A Quick Guide to HR Analytics - series recap
Andras Rusznyak
9/30/20255 min read
Ha magyarul szeretnéd olvasni a cikket, kattints ide


Who it’s for: HR Directors, Heads of HR, HRBPs who want to scale analytics beyond one-off reports.
What you’ll get: A structured path from foundations → analysis → action → culture → AI → scaling, with links to full articles and ready-to-use checklists.
How to read: Start at Chapter 1 and work forward, or jump to the chapter that matches your current maturity.
What to do next: Pick 1–2 quick wins per chapter and build a 90-day plan.
IMPORTANT NOTE:
We utilized generative AI in the making of this article.
Core idea: Shift HR from intuition-led judgment to evidence-based decisions that reduce risk and unlock growth.
Business case: Turnover, hiring delays, and misaligned investments are costly; analytics makes the cost & value visible and steerable.
What good looks like: Data clarity, repeatable insights, and actionable outputs (owners, timelines).
Read the full article → Why HR Analytics Matters
Quick wins
Define 3–5 board-ready KPIs (turnover, time-to-fill, internal mobility, QoH, engagement delta)
Convert one recurring exec question into a decision brief (KPI, threshold, action owner)
The “Why”: From gut feel to decisions you can defend
Getting started: Metrics, data hygiene, and objectives
What to standardize first:
Metric definitions (e.g., monthly turnover = exits ÷ avg headcount; QoH = 9-month performance meets/exceeds AND 12-month retention).
Data hygiene (unique IDs, timestamps, org hierarchy, location codes, reason codes).
Objectives: business-aligned, falsifiable, and time-bound (e.g., “reduce early attrition in Support L1 by 20% in 2 quarters”).
Starter checklist
Data dictionary and data owners
One source of truth for headcount, org & hierarchy
QA queries for duplicates, nulls, and timestamp logic
Read the full article → Getting Started
Descriptive tells you what happened; diagnostic explains why. Both are essential before you forecast or prescribe.
Descriptive: headcount, turnover, absence, training hours, time-to-fill, offer-accept rate.
Diagnostic: drilldowns (team/tenure/manager/location), root-cause patterns (e.g., turnover ↑ after manager change), correlations, basic hypothesis tests.
Practice pattern
1) Confirm the trend → 2) Slice where it’s largest → 3) Seek drivers → 4) Test significance → 5) Form a cause-and-effect narrative and next step.
Read the full article → Descriptive & Diagnostic Insights
Turning predictions into actions
Prescriptive analytics
What it is: Translating risk scores and forecasts into recommended decisions (who, what, when), with impact estimates.
How to do it:
Define interventions you’re willing to run (e.g., 1:1 cadence, buddy program, offer timing/positioning).
Simulate scenarios under budget/guardrails; pilot with control groups; measure lift.
Operationalize “next-best-action” with owners and SLAs; keep the human in the loop.
Read the full article → Prescriptive HR
AI now: Sentiment, chatbots, LLMs & ethics
Practical AI that helps this year (no hype, plain English):
Sentiment/VoE tagging: turn text into themes + mood with urgency.
HR policy chatbots (RAG): accurate answers with citations; ticket deflection.
Manager copilots: talking points for 1:1s and performance conversations.
Candidate assistants: scheduling, prep, consistent structured interviews (no auto-reject).
Guardrails: purpose limitation, data minimization, access control, fairness checks, human-in-the-loop.
Read the full article → AI & Advanced Techniques
How to use this guide
What happened—and why?
Descriptive & diagnostic analytics
Forecasting turnover, recruiting, and demand
Predictive analytics
Where to use predictions:
Turnover: 3–6 month risk for targeted coaching/mentoring/interventions.
Recruiting: time-to-fill and quality-of-hire likelihood by requisition/channel.
Demand: headcount needs by role/region from pipeline and seasonality.
Success criteria
Start simple (logistic regression/survival models), avoid data leakage, emphasize interpretability and actionability (thresholds + plays).
Read the full article → Predictive Analytics in HR
Measuring success: ROI, KPIs & continuous improvement
Measure what changes, not just what you shipped.
Adoption: who uses, how often, action completion.
Impact: KPI deltas vs. baseline/control (attrition, time-to-fill, acceptance, mobility).
ROI: vacancy cost avoided + performance uplift + saved spend – implementation cost.
Learning loop: quarterly reviews; retire low-impact features; codify wins.
Read the full article → Measuring Success
Strategy & culture: Operating model, governance, behaviors
Make analytics a habit, not a hero project.
Operating model: start centralized (CoE), evolve to Hub-and-Spoke as adoption grows.
Governance: data contracts, metric catalog, access controls, model cards, fairness & privacy checks.
Culture: triggers → plays → cadence; managers know what to do when a metric crosses a threshold.
Read the full article (series article on strategy & culture) → Not Just Numbers — How to Turn HR Analytics into Strategic Operations
Key use cases you can run this year
Four detailed domains with prerequisites, steps, and value:
Talent acquisition: predict QoH/time-to-fill; prescribe channel/offer strategy.
Comp & benefits: optimize pay/benefit mix under budget; monitor pay equity & compression.
L&D: measure causal impact; prescribe persona-based learning paths and manager behaviors.
Payroll & compliance: predict violations early; prescribe schedule/policy fixes.
Plus near-term “state-of-the-art” horizons for your roadmap.
Read the full article → Key Use Cases
The future: Skills, roles & scaling
From projects to products. Build a durable capability with:
Roles: Head of People Analytics, Product Manager, Data/Analytics Engineer, People Scientist, Analytics Partner, Privacy/AI Ethics, Change & Enablement.
Skills: problem-to-product, causal literacy, data contracts, observability, enablement.
Scaling: CoE → Hub-and-Spoke, portfolio of 4–5 products in 12 months, automated health checks, regular assurance.
Read the full article → Future of HR Analytics
Roadmaps you can copy
90-day starter plan
Weeks 0–2 — Baseline & guardrails
Publish metric definitions (turnover, QoH, time-to-fill, acceptance, mobility).
Fix top data hygiene issues (IDs, timestamps, org hierarchy).
Agree privacy/fairness guardrails and a minimal governance “launch gate”. [general knowledge]
Weeks 3–6 — Two products to MVP
Retention early-warning (simple model, threshold + 2 plays, human review).
TA time-to-fill/QoH lens (bottlenecks + channel/offer suggestions).
Ship decision charters: KPI, threshold, owner, SLA, evidence of completion. [general knowledge]
Weeks 7–10 — Pilot & enablement
Manager training; dashboard + action tracker; weekly trigger huddle.
Track adoption (MAU, actions closed) and early outcome deltas.
Weeks 11–12 — Review & scale decision
Impact brief to the ELT; choose features to retire/standardize; plan quarter 2.
12-month scale plan
Q2: Add pay-equity & compression monitor; publish model cards; start quarterly assurance.
Q3: Launch skills & mobility explorer; embed partners into 1–2 business units.
Q4: Automate health checks (freshness, drift, fairness); run 1–2 A/B or stepped-wedge evaluations; define deprecation policy.
End of year target: 4–5 live products, ≥70% of target managers using monthly; measurable lift in 2 KPIs.
Templates & checklists
A. Decision Charter (one-pager)
User & decision: who acts, on what, and how often
KPI & threshold: formula + alert rule
Playbook: steps, owner, SLA, evidence of completion
Data source & refresh: table/view, frequency, data owner
Assurance: privacy/fairness checks, human review point
Next review date & success criteria
B. People Analytics skills map (6-month upskilling)
Monthly problem-to-product workshop • Analytics dojo • Fairness & privacy mini-labs • Manager enablement sprints
Final thought
Your best signal of maturity isn’t a complex model—it’s a manager changing behavior because an analytics product made the next step obvious and safe. Start with small, reliable wins; measure; scale deliberately; and keep people (and ethics) at the center.
Thank you for reading this recap. Stay with us for season 2 and specials on HR Analytics. Follow us on social media to make sure that you get notified on hte latest articles.


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