Part of the people analytics cluster. This is educational, operational guidance that connects to the wider site — the employee lifecycle, employer operations, metrics and templates.
Use your own clean data, and interpret with care.
Why it matters
Decisions about people are too important for anecdote, but careless analytics dresses up bias in numbers. A disciplined framework — good questions, clean data, honest interpretation — makes a structured approach to people analytics trustworthy and useful.
The method matters more than the dashboard.
Key concepts
- Question-first analysis.
- Data quality and definitions.
- Honest interpretation.
- Reporting and governance.
Operational framework
- Start from a decision or question, not the data.
- Ensure clean data and consistent definitions.
- Interpret with context; resist over-claiming causation.
- Report clearly for the decision and audience.
- Govern data, privacy and access throughout.
Data & governance
- Which decisions analytics serves.
- How data quality is ensured.
- How privacy and access are governed.
- How insight reaches decisions.
Common challenges
- Data without a question.
- Inconsistent definitions.
- Causation over-claimed.
- Insight that never acts.
Best practices
- Start from a decision.
- Stabilise definitions.
- Interpret with context.
- Govern data and privacy.
Common mistakes
- Collecting before questioning.
- Drifting definitions.
- Causal over-claiming.
- No governance.
Measure this with the workforce planning metrics metric, put it into practice with the workforce planning template, and run it as a system via workforce risk management.
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