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Apply data quality rules on data quality assessment
Process flow

You can run a data quality assessment project to examine the quality of existing data.

You can do a data quality assessment, for example, if you:

  • Have implemented a change in the Data quality studio configuration and want to make sure existing data is in line with this change.
  • Have imported data without applying data quality policies.
  • Want to periodically check the quality level of your data.

As a data owner, you can review and process data quality assessment results. A data owner can be a:

  • General data owner who can review and process quality assessment results for all companies.
  • Company-specific data owner who can review and process quality assessment results only for an assigned company.

For each failed validation rule or duplicate check rule, a separate entry is created in the Quality assessment exceptions log.

The options you have to review and solve quality assessment results, depend on the origin of the found issues:

  • Validation rule not met.
  • Duplicate check finds possible duplicate records.

Data quality administrator Data quality administrator The data quality administrator (DQSDataQualityAdministrator) can set up and maintain: Data quality policies Data quality studio parameters Data quality studio general setup Data quality officer Data quality officer The data quality officer (DQSDataQualityOfficer) can view and process data quality assessment results. Start Start Run data quality assessment Run data quality assessment You can run a data quality assessment to examine the quality of existing data. To run a data quality assessment, you use data quality assessment projects. You can do a data quality assessment, for example, if you: Have implemented a change in the Data quality studio configuration and want to make sure existing data is in line with this change. Have imported data without applying data quality policies. Want to periodically check the quality level of your data. Procedure 1. Go to Data quality studio > Periodic tasks > Run quality assessment. 2. In the Project name field, select the desired data quality assessment projects. 3. In the Company field, select the desired companies, if desired. 4. Select Yes in the Run paid web services field. 5. Sub-task: Run quality assessment in a recurring pattern. 6. Expand the Run in the background section and fill in the fields as desired. 7. Click Recurrence and fill in the fields as desired. 8. Click OK. 9. Click OK. Notes When you run a data quality assessment project, the applicable data quality policy rules are only applied : If the 'Quality assessment' execution trigger is enabled for the data quality policy. For active data quality policy versions. To the applicable data. Start quality assessment results review Start quality assessment results review As a data owner, you can review and process data quality assessment results. For each run quality assessment project, all related records with at least one warning or error are shown in the Quality assessment exceptions log. For each error or warning message record, set the review status. Procedure 1. Go to Data quality studio > Inquiries > Quality assessment exceptions log. 2. In the list, find and select the desired record. 3. Click Change status to open the drop dialog. 4. In the Review status field, select 'In progress'. 5. In the Reason code field, enter or select a value. 6. In the Reason notes field, type a value. 7. Click OK. 8. Click Yes. 9. Close the page. What's the quality  assessment result  message about? What's the quality  assessment result  message about? Review and edit source record Review and edit source record As a data owner, you can review and process data quality assessment results. For each record with errors or warnings, you can open the source record to review the error or warning and, if desired, make changes. Usually, you review and change the source record in case of a validation rule error or warning. Procedure 1. Go to Data quality studio > Inquiries > Quality assessment exceptions log. 2. In the list, find and select the desired record with review status 'In progress'. 3. Click Open source record. 4. If desired, click Edit, and make changes to solve the errors or warnings. 5. Close the page. 6. Close the page. Notes If a Data entry workflow integration is set up for the quality assessment project, for each record with errors or warnings, you can start a data entry workflow to make the desired changes to the record. To do so, select an entry, and click Run workflow. Note: You can start only one data entry workflow for each record with errors or warnings. A record can fail several validations or duplicate checks. For each failed validation rule or duplicate check rule, a separate entry is created in the Quality assessment exceptions log. So, if you have started a data entry workflow for an entry, you cannot start another data entry workflow for another entry of the same record. Review and merge duplicates - Quality assessment Review and merge duplicates - Quality assessment As a data owner, you can review and process data quality assessment results. For each record with possible duplicates, you can: Review the found duplicates. Merge field values from the duplicate records to a chosen master record. Manually delete undesired duplicate records. Both basic and fuzzy duplicate checks are done by the quality assessment. You can review and merge found duplicates for both duplicate check types in the same way. Procedure 1. Go to Data quality studio > Inquiries > Quality assessment exceptions log. 2. In the list, find and select the desired record with review status 'In progress', and which message says that possible duplicates exist. 3. Click View possible duplicates. 4. Sub-task: Merge field values of duplicate records. 5. In the Duplicates found section, click Proceed to merge. 6. For the desired record, select the Master record check box. 7. Select the desired 'merge' check boxes. 8. Click Merge. 9. Click Yes. 10. Sub-task: Delete undesired duplicate records. 11. In Duplicates found section, in the list, find and select a duplicate record that you want to delete. 12. Click to follow the link in the Identifier field. 13. Click Delete. Note: Before you delete a record, make sure it is not referenced in another record. 14. Click Yes. 15. Close the page. 16. Close the page. 17. Close the page. Finish quality assessment result review Finish quality assessment result review As a data owner, when you have finished the review and editing of a data quality assessment record with errors or warnings, change its status accordingly. You can choose one of these statuses: Accept deviation: You did not make changes to the source record, nor merged duplicate record fields to solve the error or warning. So, you accept the deviation from the data quality policy rule. Completed: You made changes to the source record or merged duplicate record fields to solve the error or warning. Procedure 1. Go to Data quality studio > Inquiries > Quality assessment exceptions log. 2. In the list, find and select the desired record. 3. Click Change status to open the drop dialog. 4. In the Review status field, select 'Accept deviation' or 'Completed'. 5. Usually, if you set the review status to 'Accept deviation', you define a reason code to explain why you accept the deviation. However, if you have set up a reason code to indicate why you complete a record, you can use this reason code here as well. In the Reason code field, enter or select a value. Note: Based on the setting of the 'Require reason when accepting deviation' parameter, it can be mandatory to define a reason code when you accept a deviation. 6. In the Reason notes field, type a value. 7. Click OK. 8. Click Yes. 9. Close the page. End End Validation  issue Duplicate records  found

Activities

Name Responsible Description

Run data quality assessment

Data quality administrator

You can run a data quality assessment to examine the quality of existing data. To run a data quality assessment, you use data quality assessment projects.

You can do a data quality assessment, for example, if you:

  • Have implemented a change in the Data quality studio configuration and want to make sure existing data is in line with this change.
  • Have imported data without applying data quality policies.
  • Want to periodically check the quality level of your data.

Start quality assessment results review

Data quality officer

As a data owner, you can review and process data quality assessment results.

For each run quality assessment project, all related records with at least one warning or error are shown in the Quality assessment exceptions log.

For each error or warning message record, set the review status.

Review and edit source record

Data quality officer

As a data owner, you can review and process data quality assessment results.

For each record with errors or warnings, you can open the source record to review the error or warning and, if desired, make changes.

Usually, you review and change the source record in case of a validation rule error or warning.

Review and merge duplicates - Quality assessment

Data quality officer

As a data owner, you can review and process data quality assessment results.

For each record with possible duplicates, you can:

  • Review the found duplicates.
  • Merge field values from the duplicate records to a chosen master record.
  • Manually delete undesired duplicate records.

Both basic and fuzzy duplicate checks are done by the quality assessment. You can review and merge found duplicates for both duplicate check types in the same way.

Finish quality assessment result review

Data quality officer

As a data owner, when you have finished the review and editing of a data quality assessment record with errors or warnings, change its status accordingly.

You can choose one of these statuses:

  • Accept deviation: You did not make changes to the source record, nor merged duplicate record fields to solve the error or warning. So, you accept the deviation from the data quality policy rule.
  • Completed: You made changes to the source record or merged duplicate record fields to solve the error or warning.

Activities

Name Responsible Description

Run data quality assessment

Data quality administrator

You can run a data quality assessment to examine the quality of existing data. To run a data quality assessment, you use data quality assessment projects.

You can do a data quality assessment, for example, if you:

  • Have implemented a change in the Data quality studio configuration and want to make sure existing data is in line with this change.
  • Have imported data without applying data quality policies.
  • Want to periodically check the quality level of your data.

Start quality assessment results review

Data quality officer

As a data owner, you can review and process data quality assessment results.

For each run quality assessment project, all related records with at least one warning or error are shown in the Quality assessment exceptions log.

For each error or warning message record, set the review status.

Review and edit source record

Data quality officer

As a data owner, you can review and process data quality assessment results.

For each record with errors or warnings, you can open the source record to review the error or warning and, if desired, make changes.

Usually, you review and change the source record in case of a validation rule error or warning.

Review and merge duplicates - Quality assessment

Data quality officer

As a data owner, you can review and process data quality assessment results.

For each record with possible duplicates, you can:

  • Review the found duplicates.
  • Merge field values from the duplicate records to a chosen master record.
  • Manually delete undesired duplicate records.

Both basic and fuzzy duplicate checks are done by the quality assessment. You can review and merge found duplicates for both duplicate check types in the same way.

Finish quality assessment result review

Data quality officer

As a data owner, when you have finished the review and editing of a data quality assessment record with errors or warnings, change its status accordingly.

You can choose one of these statuses:

  • Accept deviation: You did not make changes to the source record, nor merged duplicate record fields to solve the error or warning. So, you accept the deviation from the data quality policy rule.
  • Completed: You made changes to the source record or merged duplicate record fields to solve the error or warning.
Related to Notes

Set up data quality assessment project

 		 

Set up reason codes

 		 

Set up data quality assessment project

 		 

Set up reason codes

 		 

Apply data quality rules on data quality assessment

 		 

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