---
name: hr-analytics
description: Build HR analytics dashboards, workforce reports, people metrics, and data-driven insights for HR decision-making. Use when creating HR reports, analyzing people data, building workforce dashboards, or presenting HR metrics to leadership. Triggers on phrases like "HR analytics", "people analytics", "HR dashboard", "workforce report", "HR metrics", "people data", "HR reporting", "workforce analytics", "people insights", "HR data", "people metrics", "turnover report", "headcount report".
---

# HR Analytics & Reporting

Transform HR data into actionable insights for strategic decision-making.

## Workflow

1. Define reporting requirements: Stakeholder needs, key metrics, frequency, format.
2. Source and prepare data: Pull from HRIS, ATS, LMS, performance platforms, survey tools.
3. Clean and validate data: Handle missing values, standardize formats, verify accuracy.
4. Build dashboards: Interactive visualizations with filters, drill-downs, alerts.
5. Generate insights: Identify trends, correlations, anomalies, and recommendations.
6. Distribute reports: Scheduled delivery, self-service access, executive summaries.
7. Act on insights: Recommend and track HR initiatives based on data.
8. Iterate: Refine metrics based on stakeholder feedback and evolving needs.

## Core HR Metrics

```
HR METRICS SCORECARD
=====================

CATEGORY 1: WORKFORCE COMPOSITION
  → Total headcount: [Number] (+/- [Number] vs. last quarter)
  → FTE count: [Number] (full-time equivalent)
  → Contractor count: [Number]
  → Headcount by department, level, location, tenure band
  → Diversity metrics: Representation by demographic at each level
  → Org chart depth: Average management span of control

CATEGORY 2: RECRUITING
  → Time-to-fill: [X] days (avg) by department and role type
  → Time-to-hire: [X] days (candidate experience metric)
  → Cost-per-hire: $[X] (total recruiting cost / number of hires)
  → Offer acceptance rate: [X]% (target: > 85%)
  → Source-of-hire: Breakdown by channel (referral, job board, agency, etc.)
  → Quality-of-hire: 90-day performance ratings, 1-year retention
  → Hiring manager satisfaction: Survey score [X]/5.0
  → Pipeline health: Active candidates per open role by stage

CATEGORY 3: ONBOARDING
  → New hire 90-day retention: [X]% (target: > 90%)
  → Onboarding completion rate: [X]% (all tasks completed by Day 30)
  → New hire satisfaction: Survey score [X]/5.0
  → Time-to-productivity: Days until first major deliverable

CATEGORY 4: PERFORMANCE
  → Performance distribution: % in each rating bucket
  → Review completion rate: [X]% (on time)
  → Goal completion rate: [X]% of goals achieved
  → Performance by department, demographic, tenure
  → Calibration variance: Consistency across managers

CATEGORY 5: COMPENSATION
  → Avg salary by role, level, department, location
  → Compa-ratio: Actual salary / midpoint of band (target: 0.90–1.10)
  → Salary equity metrics: Controlled pay gaps by demographic
  → Merit increase avg: [X]% (by performance rating)
  → Bonus payout avg: [X]% of target
  → Total labor cost: $[X] ([X]% of revenue)

CATEGORY 6: LEARNING & DEVELOPMENT
  → Training hours per employee: [X] hours/year
  → Training completion rate: [X]% of assigned courses
  → L&D investment: $[X] per employee
  → Certification rates: [X]% of eligible employees certified
  → Promotion rate: [X]% internally promoted (vs. external hire)

CATEGORY 7: RETENTION & TURNOVER
  → Overall turnover rate: [X]% (voluntary: [X]%, involuntary: [X]%)
  → Regrettable turnover: [X]% (top performers leaving)
  → Turnover by department, manager, tenure, demographic
  → Tenure distribution: % in each tenure band
  → Early turnover (< 1 year): [X]% — indicates hiring/onboarding issues
  → Stay interview completion: [X]%
  → Exit interview completion: [X]%

CATEGORY 8: ENGAGEMENT
  → Engagement survey score: [X]/5.0 (trend over time)
  → eNPS (employee Net Promoter Score): [X]
  → Pulse survey scores: By category and department
  → Absenteeism rate: [X]% (unplanned absences / total workdays)
  → PTO utilization: [X]% (are people actually taking time off?)
```

## Dashboard Design

```
EXECUTIVE HR DASHBOARD
=======================

Layout: Single-page summary with drill-down capability

ROW 1: KEY HEADLINE METRICS (big numbers)
  [Headcount] [Turnover %] [Open Roles] [Engagement Score] [Time-to-Fill]
    1,247       12.3%        23           3.7/5.0          42 days

ROW 2: WORKFORCE TRENDS (line charts)
  → Headcount trend (last 12 months)
  → Turnover trend (last 12 months, voluntary vs involuntary)
  → Engagement score trend (last 8 quarters)

ROW 3: RECRUITING PIPELINE (funnel)
  Applied → Screened → Interviewed → Offered → Hired
  2,340    1,120      456         89        67

ROW 4: DEPARTMENT BREAKDOWN (bar chart)
  Turnover by department (flag > 15%)
  Engagement by department (flag < 3.5)

ROW 5: ALERTS AND ACTIONS (table)
  ⚠ Engineering turnover at 18% (3-month avg) — investigate
  ⚠ Sales hiring pipeline below target — 14 roles > 60 days open
  ✓ Onboarding satisfaction improved to 4.2 (from 3.8)
  ⚠ Q3 engagement pulse declining in Operations — action needed

INTERACTIVE FEATURES:
  → Click any metric to drill down (by department, location, demographic)
  → Filter by date range, department, level, location
  → Export to PDF or Excel
  → Subscribe to scheduled delivery (weekly/monthly)
  → Alert configuration: Set thresholds for automatic notifications
```

## Report Templates

```
MONTHLY HR REPORT — [Month, Year]
===================================

EXECUTIVE SUMMARY:
  [3–5 bullet points highlighting key changes, concerns, and wins]

WORKFORCE SNAPSHOT:
  Headcount: [Current] ([Change] from last month)
  New hires: [Number] | Separations: [Number] | Net change: [Number]
  Open positions: [Number] ([X] critical, [X] backfill, [X] growth)

RECRUITING UPDATE:
  Offers extended: [Number] | Accepted: [Number] | Declined: [Number]
  Avg time-to-fill: [X] days ([Trend] vs. last month)
  Top sources: Referrals ([X]%), Job boards ([X]%), Agencies ([X]%)
  Critical hires needed: [List roles and status]

TURNOVER ANALYSIS:
  Voluntary separations: [Number] ([X]% of headcount)
  Top exit reasons: [Reason 1], [Reason 2], [Reason 3]
  Regrettable losses: [Number] ([Names/Roles if appropriate])
  Departments above avg turnover: [Department 1] ([X]%), [Department 2] ([Y]%)

ENGAGEMENT:
  Latest pulse score: [X]/5.0 ([Trend])
  Key themes: [Positive theme], [Concern theme]
  Action items in progress: [List]

COMPENSATION:
  Total labor cost YTD: $[X] ([Variance] vs. budget)
  Avg merit increase: [X]% (budget: [Y]%)
  Bonus pool status: [On track / At risk / Over budget]

LEARNING & DEVELOPMENT:
  Training completed this month: [Number] courses
  Completion rate: [X]% (target: [Y]%)
  Upcoming: [Key training programs]

NEXT MONTH PRIORITIES:
  → [Priority 1]
  → [Priority 2]
  → [Priority 3]
```

## Predictive Analytics

```
PREDICTIVE HR ANALYTICS
=========================

FLIGHT RISK MODEL:
  → Predicts likelihood of employee resignation within next 90 days
  → Input variables: Tenure, recent promotion, comp vs market, engagement score,
    manager changes, PTO usage pattern, internal job applications, commute distance
  → Output: Risk score (0–100%) per employee
  → Action: Managers flagged for high-risk employees; proactive stay interview

PERFORMANCE PREDICTION:
  → Predicts future performance based on hiring data, onboarding, early reviews
  → Input variables: Pre-hire assessment scores, referral source, onboarding completion,
    30-day feedback, training completion
  → Output: Predicted performance rating range
  → Action: Early intervention for at-risk new hires

TURNOVER FORECASTING:
  → Predicts quarterly turnover by department
  → Input variables: Historical turnover, seasonality, hiring volume, engagement trends,
    market conditions, compensation competitiveness
  → Output: Expected turnover rate and headcount impact
  → Action: Proactive hiring, retention strategies, workload planning

SKILLS FUTURE-CASTING:
  → Predicts which skills will be in highest demand in 12–24 months
  → Input variables: Business strategy, technology roadmap, market trends,
    current skills inventory, industry benchmarks
  → Output: Skills gap forecast by department
  → Action: Targeted L&D investment, hiring strategy adjustment

IMPLEMENTATION:
  → Data requirements: Minimum 24 months of historical data
  → Model validation: Back-test against known outcomes
  → Privacy: Employee-level predictions used for proactive support, not punitive action
  → Ethics: Regular bias audits on predictive models
```

## Integration Points

- HRIS (BambooHR, Workday, Gusto): Primary data source for employee records
- ATS (Greenhouse, Lever): Recruiting metrics
- LMS (Cornerstone, Docebo): Training data
- Performance platform (Lattice, 15Five): Performance and engagement data
- Survey tools (Culture Amp, Glint): Engagement and pulse data
- BI tools (Tableau, Power BI, Looker): Dashboard building and visualization
- Data warehouse: Consolidated people data lake
- Automation (Zapier, Make): Scheduled report delivery, alert configuration

## Edge Cases

- **Data quality issues**: Missing demographics, inconsistent job codes, system sync errors — data governance needed
- **Small datasets**: Limited statistical power; focus on directional trends, not precise predictions
- **Privacy concerns**: GDPR/CCPA compliance; aggregate reporting; anonymize individual data
- **System fragmentation**: Multiple HR systems; integration via APIs; master data management
- **Stakeholder pushback**: "Data doesn't tell the whole story" — combine analytics with qualitative insights
- **Action paralysis**: Data without action is pointless — always pair insights with recommended actions