Monitor and clean up data quality execution history

In the Data quality studio parameters, you can enable logging of data quality policy validation rules execution.

Validation rule execution results are

• Logged only when you manually add or change records. Each time a validation rule is executed, an entry is logged.
• Not logged if the data quality policy is run by a:
  • Data quality assessment.
  • Connectivity Studio data import.

You can review and process the logged validation rule execution results.

For each logged validation rule execution result, you can:

• Set the review status.
• Open the source record to make changes, if desired.

The validation status of a logged validation rule execution result can be:

• Success: The validation rule is met.
• Failed: The validation rule is not met and results in an error or warning.
• Skipped: The validation rule is not executed because its conditions are not met.

In the Data quality studio parameters, you can enable logging of data quality policy validation rules execution.

Validation rule execution results are logged only when you manually add or change records. Each time a validation rule is executed, an entry is logged.

You can clean up the data quality policy execution log once or in recurring mode.

For example, you want to keep validation history records for one month. Each week, you can do a cleanup, deleting history records older than one month.

The steps of this topic explain how to clean up the validation history.

If you have checked a record for fuzzy duplicates, you can view the fuzzy duplicate check history for that record. The history shows all fuzzy duplicate checks that are done for the record.

If values are merged from duplicate records to the selected record, you can view the merged values.

If you have checked a record for fuzzy duplicates, you can view the fuzzy duplicate check history for that record. The history shows all fuzzy duplicate checks that are done for the record.

You can clean up the logged fuzzy duplicate check history manually or in recurring mode.

For example, you want to keep fuzzy duplicate check history records for one month. Each week, you can do a cleanup, deleting history records older than one month.

The steps of this topic explain how to clean up the fuzzy duplicate check history.

If you run quality assessment projects, you can monitor the quality assessment history.

Quality assessment history is stored by:

• Quality assessment project
• Data quality policy
• Date/time
• Company

For each unique combination of these entities, a separate record is added to the quality assessment history.

You can filter the quality assessment history by:

• Type of rule: Validation rule or duplicate check rule.
• The moment the assessment is run: Today, Last 7 days, last 30 days, or All.

If you run quality assessment projects, the quality assessment history is stored.

You can clean up the quality assessment history once or in recurring mode.
For example, you want to keep quality assessment history records for one month. Each week, you can do a cleanup, deleting history records older than one month.
The steps of this topic explain how to clean up the quality assessment history.