Analytics
Using AI to Build a Data Dictionary People Actually Use
Definitions reduce confusion only when they are short and discoverable. This guide helps data teams get to shared metric understanding.
AI tools are most useful when they are attached to a specific job. For data teams, the goal is not to add another shiny tool to the stack. The goal is to create a repeatable workflow that produces a better decision, draft, prototype, analysis, or handoff.
Definitions reduce confusion only when they are short and discoverable.
In practice, that means using ChatGPT as part of a bounded process. Give the tool enough context to be useful, keep the output connected to real work, and make the human review step explicit.
The Workflow
Use this as a simple starting point. The exact details can change, but the sequence keeps the work focused.
Collect common metrics
Define source tables
Add owners
Include examples
What Good Looks Like
The output should make the next step easier. If the workflow ends with more ambiguity, more tabs, or a longer list of unresolved ideas, it is not doing its job.
A strong result gives you shared metric understanding. It should also leave a trail: what information went in, what assumptions were made, and what still needs human judgment.
Common Mistakes
Writing academic definitions
Letting definitions drift
Bottom line
Keep the human edit in the loop.
Start small, make the workflow observable, and keep responsibility with the person doing the work. AI should reduce friction, but it should not remove taste, judgment, or accountability.
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