By: Martin Rowell
Step 1: Interpreting the Data
When I was looking over this data, I did start to develop several questions in the data. The first question that came to mind was how were the performance scores given. I assumed that individuals working in difference departments have different task and a different set of standards. I was also curious why the comments on each employee was minimal or non existent. I found that peculiar because I would not be able to get more insight in why they got the performance score they did.
From what I could gather from the data, I saw that most departments have people performing well (around 3) with a few exceptions. I also noticed that when I looked at start dates, there was a general trend that the employees that were brought on later tend to have a better performance score on average than the ones who had been working longer. Another note was that not all departments have grown only a select few which were sales, manufacturing, logistics, and account services.
Step 2: Analyzing the Data
The data is telling me that most poeple in each department achieve a score close to department average but there are a few who score low. The data is also showing me that most people get the same pay expect for the managers who have a wide range of pay. From what I can, a person’s pay is not determined on performance score or how long they have been working for the company, so it would be nice to get a better understanding why some individuals get more pay than others. Do they have different tasks that set them apart? A similar questions also comes from the management department. Everyone in management started working within a seven day range but the salary variance is $125 K from the highest paying manager to the lowest. The final thing that the data isn’t showing me is the development of the employee’s performance scores over a period of time. I just have a snap shot of their scores but I don’t know if anyone is improving or decreasing in their performance, so it would be better to have a timeline of scores to see how these employees and departments have been doing.
Sadly this data is not really giving much qualitative data. There are a few employees who had one sentence comments but many employees have no comments which makes it hard to see if there is any difference between two people from the same department that share the same performance score or a glimpse into why some people have lower scores than others. It would also be nice to see more comments over a span of time as well to see if employees have been getting the same critiques. I also don’t have any feedback from the employees so I can’t confirm if there is a trend between ‘happy employees’ to ‘higher performance scores’.
Overall I think getting a list of each person’s tasks and more comments about their work would help me better understand what each employees does and why they have received the score they got.
Step 3: Conclusion of the data:
The prompt this week is asking us what would be our recommended strategy if the company wanted to shirk the budget down by roughly 10%. My strategy based on the data given would to considered letting goes those who have lower performance scores – especially those who come from a department that grew in size. I would recommended this for two reasons. The first reason is you would only be letting go poeple who’s scores show that aren’t performing up to par with others in their department. My second reason for looking more at the departments that grew is because they may have grown when the economy and the company was in better shape. With the detail that the economy contracted and the loss of sales and revenue it might be a good idea to scale back these departments.
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