The Evolution of People Analytics: A Decade of Transformation - PART ONE

By: Yuyan Sun & Cole Napper

Source: Yuyan Sun

This collaborative article is written to discuss: How the content of people analytics has evolved over the last ten years, and what were the problems then and now we are trying to solve. Subscribe to both Yuyan's Amazing Work! and Cole's Directionally Correct newsletter to follow the complete series and access more insights on people analytics.

Thank you

First, a big thank you to David Green and his Data Driven HR Monthly newsletter for being such a steadfast resource to the people analytics community, but also the primary data source for the trends in this analysis.

Context

If you've been in people analytics for any length of time, you've felt the shifts—from fighting for a seat at the table to being called into the CEO's office during a crisis. This is the first in our three-part series examining the remarkable journey of people analytics over the past decade, through the lens of David Green's comprehensive industry coverage as the source of data.

What emerged from our analysis wasn't just a story of growth, but one of transformation—of an entire profession finding its purpose and impact within organizations. The data tells us something that many practitioners have felt intuitively: People analytics has evolved from a technical specialty to a strategic function at the heart of workforce transformation.

From Outsiders to Insiders: Four Chapters in People Analytics Evolution

In 2014, people analytics practitioners occupied an uncertain position within organizations. They didn't quite fit into traditional HR, weren't exactly data scientists, and certainly weren't yet strategic partners. They were, as one early commentary in Green's newsletter put it, at risk of "losing analytics to Finance" altogether.

A decade later, the transformation is remarkable. Today's discussions center around how analytics teams are driving AI adoption strategies and reshaping organizations around skills rather than jobs. The function that once had to justify its existence is now helping shape organizational strategy.

Our analysis reveals four distinct chapters in the people analytics story, each with its own central challenges, themes, and breakthroughs: Building the Foundation, Finding a Strategic Voice, Crisis Response and Validation, and Enabling Transformation. We will address each of these phases in more detail below.

Source: Yuyan Sun

Chapter One: Building the Foundation (2014-2016)

The early days of people analytics now seem almost quaint. Organizations were debating whether predictive analytics could improve hiring, and whether HR analytics should report to HR or Finance. The field's pioneers were introducing concepts that we now take for granted.

Josh Bersin, an early champion featured frequently in Green's newsletters, predicted that 2015 would be the year analytics went "mainstream" in HR. While that prediction was optimistic, these years established important fundamentals that would shape everything that followed.

The early period introduced foundational ideas like "pure algorithmic hiring systems" (before mainstream discussions of AI bias) and the "fox vs. hedgehog approach to workforce planning" (distinguishing between broad strategic thinking and deep specialist expertise).

What's most striking about this era was its somewhat defensive posture. The field's value proposition wasn't yet about transformation—it was about capturing quick wins and avoiding failures. In a 2016 article, HR transformation expert Andy Spence asserted that despite the hype of people analytics, adoption rates remained frustratingly low. Challenges remained high for HR data to help reinvent organizations.

Teams were building capabilities while simultaneously having to justify those same capabilities. They faced skepticism from both HR leaders who questioned the need for analytics and business leaders who questioned whether HR could deliver meaningful insights.

Chapter Two: Finding a Strategic Voice (2017-2019)

By 2017, the conversation had shifted significantly. With the question of ownership largely settled, people analytics began a pivot toward strategic value. This shift is evident in the emergence of employee experience as the dominant theme in Green's newsletters during this period—mentioned more than twice as often as any other topic.

Organizations had moved beyond asking "should we do analytics?" to "how can analytics transform our business?" as successful applications of people analytics in established organizations gain publicity. The field was developing a more confident strategic voice.

Patrick Coolen, then of ABN AMRO, a frequent voice in Green's coverage, exemplified this evolution. His team progressed from delivering basic workforce metrics to developing sophisticated models linking people practices to business outcomes. The analytics function was connecting people insights to business strategy in increasingly meaningful ways.

This era also saw Organizational Network Analysis (ONA) emerge as a mainstream methodology. The ability to map informal relationships and influence networks gave analytics teams a new lens for understanding organizational dynamics beyond the formal hierarchy.

Yet alongside this growing sophistication came an important counterbalance: Ethical considerations. In 2018, Cambridge Analytica/Facebook data breach served as a good reminder to the people analytics field that ethics and data privacy is arguably the most important part of any analytics program – particularly when it comes to HR and employee data. The concept of "responsible people analytics" became increasingly important as practitioners recognized the potential risks of their expanding capabilities. Major industry conferences Unleash and the Wharton People Analytics Conference featured ethics discussion prominently during this time.

This period wasn't just about new methods—it was about new mindsets. Analytics teams were increasingly expected to think like business partners rather than technical specialists. The most successful practitioners became adept at translating between data science and business strategy.

Chapter Three: Crisis Response and Validation (2020-2022)

The pandemic marked a turning point for people analytics. Organizations suddenly needed answers to questions they'd never even thought to ask: How were collaboration patterns changing? Who was at risk of burnout? How could they maintain culture without physical proximity? And very basic yet important questions, such as, can work continue if everyone can’t see each other in person?

David Green's newsletters during this period document a field meeting an unprecedented challenge. DEI analytics became the most discussed theme, reflecting both the pandemic's unequal impacts and broader societal movements for racial justice.

Remote work analytics emerged as a critical capability. Teams who had been mapping collaboration patterns for years found their expertise in urgent demand as organizations struggled to understand the new virtual workplace.

"The ability to redeploy 700 people within a few days shows the importance of having good HR data in a time of crisis." one CHRO noted in a 2020 feature. The function that had once fought for recognition was now central to navigating the greatest workforce disruption in generations.

This era brought significant innovation under pressure. New concepts like "continuous listening framework" and "work without jobs" demonstrated the field's ability to adapt existing methodologies to entirely new challenges. The "skills-based organization" emerged as a framework for rethinking traditional job structures in a rapidly changing environment.

The final months of 2022 brought another significant development with OpenAI's release of ChatGPT, as people analytics practitioners quickly began experimenting with the tool. You could argue the introduction of Gen AI is what brought the “pandemic” phase of people analytics to an end. The democratization of these powerful language models signaled the beginning of a new phase for people analytics—one where the barrier to entry for sophisticated text analysis was dramatically lowered. Green's newsletter even included ChatGPT's own perspective on how it might impact the future of work, illustrating both the novelty and potential significance of this technology for the field.

As an example, Dawn Klinghoffer and Microsoft's people analytics team, who frequently appear in Green’s newsletters, and their analytics journey during this period were particularly instructive. Their Workplace Analytics team leveraged years of collaboration data to publish influential research on digital exhaustion and meeting overload—research that shaped how many organizations approached remote work.

Chapter Four: Enabling Transformation (2023-Present)

In this section we are writing about history as it happens. The current era of people analytics is defined by two dominant forces: AI integration and skills transformation.

These twin forces are reshaping people analytics' core value proposition into something profound: Workplace transformation. The field is no longer merely analyzing the workplace but actively reshaping it. As Adam Grant noted in a 2023 Wall Street Journal interview, this represents a fundamental shift in how organizations approach decision-making: "Every opinion you hold at work is a hypothesis waiting to be tested. And every decision you make is an experiment waiting to be run."

This perspective captures the essence of the current era—moving from doing analytics as a specialized function to enabling analytics as an organizational capability. Analytics is becoming embedded in how work gets done, decisions get made, and organizations evolve. This shift from producing insights to enabling action represents an important evolution in the field's identity and impact. At the same time, organizations are asking the question of after a pandemic, how much value is a people analytics team really bringing me during times of relative normalcy? People analytics needs to step up to answering this question.

The increase in skills-related discussions reflects a broader reimagining of how work gets organized. Organizations are increasingly questioning the long-established paradigm of fixed jobs, exploring instead how work can be organized around skills and capabilities. People analytics teams are providing the data infrastructure and insights needed to make skills-based approaches viable.

Meanwhile, AI has moved from theoretical discussion to practical application with surprising speed. New concepts like "responsible AI in HR" and "agentic AI for HR" are changing how teams approach their work. The very function that once worried about proving its basic value is now helping organizations navigate complex technological transformation.

Source: Yuyan Sun

The Changing Value Proposition: Four Ways of Creating Impact

The evolution of people analytics is perhaps most clearly seen in how its fundamental value proposition has changed over time:

Foundation Era (2014-2016): "We'll help HR become more data-driven." The initial value proposition was somewhat defensive—bring data to HR or risk irrelevance. Teams focused on basic workforce metrics and establishing credibility.

Strategic Era (2017-2019): "We'll drive competitive advantage." As the field matured, the promise evolved to connecting HR practices with business outcomes. Analytics was increasingly positioned as a source of strategic insight rather than operational reporting.

Crisis Period (2020-2022): "We'll help navigate uncertainty." During the pandemic, the value proposition centered on enabling rapid adaptation to unprecedented challenges. Analytics became essential to organizational resilience and response.

Current Era (2023-Present): "We'll enable transformation." The current promise positions analytics as critical for navigating technological change and organizational reinvention. The focus has shifted from adaptation to proactive transformation.

This evolution reflects a field that has continuously expanded its ambitions and capabilities. Each era built upon the last, creating a more sophisticated and seasoned function.

Persistent Challenges: What Hasn't Changed

Source: Yuyan Sun

Despite this remarkable evolution, several challenges have remained consistent across all four eras:

The data quality dilemma has been a constant companion. Early newsletters discussed the challenges of "messy data in HR," while recent ones address integrating disparate data sources for AI applications. What's changed isn't the challenge itself but our approach to it—moving from seeking perfect data to finding ways to derive value from imperfect data.

Similarly, the adoption gap between analytics capability and actual business impact has been a recurring theme. David Green's 2016 newsletters noted that "interest in analytics is increasing but adoption still lagging." Recent editions express similar concerns about the cost of the misalignment between analytics investment and business goals. Particularly with increasing investment in AI technology, business executives reported their top priority was now external marketplace focused, as “these investments need to create business value by helping the company achieve its growth goals.”

The skills challenge within analytics teams themselves has evolved significantly. Early concerns focused on basic statistical literacy and data visualization. Today's teams wrestle with AI literacy, ethical governance, and the delicate art of influencing without authority. The technical bar keeps rising, but the human elements of the work remain equally challenging.

To remain relevant in the future, people analytics need to focus on fixing these dilemmas once and for all, not just in one-off ways. This requires partnership between people analytics teams, HR technology, HR leaders, and vendors to all have aligned incentives and priorities. Let’s get to work.

Three Fundamental Shifts

As we conclude this first part of our three-part series, three fundamental shifts stand out in the evolution of people analytics:

First, the field has evolved from HR service to business partner. What began as a technical capability within HR has become a strategic function delivering critical workforce insights directly to business leaders.

Second, the focus has shifted from data collection to value creation. Early efforts concentrated on building basic reporting capabilities, while today's teams are focused on creating actionable insights that drive decisions and outcomes.

Finally, the purpose has transformed from employee measurement to employee enablement. The field that initially focused on monitoring workforce metrics now aims to create better employee experiences, development opportunities, and wellbeing.

These shifts haven't happened in isolation. They reflect broader changes in how organizations view their people—increasingly as sources of creative value rather than costs to be managed. People analytics has both shaped and been shaped by this evolving perspective.

In our next installment, we'll explore how the methods, tools, and technologies of people analytics evolved to support this expanding mission. We'll examine the technical capabilities that enabled each era's advancements and identify the methodological breakthroughs that pushed the field forward. Stay tuned…

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Join us for Part 2 of our series in Amazing Work! and Directionally Correct, where we'll dive into the evolution of people analytics methods, tools, and technologies—exploring how the field's technical capabilities developed to support its expanding strategic role.