How it works
The importance of Data Validation

Data validation is the process of analyzing the quality and accuracy of data to ensure that a dataset is correct and complete. By using data quality rules in Loome, you can quickly and easily identify issues, send alerts and add further context to explain why these issues have occurred and what remedial action needs to be, or has been taken. You can assign all issues found to the Data Steward who would then be able to track these exceptions, their diagnosis, and formulate an action plan that would be implemented to fix any systemic issues.

To capture this activity we can add the following additional fields to our dataset: ‘Diagnosis’, ‘Triage Category’, ‘Date Actioned’ and ‘Action Plan’. You can also add validation checks and specify a column in your dataset for each validation check.

The types of validation checks include:
  1. Date Validation
  2. Number Validation
  3. Fixed Value
  4. Database Function
  5. Format
  6. Reference Lookup

Once you have run this rule, you can view your dataset in a results table.

It will display your data validation column checks and Loome will provide a “Fail” or “Pass” status for each row. You can also see the status of each of your records, additional fields where you can now capture more information, and assign these issues to your Data Steward. Your Data Steward can then formulate an action plan and capture information in the activity fields you provided. In the following example, products were found to be missing information in the color field, so the Data Steward noted which records had failed validation for the color field and took action.

A gif showing how to create a data quality rule in Loome Monitor.
Find out More
See more about data quality validation checks at our online documentation today.