/datasource

To document a data source for AI analysis, you capture structured metadata including ownership, data type, volume, refresh frequency, schema details, and access methods. This creates a data catalog entry that links sources to owning systems and identifies consumers.

For database tables and API endpoints, capture metadata such as data type, record count, volume, refresh frequency, classification, and key schema information. Including data quality metrics and system ownership makes the data source discoverable for AI integration.

Yes, you can document Snowflake tables by creating structured entries that capture the table purpose, schema, and access methods. This links the data source to the E-commerce Platform or owning system, making it accessible for AI assistants.

The best way to structure data governance documentation for AI is to capture classification, ownership, and refresh frequency alongside schema details. This structured approach links data sources to systems and consumers, facilitating AI understanding of the data landscape.

Yes, the data source documentation includes dedicated fields for data quality metrics. Capturing these metrics alongside schema details and access methods ensures AI assistants have a complete understanding of the data source's reliability and structure.