People Data & Analytics • 8 MIN READ
Predictive HR Analytics: From Hindsight to Foresight
JUN 10, 2026
Most HR reporting tells you what happened. Predictive analytics tells you what is likely to happen next. Here is how to make the shift.
The difference between descriptive and predictive analytics
Descriptive analytics answers the question: what happened? Your monthly headcount report, turnover rate, and absence data are all descriptive. They tell you about the past and are valuable for tracking trends. Predictive analytics answers the question: what is likely to happen? A predictive attrition model identifies employees at elevated risk of voluntary departure before they resign. A predictive absence model flags teams with patterns suggesting an emerging absence problem. The distinction matters because descriptive analytics shows you problems after they occur; predictive analytics gives you the opportunity to intervene before they do.
Getting started with predictive analytics
You do not need a data science team to begin. The first step is accumulating enough historical data in a single system (your HRIS) to identify meaningful patterns. The most accessible predictive model for most HR teams is a flight risk model: identify the characteristics that historically preceded voluntary departures in your organisation. Tenure, time since last pay review, time since last promotion, engagement score trend, and manager quality score are common predictors. Once you have identified the pattern, you can flag employees who match the profile for a proactive retention conversation.
