Why is documenting data models important in dbt?

Documenting data models in dbt is crucial for facilitating better understanding and usage among team members and stakeholders. When data models are well-documented, it provides context on the purpose and structure of the data, as well as how transformations are applied. This richness of information allows users to interpret the data accurately and use it effectively in their analyses or reports.

Moreover, good documentation aids in onboarding new team members, as they can more quickly ascertain how data flows through the models and the logic behind transformations. Additionally, comprehensive documentation serves as a reference that helps in troubleshooting issues, improving collaboration among team members, and fostering a culture of transparency regarding data.

While compliance with external regulations, improving data entry efficiency, and reducing data storage costs are important considerations in data management, they do not capture the core function of documentation within dbt, which is primarily about enhancing understanding and utilization of data models.