Data Vault: Business Rule Secrets

In September 2019, Neil Strange delivered a webinar on “Business Rule Secrets” for Data Vault practitioners. Here’s a summary of the key concepts, best practices, and actionable tips from that session.

Why Business Rules Matter in Data Vault

Business rules are often underexplored in published Data Vault material, yet they are a frequent source of confusion in forums and real-world projects. Tightening up guidance and establishing good practice around business rules is essential for delivering robust, agile data warehouse and business intelligence solutions.

Understanding the Business Vault

The Data Vault architecture includes several layers:

• PSA (Persistent Staging Area)

• Stage

• Raw Vault

• Business Vault

• Information Marts

• Error Vault

• Metadata Vault

The Business Vault is an intermediate layer between the Raw Vault and Information Marts. It’s a sparsely modeled data warehouse, built on Data Vault design principles, that houses data changed by business rules. The Business Vault makes it easier to create end-user structures and can be dropped and regenerated from the Raw Vault at any time.

What Is the Business Vault?

The Business Vault is an augmentation of the Raw Vault. It overlays derived data, including:

• Calculations, ratios, allocations (business rules)

• Helper tables (PIT and Bridge tables)

• Data science model outputs (classifications, predictions)

• Quality checks (orphan detection, conformance, etc.)

All Business Vault tables are overlaid on the Raw Vault, sharing Hubs and Links, and can be reconstructed at any time from the raw data.

Why Calculate Business Rules in the Data Warehouse?

If data is needed to run the business, why isn’t it in the source systems? Often, it’s because of missing functionality in those systems. The data warehouse plugs these gaps by applying business rules and creating derived values that support business operations and analytics.

Data Consumption Rules and Helper Tables

Business rules aren’t just about transforming data—they also include:

• Grain shifts (aggregation, grouping)

• Filtering (subsetting data)

• Security (obfuscation, masking)

Helper tables like PIT (Point-In-Time) and Bridge tables are vital for performance and reporting. PIT tables help identify the correct record for each reporting event, while Bridge tables pre-calculate navigation across Hubs and Links, and can add aggregated data as extra columns.

Implementing Business Rules in Data Vault

• Derived values are usually entered into satellites (e.g., SAT_BR_CUSTOMER_DETAILS).

• Occasionally, a business rule may create a new Link or even a new Hub (e.g., for classifications).

• Use clear naming conventions (e.g., “BR” for Business Rule satellites) and version control for lookup tables when needed.

• All business rule logic should be transparent, auditable, and, where possible, reconstructable from the Raw Vault.

Best Practices and Key Takeaways

• The Business Vault is not just a layer—it’s an overlay of derived, helper, and processed data.

• Use PIT and Bridge tables for efficient, single-date effectivity and performance.

• Implement output tables as views or as added columns on Bridge tables.

• Derived values should be calculated and, if needed, fed back as new data sources.

• Always overlay Business Vault tables on the Raw Vault, sharing Hubs and Links for consistency and traceability.

• You can always reconstruct Business Vault outputs from the Raw Data, ensuring flexibility and auditability.

Conclusion

Mastering business rules in Data Vault 2.0 is key to building agile, high-performance data warehouses that deliver real business value. By leveraging the Business Vault, helper tables, and best practices for business rule implementation, you can ensure your data warehouse is both robust and adaptable.