IBM: HR ANALYTICS DASHBOARD

This is a detailed article about IBM HR Analytics Dashboard where I visualized employee attrition metrics and KPIs, I also generated insights and recommended strategies for employee retention.
Tools Used: Microsoft Excel | Microsoft Power BI
Quick Links: Power BI Dashboard | GitHub Repository
Below is a table of contents in case you want to go to any section.

Table Contents:

1. Introduction
2. Process
1. Microsoft Excel
2. Microsoft Power BI
3. Misc.
3. Finished Project
4. Overview of Data
5. Insights
6. Recommendations
7. What I Learned
8. Resources

INTRODUCTION

Here’s something about IBM: IBM (International Business Machines Corporation), nicknamed Big Blue, is an American multinational technology corporation headquartered in Armonk, New York and is present in over 175 countries. IBM is best known for producing and selling computer hardware and software, as well as cloud computing and data analytics. It is among the world’s largest employers, with over 297,900 employees worldwide in 2022.
Why I chose this project: Although this is a fictional dataset created by IBM data scientists (as claimed by the person who shared this data), I wanted to improve my dashboard creating and data analysis skill. Also this is the first time I did a project in this domain where I used my analytical skills to recommend strategies (see overall goal) based on the insights generated.
Overall Goal: “Recommend strategies to retain employee”
Questions: What is the average age of employees who left the company? What is the most common department for employees who left the company? What is the correlation between salary and attrition? What is the correlation between years of experience and attrition? What is the correlation between education level and attrition? What is the gender split of employee attrition? …etc.

Process

Overview: The original data was cleaned in Microsoft Excel, then I used Microsoft Power BI to create an interactive dashboard. I used NovyPro to host the Power BI dashboard file.

Microsoft Excel

I downloaded the csv file and converted it into spreadsheet to check if there was any garbage/null values but the data was already clean meanwhile I also familiarized myself with the data.
Below are the sample images of data column, there are total 35 columns it consists of both employee attrition and performance data.

Power BI

To view my complete dashboard click here.
My goal was to display all the key metrics leading to employee attrition in one screen.
While creating charts and cards, I faced a few problems like the values in attrition column had yes or no values so whenever I wanted to show the count of employee attrition I always had to apply filter for “yes” value so to tackle this problem I created a new column called Attrition with the values 1 and 0 to make it easy for counting this saved me some time and then I renamed the previous one as Attrition(Yes/No).

Added Columns:
Attrition – to store 1 for Yes and 0 for No w.r.t Attrition(Yes/No) column.
Age Group – to categorize the age in two groups “Adult” and “Senior”.
Salary Slab – to categorize the salaries- “Upto $5K”, “$5K-$10K”, “$10K-$20K”, “$20K+”.

I also created a measure:
Attrition Rate – to calculate the rate of attrition, please refer the formula below:
Attrition Rate = 100 * Attrition / EmployeeCount

To make the dashboard interactive I added 3 slicers to filter the whole dashboard by:
Department – Human Resources, Research & Development and Sales.
Gender – Male and Female
Age Range – Although the age is categorized in Age Group column but I added this slicer so you can select an age range of your choice, you can select both upper and lower limit with the help of slider or enter it manually.

MISC.

I used NovyPro to host the dashboard.

FINISHED PROJECT

Here is my finished project: IBM: HR Analytics Dashboard

Preview

Overview of Data

Data:

1. Average Age of Employee
2. Attrition by Department
3. Attrition by Salary Slab
4. Attrition by Years at Company
5. Attrition by Educational Background
6. Attrition by Gender
7. Job Satisfaction Rate by Job Role
8. Attrition by Job level

Average Age of Employee

Attrition by Department

Attrition by Salary-Slab

Attrition by Years at Company

Attrition by Educational Background

Attrition by Gender

Job Satisfaction Rate by Job Role

Attrition by Job Level

INSIGHTS

Below are the general insights and answers to the question from the dashboard:

• The average age of employees who left the company is 37 years.
• The Research & Development department witnessed the highest attrition, accounting for 133 of the total.
• The majority of employees who left the company belonged to the salary range below $5000 per month, comprising 163 individuals.
• Among employees who left, the highest attrition rate was observed among those who spent 1 year at the company (25%), followed by 2 years (11%) and 5 years (9%).
• The educational background of employees who left the company indicates that 89 individuals had a Life Science background, 63 had a Medical background, and 35 had a Marketing background.
• Male employees experienced an attrition rate of 17%, while female employees had a slightly lower attrition rate of 14.8%.
• The attrition rate in the Research & Development department was the highest at 56%, followed by Sales at 39% and the HR Department at 5%.
• Approximately 69% of employees who left the company fell under the salary range below $5000, while 21% fell under the $5000-$10,000 range, 8% under the $10,000-$15,000 range, and 2% under the $20,000+ range.

RECOMMENDATIONS

• Implement retention strategies for employees around 40 years old.
• Address attrition in the Research & Development department.
• Enhance compensation and benefits for employees in lower salary ranges.
• Develop employee engagement initiatives for early career employees.
• Provide opportunities for career growth and learning based on educational backgrounds.
• Pay attention to gender disparities in attrition and promote diversity and inclusion.
• Mitigate attrition in the Research & Development and Sales departments with department-specific strategies.
• Regularly review and adjust salary ranges to ensure competitiveness.

WHAT I LEARNED

How to create new column using DAX queries.
How to create a measure using DAX query.
Creating universal filters and slicers with sliders for Age Range.
Creating matrix table to display aggregate data w.r.t a column.

RESOURCES

This dataset was downloaded from kaggle, to download it click here.