What's your method for documenting data modeling decisions?

1 Why document data modeling decisions?

Documenting data modeling decisions is not only a good practice, but also a necessity in data science. It helps you and your team to keep track of the logic, assumptions, and constraints behind your data model. It also helps you to communicate and justify your data model to stakeholders, clients, or reviewers. Moreover, it helps you to maintain and update your data model as the project evolves or the data changes. Without proper documentation, you risk losing valuable information, creating confusion, or introducing errors in your data model.

2 What to document in data modeling decisions?

When it comes to data modeling decisions, there are various elements that should be documented depending on the level of detail and complexity of your data model. For instance, you should document the purpose and scope of your data model, the data sources and quality, the data modeling approach and method, the data modeling tools and techniques, and the data modeling challenges and solutions. Specifically, you should consider what problem or question your data model aims to solve or answer, where your data comes from and how reliable it is, what type and structure of your data model is being used, what tools and techniques you use to create, test, and implement your model, as well as any challenges or difficulties you face or anticipate in the process. Additionally, you should consider the advantages and disadvantages of your approach, the formats, types, and sizes of your data, how to handle missing or inaccurate data, how to normalize or aggregate your data, what alternatives or trade-offs you considered or applied in your decisions.

3 How to document data modeling decisions?

Documenting data modeling decisions can be done in various ways and formats, depending on the audience, purpose, and medium of the documentation. For instance, a data dictionary is a collection of metadata that describes the data elements in your data model. This typically includes the name, definition, type, format, length, range, constraints, and source of each data element. A data model is a visual representation of the structure and relationships of your data elements. It can use symbols, diagrams, or schemas to illustrate how your data elements are organized and connected. Additionally, a data modeling narrative is a written explanation of the rationale and process behind your data modeling decisions. It often includes the purpose, scope, approach, method, tools, techniques, challenges, and solutions of your data modeling process. All these methods help to standardize and clarify the meaning and usage of your data elements; identify and resolve any issues or gaps in your data model; document and justify choices made in the process; and provide context for your data model.

4 When to document data modeling decisions?

Documenting data modeling decisions is not a one-time task, but an ongoing process throughout your data science project. As soon as possible and as often as necessary, you should document your data modeling decisions. When you start a new project or join an existing one, document the initial scope, requirements, and expectations of your data model, as well as the data sources, quality, and availability that you have or need. Additionally, when making changes or updates to your data model, document the reasons and impacts of those changes or updates and the results and feedback from testing or implementing it. Finally, when you finish or deliver your data model, document the final version and status of it, as well as the lessons learned and best practices that you gained from your data modeling process.

5 How to improve your data modeling documentation skills?

Documenting data modeling decisions is a skill that can be improved with practice and feedback. To help you in this endeavor, here are some tips and resources to consider. First, follow the best practices and standards for data modeling documentation that are relevant to your data model. Additionally, use consistent and clear terminology, format, and style for your data modeling documentation. Furthermore, select the appropriate tools and templates for data modeling documentation that suit your needs. Leverage the features and functions of the tools and templates to automate your data modeling documentation. Lastly, review and revise your data modeling documentation regularly and thoroughly, as well as seek feedback from stakeholders or reviewers to validate it.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?