Data Dictionary (a description of all data elements - entities/tables and attributes/columns) is data modeling or documentation artifact that can be created in various scenarios and can have different functions. Let's have a look at a few examples of those functions.

1. Gathering Requirements

You might use this when you gather requirements for a new application or database. You just set up an Excel spreadsheet and ask business people what data they would like to collect. It might look like this for a CRM system.

2. Data Modeling

Later in the process of the implementation of the CRM data modeler or architect takes those requirements and tries to make a valid data model out of it. Along the UML Class diagram or ER diagrams, more detailed definition of fields should be created. It may look like this:

3. Physical Database Design

When logical mode is created, database developer or DBA needs to translate entities into database tables. This would serve as a documentation of a database and might look like this:

4. Data Mapping

Once the CRM database has been designed, data mapping for data migration needs to be prepared as a joint exercise by the business and IT. It might look like this:

Conclusion

As you have now seen, Data Dictionary may have different functions, serving different people in different phases of data lifecycles. Finally, it has a very different format and I hope to show you more formats and applications on this blog in the future. Make sure to subscribe.

Learn more

1. Logical vs Physical Data Dictionary
2. Why Is Metadata Important for Databases
3. ER Diagram vs Data Dictionary - Which is Better for Documenting Data Models

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