HR Workforce & Employee Attrition Analytics Using Power BI
Employee attrition is more than just a percentage.
Behind every attrition number are important questions:
Which departments are affected? Which employee groups have higher attrition? Are there patterns related to compensation, career growth, workload, or employee experience?
To explore these questions, I built an HR Workforce & Employee Attrition Analytics Dashboard using Microsoft Power BI.
📊 About the Project
For this project, I sourced the IBM HR Analytics Employee Attrition & Performance dataset from Kaggle, containing information on 1,470 employees.
The dataset includes information related to:
- Employee demographics
- Department and job role
- Job level
- Monthly income
- Tenure
- Promotion history
- Overtime
- Job satisfaction
- Work-life balance
- Business travel
- Employee attrition
The objective was to transform this raw HR data into meaningful business insights through interactive data visualization and analysis.
🛠️ Tools Used
- Power BI — Dashboard development & visualization
- Power Query — Data cleaning and transformation
- DAX — KPI and analytical calculations
📑 Dashboard Structure
I designed the dashboard across 5 analytical pages:
1. Executive Overview
A high-level view of workforce and attrition KPIs.
2. Workforce Profile
Analysis of employees by department, gender, age group, education field, and job level.
3. Attrition Analysis
Analysis of attrition by department, job role, age group, tenure, and overtime.
4. Compensation & Career Analysis
Analysis of income, job level, and promotion-related patterns.
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5. Employee Experience Analysis
Analysis of job satisfaction, environment satisfaction, work-life balance, and business travel.
Dashboard Overview
Here is a look at the final Power BI dashboard:
🔍 Key Insights
Some notable patterns identified from the analysis were:
- Overall attrition rate was 16.1%, with 237 employees in the attrition count.
- Sales recorded the highest department attrition rate at 20.6%.
- Sales Representatives had the highest displayed job-role attrition at 39.8%.
- Early Career employees showed the highest age-group attrition at 39.2%.
- New Joiners had the highest tenure-group attrition at 29.8%.
- Employees working overtime had 30.5% attrition, compared with 10.4% among employees who did not work overtime.
- Lower Income employees recorded the highest income-band attrition at 28.6%.
- Job Level 1 showed the highest job-level attrition at 26.3%.
These findings highlight several areas that HR teams could investigate further, particularly early-career retention, workload, compensation, and career progression.
Note: These are patterns and associations observed in the dataset; they do not establish that a particular factor directly causes attrition.
💡 From Data to Business Insights
One of the biggest lessons from this project was that a dashboard should not simply answer:
“What happened?”
It should also help stakeholders ask:
“Where is the problem?” → “Who is affected?” → “What patterns do we see?” → “What should we investigate next?”
This helped me approach the project from a business-question-first perspective rather than simply focusing on creating charts.
🚀 What I Learned
This project strengthened my skills in:
Power BI | DAX | Power Query | Data Visualization | HR Analytics | Data Analysis | Business Analysis | Data Storytelling
More importantly, it helped me understand how to transform raw data into insights that can support business decision-making.
🔗 Explore the Project
The complete Power BI project, including the PBIX file, screenshots, and documentation:
GitHub — https://github.com/SDP380/HR-Workforce-Attrition-Analytics-PowerBI
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