Data Cleaner

Garbage in, garbage out.

The Data Cleaner feature organizes your Azure Entra ID user info. It harmonizes all those little inconsistencies in departments, job titles, and locations, making sure everything is neat, uniform, and easy to manage. This helps keep your data clean and standardized.

How does the Data Cleaner work?

The Data Cleaner makes it simple to tidy up your data. It does this by first gathering all the unique departments, locations, and job titles from the users in your Azure Entra ID. Then, when you make changes to any of these unique attributes, it automatically updates all users accordingly.

Let's break it down with examples:

Example 1: Department Renaming Imagine you have some users listed under "Sales" and others under "Sales Netherlands". If you decide to rename "Sales Netherlands" to just "Sales", the Data Cleaner will ensure that all users associated with "Sales Netherlands" get updated to "Sales" across both ServiceChanger and Entra ID.

• Original Data:

  • Users A, C, and D: Department - "Sales"

  • Users B and E: Department - "Sales Netherlands"

• Change:

  • Rename "Sales Netherlands" to "Sales"

• Outcome:

  • Users B and E's Department updated to "Sales" in both ServiceChanger and Entra ID.

Example 2: Job Title Correction Suppose some users have their job title written as "Salesmanager" without a space and capitalization, while others have it as "Sales Manager" with proper spacing and capitalization. If you correct "Salesmanager" to "Sales Manager", the Data Cleaner will apply this change to all relevant users in both ServiceChanger and Entra ID.

• Original Data:

  • Users A, B, and D: Job Title - "Salesmanager" (missing space and capital letter)

  • Users C, E, and F: Job Title - "Sales Manager"

• Change:

  • Correct "Salesmanager" to "Sales Manager"

• Outcome:

  • Job Titles of Users A, B, and D updated to "Sales Manager" in both ServiceChanger and Entra ID.

Example 3: Location Standardization Let's say one user has their location listed as "Barcelona" and another as "barcelona " with a lowercase letter and extra space. If you correct the second entry to "Barcelona", the Data Cleaner will ensure consistency by updating the location of that user to "Barcelona" across both ServiceChanger and Entra ID.

• Original Data:

  • User A: Location - "Barcelona"

  • User B: Location - "barcelona " (lowercase letter and extra space)

• Change:

  • Standardize "barcelona " to "Barcelona"

• Outcome:

  • Location of User B updated to "Barcelona" in both ServiceChanger and Entra ID.