, do they fall under your authority to control? Pay attention to all the tiny little details. ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. If you have interest in participating in the webinar series, let me know here: (http://www.misc-peopleanalytics.com/lean-series) And if you know anyone else who you think would, please let them know too!
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Mike West
January 18th, 2016 In the 53 years that has passed since Dr. Martin Luther King Jr. delivered his I Have a Dream speech America has clearly come a long way, however the work of freedom is not finished. If you listen carefully the words of Dr. Martin Luther King's speech are still as relevant today as ever. While he was speaking primarily to obvious injustices against people of color at in his time - and some of these measures have progressed - specifically, black people suffer less lynchings and can actually directly influence the political process - yet, upon close inspection of facts, it is obvious there is a startling and frustrating lack of progress today. On nearly all fundamental metrics of prosperity we see massive pernicious disparity by race. How could we have worked so hard and arrived in a place not really that much different from where we started? I will not insult you with an easy or absolute answer : the 3 step process for racial equality or the 5 reasons things are or aren't as bad as some would claim. Difficult problems cannot be satisfied by the sticky residue of low hanging fruit. I will share my few observations. I don't know if the world has really become more complex, but it certainly seems like it has. Over my lifetime I have worked for over 10 different organizations in professional and nonprofessional roles and have never met a blatant racist at work (and very few even in my personal life). How much easier it would be to confront the obvious ignorance of racism directly. This type of problem seems it could be tidied up promptly. However that is not our work today. The problem we are dealing with is much much more insidious than this. I can find no racists, but of "unconscious bias" I could dredge up volumes for you. Here is a tidy summary published by the New York Times : Racial Bias, Even When We Have Good Intentions. Lacking an actual human descriptor - we are fighting an elusive thief that we are forced to call some inhuman name "unconscious bias". Who believes in something you cannot see and a story you cannot tell. Imagine the frustration of toiling hard each day, stockpiling the fruit of your labor for tomorrow's meal and finding each night some unknown stranger steals it you in the quiet of the night. You have no way of catching, identifying or accusing this stranger and nobody believes you. Your lack of food must be explained by something else - perhaps you are lazy. You have spoken about it for so long, and this injustice so unbelievable that sometimes even you wonder if you have gone crazy. This is a description of the actual horror of unconscious bias. One of the things we are doing now, different than in the past is that we are beginning to face this thief directly. It turns out that clever people have devised clever ways of actually catching the thief in the act. Here is a video of great work conducted by People Analytics at Google, speaker is Brian Welle, a former colleague of mine : https://youtu.be/nLjFTHTgEVU . Brian will blow your mind. Fundamentally, at its essence People Analytics is about using clever research methods and data to reduce mistakes of human bias, because bias causes us to make worse decisions for our business than we would have made with a more perfect understanding of truth. It was only a matter of time that People Analytics would turn its attention directly upon matters of diversity too. While justifiable in its own right as an effort for "fairness" or for "the law", but we actually don't stand against bias at work just for these reasons - we actually benefit from truth too. This is not benevolence or charity - that demeans it . The most astounding thing about working on issues of bias is that when we make decisions with less bias we benefit directly too! If you did not get this point from your reading of the book "Moneyball", or watching of the movie, go back and watch it again - you missed an important detail. They did not do this "diversity thing" because we feel sorry for people who look different or throw the ball weird - it turns out that if you like winning people who throw the ball weird might make great teammates! This is the beauty of all things good and eternal. In truth there is actually no threat to anyone. Open up the door, let every truth come in. The house only expands. We may never reach a place of perfect truth or perfect answers on this earth, but as I am reminded by Dr. Martin Luther King Jr. I too refuse to believe that there are insufficient funds in the great vaults of opportunity of this nation (as he addressed the United States of America at Washington DC in 1963) I leave you with his words, which even 50 years later, never fail to bring tears to my eyes. We are not where we wanted to be, but Dr. Martin Luther King Jr. is no less prophet if we open our minds, hearts and ears. "In a sense we've come to our nation's capital to cash a check. When the architects of our republic wrote the magnificent words of the Constitution and the Declaration of Independence, they were signing a promissory note to which every American was to fall heir. This note was a promise that all men, yes, black men as well as white men, would be guaranteed the "unalienable Rights" of "Life, Liberty and the pursuit of Happiness." It is obvious today that America has defaulted on this promissory note, insofar as her citizens of color are concerned. Instead of honoring this sacred obligation, America has given the Negro people a bad check, a check which has come back marked "insufficient funds." But we refuse to believe that the bank of justice is bankrupt. We refuse to believe that there are insufficient funds in the great vaults of opportunity of this nation. And so, we've come to cash this check, a check that will give us upon demand the riches of freedom and the security of justice." - Dr. Martin Luther King Jr. Full I Have a Dream Speech : https://youtu.be/I47Y6VHc3Ms ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. If you have interest in participating in the webinar series, let me know here: (http://www.misc-peopleanalytics.com/lean-series) And if you know anyone else who you think would, please let them know too!
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Mike West
Did you think I'd crumble? Did you think I'd lay down and die? Oh no, not I. I will survive. - Gloria Gaynor, "I will Survive". 2002, White House Station, New Jersey, Merck, Organizational Learning I am helping to organize a launch meeting for our new Performance Management process and we need to stand up a website. I studied Sociology and Psychology and finally Human Resource Management with a natural penchant for Abstract Mathematics. My skills don't extend into HTML - in 2002 we didn't have SquareSpace - At Merck we were lucky we had Internet Explorer installed. I was invited to go talk to this guy in IT about creating a website for the event. For reasons I will describe, I'll never forget this meeting. To protect his identity I will call him "Stan". I get to Stan's desk area/cube. First, I see this row of stuffed animals. Let me just stop here and provide context. At Merck, at this time, we wore suits and ties to work every day – presumably in case a member of Congress, a foreign dignitary or a nobel laureate just stopped into the office. We were East Coast Pharma. We were a 100 year old organization and one of the most productive research institutions in the world. In our minds, we saved lives and we put billions of dollars in the economy, and we proud of both - we were buttoned up. Guys like me wore single-breasted jackets. If you were at the top your suit was double, maybe triple breasted. I don't recall what Stan was wearing precisely, but by the row of stuffed animals on his desk, I knew this guy was a strange animal. I was curious how Stan got away with this. Stan told me to come in, pull up a chair and sit down. I sat next to him in his cubicle, side by side staring at a couple of computer monitors, stuffed animals behind me. He had at least three computer boxes. He asked, “what do you want ?” As I recall, at the same time I was speaking he was typing in HTML, occasionally with diversions to chat while he kept typing. With swift keyboard strokes he would move seamlessly between computers, moving things from a design environment, to test, to production. Somewhere in this exchange I thought, "I don't care about the stuffed animals, this guy is pretty cool." I thought in my mind - if I ever go start a company this is the first guy I would take. For fear of losing access to him I told no one else about this. I don't know if I actually ever said this to Stan directly. Time went on, and Stan and I went on our separate ways. A few times after I left Merck I tried to engage Stan in projects I had going, but it has never worked out. I don't know if he kept up with changing technology or not. My learning from Stan is that “most of us use computers; some of us use them differently.” Excel - “most of us have used Excel; some of us use it differently.” I share the story about “Stan” because it makes what I am going to say about Excel just a little more vivid: “most of us have used Excel; some of us use it differently.” I want you to understand the degree to which I understand, appreciate (and even love) Excel before I describe why it may be the most dangerous business application of all time. This story about Stan with HTML foreshadowed how I would some day use Excel. It didn't come easy. Nobody taught me how to work with HR data in Excel. The way I use Excel is different than most people I have ever observed working in Excel. Granted I got there somewhere after spending over 10,000 hours on it over 15 years. I sit sheets like data stores, with pivot tables that feed into lists that other tables sit on, and lookup functions that move data around, transform it into whatever I need it to be, that feed into downstream analysis, and finally, charts. I have figured out how to do multidimensional reporting in Excel. I work data through recursive algorithms in Excel. I use add-ons to run a variety of statistics. I take the charts provided in Excel and strip them down to the bones, rebuilding them into something beautiful, apparently never imagined by Microsoft. When I am working with Excel, I often don't even see the detail of the data – at times I might as well be operating in another dimension. Having said all this, I'm sure there are people who have even more advanced Excel skills than myself. I'm not saying I'm the greatest - simply that there are differences between how people use Excel, so when we talk about Excel it is important to keep this in mind. Excel can be a very powerful application. 2006, Mountain View California, Google, People Analytics There was a time at Google where I was working with employee data in Excel and developing ways to run out reports by all segments we had in the data. I had spent probably 150 hours working iterating report trials off this dataset over several weeks. I started on the Google bus on the way into the office as I travelled from Berkeley to Mountain View, worked all day at Mountain View, stopping only for food and coffee, then continued working on the Google bus on the way home at night, collapsing into bed when I arrived, and starting over again the next day. At one point, while working with the structure of the data, reaching that point just before it was ready to share and it was like the sun rising on a very dark night. In some way it was like seeing God. That or I had reached the ceiling for caffeine consumption - one or the other. If I did not see God at the very least experienced what it must be like to see pure truth. The best example I can provide of what this is like, it is like standing on the edge of the Grand Canyon or an ocean or looking at the stars in complete darkness on a crystal clear night. I have not since recreated a feeling this vivid at work - apparently I lost track of the specific Excel function for this :-) - but what remains with me today is an appreciation that there is a truth encapsulated in data and a beauty in its mathematical structure, which also happens to be powerful. If you spend enough time in it, you might see it. Don’t take my word for this, here representative thought from a series of quotes about the beauty of mathematics: “It seems to me now that mathematics is capable of an artistic excellence as great as that of any music, perhaps greater; not because the pleasure it gives (although very pure) is comparable, either in intensity or in the number of people who feel it, to that of music, but because it gives in absolute perfection that combination, characteristic of great art, of godlike freedom, with the sense of inevitable destiny; because, in fact, it constructs an ideal world where everything is perfect but true.” Bertrand Russell (1872-1970), Autobiography Thoughts about the beauty of mathematics The way that Excel is different from most other applications for working with data is that in Excel, you can actually directly see the data you are working with. There are a number of other reasons why Excel is the most used business application of all time, but I won’t bore you with the nuances. This quote sums it up: “Microsoft Excel is one of the greatest, most powerful, most important software applications of all time. Many in the industry will no doubt object, but it provides enormous capacity to do quantitative analysis, letting you do anything from statistical analyses of databases with hundreds of thousands of records to complex estimation tools with user-friendly front ends. And unlike traditional statistical programs, it provides an intuitive interface that lets you see what happens to the data as you manipulate them” (The Importance of Excel) I love my friend excel, but I'm about to shake his hand and then pummel him. The main argument against Excel has been that the things that make Excel great are also its biggest downside. First, Data Quality: Excel makes it too easy for people to make mistakes Excel makes it too easy for people to lie For starters, while it is incredibly easy to get started making spreadsheets, it’s also incredibly easy to make mistakes that cost companies millions (or billions) of dollars. In 2008, University of Hawai’i professor Raymond Panko published a summary of 13 field audits that checked spreadsheets used in ‘real-world’ environments. His analysis found that a whopping 88% of the spreadsheets had errors! In evaluating possible solutions to the spreadsheet errors he described in his 2008 paper, Professor Panko wrote: “… few spreadsheet developers have spreadsheeting in their job descriptions at all, and very few do spreadsheet development as their main task.” One problem is that since everybody has at least some knowledge of how to use Excel, many people misjudge their own expertise, as well as that of others. This is different from when how we hire and judge software developers. Business managers don't know that there is a problem (actually, lots of problems) with spreadsheets, while IT regards spreadsheets as falling outside its jurisdiction. So spreadsheet management falls into a black hole. While Excel the program is reasonably robust, the spreadsheets that people create with Excel are fragile. There is no way to trace where the data came from, when, and what was done to. The biggest problem is that anyone can create Excel spreadsheets—badly. Because it’s so easy to use, the creation of even important spreadsheets is not restricted to people who understand programming and do it in a methodical, documented way. There are a number of public examples of Excel mistakes, some with substantial impact 2012, London, "The London Whale" The problems of Excel apply to anything and anyone who’s working with data in Excel, not just HR. Here is a description of the particularly high impact example at JP Morgan: Microsoft Excel Might Be The Most Dangerous Software on the Planet. “After the London Whale trade blew up, the Model Review Group discovered that the model had not been automated and found several other errors. Most spectacularly, “After subtracting the old rate from the new rate, the spreadsheet divided by their sum instead of their average, as the modeler had intended. This error likely had the effect of muting volatility by a factor of two and of lowering the VaR . . .” The explanation in English: someone at JP Morgan was running bets (to the tune of tens of billions of dollars) in Excel and there was an error. There may have been other negligence or nefariousness going on as well, but I found the most outrageous part of the story that this sophisticated derivate work was completed in Excel in the first place. Stupefying actually. At the time I was getting paid 1000 times less money to do similar work in Human Resources- you have to ask yourself, "Maybe I should have considered a different profession?" "I could totally screw up derivatives, maybe even 1/3 as bad as this "whale guy". The other ways that Excel falls down are: Difficulty seeing workflow (e.g. how the data goes through stages) Difficulty documenting workflow, process audit trail. Difficulty with dependence - difficulty transitioning spreadsheets from one person to the next. Stale data and/or constant rework as a result of stale data. Difficult to see the real cost of manual work in Excel being performed across the organization. Inability to secure data. 2013, Austin, Texas, ACMETech, Workforce Analytics Very few people know that between the time that I worked at Children’s Medical in Dallas and started my own consulting company, two and a half years prior to joining One Model, I worked for a technology company in Austin. Let’s call them AcmeTech. AcmeTech lured me from a children’s hospital in Dallas with higher pay, better benefits, a well stocked micro kitchen, free lunch on Fridays and a ping pong table. I felt bad leaving the children. Little did I know at the time, as a result of this decision I was going to hell. When I interviewed with AcmeTech I described the important analytical work I had done in HR at Merck, PetSmart, Google and even at a very modestly funded non profit children’s hospital. The emphasis of this work was in automating analytical workflow AND then spending my time in more sophisticated and high value analysis like exit prediction models. I thought we were on the same page. Well, I started with ACMETech and soon learned that this, in fact, we were not on the same page. I was expected to create weekly HR reports for the division I supported in Excel. We were dumping data out of WorkDay into Excel, aggregating into metrics and reporting by segment. I have a history creating prototype reports just like this in Excel but these always were temporary, not long term, solutions. At ACMETech this is something they had been doing for years and there were some unique nuances to the way they were doing it that prevented full automation. My predecessor diligently showed me how to copy data from one sheet to another, change a series of things (to be recalled by notes or by memory), check for these other things that may or may not go wrong, then publish the reports out by email. A single report would take me a full day to complete and there were several different versions of these reports for different stakeholders. There were a dozens points of possible failure. There were five of us on this team doing the exact same reports for different divisions. When I raised this issue to my manager and my ‘managers manager’ I was told, “We want you to keep doing the reports the same way they were done by your predecessor.” This is my problem: somehow I had gone from at one point of the time one of the most brilliant People Analytics teams in the world to now something slightly above “human cog” in a car headed to nowhere, driving off a cliff. Here were my views on ACMETech's Excel Based Workforce Analytics Process: There was not much value in these reports as produced, relative to other work we could be completing on behalf of the organization. The incredible waste of time and money in this approach - not to mention life effort. When things eventually went wrong or people were through with this it would be our neck on the line (in the case of my division my neck specifically) The way we were running these reports were affecting the quality of experience for the recipients of the reports. I knew there were better ways that would save ACMETech HR time, money and credibility that could be put in place very quickly. My mind just didn’t work the way we were running these reports - I could operate more effectively in other capacities. At the end of the day I went back and said, “You hired me for my expertise, there is a better way.” The reply was, “Do it our way or leave”. My reply, “o.k. then”. That’s when I decided to go start my own company. I wanted to work with people who really wanted to work with me, or not at all. I acknowledge that ACMETech HR can choose to do whatever ACMETech HR wants to do and that is fine, but if you look at what they were spending each year on headcount, turnover and hire reports for their organization it was at least $500,000 and if you calculate that over 5 years they have spent at least 2.5 million for a very basic reporting framework - without any real semblance of advanced analytics we know as People Analytics and full of holes. This, my friends, is why Excel in HR is dangerous, and a great case study for why you should consider an alternative solution for analysis and reporting! There are a variety of options today, One of which is One Model - which in full disclosure I recently joined - I'm a little biased. Other options are out there: seeWhat Your HR Analytics Technology Sales Rep Doesn't Want You to Know. ---------------------------------------------------------------------------------------- Disclosures: This is a "Gloves Off Friday" post Mike West is a bad man Mike West writes way too much about People Analytics Mike West is currently VP of Product Strategy for One Model ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose.
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Mike West
"Life is a struggle, and then you die" So go make something of it. Work on something important and watch over these things. The 5 Reasons Most HR Analytics Efforts Stall: #1. Not having enough precision on what is the right problem to focus and what questions you need to answer to solve that problem. The typical fail is that you will spend a huge amount of time, money and effort to get a HR reporting environment set-up but downstream users do not use them. People say the information is nice to have - they just don’t have time to go look at your reports. Sometimes the problem is that the information has little relationship to important decisions, or little bearing on the work that anyone is doing. Often the people supported will request an infinite assortment trivial changes in the desperate hope that each change will produce a better result. Or with no specific reason provided you and your solution just go from hero to old hat over night - and you are left to wonder what actually went wrong. The problem is that you spent your resources and time working on the wrong problems and questions. More could have been accomplished with your time and effort had clarity been achieved at the outset. #2. Not having all the right data you need to answer the questions you want to answer. The worst possible outcome of analysis is to produce a statistically significant finding that increases certainty in a wrong answer. This is a common outcome of the “missing variable problem” (the unknown unknowns that wreck most analysis) This is some portion of the 80% of the variance your model did not explain. You ran the analysis but did not include the right control data, so you get an answer, but you get a wrong answer, and you have no way of knowing you got a wrong answer. Sound like a nightmare? This is not a nightmare, this is a real problem. The second worst possible outcome is when you do all the work and don’t achieve a statistically significant finding but could have produced a finding if you had included the right variables. In either case, not having a basic theory that would explain what variables to include in the analysis results in you never achieving a satisfactory outcome from your effort or you may double or triple the hours to reach a conclusive answer because many passes are required. These problems are why we pay university scientists the big bucks. Big Bucks!? O.K., not really! University scientists try harder because they know their work will be 'Peer Reviewed' by other really smart people who also know something about this topic. We don’t have this check inside organizations - we have non experts reviewing the work of experts. Major danger. #3. Expecting technology to solve the whole problem (absent analysts). You have made an important investment in supporting technology, but you may not get anything of lasting value out of this investment because you did not factor in the cost of acquiring (or creating) skilled operators of this technology. It is as if you have this wonderful piece of machinery sitting in idle. The success you have with analytics is dependent on the experience and preparation of the people working the analysis. You can achieve two different analysis outcomes with two different analysts, using the same technology! The worst part - if you get it wrong, how would you even know? Between different analysts you will find different choices about what data to include, chosen research method, as well as differences in skill in executing chosen method. Clearly, you need to think about analysts, but you must also think about the rest of the organization. It does not help you to have this group of "really smart data people” and nobody else who knows how to use their work. You need everyone on the same page on where we are trying to go, what everyone's role is, and how it all fits together. #4. Expecting your analyst to solve the whole problem (absent the right technology & support). Analysts are evaluated based on results. Some other HR roles can produce activities (implementing programs, policies, processes and systems) and we declare victory at the conclusion of the activity without respect to impact (which conveniently is never measured). Success is defined as completion of the project on budget and on time, then on to the next. Analysts do not have this privilege! For Analysts, the proof is in the pudding. If you tell the truth, "based on our data and the tools I have I found nothing of lasting significance to you" - your reward is you don't get invited back to the meeting. Analysts either produce very little value and stick around (satisfied with the activity for pay, as opposed to outcomes) or they leave for another opportunity to do better analysis. They either have a fire in their eye or they don't. You want the one who cares, or don't bother even starting. You have made an important investment in a person, but you can get nothing of lasting value out of this person without providing the tools and support they need to complete their work. Managing an environment like this is difficult, but not impossible - it requires skill and care. #5. Expecting results without someone putting in hard work. Your typical project management wisdom applies - choose one out of three : time, quality or cost. Every new question you want to answer will involve investment in new data collection, cleanup, transport, joining, reshaping, statistics and figuring out how to best communicate the results. We inevitably want automation of routine analytical workflow, but there is a first and second priority constraint : what should be made routine? How can it be made routine? We (technologists) will try our best to design out of this, but the first pass is best handled by a human - this will be hard to get around. It will fail if no one has put in the work. This doesn’t necessarily mean you have to do all the work - or even the hard work - just that somebody does and there is no way to escape this cost. You can bring in consultants to do the work, you can hire enough people in your organizations to do the work, or you can buy packaged solutions that help with part of the work. In this you will be making big trade offs on time, quality of delivery and cost. Beware - no silver bullet will kill this beast. You must make a commitment to ongoing refinement of the analytical process or you get an analytical process that really does nothing for you. If you get into the real day-to-day work of HR Analytics, the People Analysts are dealing with data that generated for some other purpose that does not conform to basic needs and expectations of our existing purpose. The best way to understand what must be done to automate an analytics workflow is to have someone work through the analysis one time to understand what data is there, what is not, what is wrong, and figure how what is there must be improved for successful analysis to occur. Often we implement expensive reporting solutions on the hope that these will produce useful insights. Why invest in automation (repeatability) if you can not achieve a useful finding through a manual effort? Hope? Hope is a great attitude to apply to all situations, but not a great strategy. Why not run through it manually one time and figure out if it is worth automating? The most important question is - when you got to the end of it all manually, do you end up with a report that is useful to the organization? If you did, great, now is the right time to make decision about automation. ---------------------------------------------------------------------------------------- People Analytics is difficult, no doubt about that, but... I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems.
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Mike West
“This the real world, homie, school finished. They done stole your dreams, you dunno who did it.” Kayne West (no relation) A New People Analytics Blog Series : Gloves Off Friday! At the moment, in People Analytics I see a lot of the same keywords repeated over and over. Two examples: insights and storytelling… I find myself thinking: It is great you are using the right words. These are what we want out of our analytics system, and great claims, but what do these words really have to do with your application today? Are these words real or are they hype? The first hard truth is this: most of the time enterprise analytics system keywords are just marketing hype. Sell the people what they want to be sold. Keep in mind if an existing system has any real market traction it probably was designed well before we even started using the keywords we use today. If the product was designed 5-10 years ago, you might ask: so really, what has changed? Are these just old solutions packaged a new way? If these systems have existed all this time, why are they suddenly going to help HR in a different way now that they didn’t in the past?” My suggestion is this - when you peer under the hood of a reporting system look at the following: 1.) make sure you are finding what would constitute viable insights or stories to substantiate the insights and storytelling claim. 2.) make sure you can find examples of insights you could only derive in this system (not something you could substitute almost any other system of the same family for to achieve the same result) 3.) make sure that the way these insights are achieved will work in the real world (that it based on clear and accurate assumptions and with a process that is scalable) I’m might be going out on a limb by myself, and maybe I could get fired for saying this, but I will stand on this view: at the moment, insights and stories are mostly functions of human beings – not systems! Let me say it again: insights and stories have mostly to do with the observations of the operators of systems, analysts; little to do with the systems themselves. My position could change someday, but for now I’m not holding my breath. Instead, when you review a potential solution you should ask better questions: How does this application enable my analysts to derive insights in a different & better way than any other application? What do analysts think about this application? The second hard truth is that the problem addressed by the analytics systems of today is the efficiency of the analyst at producing the insight, not the insights. Despite differences in features and presentation, there really is not a lot of meaningful difference between most broad use case enterprise reporting applications in the range of possible insights produced. If you see that the essence of the problem these application are designed to solve is the efficiency of insight production, you will know how to better prioritize your decision. By nature the principles that underlie the use of technology are: automation, repeatability & scalability. How does this application support automation, repeatability & scalability of the analytical process? Where does the data come from? How is the data loaded? How do we deal with the unique nuances of our organization and quirkiness of our data? What do I do when we change our underlying systems? How can this application adjust to changes? What level of expertise is required to do the work? Who will do the work? Pay very careful attention to those features that promote a sustainable path to insights (with less manual work per insight produced) - producing ongoing long-term efficiencies in the analytics process. The devil is in the details. Ask the people who do the work what they think. This statement is not intended to overemphasize the importance of efficiency over all else– I’m just saying let’s be clear about what real problem we are solving for when we implement a technology system – the alternative is a disaster waiting to happen. The system will eventually roll out and it will eventually be held to the standard of how it was promoted. What I mean is this : if the assumption is that after this is implemented there will be no analysts required, does the system actually produce insights without an analyst? If the major difference in this application is how it can bypass the analyst to get the insight directly to the downstream user, you must observe, does the downstream user take on the work to go in to see those reports? Do the reports viewed by the downstream user translate directly into any useful action? Is the insight better than what could be achieved with the assistance of a specialized analyst. Be honest. If the answer to any of these questions is no, but you sold it as so, you will be tearing that solution out in a few years. Fool me once_________, Fool me twice _________? The third hard truth is that THERE ARE REAL DIFFERENCES in how the enterprise analytics systems approach efficiency in the production of insights. The first great simplifier - consider, where is the system maintained and delivered? Is it an “On Premise" or “Cloud” solution? The keywords here are: Cloud, Software as a Service (SaaS) This is the first major branch in your decision tree. Cloud and SaaS are not very human words and at this point already feeling a little tired out, but are still very important to pay attention to. There are technical nuances to the definition of cloud but in layman's speak essentially we are getting at is: are all customers on the same instance over the internet (cloud) or does each customer maintain their own instance on their own servers or desktops (on premise)? Software as a Service usually goes together with cloud - this refers primarily to the method of payment. With a SaaS solution you are renting the software, rather than buy it. My opinion. If you are not moving to the cloud “You are going the wrong way!…” For fun, here is a great wrong way clip – https://youtu.be/_akwHYMdbsM - I just love John Candy and Steve Martin together in this movie. For example - there is a reason Google just pulled out their homegrown Human Resource Information System (HRIS) –GoogleHR (GHR), which giant teams of Google engineers had worked on for over 10 years, replacing it with WorkDay, a cloud, Software as a Service (SaaS) HRIS. The reasons are: A.) Your business is XYZ, not whatever this is. Unless Google planned on getting into the HRIS business, Google had to ask, what the hell are we doing fooling around with HRIS? A very good question. B.) You will spend less on a cloud solution than an on premise solution. The cost of cloud infrastructure is spread among all customers, as opposed to a single entity. Fundamental economics, 9 out of 10 times cloud will be a better value than on premise. Also, because you are billed for your use of this software over time, if somewhere down the road you don’t like it you can go with another solution. You just turn it off! This is a lot easier pill to swallow than a PeopleSoft or Oracle implementation used to be. C.) Cloud companies are faster innovators. Cloud companies have a single instance to invest continuous innovation in – consequently they push more frequent updates out to all customers on one platform - therefore they are faster innovators. Google is a cloud company too, this fact is too close to home to miss. Technology is evolving so rapidly - if you buy something that sits on premise (or build it), it will be out of date before it is fully implemented. ------------------------------------------------------------------------------------------- By the way, I’m NOT for or against WorkDay specifically. WorkDay is just an example the overwhelming trend in HR going into the cloud. We were not sure if we wanted our HR data in the cloud at first – now the market is tipping to the cloud dramatically! There are a number of different cloud HRIS product options. Even the old on premise providers (Oracle and SAP) have options that are cloud now. WorkDay is just a working example of an HR application in the Cloud that the market has already wrapped its mind around. -------------------------------------------------------------------------------------------- If you are looking at an enterprise analytics solution that is not in the cloud and not delivered as software a service – and you don’t have a really unique good reason for this - you are probably making a mistake. The next consideration to pay attention to in HOW is : what functional use case or domain was this enterprise analytics system designed for? This is the next big divider – here is where we get into the nuances of important less obvious choices you will be making. The second great simplifier - is this system designed for generalized purposes or specific? This is another major branch in your decision tree. Option A : a generalized analytical system that can theoretically be applied to any analytical problem (but that is not designed specifically for any). Option B : a solution that is designed specifically for a particular domain, customer type, or use case. To use the HRIS example again, back when we were actually debating questions like this you could a.) build your HRIS system (an HR database) on a generalizable database structure – say generic Oracle – or b.) go with a database designed specifically for HR - PeopleSoft, Oracle HR, SAP HR, Lawson HR, Ultimate Software, WorkDay, etc… It took us time to figure this out, but the market decided that buying a system designed for HR is much better. Do you really want your IT team to be learning, following and servicing obscure changes to payroll, compensation or arcane HR needs that ultimately drive database design? That argument was long ago concluded. Overwhelmingly, with no uncertainty, the big girls and boys do not build and service their own HR databases. It turns out that what you are using the database for impacts important design decisions! It also follows out that customizing a generalized solution for an HR purpose is overwhelmingly more expensive over time. (because of labor costs and other problems unforeseen at the outset). Option A : Generalized Analytical System. Option A - Generalized, may on the surface look less expensive because it is a single solution that can applied across many functions, however you have to factor in the labor costs to bend it to the reality of each business function and their needs and maintain that. You won’t want your critical IT and Software Eng. teams working on HR stuff, so don’t do it. HR problems are notoriously needy and difficult. Stay out of it. The big costs in technology are not in software, they are in the design and set-up. For example, I can buy a single license of Tableau for $2000 (+ $100K+ on a Tableau server to distribute that report over the company intranet with security), but to apply Tableau to my HR reporting needs I might actually “spend” $300K on IT labor for build of my ETL (extract, transform, load) and Data Warehouse path, which must occur prior to delivery of the data into Tableau. After this I will probably spend another $100K in labor to get my Tableau dashboard designed to do what I want it to and to look good. Was this actually a $2000 solution? No. Not counting the server (presumably used by other business functions outside HR) the solution actually cost me $402K, and possible a lot more. I will go through those labor costs several times while iterating towards the right set of metrics and reports for HR on a generalized analytical platform. Who will support me when I need to change the ETL? I have been involved in one way or another with these generalized solutions four times over my career: Cognos at Merck, MicroStrategy at PetSmart, MicroStrategy at Google and Tableau at Jawbone. Google simultaneously experimented with Tableau and ClickView and MicroStrategy. There were people at Google who called MicroStrategy, “MicroDisaster” or “MicroSadly”, or (insert your own hilarious Micro explicative). The general consensus was, it actually may be a great solution, we are not sure, but WAY to difficult to implement and WAY complex for the typical user. Keep in mind, this was GOOGLE! Do you know the kind of people they hire? I’m wondering if anyone can figure out MicroStrategy? Sad indeed. In some regards, Tableau and ClickView is designed to be more accessible but get into the nuances of Tableau and ClickView you will see they are going down the same path. Arcane nuances. Sub menu within sub menu. Flip this little switch in submenu 24, under the heading of a new word we invented, and then the report will work right… Seriously, that is your solution? Tableau promotes this as a simple solution, FOR EVERYONE. I'm holding them to it. Do you know what Tableau Professional Support Services costs per hour? $250 per hour. I once spent $5000 in Tableau Professional Services to find out that if I changed the way the data looked to Tableau on the way in then everything would work right, if I didn’t there was no solution to my problem. Major question - who is going to design and support the right ETL to accommodate this thing I am buying, what does the solution really cost? Choose carefully. I can go down the line but the premise is about the same – you might save costs on software by leveraging the same software across functions of your organization, but you give that back on labor costs bending those applications into your functional needs and use cases. It may not be a silver bullet. It may not be cheaper. It may not be easy. The other non-obvious consideration here is that if you are lucky enough to have a Business Intelligence (BI) team - IT people who specialize in business reporting - HR may be able to get their attention briefly, but my experience has been that 4 out of 4 times they tire very quickly of this BI ignorant HR person and their silly needs. The BI people go away. They disappear. The result is a solution that never gets where you want it to go and ultimately doesn't work. If you are going to go in house, you need dedicated resources - dedicated resources cost money. You also have to find some really smart technology people who actually like and want to work on HR problems, rather than try to invent the next Facebook. Non coincidentally, this is extremely hard - even at Facebook. Especially at Facebook. ------------------------------------------------------------------------------------------ For the record, Tableau could be a great downstream data exploration and data visualization application, IF YOU HAVE a viable data warehouse and ETL solution in place for HR. ------------------------------------------------------------------------------------------ Option B : Specialized Analytical System For HR Here is a sample of options for varied HR Analytics purposes (alphabetical order) CultureAmp CruncHR Glint HiQ Lab One Model OrcaEyes Sapience Analytics SAP SuccessFactors Workforce Analytics (used to be InfoHRM) Visier ZeroedIn (Folks, feel free to add other HR Analytics applications to the comments of this post and I will edit this list to include those later) Maybe someday I will do a detailed analysis of all these applications – for now, here are the main questions you should ask as you evaluate each option? (you and your team need to answer these in a no-spin zone) What is the focus of this application? (consider depth, breadth, etc.) What are the 1-3 main differentiators of this application relative to its peers? What non-transparent assumptions underlay those 1-3 main differentiators? What other data management effort and applications are required to produce the final insights and stories that I am looking for? What is the data management strategy to get the data from your varied HR systems to these environments? (consider at the go live and consider the ongoing refresh) This was a Gloves Off Friday Post from Mike West Disclosures: Mike West is a bad man Mike West writes profusely about People Analytics Mike West now works for One Model
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Mike West
To whom it may concern, For the last 2 years I am proud to have run my own People Analytics consulting company - PeopleAnalyst, which I like to call the first Independent People Analytics Design Company - but On January 1st - I will be joining One Model. These are the reasons: 1. I believe that People Analytics is important to the future of HR, the future of business and humanity – perhaps one of the most important business trends in our lifetime. I recently shared the principles I hold, supporting this thought here: Future of Human Resources - in 10 Principles. Beyond these principles I frequently try to point out that we had Accounting before we had Finance, we had Sales before we had Marketing, and we had HR (People Operations) before People Analytics. In every historical case, the application of an analytical framework to the pre-existing operational function revolutionized the way business was done, and those who were early to it were able to exploit lucrative informational advantages for a brief period of time before they became ubiquitous. In face of history these changes did not occur that long ago but today we think of them as always being the way they are. People Analytics’ time is now – in the future it will be “required for entry” in the big girls/boys club, but not be as much of an advantage as it is now. Pay very careful attention to the investment companies like Google and Walmart have made in People Analytics – 30+ people each and growing, going back several years. These companies are not stupid – this says something - they saw something. Google is “cleaning up” on the application of data to the People Operations/Talent area and in many markets they are a force to be reckoned with - nobody is anywhere near them on what they offer and how they do HR today. They are a steamroller. 2. Long ago I decided that my work - the application of data, math and science to HR - is my reason for being and part of the intentional legacy I want to leave on this earth. My commitment to what we now call People Analytics is unchanging - the key for me as I go through life is just figuring out where my efforts will have the most impact. I make moves when I sense the math on this has changed. Merck -> PetSmart -> Google -> (brief cross functional divergence)-> Children’s Medical ->PeopleAnalyst (my consulting company - the first People Analytics design company) -> now One Model… As we move forward, I think my area of greatest contribution will be to embed my unique way of approaching People Analytics into a technology environment, making it more realistic, accessible and affordable to more organizations. It is quite an amazing thing for a guy like me to have access to an engineering team with seed funding – I’m not going to pass on that opportunity. 3. Team - I believe in the magic of teamwork. I saw this video, which reminded me of what can be accomplished with teamwork : http://wapo.st/1U19g0M Gives me chills - the good kind. InfoHRM --> Success Factors --> SAP If you know anything about the history of enterprise reporting solutions for HR, the foundational predecessor to modern HR Analytics, you will find that the engineering team at One Model has a very interesting pedigree. Going back 10 years, the only system in this space was a little company called InfoHRM – they were out on the leading edge of HR reporting, essentially running a “cloud-like” solution before we even knew what the cloud was. InfoHRM was acquired by Success Factors (purportedly to help them crack the HR data reporting challenges they couldn’t seem to solve on their own), and then Success Factors was later gobbled up by SAP. I don’t know the whole behind the scenes story, but my general impression of what happens to people in these big company acquisitions is that how the product is perceived, the dynamics of working for an organization, as well as where you fit into all that really change. These guys fell out of that. When you see people who helped built a product category before anybody else was doing anything like it, who say now we are building something better knowing what we know now, you stop and listen - at least I do. TechStars – The Top 1% of Startups Another thing I really like about One Model is that they came out of the TechStars Accelerator program. Accelerators like TechStars are super-selective--less than 1% of applicants get in. You could say they are pickier than schools like Harvard, Stanford and MIT. In addition to direct assistance in getting the business model on the right track, and the well know “Pitch Day” TechStars alumni have access to a network of investors and advisors for life. In the Startup world, access to capital matters and access is primarily determined by your network. An element of this may seem like a superficial game of “who you know and who knows you”, but an element of this is ability to get to people who have been through good and hard times and can help you solve really difficult problems. Austin - SXSW and Food Trucks about say it all. These guys came to Austin to launch their company a little after the time that I did. I’m an HR guy – I’m all about culture and Austin has the right culture for me. Austin is hip (some call it weird), is second only Silicon Valley for startup community, in a US state that is friendly to business, has a lower cost of living than either US coast, and is well positioned geographically for US enterprise sales – 4 hours by plane to either coast and within driving distance or very short plane flight of 3 other major cities (Houston, Dallas and San Antonio). The startup community is tight-nit, collaborative and with a lot less of the showmanship and games you see in the Silicon Valley – I think a higher percentage of people here take creating a real business more seriously. Beyond these intangibles, when it comes to HR data, keep an eye on Austin, this is where it will be, there is some important stuff going on this space here right now. Mostly I just love Austin – it is an island of authenticity and creative energy unlike anywhere else. 4. Product Focus – oh where we can go together. One of the big mistakes I see in the field right now is that most people that are thinking at all about the space are thinking too narrowly. They think People Analytics is just one type of question, one type of data, or the application of a certain method of working with data. Let’s say prediction, for example. Examples include, how do you predict hires who will perform well in your environment or how do you predict what people are most likely to leave in a given time frame. However, some of these strike me as gimmicks - not standing up on real solid data - People Analytics is much more than this. For example - I have personally worked with data on HR on decisions involving how organizations select (staffing), onboard, pay (compensation), perk (benefits), the origins of happiness and motivation at work, quality of managers, employee commitment/turnover, performance, diversity, learning/training, time off policy, the relationship of HR outcomes to sales outcomes, etc… Others have worked on topics I haven't - the list continues. On top of the varied subject matter focus, you can focus on how you collect data, the tools to make data flow more efficiently, the methods you can use to analyze it, statistics, how you visualize the data, how you distribute to other people, etc. Any and all of these are potentially viable areas of business focus that you will see niche products in. As methods, machine learning algorithms and prediction are hot right now – all these are in our future, but we still have a lot of work to do on them. Here are the main perceptions I will offer on product focus at this time: People Analytics is eclectic, expansive and inclusive. In its essence, People Analytics is the systematic application of behavioral science and statistics to Human Resource Management to achieve probability derived business advantages. We need solutions that enable analysts to be better analysts, in the world of possibilities, not try to replace the analysts entirely. We need solutions that create more heros, not less. People tire quickly of gadgets and nobody wants to purchase and manage an ever-expanding assortment of gadgets (or only if they are all made by Apple). I’ll put it another way - One Model looks more like an aircraft carrier to me than a paper airplane. Organizations operate as holistic systems, therefore the answers to problems span across areas of specific functional responsibility, expertise and operational data stores. We have a lot of silos of data in HR – HR has undergone progressive advances through technology specialization and will continue to. The great irony is that the future of HR Analytics may be just reversed: synthesis, not specialization. The differentiating premise of One Model is synthesis. Many advantages will stem from this vantage point. If you care about synthesis of data in HR One Model should be on your short list. To do any analytics, simple or advanced (prediction, forecasting, optimization), accurately, regularly, in a timely and efficient way requires address of sprawling un-integrated operational HR data sources and process. One Model decided to start with the ‘data munging’ fundamentals and build from there. That doesn’t sound sexy and is a little more difficult initially to get the same kind of PR as a result, but it is important, and they will steadily deliver increasing value on that foundation, offers a lot of possibilities, and takes customers into the future in steps, not all at once. Imagine showing up to the board room with predictions about employees but you can't accurately answer or get to quickly any of the basic employee ins and outs questions. Begging the question - do you really know your workforce? What exactly do you do here anyway? I'm all for prediction and One Model is going there but don't over promise, really get to know HR data specifically, get the 'data munging' fundamentals right, organize more sources data more efficiently than anyone else, and delight and surprise the customer progressively as you go. I agree with this – I think it works better, fits the needs of today's HR function, and matches my practical MidWest (US) values. Cloud / Software as Service is here, is the future. The premise of One Model is that they can invest big in infrastructure and innovation on that infrastructure and distribute those gains to everyone. It only gets progressively better and more efficient over time. Why should every company invest in homegrown infrastructure for HR Reporting and Analytics independently? To reinvent all HR Analytics workflow internally at every organization is unrealistic for most organizations as it is a ludicrous business proposition. We no longer design our own homegrown HRIS systems today - why create and maintain our own technology infrastructure for HR Reporting and Analytics? I think 5-10 years from now we will look back and wonder why we used to do this at all, evoking the puckered sneers that “legacy HRIS solutions” get today. Don’t get me wrong - you should invest in ultra advanced or niche innovations in analytics unique to your business, in your environment, however in order to have the time and resources available to apply that kind of focus, you can rent everything else. How about getting on a platform that can speak to those applications and everything else. Like I do, these guys believe in "play nice with others" and that good guys do win too sometimes. You want to come along for the ride? ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 10+ years of experience building People Analytics from the ground up at companies such as Google, Merck, PetSmart, Children's Medical, Jawbone and other places. Mike's passion is to develop thought leadership and to cross pollinate the frameworks and processes he helped develop and pioneer as an employee at these places. Mike spends most of his time teaching, coaching and writing on all things People Analytics.
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Mike West
“Men* wanted for hazardous journey. Low wages, bitter cold, long hours of complete darkness. Safe return doubtful. Honour and recognition in event of success.” Serious question. If you stumbled on the employment ad above, would you respond? The ad is purported to have been posted by Ernest Shackleton to recruit people (“Men*”) for his Endurance expedition to the South Pole. There is some debate whether the ad was actually written by Shackleton, never-the-less whoever wrote it could get credit for compelling ad copy, as well as possibly the first example of a realistic job preview. *This was around 1914, and apparently whoever actually wrote this was unaware of woman’s interest in suffering in the workplace for 25% less than men! This reminds me of a pernicious glass ceiling and wage disparity problem – and now suddenly taking a little boat down to the South Pole doesn’t actually sound so challenging to me. Lots more I can say about the journey into problems of diversity and gender, but we will circle back to HR data and diversity another time. For now, let’s just continue with our juxtaposition of the cold, icy cold, Antarctic and HR Analytics, in general. A Brief Summary of The Expedition (Wikipedia): Endurance became beset in the ice of the Weddell Sea before reaching Vahsel Bay, and despite efforts to free it, drifted northward, held in the pack ice, throughout the Antarctic winter of 1915. Eventually the ship was crushed and sank, stranding its 28-man complement on the ice. After months spent in makeshift camps as the ice continued its northwards drift, the party took to the lifeboats to reach the inhospitable, uninhabited Elephant Island. Shackleton and five others then made an 800-mile (1,287 km) open-boat journey in the James Caird to reach South Georgia. From there, Shackleton was eventually able to mount a rescue of the men waiting on Elephant Island and bring them home. On the other side of the continent, the Ross Sea party overcame great hardships to fulfill its sub-mission. Aurora was blown from her moorings during a gale and was unable to return, leaving the shore party marooned without proper supplies or equipment. Nevertheless, the depots were laid, but three lives were lost in the process. Clearly the expedition failed to accomplish all of its objectives – yet it is recognized as an epic feat of leadership, endurance and one of the last of “great expeditions”. Some time ago I came across the ad, which tickles my sick sense of humor and my imagination. Who would take this job. I wonder - who wouldn’t take this job? In my mind I notice subtle similarities to the choices I have made in my career – which circles on the question how to change Human Resources, if not the direction of work as a whole, with data. Despite difficulty and a seeming complete lack of possibility for fame in this work, I am excited by what I do and I love every minute of it. I probably would have responded to the ad. I am also intrigued by others who take an extended interest in my strange field - partly because historically extended interest has been rare and partly because it is far from what most would consider a "rewarding career". I have been tracking new roles in HR Analytics, Talent Analytics, Workforce Analytics (what I call People Analytics) through Job Postings off and on for a long time. More recently, I have been searching for these people on LinkedIn. It is a question that begs to be answered – who are these people, what is their story, what do they care about, what do they want to achieve, how are they received within their HR organizations, what are they working on, what are their struggles? Those questions are a work in progress. For now, the fresh faces and backgrounds I see in these roles in pictures and Linkedin headlines is already truly inspiring to me. They surpass my imagination in magnitude and breadth and to me represent "pure energy". I think the people who are drawn to these roles are special and amazing. I have no doubt that as a collective they/we will ultimately work through the difficulties to reach our destination (Destination? This too is a great question). “Honour and recognition” hopefully forthcoming. In real life who would respond to an ad like Shackleton’s? I think a high proportion of those people today would be these People Analysts. The Vast Expanse of Human Resources The real HR is vastly underestimated and mostly lost on most people without direct exposure to a leadership role in HR in a large modern organization. By large I don't mean 500, I mean 50,000. HR doesn't really come into the spotlight until you reach 5,000 employees. At this point, you start to realize the inherent value in getting HR right - that is, if it is not too late for you! If you reach 50,000 employees you probably have figured HR out and now you preach it, never-the-less you are in a world of much bigger HR issues. If you are in a leadership position in HR for one of these companies few people know your precise struggles. It is a lonely world. If you could peer inside the little windows you would see that HR in large modern organizations is a complex network of technology, policy, process and people within discrete areas of specialization. HR can be grouped broadly in categories of Staffing (Sourcing, Recruiting, Onboarding), Total Rewards (Compensation & Benefits), Diversity, Talent Management and Organization Effectiveness (Performance Management, Succession Planning, Organization Design), Training and Development, Employee and Labor Relations, and HR Law & Govt. Compliance.It is a veritable alphabet soup of things to learn, with an entire language of its own. Sounds crazy, but yes, it is a diverse world of very real and very different things, all in this thing we call "HR". If you want to dive deeper you can find more granular silos of responsibility, but I will refrain from the arcane details and just leave it at the high level. I always forget - how many ways can eskimos describe snow?, and why? I don't know the answer, however I can surmise that they must spend a lot of time with the stuff. This is a good place to pause – are you still with me? Here is a more down to earth human story for you. Several times I have spoken to recruiters (an actual real life role in HR), and as I attempted to describe the complexity of tieing out data from many HR sources to some meaningful business conclusion and what that actually requires I have been interrupted with a reply something like - “It is nice you have had exposure to all those other things, but to be clear in this role you will deal exclusively with HR data. Are you o.k. with that?” With HR data? After only just having described a variety of HR data sources, not really even making the point about how these sources should be tied to business data, I am perplexed at who and what I am dealing with here. I am left to assume that by HR, they mean Staffing, a subset of HR and the rest of it is lost on them. Clearly, per their guidance it will also be lost from me and the organization if I take this job under these conditions. Do I correct them, or proceed? I am sorry to be blazingly disrespectful, but I have been tempted to stop right there and say, “Hello, Hello, are you still with me?" Should I proceed ahead or turn back? O.k., for now I will spare everyone the graduate level course in Human Resources but if you would like one they offer specialized programs in this stuff at the University of Minnesota, Cornell, Rutgers, University of Illinois, USC, somewhere in Michigan, and a few other schools! Some of the world's most respected CHROs of the largest organizations of the world came from those programs - I’d be happy to make introductions. By now you are rolling your eyes at me. No problem, I’m used to this. I am not sure what other people were doing when Shackleton was hacking away at the ice that was destroying the hull of his ship - I am sure his choices in life made a few eyes roll as well. Leadership and decision-making for HR sub-functions is distributed among members of the HR Leadership Team (HRLT). In large organizations the HR Leadership Team generally consists of 7-10 Sr. Directors reporting to a “Chief Human Resources Officer”(CHRO, SVP of HR, etc.). The Chief Human Resource Officer may have leadership, decision-making authority and budgetary control over the HR function, but in some cases the big budgetary decisions may go all the way up to the CEO and/or the board. Staff/Employees/Humans typically represent a spend of 50-85% of most modern organizations revenue. Usually over 75%. Stop - check it out in the annual reports, these days available on the internet. These are real numbers. Where is the profit? It is the little bit that is left over after all those people are done with their work and paid. The shareholders are buying a share in that little bitty slice. "Our most important asset". Have we looked at how we do HR with data - not much, not really - we know it is somewhere down there - it's a line item. In most advanced organizations the head of HR will report to the CEO, but often they report to the head of Operations or Finance – this varies by company. It is usually a sign post – a little piece of ice floating – be sure to keep an eye on it, something big may actually be under this ice. Regardless, wherever they report, it is contingent upon the CHRO to drive alignment of HR goals with “the business” and alignment between HR sub-functions on a central strategy and operational architecture for Human Resources. Seen as a “support function” annual HR goals and budgets typically FOLLOW development of business strategy and goals. HR goals are assembled in a last minute hurried manner sometime AFTER all the other business functions have had their shot at the podium. If those others strategies and goals are not yet fully clear, or the HR/People consequences cannot yet be ascertained with the information on hand, or the HR leadership team fails to communicate the people implications of the business goals, then HR will follow the business in a misaligned and disheveled path. In my experience, these are accurate actual descriptions of HR goals. Whether or not the HR team is able to formulate a coherent plan HR has last shot at organizational resources - consequently HR efforts are often unclear, underfunded and unrealistic (relative to the magnitude and difficulty of the goals). Examples of strategic HR objectives include: “Improve Employee Morale”, “Improve Employee Engagement”, “Reduce Employee Turnover”, “Change our Company Culture”... Hey, a big challenge excites me, but FYI these population level averages are resistant to dramatic changes. I’m not saying they don’t matter or can’t ever change – the polar icecaps are melting as we speak - I am saying moving these things in a different direction than they want to go requires serious attention to detail, resources, teamwork and commitment. It starts with data – let’s try to have a look at it together. Before Data, HR Systems Historically HR has vacillated between a single system ERP (Enterprise Resource Planning System) or HRIS (Human Resource Information System) that is average at everything or a series of “best of breed” applications that are better at a single purpose functions (Applicant Tracking Systems, Performance Management Systems, Compensation Systems, Learning Management Systems, Time and Labor Systems, etc…) Since HR CHROs and/or critical stakeholders in HR change over every 2-4 years you can guarantee exchange of favored technology to solve whatever shortfalls exist. Meanwhile, within a few years of implementing a solution we have new leadership and who prefer a newer, more optimistic path. Never wanting to get trapped in the ice, there is constant pull towards fragmented systems stemming from HR sub-function implementation of “best of breed” applications that align with HR sub-function operational objectives. If I am the head of Staffing and my team is tasked with improving Staffing, why would I settle for anything less than the best Applicant Tracking System? If I am the head of Compensation and my team is tasked with getting a grip on Total Rewards, why would I settle for anything less than the best Compensation Management System? So on and so forth. There is nothing wrong with this, however it results in a lack of technology optimization across sub-function silos and some important data consequences that must be addressed before or during reporting and analytics. Larger, established HRIS systems (PeopleSoft, Oracle, SAP, Lawson, and recently Workday), have more dependencies to worry about and thus are inherently slower to innovate than the collective of single purpose systems. The core systems will always lag in one or more sub-function operational areas. For example, even 10-15 years ago you could facilitate the Performance Management on SAP or Oracle HRIS, however Success Factors came along and offered a solution that was designed for this and consequently much better at it. So for 5-10 years Success Factors took the HR market for Performance Management, creating a hundred million dollar market and adding yet a new system category for HR to manage and integrate. Success Factors was later acquired by SAP (for 3.4 Billion) , and to this day can still be purchased by your organization as a stand-alone Performance Management application, with or without SAPs HRIS product. To this day most of the organizations I have worked with or for have NOT fully integrated Performance reporting between Success Factors and their HRIS. They prepare for the process to begin by manually setting and loading a file. There is someone deep within the bowels of the organization doing several spins on this. Probably also with a smart phone going off widely in the middle of dinner with the family - if this person even gets dinner with the family for a month or two of their life. Getting it out and rejoined to a changing organization and broader analytical purposes is another thing entirely. You will then discover, you can't export all the data you want directly from the same report, and there may not even be a common key! You might ask, “You mean to tell me we have no efficient method to join and report this data that is central to HR and to the business? Are you kidding me?" No. We have seen the same in Staffing, Learning Management, and Compensation. Compensation is especially bad - I like to call Compensation Planning (an annual event at every large company) a "planned emergency". 99.9% of the problems is embed in systems, 100% of it is caused by choices made by humans. By virtue of the many simultaneously moving fronts of sub-function innovation HR will undergo constant fragmentation and change in systems. Innovation in HR systems is good for us, but it is also very disruptive. Let’s Talk About HR Data Contrary to widespread belief HR actually has a lot of data, and a lot of good data. It is just locked away in systems not designed at all for reporting or analysis. Universally, HR systems are designed to facilitate operational processes and while they can perform their intended operational functions well (by design), they often do so at the expense of reporting and analysis. I will take a bucket of ice water over the head if someone shows me a single HR system that can perform a chi-square or binary logistic regression out of the box. How about any statistics beyond addition, subtraction, multiplication and division . We are waiting. Still waiting. Frankly, I like to keep my statistics and data visualization software options open – I don’t want my HR system to do everything for me, but I do need to be able to get the data out of my HR systems. Often even this seemingly basic task (get the data out) is difficult. Apparently, nobody thought of about getting the data out. If you are laughing, stop laughing. I’m not kidding. We can’t get deep insights or complete picture of the story until we get the data out of multiple systems, join it, and have a look at it through applications designed to work with data for reporting and analysis. Most HR “Analysts” are cobbling data together inefficiently with manual, undeveloped or broken data process. Some 80% of the effort of HR Analyst efforts are attributed to manual augmentation of non existent or inefficient data workflow. They spend very little time on actual science, statistical analysis and presentation of analysis. I have been an HR Analyst in one form or another for over 15 years, I speak to HR Analysts weekly, and I have seen the surveys. HR data are/is complex because people and organizations are complex and the sub-functions to support HR (as described at high level in above) are varied. There is hardly any similarity in reporting between Compensation and Benefits, let alone Staffing, Training or Employee Relations. Who owns Turnover reporting? Who owns the Employee Survey? These come from completely different systems, with different data structures and different metrics. The desired metrics can escalate into the hundreds and with variations in the thousands. Most HR metrics are compound and with dozens of potentially relevant dimensions to monitor. Let's take a look at a very commonly produced and seemingly simple HR metric: “Employee Attrition/Turnover”. This metric is formulated as a compound calculation (Time Period Exits / Time Period Average Headcount). Now before you shout "Eureka! - I have it - you just divide this number by this other number" keep in mind that you will need to calculate this by segments along multiple dimensions: location, division, business unit, tenure, grade/pay group, performance group, age, ethnicity, gender, etc.… You may have 50 locations. Some locations have 5 people and some have 15,000 people. What if you want division by location by gender. Also keep in mind that in annual form the denominator (average headcount) requires 12-13 or more data points for each of subset of each dimension, and these subset counts must all align in definition and time period consistently with the numerator. Also keep in mind that organizations can be expressed in different ways (people reporting relationships or cost center reporting relationship, which do not typically match), and that the only constant is that organizations are constantly changing. Imagine a data set that changes simultaneously along multiple dimensions over time and you are trying to report on this consistently over time to demonstrate a "trend" or "story" - how exactly does this work? If you are flabbergasted, don't worry, most of us People Analysts can work this problem in our sleep. This isn't even the hard stuff. I just described the calculation of one metric, now multiple this effort by 20 more metrics with data sitting across different systems and try to derive some meaning from that. Shouldn't we be analyzing this stuff together to tell a story, not independently? Most of the people doing good work on employee turnover have long ago moved on to more advanced ways of looking at exits (logistic regression, survival analysis, hazard curves, predictive models, etc.) and incorporate data from many many more data sources. I can ask employees three questions and based on their answer tell you if their chance of leaving in the next year is "about average, 2x average or 3x average" without knowing anything else about them. Give me their job and salary and tenure and a few other details and we can clean up on this problem. Let me join it to performance and compensation and now you can decide carefully how you want to distribute the limited budget you have to work with. Why not? If you are not working on this, what exactly are you working on? Speaking of clean up. For most organizations, you will get started on a seemingly simple problem, and bang into an inconsistency or perceived data quality problem, which requires people sitting in another sub-function you have no authority over to make a decision, fix a process, or change how they do something. This is not simple any longer – this is really difficult. You can circle on the same issues for years. Welcome to People Analytics, Are You Sure You Really Want This Job? Then There Were People HR professionals are not selected for a background in technology, math or science and so depend on people outside of HR for augmenting support in these capacities. Since HR is seen by “the business” as lower in priority than other business functions (software engineering, finance, marketing, sales, etc.), and therefore not a very prestigious appointment for anyone - IT and data science support suffers. The head of HR is on the phone about something they want and the head of Sales, which call should I take? If HR is already underfunded for its goals, HR IT is even worse, not receiving adequate talent pool or funding. Budgets are divided among the heads of functional HR silos and so no unifying technology solution can be reached. There are simply too many different jobs to be done in too many different systems. Data architecture for this is an after-thought. Each Sr. Director will compete for the time, attention and resources of whatever HRIS or HR Analytics professionals exist to try to achieve their objectives first (at any and all cost to others –their performance rating is on the line!). If they cannot get that attention they want from IT or from their HR Analysts they will try to go outside the organization for the support. Talk about being crushed in the ice. You will be loved and despised, sometimes consecutively, sometimes simultaneously. How this can happen is one of the great mysteries of the universe - outside of light being possible to be described both as a particle and wave. It is sort of like the possibility of dying of thirst while standing on water (ice). Input fire and maybe drown. Now I am being dramatic. This is just to say that I think Shackleton and company did a pretty good job, given the odds. I have had opportunity to be in these data oriented HR roles for variety of very well respected organizations (Merck, PetSmart, Google, one of the best Children’s Hospitals in the country to name a few) - believe me when I say these HRIS and HR Analytics folks have many more “bosses”, a combined list of objectives much longer, technically more complicated, and with less resources than anyone else. There high turnover among HRIS and HR Analytics professionals for a reason. You got one. Great. I hope you have a backup plan! Other People Analytics posts by Mike West ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 10+ years of experience building People Analytics from the ground up at companies such as Google, Merck, PetSmart, Children's Medical, Jawbone and other places. Mike's passion is to develop thought leadership in HR and to cross pollinate the frameworks and processes he helped develop and pioneer as an employee at these places. Mike spends most of his time teaching, coaching and writing on all things People Analytics.
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Chris Butler
The One Model team has a huge amount of experience in the HR data and analytics field. Our careers started at Infohrm, the world’s first SaaS workforce analytics provider. Infohrm was acquired by SuccessFactors in 2010 and we later moved into SAP with their acquisition of SF in 2012. As a result we’ve worked with more customers across more data sources than just about anyone else in the world. Customers with 200 employees right through to 600,000 employee behemoth organizations. This experience has earned us a unique perspective on how organizations currently use their people data, how they could be using their data in a perfect world and the amount of supporting technology that is available to them. We’ve learned that data and the correct management of it, is the real key to organizations becoming successful with their talent analytics programs. Every company I have ever met struggles with their HR data. Visualization tools are a red herring to true capability without a properly constructed and maintained method for bringing together all of your HR technology data. It will give you some early wins but you’ll soon outgrow the offered capability with nowhere else to go. Analytics, planning, and even application integration should flow as a natural byproduct of a well-executed data strategy. This is what we bring to our customers with One Model. All of your HR technology data brought together in a single unified source, automatically organized into expert built data models ready for intelligence and to support any other use case. With all of your data together regardless of the source the opportunities for using this data, for choosing better business software, and interaction between data sets become limitless. Our passion is for this data set and the HR challenges we can solve with it. We have always wanted to be able to build without restriction, the tools to collect data, to build the calculations, algorithms, and thought leadership initiatives we know our customers want. One Model is architected exactly for that, highly automated, flexible, intuitive, and open to use any other toolset you may have already invested in or want to invest in. Easily use tableau, qlikview, excel, and successfactors workforce analytics. See how we compare to the competition. We are looking for more great customers to come on board and help us refine our roadmap and prioritize capabilities important to you. On premise or cloud sources we’re ready to onboard your data and give you complete control, please contact me if you would like to join our customer engagement program.
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Stacia Damron
It’s sounds ridiculous, but it’s true. According to the New York Times, 4.2% of women held CEO roles in America’s 500 largest companies. Out of those same 500 companies, 4.5% of the CEO’s were named David.* While shocking, unfortunately, it’s not incredibly surprising. Especially when a whopping 41% of companies say they’re “too busy” to deploy diversity initiatives. But for every company out there that’s “too busy”, there are plenty of others fighting to get it right. Take Google, for example. In 2016, Google’s tech staff (specifically tech roles - not company-wide roles) was 1% Black, 2% Hispanic, and 17% women. They announced a plan to invest 150 million in workforce initiatives. The tech staff is now 2.5% Black and 3.5% Hispanic/Latinx, and 24.5% female, according to their 2018 diversity report. So what does that mean? It means that even the brightest and most innovative companies have their work cut out for them in regards to improving diversity. Change doesn’t happen overnight. Diversity breeds innovation; a diverse talent pool leads to diverse ideas. Get this; a Forbes article touts that transitioning a single-gender office to a team equally comprised of men and women would translate to 41% in additional revenue. “Metrics” (which is just a fancy word for data btw) don’t lie. It’s important to set, track, and monitor workforce diversity goals - especially when we have more tools than ever at our disposal to do so. Over the past few years, here at One Model, we've seen a huge push for placing a priority on monitoring diversity metrics. In 2016, a Fortune 100 financial services organization, Company X (name anonymized) selected One Model’s platform to measure and monitor company-wide trends in diversity data and metrics. As their people analytics and workforce planning solution, One Model allowed them to not only better report on their data - but also more easily track and monitor changes, determine key KPIs, and see how improvements they’re making internally are affecting the data. More Accurate Data = Better Reporting. During Company X's transition from SAP to Workday, they used One Model to retrieve and migrate survey data. This platform allowed them to combine and normalize the data from several sources, enabling the team to report off of it as one source. The successful migration provided the HR team with the recovered data and prevented the team from having to redeploy the survey, allowing them to more accurately reflect their current diversity metrics and progression towards goals. This was a win. Here’s the challenge: When pulled together, the data referenced above indicated that out of several thousand employee responses, a number of employees failed to select or identify with one of the given race selections. This represented a sizeable portion of the employees. One Model’s software helped them identify this number. Once they realized this, they realized they had an opportunity to setup other processes internally. They did just that - which helped identify 95% of the employees who fell within that group, obtaining vital missing data that raised the percentage of diversity within the organization. Determining Key KPIs and Measuring Improvements Furthermore, Company X used the One Model platform to identify and reward the departments that successfully hit their recruitment-based diversity goals. This allowed the team to survey these departments and identify the hiring trends and best practices that led to these improved diversity metrics. By identifying specific process and KPI’s surrounding these diversity metrics, departments that successfully met their goals could share recruiting tactics and best practices to ensure appropriate actions were taken to maximize diversity throughout the whole of the recruiting pipeline. Company X is currently implementing these processes and working towards replicating a similar outcome amongst other departments in need of workforce diversity improvement. Tracking and Monitoring Changes Last but not least, Company X wanted more visibility into why females had a lesser presence in managerial roles within the organization. While, male to female promotions were equal. (This past year, 32 people were promoted. 55% of promotions (16 people) were women), there were significantly more males than females in managerial roles. Upon reviewing the data, they learned that out of the company’s requisitions, females applicants only made it to certain stages within the interview process (namely, an in-person interview) 50% of the time. Half the time, the only applicants that made it to a particular stage were male. They determined a hypothesis surrounding a particular KPI - that if more females made it to this particular stage, the odds were higher that more females would fill these roles. Company X set a goal that they wanted a female candidate make it to a manager interview stage 80% of the time. They are testing different methods on how best to achieve this, and with One Model's help, they are able to measure the effectiveness of those methods. By providing this visibility, One Model’s platform is currently helping them monitor their progress towards this goal, and allows them to see the affect - the direct impact on numbers of M/F managers in real-time. Company X is one of the many companies that has realized and embraced the importance of diversity in workforce planning. We’re confident they’ll eventually hit their goals, and we’re proud to be a part of the solution helping them do so. Is your company ramping up it’s People Analytics Program or diving into workforce diversity initiatives? One Model can help you better view and report on the data associated with your diversity goals. Here are just a few of the top metrics companies are currently focusing on: Recruitment Metrics Representation Metrics, such as: Minorities / URMs Veterans Women IWDs Staffing/Placement Metrics Transaction Metrics Training Metrics, such as: Penetration of diversity-related training, general training participation rates, and demographics of talent pipeline Advancement Metrics External Diversity Metrics Culture / Workplace Climate Metrics *based on 2016 NYT data. Want to see what One Model can do for you? Scheduled some time to chat with a One Model team member. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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This past Tuesday, Chris Butler, Founder & CEO of One Model and Phil Schrader, One Model's Product Evangelist, got together to present best practices on SuccessFactors regarding people analytics. Didn't get a chance to view it live? Enjoy the webinar recording below!
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