People Analytics is one of the fastest-growing areas in HR—but if you're wondering what People Analytics actually involves, or how to build a People Analytics career, you're not alone. Many new leaders quickly discover that success depends on far more than analytics skills—it requires navigating a complex data supply chain.
In a new People Analytics role, even experienced professionals often find themselves grappling with challenges that go well beyond a new work environment. Though hired to deliver workforce insights and instill a data-driven mindset, they soon face upstream challenges in what we call the People Data Supply Chain—a web of systems, stakeholders, and limitations that restrict access to the data and tools they need across HR functions.
Within weeks, new People Analytics leaders often find themselves working more closely than they expected with data engineering, technology, HR operations, and senior leaders—just to achieve one thing: clean, strategic, and actionable workforce data.
For those beginning a People Analytics career, working through these challenges may feel overwhelming, but it is, in fact, the foundation of high-impact analytics work.
It can help to know that this is the gig; you're not alone. It comes with the territory. To that end, the image below depicts the 30,000-foot view of this uncharted path. Dive deeper into the experience below or listen to Richard Rosenow’s keynote at People Analytics World in London on why it’s so hard to grow in People Analytics—and what data-savvy professionals can do about it.
We’re going to follow a fictional People Analytics leader who just joined a large tech company. The company has been curious about People Analytics for some time and is finally ready to dip their toes into the water. This new leader is tasked with building their People Analytics function from the ground up.
It’s very instructive to know upfront that the root problem isn’t people or effort—it’s misalignment of data ownership, system setup, reporting expectations, org structures, and tech stacks. These factors quietly but powerfully constrain even the most capable leaders.
The new People Analytics leader begins by taking stock of available data, identifying extraction points, assessing availability, and persuading stakeholders to grant access. Along the way, they must also learn how the organization defines its metrics. These early steps are critical because People Analytics isn’t just about pulling reports. It’s about building analytical models that can turn workforce data into real insights.
But like many companies still catching up to the “analytics revolution,” this one hasn’t prioritized data infrastructure. Key information is scattered, outdated, or simply unavailable for People Analytics work.
The leader quickly learns to be resourceful— borrowing, patching and improvising wherever possible. That’s long been a trait of People Analytics professionals: generating value despite limited access. But as the questions grow more complex, issues of data quality will start to surface and scrappiness will only go so far.
Our People Analytics leader soon hits a wall, not because data is missing, but because system configurations and reporting structures are misaligned. Despite the team's investment in advanced HR systems like Workday, SuccessFactors, and Greenhouse, generating reliable, actionable insights remains a struggle.
To troubleshoot, the leader focuses on tasks typically assigned to HR analysts or IT: resolving system issues, reworking configurations, and collaborating with tech teams, diverting even more time from their core analytics work.
This would be challenging enough on its own. But HR technologists and IT teams often aren’t equipped for these data issues either. Their focus is typically on launching scalable systems and enhancing workforce experience, not on ensuring data is ready for advanced analytics. Once a system goes live, their role is usually complete, leaving gaps the People Analytics team must fill.
To be fair, People Analytics is still new territory for many technologists. But recent waves of costly rework have encouraged stronger partnerships betwwn functions. It’s now more common for People Analytics teams to be involved early in HRIS and tech implementations.
Eventually our leader learns that the technologist hat stays on. HR enterprise systems are highly configurable and rarely implemented the same way twice. While this flexibility supports business complexity, it also creates massive variation in how data is structured and surfaced.
And since upstream teams define system requirements, People Analytics rarely controls configuration decisions—yet they’re the ones who feel the consequences.
Now our People Analytics leader encounters a fundamental truth about technology: without standardized processes, documented operational methods, clear documentation, and operational guardrails, even the best systems will fail. When processes are inconsistent or ad hoc, the data they generate is unreliable—and no amount of analysis can reconnect it to meaningful business goals.
Take, for example, an Applicant Tracking System (ATS). It depends on consistent process flows to produce clean data. But if a recruiter, eager to close a candidate, bypasses key steps or stages, the ATS won’t accurately reflect activities—and the people data becomes fragmented.
Even the best recruiting tools require defined process maps, clear logic, and agreement on what steps come first—or how edge cases like evergreen requisitions should be handled. Tools that promise to automate or simplify HR functions don’t eliminate the need for upfront process design and operation rigor. Requirements still need to be gathered. Standards still need to be set. New tech can’t fix broken operations; and it certainly can’t replace the need for strong documentation, change management, and cross-functional support.
Armed with this knowledge, the leader now steps into an unexpected role: HR operations partner. They begin working side-by-side with ops teams to standardize workflows, map business logic, and define consistent practices for data entry. This upstream work directly shapes downstream configuration and data architecture decisions for People Analytics.
It’s worth it. Clean data starts with clean processes. But just as progress is made, a deeper issue surfaces: there’s no clear workforce strategy guiding these efforts. You can't standardize your way out of confusion. Without a strategic direction, even the best operational improvements risk becoming disconnected, temporary fixes.
The organization is at a crossroads. And the People Analytics leader is headed toward one final role.
By this stage, our People Analytics leader has learned that data standardization alone isn’t enough. Without a clear workforce strategy, operational consistency lacks direction. A documented strategy is needed to define how HR resources its programs, processes, and technology to achieve business goals.
Strategy becomes the guiding light for People Analytics, enabling the team to align their work with organizational outcomes and measure its true impact.
The most mature People Analytics teams don’t just respond to strategy–they shape, support, and influence it. While the CHRO sets the strategic vision, our leader collaborates closely to synthesize business needs, assess current HR capabilities, and prioritizes initiatives across HR. Leaders with strong skills in strategic planning and project execution bring immense value to their organizations and to their own People Analytics career.
This strategic alignment makes it possible to scale operations, accelerate delivery, and optimize technology investments. With the right strategy, systems, and operational processes in place, the team finally gains access to clean, connected data that directly supports business priorities.
And with that, the leader can return to the heart of their role. But this journey has revealed something deeper than the need for better data. It has revealed the entire people data supply chain and the critical role People Analytics plays in holding it together.
You’ve just seen how the People Analytics journey moves through data engineering, HR tech, operations, and ultimately into strategy. It’s not a straight line—and it’s not a solo effort.
Within this environment, a new role is quietly taking shape: someone who can connect the dots. That role—the Workforce Systems Leader—is critical to helping People Analytics teams do their jobs: navigate complexity, close data gaps, and finally focus on delivering strategic insights.
In this whitepaper, author Richard Rosenow covers:
If you’ve lived this journey—or are looking to the future—this guide is for you.
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.