data-dictionary

To create a data dictionary, define and document each data element with its business description, data type, format, source, owner, rules, and lineage. This living dictionary centralizes definitions across database schemas, ETL processes, and reporting datasets to reduce ambiguity.

Documenting data ownership and business rules requires centralizing data definitions, sources, and governance rules for each field in reporting datasets. This ensures auditability and aligns data understanding across business units by clarifying field meanings and relationships.

Yes, you can document entity relationships and data lineage across business units by applying a centralized data dictionary to your data models and schemas. It captures entities, field relationships, and governance rules to ensure consistent data definitions and ownership.

Field descriptions for analytics and reporting datasets should include a business description, data type, format, source, owner, rules, and lineage. This documentation clarifies data meanings and ensures consistent usage across ETL processes and business intelligence reporting.

You need a data dictionary for your data models when ambiguity around field meanings and data ownership arises across business units. It defines data elements, sources, and business rules to align data understanding for analytics, ETL processes, and reporting.

Yes, this approach supports generating a data catalogue and CRUD matrix for governance by documenting entities, fields, and business rules. It establishes data ownership, definitions, and lineage to provide auditability and reduce ambiguity across the organization.