Employee Performance Analysis

Companies generate a lot of data with regards to their employees. I came upon this employee dataset and decided to visualize it through PowerBi.

The dataset was obtained from here. A problem I had with this particular one was deciding which data was relevant as it contained more than twenty columns.

In gauging employee performance, a performance rating is given, for this particular dataset, the rating was in levels of 1,2,3 and 4. These represent Low, Good, Excellent and Outstanding respectively.

From our dataset, factors that could affect employee performance rating were many for the picking; hourly rate, job satisfaction, overtime, environmental satisfaction, years of experience e.t.c. The best performing department could also be looped into this analysis.

For the data cleaning and pre-processing, the data did not need much alteration. Missing values were few and were discarded without major changes to the remaining dataset. The variables that had levels to them for example work life balance were well defined and labelled appropriately, see the insert below. An age-group column was added to segment the employees into groups.

The data was clean, correct data formats were in place and the analysis began. To kick off, a general overview of the data to give a clear picture of the company’s staff was included. It had the total headcount, tenure, years of experience, gender, employees by department, educational background of the staff, years of experience, the average age of the employees, attrition levels and so forth. I assumed the attrition was the number of employees who had intentions of leaving the company. The snapshot below gives a clear picture.

In assessing the performance of the employee, I aimed to assess the relationship between the performance rating and to determine whether the factors below affect performance in any way.

Job Satisfaction

Overtime worked

Job Involvement

Work Life Balance

Hourly rate

Years of Experience at the current role

Percentage of last salary hike

Years since the last promotion.

The visualization is as shown in the snapshot below. From the data, the average performance rating steadily drops to employees with high job satisfaction then sharply increases to the employees with very high job satisfaction. The performance rating also steadily increases with increasing work life balance. The department with the highest average performance rating is Development. The years of experience do not greatly affect the performance rating though there was a notably low average rating in the employees who were in their 16th year of service. The average performance rating declined with increasing hourly rate. The average performance rating was higher for employees who received a higher percentage increase in their salary. The average performance rating declined with increasing number of years before the last promotion and the employees who worked overtime had a higher performance rating.

From the findings it is notable that money is a motivator of better performance at work as seen from the salary rate increase and the hourly rate. The Development department should probably be awarded for their work. The company can reduce the time it takes for promotions to motivate their staff . The company can motivate their staff to improve their work life balance ultimately improving their performance at work. The company should be cautious to avoid bias when looking at the overtime factor when evaluating performance. This was an interesting project and I can’t wait t share the next one.