This assessment evaluates the maturity of an organization's people analytics capability across five critical dimensions: data foundation and quality, reporting and visualization, advanced analytics and predictive modeling, workforce planning integration, and analytics team and governance. The output identifies where analytics enables decision-making and where data gaps undermine every other HR initiative. [src1]
What this measures: Reliability, completeness, and accessibility of workforce data across systems.
| Score | Level | Description | Evidence |
|---|---|---|---|
| 1 | Ad hoc | Data scattered; no single source of truth; basic fields missing | Spreadsheets; partial HRIS; duplicates; unreliable dates |
| 2 | Emerging | HRIS primary but quality issues; some integrations; manual transfers | 60-70% field completion; CSV transfers; no steward; inconsistent jobs |
| 3 | Defined | HRIS is system of record; automated integrations; data dictionary; audits | 90%+ completion; automated feeds; dictionary documented; quarterly audits |
| 4 | Managed | Data warehouse integrating all systems; real-time; governance program | Warehouse with HR+finance; daily refresh; governance committee; quality KPIs |
| 5 | Optimized | Data mesh; real-time streaming; AI quality monitoring; self-service | Data mesh architecture; AI anomaly detection; catalog with lineage |
Red flags: Systems disagree on headcount; no data dictionary; same data entered in multiple places. [src1]
Quick diagnostic question: "Would HRIS, payroll, and finance agree on headcount right now?"
What this measures: Ability to produce and distribute workforce reports and dashboards.
| Score | Level | Description | Evidence |
|---|---|---|---|
| 1 | Ad hoc | No standard reports; Excel when requested; reports take days | Ad hoc Excel; no cadence; leaders don't see HR data; 3-5 day turnaround |
| 2 | Emerging | Standard reports exist but manual; basic HRIS reporting | Monthly headcount via email; turnover calculated manually |
| 3 | Defined | Automated reporting; HR dashboards in BI tool; self-service for basics | Automated monthly dashboard; BI tool with HR models; leaders access directly |
| 4 | Managed | Real-time dashboards; embedded analytics; storytelling with data | Daily refresh; embedded in manager workflows; benchmarks and trends |
| 5 | Optimized | AI-generated insights; natural language querying; predictive alerts | AI surfaces insights; NLQ; democratized access |
Red flags: Headcount report takes >24 hours; no HR dashboard; leadership doesn't see workforce data. [src2]
Quick diagnostic question: "What HR dashboards does leadership see regularly?"
What this measures: Ability to move beyond descriptive reporting to diagnostic, predictive, and prescriptive analytics.
| Score | Level | Description | Evidence |
|---|---|---|---|
| 1 | Ad hoc | No analytics beyond basic reporting; decisions on anecdotes | No statistical analysis; gut-feel talent decisions; no analytics team |
| 2 | Emerging | Some diagnostic analysis; Excel-based; limited statistical capability | Occasional deep-dives; single analyst; no predictive capability |
| 3 | Defined | Regular diagnostic analysis; first predictive models; models validated | Python/R used; attrition model built; accuracy measured; results presented |
| 4 | Managed | Predictive models in operations; prescriptive analytics; A/B testing | Risk scores visible to managers; recommendations active; model drift monitored |
| 5 | Optimized | AI/ML continuously learning; real-time alerts; causal inference; ONA | Auto-retraining; real-time flight risk alerts; causal models; scenario simulation |
Red flags: No analytics beyond headcount and turnover; no predictive models; data science never worked on HR data. [src4]
Quick diagnostic question: "Can you predict who is likely to leave in the next 90 days?"
What this measures: Whether analytics feeds into strategic workforce planning.
| Score | Level | Description | Evidence |
|---|---|---|---|
| 1 | Ad hoc | No workforce planning; headcount is annual budget line item | Budget-only headcount; no demand modeling; reactive hiring |
| 2 | Emerging | Basic headcount planning; some attrition forecasting; annual exercise | Revenue-based ratios; historical attrition assumed; no skills planning |
| 3 | Defined | Structured process with analytics input; supply-demand for key roles | Annual cycle with analytics; demand models; 2-3 scenarios; quarterly review |
| 4 | Managed | Continuous planning; skills-based alongside headcount; buy-build-borrow | Real-time data; skills demand drives L&D; mobility analytics; monthly updates |
| 5 | Optimized | AI-driven planning; predictive supply-demand; dynamic simulation | AI predicts 18-24 months; Monte Carlo simulation; integrated with strategy |
Red flags: No workforce planning; headcount disconnected from strategy; no skills-based planning; plan is one spreadsheet. [src5]
Quick diagnostic question: "How do you forecast workforce needs 12-18 months out?"
What this measures: People, structure, skills, and governance enabling analytics as strategic capability.
| Score | Level | Description | Evidence |
|---|---|---|---|
| 1 | Ad hoc | No dedicated role; HRIS admin pulls data; no analytics skills; no governance | HRIS admin is "analytics"; no statistical skills; analysis when insisted |
| 2 | Emerging | One analyst; basic reporting skills; informal governance; cost center | Single analyst; BI training; no formal governance; email-based requests |
| 3 | Defined | Dedicated team (2-5); mixed skills; formal governance; roadmap exists | Analyst and engineer roles; governance policy; prioritized roadmap |
| 4 | Managed | Mature team with specialized roles; operating model; embedded partners | Data scientists, engineers, consultants; COE with partners; impact measured |
| 5 | Optimized | World-class team; analytics culture embedded; all HRBPs data-literate | External recognition; HRBPs data-literate; thought leadership |
Red flags: No dedicated role; HRIS admin is sole source; no statistical training; requests take weeks. [src3]
Quick diagnostic question: "Who is on your people analytics team, and what is their background?"
Overall Score = (Data Foundation + Reporting + Advanced Analytics + Workforce Planning + Team & Governance) / 5| Overall Score | Maturity Level | Interpretation | Next Step |
|---|---|---|---|
| 1.0 - 1.9 | Critical | HR cannot answer basic workforce questions; decisions on anecdotes | Fix data foundation — clean HRIS, data dictionary, automate reporting |
| 2.0 - 2.9 | Developing | Basic reporting exists but reactive; limited diagnostic ability | Automated dashboards; first analyst; data governance; diagnostic analysis |
| 3.0 - 3.9 | Competent | Team and infrastructure in place; predictive models emerging | Embed models in operations; workforce planning; data science skills |
| 4.0 - 4.5 | Advanced | Analytics drives decisions; models in production | AI/ML optimization; causal inference; ONA; scenario simulation |
| 4.6 - 5.0 | Best-in-class | Industry-leading capability; competitive differentiator | Maintain leadership; pioneer methods; mentor peers |
| Weak Dimension (Score < 3) | Fetch This Card |
|---|---|
| Data Foundation | Data foundation deep-dive |
| Advanced Analytics | L&D Maturity Assessment |
| Workforce Planning | Performance Management Assessment |
| Segment | Expected Average | "Good" Threshold | "Alarm" Threshold |
|---|---|---|---|
| Growth (100-500 employees) | 1.5 | 2.2 | 1.0 |
| Mid-market (500-2,000) | 2.3 | 3.0 | 1.5 |
| Enterprise (2,000-10,000) | 3.0 | 3.7 | 2.2 |
| Large enterprise (10,000+) | 3.6 | 4.2 | 2.8 |
[src6]
Fetch when a user asks to evaluate HR data and analytics capability, diagnose why HR cannot answer workforce questions reliably, build a business case for analytics investment, or assess readiness for AI-powered workforce tools.