People Analytics
What is people analytics and what questions does it answer?
People analytics is the practice of using data to make better decisions about an organization's workforce. It includes descriptive analytics (what happened), diagnostic (why), predictive (what will happen), and prescriptive (what to do). Common questions include which employees are at risk of leaving, which managers drive the best outcomes, where pay gaps exist, and which programs actually move engagement. The discipline grew from HR reporting in the 2010s to a dedicated function with data scientists at most large employers.
People analytics has matured from HR reporting to a discipline that produces real business outcomes at the employers that invest in it. The Josh Bersin Company's 2025 People Analytics Maturity Model shows roughly 18% of employers at the top maturity level (predictive and prescriptive analytics tied to business outcomes), with most still doing primarily descriptive reporting. The gap between reporting and analytics is investment in data engineering, statistical capability, and willingness to change practices based on findings.
What People Analytics Actually Answers
Which employees are at flight risk and why. Which managers drive the best engagement and retention. Where pay gaps exist after controlling for legitimate factors. Which sourcing channels produce the best hires. Which learning programs predict performance gains. Which workforce changes accompany business growth.
People Analytics Maturity Levels
Descriptive: HR reporting on what happened. Diagnostic: why it happened. Predictive: what will happen if patterns continue. Prescriptive: what specific actions to take. Most organizations are stuck at descriptive; the value is in moving up.
Common People Analytics Tools
HRIS reporting (Workday, UKG). Standalone platforms (Visier, ChartHop, OneModel). Data science on extracted data. Most employers use a combination, with the standalone platforms providing easier access to the analyses HR teams want without engineering support.
Building People Analytics Capability That Drives Decisions
Invest in clean HRIS data first; bad data produces bad analytics. Hire or train analytical talent. Tie analyses to specific decisions, not vanity reports. Measure intervention impact. See human resource metrics for the metrics layer and employee engagement for the most common analytics use case.
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