📄 No One’s Reading Your Data Dictionary

You wrote a data dictionary.
It lives in a tab of a spreadsheet.
It’s outdated. No one uses it.

Meanwhile, teammates keep asking:
“What does cust_flag_1 mean again?”

😩 Why This Hurts

Poor documentation leads to:

• Confusion across teams

• Misused columns

• Delays in analysis

• Broken pipelines from misunderstood data

A good data dictionary should save time, not waste it.

✅ Make Docs That People Actually Use

Here are a few ways to make documentation useful — and effortless.

1. Auto-Generate Docs from Your Schema

If your data is in a SQL DB, generate docs using tools like:

• dbdocs.io – clean, clickable online docs

• SQLModel + FastAPI – auto-generated schema docs via OpenAPI

• Dataedo or Metabase – for larger orgs

📌 No more manually updating column definitions.

2. Use Markdown, Not Excel

Text > Tabs.

Example:

### customer_id
- Type: int
- Description: Unique customer identifier

### cust_flag_1
- Type: boolean
- Description: True if customer opted in to email

🟢 Easy to version control
🟢 Easy to read
🟢 Easy to share via GitHub, Notion, or Confluence

3. Make It Discoverable

Documentation no one can find is as good as no documentation.

📌 Pin it in your team’s Slack channel
📌 Link to it in dashboards
📌 Include it in every notebook or repo README

💡 Bonus Tip: Treat Docs Like Code

Version it.
Update it when data changes.
Assign ownership.

Better documentation = faster onboarding and fewer questions.

📊 Poll

Where do you store your team’s data dictionary?
Click to vote — results shared in next issue.