📊 HR Attrition Analytics - Power BI Project

📝 Overview

This Business Intelligence (BI) project focuses on analyzing HR attrition data using Power BI, aiming to uncover critical workforce trends and support strategic retention efforts. Over a 4-week timeline, the BI team collaboratively transformed raw HR data into insightful dashboards and actionable recommendations.

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🚀 Project Objectives

• Identify key drivers of employee attrition.
• Provide a data-driven foundation for HR decision-making.
• Visualize demographic, satisfaction, and performance patterns.
• Recommend strategic initiatives for retention and equity.

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🧑‍💻 Team & Tools

Team Members:

• Ahmed Mohsen (Me)
• Abdulrahman Hasib
• Ahmed Ibrahim
• Ibrahim Ahmed
• Ali Gamal
• Huda Moussa

Tools & Technologies Used:

• Power BI
• Power Query (M Language)
• DAX (Data Analysis Expressions)
• Microsoft Excel

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📅 Project Timeline

Week	Phase	Key Activities
1	Research & Planning	Define scope, understand dataset, set communication plans
2	Data Cleaning	Remove duplicates, transform columns, create conditionals
3	Modeling & DAX Development	Build star schema, apply calculated fields and relationships
4	Dashboarding & Finalization	Create dashboards, validate insights, compile recommendations & presentation

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📂 Dataset Description

The dataset included employee records with columns such as:

• Age, Education, Gender, Job Role, Department
• Attrition (Yes/No)
• ReviewDate, Job Satisfaction, Performance Rating
• Business Travel, Overtime, Hire Date

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🧹 Data Cleaning Process

Performed in Power Query:

1. Removed Duplicates:
   Ensured unique and valid employee records.

2. Added Conditional Columns:

   • Mapped Education levels (1–5) → "Below College" to "Doctor".
   • Grouped Age into ranges (18–25, 26–38, etc.).
   • Converted Attrition → "Still Working" / "Left The Company".

3. Custom Columns:

   • EndDate & InvalidReview to identify and filter faulty data.

4. Standardization:

   • Replaced inconsistent values.
   • Transformed column types using Table.TransformColumnTypes.

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🧠 Data Modeling

Implemented a star schema model with fact and dimension tables.

Fact Tables:

• FactEmployee: Tracks attributes like Department, Travel, Education.
• FactPerformanceRating: Contains ratings and review dates.

Dimension Tables:

• DimDate: Time hierarchy (DayName, DayNumber, etc.).
• DimSatisfactionLevel / DimRatingLevel: Satisfaction scales.

Relationships were established using EmployeeID and temporal keys to support slicing data across different perspectives.

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📊 Dashboard Features

The Power BI dashboard was divided into 4 key pages:

1. Overview:

   • Workforce size, education levels, department distribution, general stats.

     

2. Demographics:

   • Age, gender, marital status, ethnicity, and salary distribution.

3. Performance Tracker:

   • Individual employee satisfaction and performance over time.

   

4. Attrition Insights:

   • Attrition rate, inactive count, hire/tenure date, travel frequency, and overtime.

Each visual enabled drill-through and filtering for deeper exploration.

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🧮 DAX Usage

Custom metrics were calculated using DAX, including:

• Attrition rate by department and job role.
• Time intelligence metrics: Year, Month, Day from ReviewDate.
• Measures to track satisfaction averages and performance over time.

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💡 Key Insights & Recommendations

1. Target Age Group 18–30:
   Introduce mentorship programs to retain younger employees.

2. Ethnicity Pay Gap:
   Perform salary audits and implement transparent pay scales.

3. New Hire Support:
   Improve onboarding and offer early-career mentorship.

4. Overtime Risks:
   Redistribute workload or hire to ease excessive hours.

5. Travel Frequency:
   Balance business travel assignments across staff.

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📈 Impact & Lessons Learned

• Strategic Value: Helped HR visualize critical metrics and formulate retention strategies.
• Team Collaboration: Effective use of cloud tools, regular check-ins, and defined task ownership led to successful project execution.
• Challenges Overcome: Initial data complexity was addressed through iterative cleaning and modeling.

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🏁 Conclusion

This BI project provided the organization with a scalable, interactive, and insightful dashboard that transformed raw HR data into actionable decisions for reducing attrition, enhancing satisfaction, and driving organizational growth.