MeriSKILL - HR Attrition Analysis

This repository serves as my documentation for the MeriSKILL HR Attrition Analysis - Power BI Project.

It was created as a learning project as part of my Internship assignment at: MeriSKILL during May - June 2024.

It showcases my competancy to work with Microsoft Power BI and demonstrates my proficiency in essential Power BI concepts like Data Profiling, ETL with Power Query, Semantic Data Modelling, DAX Measures & DAX Calculated Columns, Dashboard Designing, Visualization, Conditional Formating, Filters, Bookmarks, Page Navigation, Publishing and Report Optimization etc.

The entire project has been implemented using Microsoft Power BI Desktop 2.128.751.0 and published on Microsoft Power BI Service.

---

Contents:

Please find the sectional links for the project below:

• Live Dashboard Link
• Project Objective
• Tools used & Methodologies implemented
• About the Dataset
  • Data Dictionary
  • Data Integrity
  • HR Employee Attrition Raw Data
• Project Implementation
• Analysis Insights
• Data-driven Recommendations

---

Project Objective:

In this report we navigate through the core of HR Analytics, from meticulous data collection to the art of deriving actionable insights from employee attrition data. It involves identifying patterns and optimizing processes that breathes life into our organizational strategies.

HR analytics, also known as Human Resources analytics or talent analytics, is the systematic application of data analysis and statistical methods to human resources data. It involves gathering and analyzing data related to an organization’s workforce to make informed decisions and drive improvements in HR processes, policies, and strategies. The primary goal of HR analytics is to optimize the performance, engagement, and overall effectiveness of an organization’s workforce.

By leveraging HR analytics effectively, organizations can align their human resources strategies with business objectives, enhance organizational performance, and create a more engaged and productive workforce. It’s about leveraging data to make informed decisions that impact both employees and the organization positively.

Tools used:

1. Microsoft Power BI: for Data Cleaning, Data Analysis, Data Visualization & Dashboarding
2. GitHub - for Documentation

Skills & Methodologies implemented:

1. Data Cleaning: Power Query
2. Data Manipulation: DAX Measures & Columns
3. Data Modelling and Normalization
4. Data Visualization
5. Dashboarding: Filters, Custom Icon Buttons, Bookmarks, Page Navigation
6. Report Publishing: PBI Service and Report Optimization
7. Documentation

---

About the Dataset:

The original dataset is a single file with 1470 records and 35 columns and contains employee related parameterized data that has been categorized into 3 broad types: Deomographics, Turnover & Wellbeing.

Attrition Data Dictionary:

Column Name	Type	Description
Age	int	The age of the employee
Attrition	char	Whether the employee has left the company (Yes) or not (No)
BusinessTravel	char	Frequency of business travel (e.g., Rarely, Frequently)
DailyRate	int	Daily rate of pay
Department	char	The department where the employee works
DistanceFromHome	int	Distance of employee’s residence from the workplace
Education	int	Employee’s education level
EducationField	char	Field of education
EmployeeNumber	int	Unique identifier for each employee i.e Primary Key
EnvironmentSatisfaction	int	Employee’s satisfaction with their work environment
Gender	char	Employee’s gender
HourlyRate	int	Hourly rate of pay
JobInvolvement	int	Level of involvement in the job
JobLevel	int	Job level or position in the company
JobRole	char	Employee’s role or position at work
JobSatisfaction	int	Employee’s job satisfaction level
MaritalStatus	char	Employee’s marital status
MonthlyIncome	int	Monthly income of the employee
MonthlyRate	int	Monthly rate of pay
NumCompaniesWorked	int	Number of companies the employee has worked for
OverTime	char	Whether the employee works overtime (Yes) or not (No)
PercentSalaryHike	int	Percentage increase in salary
PerformanceRating	int	Employee’s performance rating
RelationshipSatisfaction	int	Employee’s satisfaction with their relationships at work
StockOptionLevel	int	Employee’s stock option level
TotalWorkingYears	int	Total number of years the employee has worked
TrainingTimesLastYear	int	Number of times the employee was trained last year
WorkLifeBalance	int	Employee’s perceived work-life balance
YearsAtCompany	int	Number of years the employee has worked at the company
YearsInCurrentRole	int	Number of years the employee has been in the current role
YearsSinceLastPromotion	int	Number of years since the employee’s last promotion
YearsWithCurrManager	int	Number of years with the current manager

These columns collectively provide valuable information about employees in the dataset, which can be used for various HR analytics and decision-making processes.

Data Integrity:

ROCCC Evaluation:

• Reliability: LOW - The raw dataset is created and updated by MeriSKILL. It has only 1 file. There is no information how the data was collected or preprocessed.
• Originality: MED - First party provider (MeriSKILL)
• Comprehensiveness: HIGH - Only 1 CSV File was provided however the availability of 1470 records and 35 columns is quite adequate for an in depth analysis on the influence of various factors towards employee attrition.
• Current: LOW - Dataset creation date was not documented/provided by MeriSKILL. So its not very relevant and hence the analysis needs to be comprehended as a general (not year-specific) trend.
• Citation: LOW - No official citation/reference available.

---

Analysis Insights:

Employee Demographic Patterns:

• The age category of Adults (26 – 44 yrs) is home to the highest workforce, with 876 Active and 157 Attrited employees. However the age category of Young Adults (18 - 25 yrs) despite having 2nd lowest Active employee count are affected with the highest Attrition rate of upto 32.5 % in Males and upto 41.9% in Females. Perhaps it’s a testament to the wisdom and stability that comes with age.

• Male employees dominate our workforce, constituting 882 out of 1470 individuals. However, Female employees display a lower Attrition rate at 14.8 %, contrasting to higher Male Attrition rate of 17 %.

• Attrition rate seems to be higher among employees with Single Marital status of around 23 - 26 % regardless of the employee gender.

• While Bachelor employees boast the highest Active employees they are also affected with the 2nd highest Attrition rate of 17.3 % trailing just behind High School employees (18.2 %), considering the wide range of opportunities available for them in the job market, which narrows down as employees complete their Doctorate (10.4 %).

• HR (25.9 %), Technical Degree (24.2 %) & Marketing (22 %) field employees have Attrited highest in the company.

• Attrition increases the longer employees have to travel to office with maximum Attrition rate of 42.9 % seen around the 24 Km mark indicating that the majority of employees prefer a close commute, reflecting an aspect of convenience and local integration.

Employee Turnover Patterns:

• There seems to be a direct correlation between Average Monthly Income (AMI) and Attrition. Managers earn the highest AMI of 17.4K $ and have one of the lowest Attrition rates of 5.5 % while Sales Representative earn the lowest AMI of 2.6K $ and have the highest Attrition rate of 39.8 %. The Attrition rate goes up with decreasing AMI hinting at the interplay between financial satisfaction and employee retention.

• Attrition in general goes down as employees scale the corporate ladder as seen with Entry level & Executive employees showing 26.3 % & 4.7 % Attrition rate.

• As employees spend more time in the company, Attrition rate decreases broadly from 36.4 % at 0 yrs in the company to 6.7 % at 22 yrs in the company post which it displays a contrasting rising trend till 40 yrs in the company when it reaches 100 % mark.

Employee Wellbeing Patterns:

• Employee Satisfaction trends contradict the Attrition trend as expected. As Employee Satisfaction, Relationship Satisfaction & Job Satisfaction metrics increase, employee attrition decreases from around 23 % to 13.2 %.
• Similar to Employee Satisfaction as Employee Job Involvement increases, the Attrition rate decrease from 33.7 % to just 9 %.

• Attrition goes up from 8 % to 24.9 % as employees need to almost Never Travel to Frequently Travel for business.

• Work-Life Balance metric provides a bell curve trend of Attrition as both Poor & Very Good WLB leads to 24.4 % attrition while Fair & Good WLB leads to 15.5 % attrition on average.

• Increasing Salary Hike % leads to decreasing Attrition among employees across all job levels.

---

Data-driven Recommendations:

Employee Retention and Job Satisfaction:

• Conduct comprehensive surveys and engage in direct dialogues with employees to understand the underlying reasons for high attrition (Avg 23 %) due to Job/Environment/Relationship dissatisfaction.
• Implement measures to improve Work-Life Balance, especially for long-standing employees, to reduce Attrition rates upto 14.2 %.
• Recognizing the importance of proximity to work in their daily routines, allow Working from Home (WFH) for employess living with distance of more than 11 Km to reduce travel frequency and drop Attrition rates from 24.9 % to just 8 %.
• Ensure Salary Hike % is at least 15 % or higher to drastically cut down the attrited employees by almost 50%.

Training and Development:

• Offer training opportunities for Entry-level and Junior employees to bridge any skills gaps, particularly in emerging technologies like Python to complement existing skills in SQL, Excel, Power BI so that they can climb the ladder to be Executive/Sr. Executives dropping the attrition by upto 22 %.

Financial Incentive Programs:

• Review the Salary compensation packages to ensure they are competitive and aligned with industry standards, particularly for job roles with high attrition rates like Sales Representatives (39.8 %), Sales Executives (17.5 %), Lab Techs (23.9 %) & Human Resources (32.1 %) across the Sales, HR and R&D departments.
• Evaluate the effectiveness of Low & Moderate value Stock Options and other incentive programs to gauge their impact on employee engagement.

Gender Diversity and Inclusion:

• Develop initiatives to promote gender diversity at all organizational levels and address any disparities in attrition rates between Male (17 %) and Female (14.8 %) employees.
• Establish mentorship programs and support networks to encourage the career growth and retention of Male employees within the organization.

Data-Driven Decision Making:

• Establish regular feedback mechanisms to allow employees to voice concerns, share suggestions, and provide insights into improving workplace dynamics.
• Develop and refine data-driven models to anticipate attrition trends, enabling proactive strategies to retain valuable talent.

By implementing these recommendations, the organization can foster a positive work environment, improve employee satisfaction and retention, and drive organizational growth and success.