Managing Data Quality | Kada Knowledge Library

K helps you store, track, measure and manage data quality across your entire data ecosystem — giving your team a single place to monitor results, assign accountability, and take action when issues arise.

K can integrate and store Data Quality results from the following Data Quality Applications (DQ Apps):

Great Expectations / DBT Tests
Snowflake Data Metrics Functions

Viewing data quality results

When a data quality test has been loaded from a DQ App, a Quality tab will appear on the Data Profile Page for the target of the test (e.g. results for a column-level test appear on the column profile page).

From the Summary section you can review DQ scores across all dimensions at both the table and column level. The Details section shows the DQ trend over time — hover over any day and click a bar to see a full list of tests run that day and whether each passed or failed.

Explore this section

Use the pages below to go deeper on specific aspects of managing data quality in K:

Page	What it covers
Data Quality Scores	How DQ scores are calculated, aggregated by dimension, and tracked over time using the DQ Score Delta
Data Quality Dimensions	The 6 dimensions K uses to measure quality: Accuracy, Completeness, Consistency, Integrity, Timeliness and Uniqueness
Aggregating Data Quality Metrics by Collections	How to roll up DQ scores to Domains, Collections and other groupings
Assigning an Owner/Steward to a Data Quality Test	How to assign accountability for a DQ test to a specific Owner or Steward
Raising an issue from a Data Quality Test	How to log and track a data quality issue directly from a test result