HR and People Analytics applies statistical methods, data science, and business intelligence techniques to workforce data β transforming headcount records, survey responses, payroll figures, and collaboration logs into evidence-based decisions about hiring, retention, compensation, development, and organisational design. The field spans descriptive reporting (what happened), diagnostic analysis (why it happened), predictive modelling (what will happen), and prescriptive analytics (what should we do). Coverage below moves from foundational workforce metrics through advanced retention modelling and organisational network analysis.
What This Cheat Sheet Covers
This topic spans 14 focused tables and 120 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Headcount and Workforce Composition Metrics
| Concept | Formula / Syntax | Notes |
|---|---|---|
All active employees on snapshot date | β’ Point-in-time count; distinguish active vs on leave vs contractors β’ Use consistent snapshot date (e.g., last day of month) across periods | |
Headcount β (on leave + inactive records) | β’ Excludes employees on extended unpaid leave β’ Used as denominator for per-employee KPIs | |
FTE = Ξ£(hours worked per week Γ· standard full-time hours) | β’ E.g., two 20-hr/wk employees = 1.0 FTE β’ Normalises part-time workforce for cost and productivity comparisons | |
Full-time / Part-time / Contractor / Temporary | β’ Segment workforce mix by employment category β’ High contractor ratio signals cost-flexibility strategy or classification risk | |
IC / Manager / Director / VP / C-Suite counts | β’ Track pyramid shape over time β’ Widening base relative to top = healthy growth; shrinking middle = management compression |