Project Analysis: Week 4-JW Technology

This week’s visualization focused on fine tuning the strategies behind dashboard design by focusing on HR data.

JW Technology is interested in understanding employee turnover to better plan the organization’s long term growth. By delving into the when’s, why’s and who’s, the organization intends to spot trends and do a root cause analysis. This will help the company understand which groups need to begin actively recruiting for additional talent.

To do this, I created 2 vizzes per category(when, why, who). Let’s focus on each.

WHEN

For discrete date part of ‘quarters’ , I created a line chart to understand when employees are terming the most.

To understand the length of tenure with the company, I created a bar chart focusing on turnover buckets, using the ‘Case statement’ and ‘IF-Then’ analysis. I also used color minimistically to highlight the period when maximum employees were terming.

WHO

I used a bar chart to show % of turnovers per department.

For understanding which positions, termed the most, I used a bar chart focusing on % of terminations per position.

WHY

For categorical data, more specifically having only 2 categories, I used a pie chart to focus on the reason behind the termination.

A pie chart expresses a part-to-whole relationship in your data.Each slice represents one component and all slices added together equal the whole.

Pie charts should primarily be used when:

1. If you want your audience to have a general sense of the part-to-whole relationship in your data and comparing the precise sizes of the slices is less important.
2. To convey that one segment of the total is relatively small or large.

In this case, there is a clear distinction between the % contribution of 2 categories. If the % contribution of the categories would have hovered around 50–50, the pie chart would not have been the ideal chart to use.

To understand reasons behind turnovers, I created a pictogram where each dot represents an employee. The blue color focuses on those employees who got terminated voluntarily. The color legend, helps in understanding the usage of blue here.

ANALYSIS

1. Majority (36) of the employees were with the company for a year. Most employees churned in Q2 and Q3 of the fiscal years in consideration. The churn % was 10 times higher for the production department than the
   software engineering and admin departments.
2. The principal data architect and enterprise architect saw the most churns at 100%. This implies that those who get hired under these positions also leave. This position goes by the hire and fire policy. There are no
   such employees who can be classified as being with the company for a long period of time. These 2 positions churn at 1.5 times the second most churned position ie of an administrative assistant.
3. 85% of the employees leave the company voluntarily. Of those who leave the company voluntarily, 23% of the employees cite ‘another position’ as the reason .16% of the employees who leave the company voluntarily
   cite being ’unhappy’ at the company. Of those who left the company, involuntarily 40% of the employees cite ’attendance’ as their reason for churn. Clearly, the company needs to look into its policies of ‘hire and fire’ and employee wellness if it wants to reduce attrition rate.

What do you think of the analysis? Did you find it useful?

Please like or comment on the post if there is something that you found interesting or want to draw my attention towards.

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