Best Practices for Reference Data Management

Good naming conventions make your reference data more accessible to business users. Follow these key principles:

When modeling your data, keep reference tables focused on single concepts to maintain clarity. Use hierarchical structures for naturally nested data like organizational charts or product taxonomies. For complex scenarios where you need to bridge different systems, create dedicated mapping tables rather than trying to force everything into a single structure.

To get started with creating tables, see Create Reference Data Tables.

Your import strategy should start with clean, stable datasets for your first implementations. When data quality is already good in your catalog, use catalog items as sources. However, always set up validation rules before bulk importing from external systems to catch quality issues early.

Learn how to Work with Reference Data Records for day-to-day operations.

For hierarchical data like organizational structures:

Cross-system mapping requires creating master value sets for concepts used across multiple systems. Build translation tables that map system-specific codes to master codes, then use these mappings in data transformations to ensure consistent reporting across your organization.

Effective governance starts with clear role assignments:

Your access patterns should grant broad view access to enable self-service while restricting edit access to qualified stewards. Use approval workflows for high-impact changes to maintain data integrity without slowing down routine operations.

For detailed setup instructions, see Set Up Access and Governance.

Successful self-service depends on strong catalog integration. Publish stable reference data to make it discoverable, and add clear descriptions explaining data purpose and usage. Tag data appropriately for easier search, making it simple for users to find what they need.

Learn more about Work with Published Reference Data.

Empower your users by training business teams to find and download reference data independently. Provide APIs for automated integration and create standardized export formats for common use cases. This reduces the burden on IT teams while improving data accessibility across the organization.

Strong validation rules form the foundation of reliable reference data. Set up these essential checks:

These automated checks catch issues before they impact downstream systems.

For hands-on guidance, see Create Validation Rules.

Quality monitoring requires regular attention to data quality reports and prompt addressing of validation failures. Use your reference data to validate other datasets across the platform, creating a virtuous cycle where good reference data improves overall data quality.

Effective change management uses approval workflows for all published data changes while maintaining full audit trails of who changed what and when. Communicate changes to downstream data consumers so they can adapt their processes accordingly.

For step-by-step guidance on approvals, see Publish Your Data.

When planning bulk operations:

Successful integration requires well-planned data export strategies:

For implementation details, see Export to Database.

Platform integration becomes powerful when you use reference data in data quality rules, reference tables in data transformation workflows, and include reference data in reporting and analytics. This creates a comprehensive approach where reference data becomes central to your data operations.

Explore Improve Data Quality for data quality integration patterns.

Organizational rollout works best when you start with high-impact, stable datasets and gradually expand to more complex scenarios. Train champions in each business area who can support other users and advocate for best practices.

Performance optimization becomes important as you scale:

For performance considerations, see Known Limitations to understand system constraints.