HR Analytics, Metrics and Technology
From data to decisions
Candidates must distinguish descriptive, diagnostic, predictive and prescriptive analytics, and know the difference between a metric, a KPI and a dashboard. Core HR metrics include turnover and retention rates, cost per hire, time to fill, absence rate, revenue per employee, engagement scores, span of control and training hours. Understand how to calculate them and what each does and does not reveal.
Technology and data quality
Know HRIS, applicant tracking systems, learning platforms and people-analytics tools, plus data governance principles: accuracy, consent, security, retention and fairness. Algorithmic decision-making raises bias and transparency risks. Candidates should understand correlation versus causation, sampling, benchmarking and leading versus lagging indicators.
Traps in questions
Beware options that present a lagging indicator (turnover) as predictive of future outcomes, or that treat correlation as causation. Questions may confuse turnover rate with wastage or treat high engagement as automatically raising productivity. Watch for metrics that are easy to game, and for analytics projects that lack a decision owner.
Worked example
Average headcount 200, leavers 30 in a year: turnover rate = 30 ÷ 200 = 15%. If voluntary leavers are 18 and involuntary 12, voluntary turnover = 9%. Rule of thumb: always state the denominator and period, and separate voluntary from involuntary before drawing conclusions. High early-tenure turnover points to selection or onboarding; high long-tenure turnover points to career or reward issues.