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Tutorial: Create validation rules that use reference data

This tutorial demonstrates how to create data quality rules that validate your operational data against approved reference values. You’ll learn to use your reference data tables as the source of truth to check data quality in external datasets.

Scenario overview

You maintain a reference table of valid values (such as territories, departments, or product categories) and need to validate your operational data against these approved values. For example, you might have:

  • A customer dataset from your CRM system that you want to validate against approved territory codes.

  • A sales dataset where product categories should match your standardized product catalog.

  • An employee dataset where department codes must align with your organizational structure.

This tutorial shows you how to create data quality rules that automatically flag records in these operational datasets when they don’t match your standardized reference values, helping maintain data consistency across your organization.

What you’ll learn

  • How to create data quality rules that check operational data against reference values.

  • How to apply validation rules to your datasets.

  • How to evaluate and interpret data quality results.

What you’ll need

For this tutorial, prepare:

  • Reference data table: A table containing your approved reference values (for example, valid territory codes, department names, or product categories).

  • Operational dataset: A catalog item containing the data you want to validate (for example, customer data, sales records, or employee information).

  • Matching attributes: Identify which attribute in your operational data corresponds to values in your reference table.

Prerequisites

Before starting this tutorial, ensure you have:

  • Appropriate role on reference data tables:

    • Owner or Editor role to create and modify reference data tables.

    • Approver role to publish changes (if using approval workflows).

    • At minimum Viewer role to access table data.

  • Working knowledge of the Data quality and Transformation plans modules.

  • Sample data or catalog items to work with.

For information about roles and permissions, see User roles.

Step-by-step instructions

  1. Prepare the reference data

    1. Open your reference data table.

    2. Add, modify, or delete reference values as needed.

    3. Publish the changes to make them available for validation.

  2. Create the validation rule

    1. Navigate to Data Quality > DQ rules.

    2. Create a new rule.

    3. Define the rule logic:

      • Select is from or is not from reference data catalog item.

      • Choose your published reference table.

      • Select the appropriate attribute for comparison.

    4. Save and publish the rule.

  3. Apply the rule to your operational data

    1. Go to your operational data catalog item (the data you want to validate).

    2. Create a new Data Quality configuration.

    3. Apply the reference data validation rule to the relevant attribute.

    4. Configure the rule parameters as needed.

  4. Run data quality evaluation

    1. Execute the data quality evaluation.

    2. Review the DQ score and validation results.

    3. Identify records that don’t match your reference values.

    4. Analyze any data quality issues found.

  5. Test and maintain your validation rules

    1. Return to your reference data table when you need to add new valid values.

    2. Publish additional reference values as your business requirements evolve.

    3. Rebuild the validation rule to include new reference values.

    4. Re-run the data quality evaluation to see updated results.

    5. Compare the updated DQ scores to measure improvement.

Expected outcome

You now have an automated validation process that ensures your operational data matches approved reference values. The system will flag any records with invalid codes or values, helping maintain data quality standards across your datasets. This gives you confidence that your operational data adheres to your organization’s data standards.

Next steps

After completing this tutorial:

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