---
name: people-analytics
description: Apply data analytics to people decisions including workforce dashboards, predictive modeling, turnover analysis, productivity metrics, and ROI measurement for HR programs. Use when building HR dashboards, analyzing people data, creating predictive models, measuring HR impact, or developing people insights. Triggers on phrases like "people analytics", "HR analytics", "workforce analytics", "people data", "turnover analysis", "predictive analytics", "HR dashboard", "people insights", "HR metrics", "workforce insights", "data-driven HR", "HR BI", "people intelligence".
---

# People Analytics

Transform HR data into actionable insights for strategic people decisions.

## Workflow

1. Define questions: What business problems need data-driven answers?
2. Collect data: HRIS, engagement surveys, performance systems, external data.
3. Clean and prepare: Data quality, integration, standardization, privacy.
4. Analyze: Descriptive, diagnostic, predictive, prescriptive analytics.
5. Visualize: Dashboards, reports, stories that drive understanding.
6. Act: Translate insights into recommendations and actions.
7. Measure impact: Did data-driven actions improve outcomes?
8. Iterate: Continuous improvement of analytics capabilities.

## Analytics Maturity and Types

```
ANALYTICS MATURITY MODEL
==========================

LEVEL 1: DESCRIPTIVE (What happened?)
  → Headcount by department, location, role
  → Turnover rate by month, quarter, year
  → Time-to-fill, time-to-hire
  → Absenteeism rates
  → Diversity demographics
  → Engagement survey scores
  → Tools: HRIS reports, basic spreadsheets, dashboards

LEVEL 2: DIAGNOSTIC (Why did it happen?)
  → Turnover drivers: Regression analysis of exit data
  → Engagement correlation: What factors drive engagement scores?
  → Hiring source effectiveness: Quality by source
  → Cost-per-hire by department
  → Absenteeism patterns: Day of week, season, manager correlation
  → Tools: Pivot tables, statistical analysis, correlation, segmentation

LEVEL 3: PREDICTIVE (What will happen?)
  → Flight risk scoring: Which employees are likely to leave?
  → Hiring demand forecasting: Future headcount needs
  → Promotion likelihood: Who is ready for advancement?
  → Training effectiveness prediction: Which programs drive outcomes?
  → Revenue per employee forecasting
  → Tools: Machine learning, regression models, clustering, classification

LEVEL 4: PRESCRIPTIVE (What should we do?)
  → Optimal hiring strategy: Where to focus recruiting
  → Retention intervention targeting: Who to retain and how
  → Workforce optimization: Ideal team composition
  → Compensation optimization: Market-aligned, equity-compliant
  → Program ROI modeling: Investment recommendations
  → Tools: Optimization algorithms, scenario modeling, recommendation engines

ORGANIZATIONAL READINESS:
  → Data infrastructure: Integrated HRIS, data warehouse
  → Data literacy: HR and business leader comfort with data
  → Analytics team: Dedicated people analysts or partnership with data science
  → Culture: Data-driven decision-making norms
  → Privacy and ethics: Governance, consent, bias mitigation
```

## Key Metrics Dashboard

```
CORE PEOPLE METRICS
====================

WORKFORCE COMPOSITION:
  → Total headcount (by department, location, level, type)
  → Full-time equivalent (FTE) count
  → Contingent worker ratio (% contractors vs. FTE)
  → Manager-to-employee ratio (span of control)
  → Organizational layers (hierarchy depth)

RECRUITING:
  → Time-to-fill: Job posting to acceptance (by role, department)
  → Time-to-hire: Application to acceptance
  → Cost-per-hire: Total recruiting cost ÷ number of hires
  → Quality-of-hire: Performance rating at 6/12 months, manager satisfaction
  → Offer acceptance rate: Offers accepted ÷ offers extended
  → Source-of-hire: % by source (referral, job board, direct, agency)
  → Candidate experience score: Post-application survey

Talent and Performance:
  → Performance distribution: % in each rating category
  → Promotion rate: Internal promotions ÷ eligible employees
  → Internal mobility rate: Lateral moves + promotions ÷ total workforce
  → High-potential identification: % of workforce identified as HiPo
  → Succession readiness: % of critical roles with ready successor
  → Manager effectiveness: Team engagement, retention, performance correlation

ENGAGEMENT AND CULTURE:
  → Overall engagement score (% agree/strongly agree)
  → eNPS (Employee Net Promoter Score)
  → Pulse survey scores (by topic and frequency)
  → Psychological safety score
  → Inclusion index score
  → Manager effectiveness score
  → Trust in leadership score

RETENTION AND TURNOVER:
  → Voluntary turnover rate: Voluntary separations ÷ average headcount
  → Involuntary turnover rate: Involuntary separations ÷ average headcount
  → Regrettable turnover: Top performers who left ÷ total top performers
  → New hire turnover: Separations within first 12 months
  → Tenure distribution: Average tenure, median tenure
  → Retention rate by cohort: 1-year, 2-year, 3-year retention
  → Flight risk score: Predictive model output for attrition risk

LEARNING AND DEVELOPMENT:
  → Training hours per employee
  → Training completion rate (mandatory and elective)
  → Training ROI: Performance improvement post-training
  → Promotion readiness: % meeting competency requirements
  → Skills gap analysis: Required vs. possessed skills
  → Development program participation rate

COMPENSATION:
  → Average salary by grade/role/location
  → Compa-ratio distribution: % below, at, above midpoint
  → Pay equity ratio: Gender, race pay ratios by role
  → Merit increase average: % increase by performance rating
  → Turnover cost: Estimated cost of separations (by role)
  → Revenue per employee: Total revenue ÷ average headcount

WELL-BEING:
  → Absenteeism rate: Unscheduled absences ÷ total workdays
  → Presenteeism estimate: Productivity loss while at work
  → EAP utilization rate: Unique users ÷ eligible employees
  → Well-being survey score
  → Burnout indicator: Survey-based or behavioral proxy
  → Work-life balance score
```

## Predictive Analytics

```
PREDICTIVE ANALYTICS USE CASES
================================

1. FLIGHT RISK MODELING
   Purpose: Identify employees at high risk of voluntary turnover
   Data inputs:
     → Tenure (highest risk: months 6–18 and years 3–5)
     → Last performance rating (both high and low performers at risk)
     → Last promotion date (stagnation risk)
     → Commute distance / remote status
     → Engagement survey scores (declining trend = risk)
     → Manager change (new manager = transition risk)
     → Compensation vs. market (below market = flight risk)
     → Training participation (low engagement = disengagement)
     → Peer network changes (friends leaving = network effect)
   Output: Flight risk score (0–100) for each employee
   Action: Targeted retention interventions for high-risk employees

2. HIRED QUALITY PREDICTION
   Purpose: Predict which candidates will become top performers
   Data inputs:
     → Source of hire (referral, campus, agency, etc.)
     → Assessment scores (cognitive, personality, skills)
     → Interview panel ratings
     → Education and prior employer quality
     → Time in previous roles (tenure pattern)
     → Career path consistency
   Output: Quality prediction score
   Action: Refine hiring criteria; weight sources and assessments

3. PROMOTION READINESS
   Purpose: Identify employees ready for advancement
   Data inputs:
     → Performance ratings (consistency and recency)
     → Competency assessments (current vs. next level requirements)
     → Stretch assignment success
     → Manager recommendation
     → Peer feedback (360 scores)
     → Learning completion (required development for next level)
   Output: Readiness score and recommended timeline
   Action: Accelerate or develop; inform succession planning

4. TRAINING EFFECTIVENESS
   Purpose: Predict which training programs drive performance improvement
   Data inputs:
     → Pre/post performance metrics
     → Training completion and engagement
     → Manager feedback post-training
     → Behavioral change indicators
     → Business outcome correlation
   Output: ROI estimate per program; recommendation for continuation/investment
   Action: Optimize L&D spending; focus on high-impact programs

5. OPTIMAL TEAM COMPOSITION
   Purpose: Predict team performance based on composition
   Data inputs:
     → Skills diversity and complementarity
     → Tenure mix (new + experienced)
     → Personality/cognitive diversity
     → Size and span of control
     → Historical team performance data
   Output: Team effectiveness prediction
   Action: Inform team design, project assignments, hiring priorities
```

## Data Governance and Ethics

```
PEOPLE ANALYTICS GOVERNANCE
==============================

DATA PRIVACY:
  → Anonymization: Remove PII from analytical datasets where possible
  → Aggregation: Report at group level (minimum 5 individuals)
  → Access controls: Role-based data access; analysts see raw data, managers see aggregated
  → Consent: Employee awareness of data usage for analytics
  → Compliance: GDPR, CCPA, local privacy regulations
  → Data retention: Defined retention periods; secure deletion

BIAS AND FAIRNESS:
  → Algorithm audit: Test models for disparate impact across demographics
  → Fairness metrics: Equal opportunity, demographic parity, individual fairness
  → Human oversight: Algorithms inform, not replace, human decisions
  → Transparency: Explainable models; avoid "black box" decisions
  → Challenge process: Employees can contest algorithm-driven decisions

ETHICAL PRINCIPLES:
  → Beneficence: Analytics should improve employee experience
  → Non-maleficence: Do no harm (avoid surveillance, punishment)
  → Autonomy: Respect employee privacy and choice
  → Justice: Fair and equitable treatment across groups
  → Transparency: Be open about what data is collected and how it's used
  → Accountability: Clear ownership for data use and outcomes

ANALYTICS CHARTER:
  → Purpose: Why we do people analytics
  → Principles: Ethical guidelines for data use
  → Governance: Who approves analytics projects
  → Standards: Data quality, methodology, reporting standards
  → Review: Annual ethics review of analytics practices
```

## Integration Points

- HRIS: Primary data source (employee records, compensation, performance)
- Data warehouse: Integrated people data lake for advanced analytics
- BI tools: Tableau, Power BI, Looker for dashboarding and visualization
- Survey platforms: Engagement, pulse, climate data
- ATS: Recruiting data for hiring analytics
- LMS: Learning and development data
- Time and attendance: Absenteeism, productivity proxy data
- Finance systems: Cost data, revenue per employee, ROI calculation
- Communication tools: Data-driven storytelling, insight distribution

## Edge Cases

- **Small organizations**: Limited data volume; focus on descriptive analytics; use benchmarking
- **Data silos**: Multiple systems; integration challenges; data quality issues
- **Data quality**: Incomplete, inconsistent, or inaccurate data; governance needed
- **Privacy concerns**: Employee resistance; transparent communication about data use
- **Over-analysis**: Analysis paralysis; focus on actionable insights, not just data
- **Algorithmic bias**: Models may perpetuate existing bias; regular auditing required
- **Global analytics**: Data residency laws; cross-border data transfer restrictions