Improve Data Quality

Reference data becomes more powerful when integrated with your data quality processes. This guide shows you how to leverage reference data to improve overall data quality.

Validation rules

Create rules that validate incoming data against your reference tables:

Is from Reference Data Catalog item: Ensures values exist in approved lists.
Is not from Reference Data Catalog item: Identifies potential data quality issues.
Automatic rule updates when reference data changes.

Benefits of reference data validation

Data consistency: Ensures values across your organization conform to approved standards.
Error prevention: Catches invalid data before it impacts downstream processes.
Automated quality checks: Rules automatically update when reference data changes.
Single source of truth: Maintains centralized control over valid values.

These rules help ensure data consistency across business operations and prevent errors that could impact billing, inventory, logistics, and reporting processes.

Improving data quality through reference data

As you improve your reference data, data quality scores automatically improve:

Start with basic validation against existing reference data.
Identify gaps where valid codes are missing from reference tables.
Add missing codes through the governance process.
See immediate improvement in data quality metrics.

Creating feedback loops for continuous improvement

Establish processes that use data quality results to enhance reference data:

Monitor validation failures: * Regular review of failed validation records * Pattern analysis to identify missing reference values * Business validation of apparently legitimate codes that fail validation

Enhance reference data based on findings: * Add confirmed valid codes discovered through failure analysis * Remove or deprecate codes that are no longer valid * Update relationships between reference datasets

Measure improvement: * Track validation pass rates before and after reference data enhancements * Monitor the frequency of new validation failures * Document the business impact of improved data quality

Example 1. Example improvement cycle

A data quality rule validates customer industry codes against a reference table. Analysis shows 15% of records fail validation, but many appear to have legitimate industry codes. Investigation reveals the reference table is missing recently added industry classifications. After adding the missing codes and republishing, validation pass rates improve to 97%.

Reference data as a data quality strategy

Make reference data central to your data quality approach:

Use profiling results to identify potential reference data domains.
Create reference tables from deduplicated source data.
Establish feedback loops where data quality issues drive reference data improvements.

Measuring success

Track these key metrics to demonstrate value:

Operational efficiency

Reduction in manual data preparation time.
Faster time from data discovery to production use.
Decreased effort in maintaining multiple data copies.

Data quality impact

Improvement in validation rule pass rates.
Reduction in data quality issues across systems.
Fewer data-related incident reports.

Organizational adoption

Number of systems using centralized reference data.
Self-service download/API usage growth.
Reduction in duplicate reference datasets across the organization.

Governance maturity

Streamlined change approval processes.
Improved compliance with change management procedures.
Enhanced data lineage visibility and documentation.

Next steps after implementation

Optimize and expand: Monitor validation performance and expand to additional datasets.
Scale your approach: Best Practices - Patterns for rolling out across your organization.
Consolidate scattered data: Onboard Reference Data - Bring more reference data under governance.
Explore other scenarios: Common Use Cases - Get inspiration from other real-world scenarios.

Was this page useful?