QUICK FILTERS
Featured
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 Or perhaps you’re ready to expand into new markets, but the local talent is scarce, and your remote work policies are outdated.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!
Read Article
Featured
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.
Read Article
Featured
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.
Read Article
Featured
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!
Read Article
Featured
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
Read Article
Featured
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
Read Article
Featured
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.
Read Article
Ready to Get the Most Out of Your HR Data?
