HR Analytics and People Metrics Course

Why this module matters: Analytics fails before the spreadsheet opens when the team starts with “what data do we have?” rather than “what decision are we trying to improve?” This module establishes a problem-first method and the definitions required for trustworthy analysis.

• Distinguish HR reporting, HR metrics, people analytics and workforce planning
• Translate vague leadership questions into testable analytical questions
• Identify the decision, population, period and outcome before selecting a metric
• Define numerators, denominators and time windows consistently
• Build a practical metric dictionary and assign data ownership
• Map HR data sources across HRIS, ATS, payroll, surveys, learning and performance systems
• Assess whether two datasets can be combined safely
• Identify duplicates, missing values and inconsistent employee identifiers
• Separate decision-useful measures from vanity metrics
• Document assumptions so another analyst can reproduce the result

Workshop: Turn three common HR questions into measurable analytical questions and a data plan.

Why this module matters: A metric becomes useful when it helps locate a problem. Organisation-wide averages often hide the team, role, manager, tenure band or location where the pattern actually exists.

• Calculate and interpret headcount, FTE and workforce-mix measures
• Analyse voluntary, involuntary, regrettable and first-year turnover separately
• Examine time to fill, funnel conversion, source effectiveness and early retention
• Interpret absence and attendance measures without oversimplifying cause
• Analyse internal mobility, promotion and succession indicators
• Connect learning participation to capability questions without claiming unsupported impact
• Segment by meaningful business dimensions while avoiding unnecessary personal-data exposure
• Use cohort analysis to compare groups over time
• Choose benchmarks carefully and distinguish external comparison from internal trend
• Identify when a metric movement is material enough to investigate

Workshop: Diagnose a workforce problem from a deliberately messy dataset.

Why this module matters: People analytics becomes dangerous when a clean chart creates more confidence than the evidence deserves. HR decisions affect careers, pay and opportunity, so analytical discipline matters.

• Distinguish description, diagnosis, prediction and prescription
• Understand correlation versus causation using HR examples
• Recognise confounding variables and alternative explanations
• Avoid interpreting a relationship as an intervention effect without adequate design
• Use cross-tabs, distributions and trend analysis before jumping to complex models
• Interpret survey relationships without treating every difference as meaningful
• Understand sample size, response bias and missing-data effects at a practical level
• Identify leading and lagging indicators
• Test whether a retention, performance or engagement pattern is concentrated in a subgroup
• Decide when advanced statistical or data-science support is genuinely required

Workshop: Challenge five common HR conclusions and rewrite them to match the strength of the evidence.

Why this module matters: Workforce planning requires a view of future demand, but forecasts are assumptions about the future, not facts. Good analytics makes those assumptions visible and gives leaders scenarios rather than false precision.

• Connect business plans to workforce demand
• Distinguish roles, headcount, capacity and skills when forecasting need
• Analyse internal supply, attrition, mobility and critical-role coverage
• Build simple demand-and-supply scenarios
• Identify skills gaps without reducing capability to a single self-rating
• Model alternative hiring, development, redeployment and automation responses
• Use predictive indicators cautiously and understand model limitations
• Test sensitivity to assumptions rather than present one forecast as inevitable
• Integrate localisation, nationalisation or other workforce-policy constraints where relevant
• Communicate uncertainty so leaders understand the range of possible outcomes

Workshop: Build three workforce scenarios for one business unit or critical role family.

Why this module matters: Analysis creates no value if decision-makers cannot understand what changed, why it matters, how confident the conclusion is and what decision is required. It can also create harm when workforce data is used intrusively or without appropriate governance.

• Design a dashboard around decisions rather than metric density
• Choose visual forms that make comparison and change clear
• Write analytical commentary that distinguishes fact, interpretation and recommendation
• Structure a senior-leadership workforce story in one page
• Identify privacy and ethical risks in people data and employee monitoring
• Apply proportionality, transparency and access-control principles
• Test measures and models for bias or uneven impact
• Use AI tools to support analysis without outsourcing judgement or exposing sensitive data
• Document limitations and confidence honestly
• Convert findings into actions with owners and follow-up measures

Capstone: Present a people-analytics briefing that recommends one workforce decision and defends the evidence behind it.