HR Process Optimization & Workflow Analytics

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

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Project Overview

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

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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

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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

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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