The Challenges in Building a Strong Function Around People Analytics

In “The Challenges in Building a Strong Function Around People Analytics”, Arun Chidambaram and Michael Housman examine why, despite widespread media attention, most organizations are still in the early stages of using data effectively to manage their workforce.

The authors note that People Analytics—also referred to as Human Capital Analytics or Workforce Science—represents a fundamental shift away from intuition-driven HR decision-making toward evidence-based approaches. While this shift mirrors changes already seen in marketing, finance, insurance, and sports analytics, adoption within HR remains limited.

Survey evidence cited in the article highlights the gap between perception and reality: only 4 percent of organizations have achieved true predictive analytics capability, and just 14 percent have conducted meaningful statistical analysis of employee data. For the vast majority of firms, the opportunity is not incremental improvement but foundational progress.

To help organizations jump-start their People Analytics journey, the authors outline five core lessons.

First, don’t try to eat the elephant. People Analytics encompasses a wide array of tools and techniques—from machine learning to neuroscience—and it is easy to become overwhelmed. Successful analytics functions start small, often with descriptive reporting, and build momentum through early wins rather than large, expensive, and politically risky integrations.

Second, hire right, not bright. People Analytics is inherently interdisciplinary. Effective teams require a combination of I/O psychology, statistics or economics, and experienced HR practitioners. Equally important is sequencing these hires correctly; bringing in advanced technical talent before the organization is ready can stall progress.

Third, pick the right fruit. Early projects should focus on questions with clear business relevance and broadly accepted outcomes, such as identifying the most effective sourcing channels or quantifying the impact of manager effectiveness. These projects should fit into a longer-term roadmap spanning the full employee lifecycle—from hiring through separation.

Fourth, good ingredients make a high-quality meal. Data quality alone is insufficient. Organizations must also consider data availability, coverage, objectivity, periodicity, and repeatability. Value emerges from combining multiple data sources—HR systems, surveys, operational metrics, and external benchmarks—in the right proportions.

Fifth, you need two hands to clap. People Analytics initiatives fail when conducted in isolation. HR must be treated not as a passive recipient of insights but as an active partner throughout the process. Engagement, trust, and shared ownership are critical for translating insights into action.

The authors conclude that, despite increased attention, no organization has fully “cracked the nut” of People Analytics. However, this should not discourage newcomers. With deliberate planning, disciplined project selection, and close collaboration with HR, organizations can avoid common pitfalls and dramatically increase their chances of success.

Ultimately, the article emphasizes that strategic thinking and advance planning are more valuable than sophisticated analytics alone. In People Analytics, a small amount of foresight can outweigh a large amount of computation.