Monitor Data Quality of Reference Data
Monitor which validation rules are applied to your reference data and track how well your data meets quality standards. This page focuses on assigning and configuring rules in the Data quality tab.
The Data quality tab displays validation rules assigned to your reference data attributes and shows how many records pass each rule. Use this tab to monitor data quality, identify validation issues, and maintain data consistency across the platform.
Where data quality results appear
After evaluating data quality rules, validation results appear in multiple locations:
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Data tab - Data quality column: Validation status for each record. Select the icon to see failed rules.
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Data tab - Attribute headers: Quality indicator bar showing pass rates for each attribute.
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Data tab - Toolbar: Dataset-level quality summary with Evaluate DQ button.
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Data quality tab: Rules management view showing which rules are assigned to each attribute (described in this page).
| Data quality results are currently available in Draft datasets only. Published and In Review datasets do not display data quality results. |
For details about using quality results in the Data tab, see Monitor Data Quality.
Access the Data quality tab
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You must have at least Viewer access to the table
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To manage rules, you need Editor or Owner role
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Go to Reference data > Tables.
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Select your table.
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Select the Data quality tab.
Understanding the data quality view
Attribute rules table
The main table shows:
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Attribute: The name of the attribute (column) in your table
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Data type: The data type of the attribute (string, number, date, etc.)
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Applied rules: Validation rules assigned to this attribute, displayed as tags
Each row represents one attribute in your table. The applied rules column shows all validation rules currently assigned to that attribute.
How rules are displayed
Rules appear as color-coded tags in the Applied rules column:
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Rule name: Select to view the rule definition in the Data quality module (opens in a new tab)
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Dimension color: Colored according to its quality dimension
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Quality percentage: Percentage of records that pass validation
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Error indicator: Red warning icon if the rule is incompatible
| State | Appearance | Meaning |
|---|---|---|
Active rule |
Colored tag with percentage |
Rule is evaluating; percentage shows pass rate |
Incompatible rule |
Red error indicator |
Rule doesn’t match the attribute’s data type |
Quality dimensions
Quality rules are organized by dimension (color-coded for quick visual reference):
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Completeness: Missing or empty values
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Accuracy: Data validation against patterns or reference values
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Consistency: Format or type consistency
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Uniqueness: Duplicate detection
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Conformity: Standards compliance
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Validity: Data type and value validity
View applied rules and details
All rules appear in the Applied rules column with their quality percentage, dimension color, and status. Hover over a rule tag to see the full rule description.
Incompatible rules
Rules marked with a red error icon are incompatible with the attribute’s data type. For example, a numeric range rule assigned to a text attribute.
To fix an incompatible rule:
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Select the rule tag to open its definition in the Data quality module
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Review the rule configuration and adjust it to match the attribute’s data type
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The rule automatically resumes validation once fixed
Add rules to attributes
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You must have Editor or Owner role
Understanding quality metrics
The percentage on each rule tag shows what percentage of records pass validation. The percentage updates automatically based on your data and evaluation schedule.
Hover over a rule tag to see additional information:
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Full rule name and description
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Rule parameters
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Quality dimension
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Current evaluation status
Related areas
Select any rule tag to open its definition in the Data quality module in a new tab.
Other tabs:
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Data tab: View records and their validation status
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Data structure tab: Manage attributes
Integration with Data quality module
Rules are defined in the Data quality module and assigned to reference data attributes on this tab. Changes to rule definitions automatically update here.
To create or modify rules, go to the data-quality:use-case-creating-rules.adoc, then return to your reference data table and assign them.
For more on improving data quality through reference data, see Use Reference Data for DQ Validation.
Best practices
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Apply rules to attributes used in calculations, lookups, or key business decisions.
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Use meaningful rule names so you can quickly understand what each rule validates.
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Monitor the Data quality tab regularly to catch issues early.
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Address validation failures promptly by editing records in the Data tab.
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Fix incompatible rules or remove them if no longer needed.
Troubleshooting
Rule shows as incompatible
Cause: The rule’s requirements don’t match the attribute’s data type.
Solution: Select the rule tag to open it in the Data quality module and adjust the configuration to match the attribute’s data type.
Quality percentage is very low
Cause: Many records are failing validation.
Solution: Determine if the rule is too strict or if the data needs fixing. Either adjust the rule in the Data quality module or edit the data in the Data tab.
No rules are assigned to an attribute
Cause: No rules have been added yet.
Solution: Select the Add button to assign rules to the attribute.
Next steps
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Work with Reference Data Records - Edit and fix quality issues
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Publish and Approve Reference Data - Make changes available
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Use Data Quality Integration - Create feedback loops for continuous improvement
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