Documentation records enough detail for other people to understand, repeat, or extend analytics work. For a project, that means preserving its questions, rationale, methods, datasets, measures, tests, and code. For a data resource, it means explaining its purpose, structure, and use. The goal is not to expose every internal detail in a stakeholder deliverable, but to make those details available when someone needs to investigate a result or continue the work. section 2.3.2 section 11.3.3 section 2.3.1
Useful records serve different readers:
- For teammates, retain the project steps, queries, code, and reasons for decisions. Someone should be able to rerun the work after its original analyst leaves, or understand its context years later. section 2.3.2
- For stakeholders, summarize the purpose and rationale behind the analysis, including the information that motivated its questions. Keep more detailed working records available for follow-up. section 2.3.2
- For data users, turn warehouse metadata into searchable, human-readable descriptions. An information schema can identify tables, columns, types, and relationships; added descriptions explain what those resources mean and how to use them. section 11.3.3
Documenting a shared data model
After creating a users dimension table in dbt, an analytics engineer documents its design, transformations, underlying sources, and the meaning of each new column. An update process and sample queries help other warehouse users adopt the model rather than reconstruct its logic independently. section 11.4.1
Documentation supports reproducibility when it gives peers enough detail to recreate the methods and data behind a result. The extent of that work may depend on an organization’s time and resources. Even when a fully reproducible workflow is not in place, keeping detailed records makes later retesting, reuse, and follow-up more practical. section 2.3.1 section 2.3.2
