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HR Process Optimization & Workflow Analytics

Tech Stack: Python · SQL (SQLite) · Power BI
Timeline: Mar 2026 – Present

Project Overview

Analyzed HR workflows across 300 employees to identify operational bottlenecks, reduce onboarding inefficiencies, and support data-driven resource allocation decisions.

Key Findings

Metric Value
Overall Attrition Rate 23.3%
Highest Attrition Dept Sales (31.4%)
Avg Onboarding Duration 23.8 days
Onboarding Bottleneck IT Setup (avg 9.7 days)
Leave Approval Rate 88.4%
Avg Performance Score 3.38 / 5.0
High Performers 14.0% of workforce

What Was Built

1. generate_data.py

Builds a realistic HR dataset with 4 tables:

  • employees — 300 records with dept, level, salary, performance, attrition
  • onboarding — 1,500 step-level records tracking each stage's duration
  • leave_requests — 1,790 leave records with type, duration, approval
  • performance_reviews — 492 semi-annual review records

2. analyze.py

Full Python analysis pipeline:

  • Attrition analysis by department and job level
  • Onboarding bottleneck detection (IT Setup = main delay)
  • Leave & resource utilization by department
  • Performance insights and high-performer segmentation
  • Exports 5 Power BI-ready CSVs + dashboard PNG

3. queries.sql

8 structured SQL queries covering:

  1. Attrition rate by department
  2. Onboarding bottleneck per step
  3. Employees with slowest onboarding (top 10)
  4. Leave utilization & approval rate by department
  5. Attrition risk scoring (performance + leave cross-analysis)
  6. Performance trend by department
  7. Salary vs performance (resource allocation efficiency)
  8. Monthly attrition trend

4. Power BI Dashboard

Import these CSVs into Power BI for dashboards:

  • powerbi_kpi_summary.csv — KPI cards
  • powerbi_attrition_by_dept.csv — Bar chart
  • powerbi_onboarding_bottleneck.csv — Funnel / bar
  • powerbi_leave_by_dept.csv — Table / matrix
  • powerbi_performance_by_dept.csv — Gauge / bar

How to Run

# Step 1 — Install dependencies
pip install pandas numpy faker matplotlib

# Step 2 — Generate dataset
python generate_data.py

# Step 3 — Run analysis
python analyze.py

# Step 4 — Open SQL queries in DB Browser for SQLite
# File: output/hr_analytics.db

# Step 5 — Import CSVs from output/ into Power BI