Human Resources teams collect large amounts of employee data every day, but raw data alone cannot answer important business questions.
This project demonstrates how Business Intelligence can transform HR data into meaningful insights that help organizations improve employee retention, monitor workforce performance, and support data-driven decision-making.
Using Microsoft Power BI, Power Query, DAX, and Data Modeling, I built an interactive HR Analytics Dashboard that enables HR managers and business leaders to quickly identify workforce trends, monitor attrition, evaluate employee satisfaction, and make strategic HR decisions.
Rather than simply creating charts, the objective of this project was to solve real HR business problems through data.
Organizations often struggle to answer critical workforce questions such as:
- Why are employees leaving the organization?
- Which departments experience the highest attrition?
- Which job roles are most affected?
- What is the current workforce size?
- How satisfied are employees?
- Are employees maintaining a healthy work-life balance?
- Which age groups require greater retention efforts?
- Which departments consistently perform well?
Without centralized reporting, HR teams spend significant time preparing manual reports instead of focusing on strategic workforce planning.
This dashboard addresses those challenges by providing a centralized, interactive Business Intelligence solution.
The primary objective was to develop an executive HR dashboard that enables decision-makers to:
- Monitor workforce performance
- Track employee attrition
- Identify high-risk departments
- Measure employee satisfaction
- Evaluate work-life balance
- Support workforce planning
- Improve employee retention strategies
- Enable faster data-driven HR decisions
- Microsoft Power BI
- Power Query (ETL)
- DAX (Data Analysis Expressions)
- Star Schema Data Modeling
- Interactive Dashboard Design
- Business Intelligence
- Data Visualization
Understanding HR business objectives and defining key performance indicators.
Collected HR datasets containing:
- Employee Information
- Employee Survey Data
- Manager Survey Data
Performed data preprocessing using Power Query:
- Removed duplicate records
- Handled missing values
- Fixed inconsistent formatting
- Trimmed unwanted spaces
- Corrected data types
- Validated Employee IDs
Applied ETL processes including:
- Merge Queries
- Query Validation
- Column Standardization
- Relationship Preparation
Created a clean Star Schema using EmployeeID as the primary relationship.
Developed dynamic business measures including:
- Total Employees
- Active Employees
- Attrition Count
- Attrition Rate
- Average Salary
- Average Satisfaction
- Highest Attrition Department
- Highest Attrition Job Role
- Best Performing Department
- Dynamic Insights
- Dynamic Recommendations
- Ranking Measures
- Conditional Formatting Measures
Designed a modern dark-themed executive dashboard with dynamic filtering and interactive visuals.
- 👥 Total Employees
- ✅ Active Employees
- 📉 Attrition Count
- 📊 Attrition Rate
- 💰 Average Salary
- 😊 Job Satisfaction Score
Users can dynamically filter the dashboard using:
- Department
- Job Role
- Gender
- Education Field
- Business Travel
- Attrition Status
Every visual updates instantly based on filter selection.
The dashboard provides insights into:
- Employee Attrition by Department
- Employee Attrition by Job Role
- Employee Attrition by Age Group
- Average Monthly Income by Job Level
- Executive KPI Cards
- Dynamic Business Insights
- Actionable HR Recommendations
The analysis revealed several important workforce trends:
- Research & Development experiences the highest employee attrition.
- Sales Executive is the most affected job role.
- Employees aged 26–35 represent the highest attrition group.
- Overall attrition rate is approximately 16%.
- Average employee satisfaction remains moderate, indicating opportunities for engagement improvement.
- Higher job levels generally receive higher salaries.
- Workforce metrics can be monitored dynamically using interactive filters.
Based on the analysis, HR leadership can consider the following actions:
- Prioritize retention strategies within Research & Development.
- Improve engagement programs for Sales Executives.
- Strengthen employee recognition initiatives in high-performing departments.
- Monitor satisfaction and work-life balance regularly.
- Develop targeted retention plans for employees aged 26–35.
- Use dashboard insights during quarterly workforce planning.
The project combines multiple HR datasets to build a unified analytics model.
Included datasets:
- General Employee Data
- Employee Survey Data
- Manager Survey Data
The datasets were merged using EmployeeID to create a consolidated employee analytics model.
- Dashboard Design
- KPI Development
- Executive Reporting
- Business Storytelling
- Data Cleaning
- Data Transformation
- ETL
- Exploratory Data Analysis
- Power Query
- Data Modeling
- Star Schema
- DAX
- Interactive Visualizations
- HR Analytics
- Workforce Analysis
- Employee Retention Analysis
- Business Recommendations
- Decision Support Reporting
HR-Analytics-Dashboard
│
├── Dashboard
│ └── HR Analytics Dashboard.pbix
│
├── Dataset
│ ├── general_data.csv
│ ├── employee_survey_data.csv
│ └── manager_survey_data.csv
│
├── Images
│ ├── dashboard.png
│ ├── insights.png
│ └── data-model.png
│
├── README.md
│
└── LICENSE
Potential enhancements for future versions include:
- Department-wise drill-through pages
- Employee retention prediction using Machine Learning
- Workforce forecasting
- Diversity & Inclusion dashboard
- Recruitment pipeline analytics
- HR KPI scorecards
- Power BI Service deployment with Row-Level Security (RLS)
Rehan Khan
💼 LinkedIn: (www.linkedin.com/in/rehan-khan-163896327)
💻 GitHub: https://github.com/REHANKHANN20
Feedback, suggestions, and contributions are always welcome.