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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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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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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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Featured
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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Featured
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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Featured
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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Featured
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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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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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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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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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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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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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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Featured
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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Featured
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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Featured
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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