Filters for data quality checks

Filters help you narrow down the scope of a data quality rule by applying it only to specific rows. This is useful when you want to check conditions only for a subset of the data — for example, for values above a certain threshold or for a specific category.

What are filters used for?

Filters allow you to:

• Apply a rule to only relevant data points

• Exclude irrelevant cases from validation

• Segment your checks for better accuracy

They are especially useful for:

• High-value transactions

• Specific departments, regions, or categories

• Date-based windows (e.g. last 30 days)

Only apply the rule to records matching the filter logic

Available filters by column type

For numeric columns

• Equals

• Doesn't equal

• Greater than

• Greater than or equal

• Less than

• Less than or equal

• Between

• Empty

For text columns

• Equals

• Doesn't equal

• Begins with

• Ends with

• Contains

• Does not contain

• Empty

For date columns

• Empty

• Not empty

• Within a specific range

Example use case

Scenario: You only want to check if values are “not empty” for high-value transactions in a specific department.

Solution:
Apply the following filters:

• First filter: Awarded Amount > 10,000

• Second filter: Department Code = POL

• Condition: Not empty

This ensures that the rule only applies to relevant records — focusing on important cases within the POL department, and avoiding noise from lower-priority data rows.

Notes

• Multiple filters can be combined with AND or OR logic

• Learn more: Segmentation for data quality rules, Working with the rule detail screen, Rule creation via DQ-AI Assistant