Best Practices for Reference Data Management
Essential patterns and practices for scaling your reference data management across the organization. Reference Data Management is a journey. Start with quick wins, establish good practices, and scale systematically as your organization’s data maturity grows.
Data Organization
Structure your data for success
Good naming conventions make your reference data more accessible to business users. Follow these key principles:
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Use consistent, business-friendly table and column names.
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Include purpose in table names (like "Product_Categories" rather than "Table_1").
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Avoid technical codes that business users won’t understand.
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.
Manage data relationships
For hierarchical data like organizational structures:
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Implement parent-child relationships and use tree visualization to spot structural issues.
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Balance hierarchy depth carefully - too many levels become unwieldy, while too few levels lose important distinctions.
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.
Access Management
Set up appropriate governance
Effective governance starts with clear role assignments:
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Data Owner: Business representative accountable for accuracy.
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Data Steward: Handles day-to-day operational updates.
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Approver: Reviews and publishes changes.
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Viewer: Read-only access for consumers.
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.
Enable self-service access
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.
Quality and Validation
Maintain data accuracy
Strong validation rules form the foundation of reliable reference data. Set up these essential checks:
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Format validation for standards like country codes (must be exactly 2 characters).
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Uniqueness checks for key identifiers.
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Completeness rules for required fields.
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.
Handle updates effectively
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:
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Schedule them during low-usage periods.
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Test changes on subsets before applying to full datasets.
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Use AI assistance for pattern-based updates, but always review results.
Integration Patterns
Connect with other systems
Successful integration requires well-planned data export strategies:
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Set up automated exports to downstream systems.
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Use data transformations for format conversion.
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Schedule regular synchronization to maintain consistency across your data ecosystem.
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.
Scale across the organization
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:
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Monitor table size and query performance.
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Archive historical versions when appropriate to maintain system responsiveness.
For performance considerations, see Known Limitations to understand system constraints.
Next steps
For specific implementation guidance, see:
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Common Use Cases for situational guidance.
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Advanced Workflows for step-by-step learning scenarios.
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Get Started and Work with Reference Data sections for specific tasks.
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Troubleshooting for resolving common issues.
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