2020: Week 7

As a little aside, Preppin' Data turns 1 year old on Thursday 13th February 2020. To celebrate the day, I just wanted to say thank you to a number of people who make this weekly challenge run:
  • Jonathan Allenby - my fellow Dr Prepper, who, without asking a question by a coffee machine in London, we'd never have a Prep based challenge. He's taken on a lot to keep this all running and providing great solution posts from day one
  • Tom Prowse - who is well on his way to becoming a Dr Prepper in his own right. Lots of great solution posts and filling the gaps for Jonathan and I when we need support - thank you. 
  • Jenny Martin - throwing up challenges, spotting issues and now providing video walkthroughs of the challenges at dataprepsolutions.com 
  • Bona Wang, Donna Coles, Jack Parry, Kate Brown and Rosario Gauna for finding errors and posing new challenges and generally encouraging Jonathan and I in the early days to keep us going.
Thank you all and hopefully we can keep coming up with great challenges as the Prep Development team (Rahim, Maraki, Zaheera and co) keep coming up with more great functionality. 

Enough of the thank you's, back to why you are here - Week 7's challenge. This week one of our supporters, Bona, and I have collaborated on an idea for a challenge around a common issue when processing data - counting something that no longer exists in the dataset. With GDPR and other consumer focused legislation becoming more prominent, customer records are disappearing rapidly once a customer leaves a business (for good reason). However, as an analyst, this makes certain tasks more challenging, like counting how many 'x' there were in a business at anyone time. 

For this challenge we are looking at employees who have left the business over time as well as understanding who still remain. 


Current Employees

Reporting Dates

  • Input Data
  • Create a full list of employees
  • Create an end date for current employees that are yet to leave (use 1st March 2020) to help with calculations
  • Create a scaffold month that can have the Employee data joined on to it
  • Form your measures for analysis:
    • Count number of employees in employment by the company that month.
    • If the employee leaves in that month, remove them from the reporting within the month of leaving.
    • Avg Salary per employee per month
    • Total Salary paid by the company per month
  • Output the data


One file:
  • 4 Columns:
    • Month
    • Total Monthly Salary
    • Avg Salary per Current Employees
    • Current Employees
  •  13 Rows (14 including Headers)
The outputs can be found here for comparison. Don’t forget to fill the participation tracker and share your solutions using #PreppinData on Twitter.


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