People Analytics

Introduction

Welcome to my People Analytics data project. As the Great Resignation continues in the US Labor Market, I found great interest in understanding a ficitonal entity's employee records to find out what trends were in employee termination and how the company can retain current talent. The insights I provide the company will enable the opportunity to act on the data and monitor their talent in real-time to improve employee satisfaction and job retention rates. For purposes on this data project, the company will be titled R&D Solutions. To view the slide deck created for this analysis, please click here. 

Phase 1: Ask

What is the problem I am trying to solve?

R&D Solutions wants to understand their employee attrition rate and identify factors that are driving it. With this information, the business can make decisions to improve retention rates and increase overall job satisfaction. As the analyst, I will be providing insights to current attrition rates and workforce survey data.

• The business task is to identify trends in employee attrition and interpet employee survey data.

• What areas of the business are driving attrition?

• How did employees score the business on the workplace survey?

Phase 2 & 3 : Prepare and Process

The data has been made publically available through Kaggle by Prashant Patel and can be found here. Prashant provided a link to IBM Watson Analytics Blog that is no longer in service for the specific licensing. This dataset is fictional and should not be used in any commercial manner or relied on for real-life insights. 

Upon initial exploration of the data, the dataset contains 1470 rows of employee information with 35 columns ranging from age & gender to workplace survey data. After preparing and exploring the data, I chose to utilize Tableau Desktop to conduct my analysis and create compelling visualiations.

I began my analysis by calculating attrition rates for the entire workforce and department-specific attrition. The company had a workforce of 1470 employees and showed a 16% turnover. As I increased the level of detail for active vs terminated employment, it became clear which divisions and job roles were exceeding the system level rate. 

Following a general overview by department and job role, I began to seek insights through data points such as Age and Company Seniority. Results of further analysis can be located within the presentation and Tableau Dashboards.