What's your data modeling documentation process?

Documenting our data models is like leaving a breadcrumb trail in the forest – it guides us back if we're lost! It ensures clarity and transparency, helping the team understand data structure and flow. Plus, it aids reproducibility, ensuring we can replicate insights. And don't forget knowledge transfer – it prevents knowledge loss as team members come and go, fostering continuity.

Documenting data models is like leaving a map for others to navigate through the intricate terrain of your data infrastructure. It's not just about making things easier for your team but also ensuring that your models remain reliable and consistent over time. By documenting the rationale behind your decisions and the intricacies of your models, you're not just creating a roadmap for others but also laying down a foundation for trust and transparency within your data ecosystem.

It is a good idea because if you don’t how can consumers know what to take from where and how can the business validate if it is aligned to the business logic they explained?

Data modeling documentation process involves documenting the purpose and scope of the model, data sources and schemas, assumptions and constraints, and the rationale behind modeling decisions. Create entity-relationship diagrams or schema diagrams to visualize the data structure and relationships. Additionally, document data transformations, business rules, and validation checks applied during modeling. Ensure documentation is comprehensive, understandable, and accessible to stakeholders, facilitating effective collaboration and decision-making throughout the project lifecycle.

Directly integrating documentation into code, in my experience, improves comprehension and maintainability. It lowers the chance of misunderstanding by ensuring alignment between the code and its intended functionality. The effectiveness of development can be hampered by an over-reliance on external material, which might result in obsolete or disjointed information.

To further enrich your data model documentation: - Include version histories for your data models to track changes over time. - Incorporate feedback logs to document any modifications made based on stakeholder input. - Add use cases or scenarios to explain how the data model serves different business or analytical needs. - Ensure accessibility by using plain language and visual aids to make the documentation understandable for non-technical stakeholders.

For me, Tableau Prep Builder is much better than the tools mentioned here. It is not intended as a documentation tool; it’s a data prep tool. However, you can document any step and see the effect of any step. Even if you use it for documentation and rapid prototyping only, it is a fantastic tool. It can write to databases like Oracle, MS SQL, MySQL or Postgres. On the end-user end, a data catalogue and or a self-service solution to read the metadata of a data model and document them: number of fields, data type, primary keys, and % of blank rows…

To improve your data modeling documentation process, consider: - Incorporating version control to track changes and updates. - Using interactive documentation tools that allow users to query and explore the data model. - Establishing a peer review process to ensure accuracy and completeness. - Integrating documentation with your data models in the database, using comments or metadata for automatic documentation generation.

To share and reuse your data modelling documentation: - Consider integrating interactive elements or tools that allow users to explore the data model visually, such as clickable diagrams or dynamic schemas. - Offer training sessions or workshops to walk stakeholders through your data models, promoting better understanding and effective use. - Establish a feedback mechanism to gather insights on the documentation’s usability and areas for improvement, fostering a culture of continuous enhancement.

The documentation needs to start very early but in a dedicated session after the stakeholders signed the requirementa of what business questions the data model needs to answer. The better dashboard developers or business analysts or business understand the data model the more efficient they can leverage it. During the construction of the data model it helps if a person with deep domain knowledge is working together with the person who builds the data model. Yes, it takes time but it will save the double amount of time.

In my data modeling documentation process, I meticulously outline project objectives, data sources, transformations, modeling techniques, and performance metrics. I prioritize clarity and transparency, ensuring stakeholders understand the rationale behind model choices and any associated limitations. By presenting information in a user-friendly format with visuals and examples, I facilitate comprehension and collaboration across teams, fostering effective communication and knowledge sharing.

The documentation for the data model can begin with a list of available data fields, different data sources, granularity of data in various tables, primary/ foreign keys, and freshness of available data. The business and data stakeholders can provide invaluable insights on the needed metrics and how to derive them by transforming the data. Final metric logic, decided level of aggregation, necessary/ unnecessary fields, data structure, and data nuances, if any, should be documented over time. To build an optimized data model and for easy modification in the future to accommodate new metrics or redefine existing metrics as per changing business requirements, proper documentation is quite crucial.