2023: Week 31 - HR Month - Filling in Missing IDs

Challenge by: Kelly Gilbert

Validated by: Abiramm Thavajothy

After a successful HR Month at the end of 2022, Kelly has returned with more real-life (fake data) challenges for us! Since Kelly's data prep tool of choice is Alteryx or Python, we also enlisted some help from colleagues at the Data School to ensure the logic works in Tableau Prep as well. Thanks for your help with this one Abi! Over to Kelly:

Over the next few challenges, we’ll be using fake employee data for a small network of distribution centers. The data is fake, but the challenges are based on real people analytics challenges.

In this challenge, we have two HR tables: one that contains the list of employees (with their name, DOB, etc.), and one that contains a monthly snapshot of which employees worked at each DC during the month.

We want to be able to join the two tables, but sometimes the employee_id is missing from one (or both!) of the tables. We do have a second ID (the guid), but sometimes that is missing as well! Fortunately, we have at least one of those IDs present for each record. 

The goal for this challenge is to fill in the missing IDs in both tables, so they can be joined later.

Note: throughout this month, you’ll see the abbreviations, “ee”, which is short for employee, and “DC” which is short for distribution center

Inputs

1. Employee Dimension Table (ee_dim_input.csv) 


2. Monthly Snapshot Table (ee_monthly_input.csv) 

Requirements

  • Input the data
  • Create a lookup table:
    • Find the unique employee_id / guid combinations in each table
    • Union the results together and remove any duplicates
    • Filter out any rows where one of the IDs is missing
  • Join the main table with the lookup table on employee_id (make sure to keep all records from the original table, whether or not they match)
  • If the guid is missing from the main table, replace it with the guid from the lookup table.
  • Join the result to the lookup table on guid (make sure to keep all records from the original table, whether or not they match)
  • If the employee_id is missing from the main table, replace it with the value from the lookup table
  • Repeat previous steps for both the employee table and the monthly table.
  • Make sure that there are no nulls in the employee_id and guid fields in both tables
  • Output the results (two files)

Outputs

1. The employee dimension table, with missing IDs filled (ee_table_v2.csv) 
  • 10 fields
    • employee_id
    • guid
    • first_name
    • last_name
    • date_of_birth
    • nationality
    • gender
    • email
    • hire_date
    • leave_date
  • 537 rows (538 including headers)
2. The monthly table, with missing IDs filled (ee_monthly_v2.csv) 
  • 6 fields
    • employee_id
    • guid
    • dc_nbr
    • month_end_date
    • hire_date
    • leave_date
  • 7,440 rows (7,441 including headers)
You can download the output from here.

After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @kelly_gilbert, @AbirammT, @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 

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