You can follow below to create a sustainable layered governance framework across the Medallion architecture separating technical metadata from business semantics.

Layered Strategy

• Bronze Layer (Technical & Operational) - Keep the documentation minimal and automated. You can focus strictly on upstream lineage, source systems, ingestion frequencies and raw formats (Ingested via DLT from SAP ERP). Keep the business definitions at a high level or try to avoid as schemas can change upstream and as users generally don't consume raw data.

• Silver Layer (Conformed & Classified) - Business context begins here. You can ensure standardized schemas (naming, types, time zones etc) and use column comments to explicitly document data cleaning or transformation rules applied. You can introduce Governed Tags here for data classification (PII, sensitive, public etc) to drive downstream security policies.

• Gold Layer (Consumption Ready Semantics) - Its the main & primary documentation target for business users and BI tools. Ensure that every column has a rich business definition aligned with the organization’s master business glossary. Maintain comprehensive compliance tag coverage for data ownership and discovery.

Unity Catalog Execution

Unity Catalog provides various features to support the governance.

• Column Comments - You can focus on adding calculation logic, edge cases and provide concrete examples such as order_status_code: The operational lifecycle state of the order. Valid values: PENDING, SHIPPED, DELIVERED, CANCELLED.

• Governed Tags - Use tags as structured & queryable metadata. You can establish an upfront taxonomy (e.g., Data_Classification: PII, Business_Domain: Finance) and then leverage these tags directly in security policies based on roles.

• Constraints - You can use CHECK constraints strictly to actively enforce data quality (as CHECK (age >= 0)). Document the rationale behind the constraint inside the column comment.

AI Consumption

You can follow below to ensure Unity Catalog's metadata is useful for AI cases

• Enforce Structured Formats - Use deterministic patterns in column comments (such as always ending with Valid values: [X, Y, Z]) allowing LLM parsers to accurately extract categorical boundaries.

• Define Relationships - Ensure primary and foreign key relationships are explicitly defined in the schemas. UC uses these to help AI engines write accurate multi-table joins.

• Publish Freshness Metrics - You can attach metadata regarding data freshness, SLA targets and update frequencies to your table properties to allow AI models know if they are pulling stale data.