HR Analytics Dashboard built Power BI

HR Analytics Dashboard built in Power BI to explore workforce attrition trends and support HR decision-making.

Project Overview

This HR Analytics Dashboard analyzes employee attrition patterns across demographics, job roles, salary bands, and tenure. The goal of the project was to build a clean, interactive dashboard that helps HR teams understand why employees leave and where strategic interventions are needed.

This project demonstrates my skills in:

• Power Query data cleaning

• DAX measure creation

• Data modeling

• HR analytics

• Dashboard design and storytelling

Data Preparation (power Query)

All data cleaning and transformation was completed in Power Query before modeling. Key steps included:

• Removing nulls and duplicate records

• Standardizing text finds (Department, JobRole, Education)

• Formatting numeric fields (Age, MonthlyIncome, YearsAtCompany)

• Creating helper columns;

Attrition_Count (1 = Yes, 0 = No)

Age_Group (18–25, 26–35, 36–45, 46–55, 55+)

Salary_Band (Up to 5K, 5K–10K, 10K–15K, 15K+)

These transformations ensured the dataset was clean, consistent, and ready for analysis.

Data Model Design

The dashboard is powered by a clean relational model built in Power BI:

• HR_Data (Fact Table): Core employee records with age, salary, tenure, attrition, and job role

• Departments Table: Normalized department names linked to HR_Data

• Jobs Table: Job roles and IDs for role-based analysis

• Education Tables: Education levels grouped for attrition insights

All tables are connected via one-to-many relationships, enabling clean filtering, accurate aggregations, and scalable reporting.

DAX Measures Used in the Dashboards

1. Attrition Rate

This measure calculates the percentage of employees who left the company

Attrition_Rate = SUM(HR_Data[Attrition_Count]) / SUM(HR_Data[EmployeeCount])

2. Employee Count

Counts the total number of employees in the dataset.

Employee_Count = COUNTROWS(EMPID)

3. Attrition Count

Counts the number of employees who left the company

Attrition_Count = CALCULATE(COUNTROWS(HR_DATA), HR_DATA[Attrition] = "Yes")

4. Average Age

Average_Age = AVERAGE(HR_DATA[Age])

5. Average Salary

Average_Salary = AVERAGE(HR_DATA[MonthlyIncome])

6. Average Tenure

Average_Tenure = AVERAGE(HR_DATA[YearsAtCompany])

Dashboard Highlights

Summary KPIs

• Employee Count: 1,480

• Attrition Cases: 238

• Attrition Rate: 16%

• Average Age: 37

• Average Salary: 6.5K

• Average Tenure: 7 years

These KPIs provide a quick overview of workforce health.

Attrition Insights

By Gender

• Male: 151

• Female: 87

By Age Group

• 26-35: Highest attrition (116 cases)

• 18-25: 44

• 36-45: 43

• 46-55: 27

• 55+: 8

By Education

• Life Sciences: 37.39%

• Medical: 26.47%

• Marketing: 15.13%

• Technical Degree: 13.45%

• Other: 4.62%

By Salary Band

• Up to 5K: 163

• 5K-10K: 49

• 10:15K: 21

• 15K+: 5

By Years at Company

Attrition peaks at:

• 1 year (59 cases)

• Smaller peaks at 4 and 6 years

By Job Role

• Reach Scientist: 100

• Human Resources: 58

• Sales Representative: 44

• Laboratory Technician: 31

Key Insights

• Younger employees (26-35) show the highest turnover

• Lower salary bands experience significantly more attrition

• Most attrition occurs within the first year, suggesting onboarding or job-fit issues

• Education background influences attrition patterns

These insights help HR teams target retention strategies more effectively.