How to Document a Power BI Report (and Why You'll Regret Not Doing It)

There's a recurring post on the Power BI subreddit that goes something like this: "I just inherited a report with 180 measures and zero documentation. Where do I even start?"

The replies are always sympathetic. Everyone's been there. And yet, almost nobody documents their own reports either.

It's not because people don't understand the value. It's because writing documentation is genuinely tedious when you're already behind on deadlines. So let's talk about what actually belongs in a Power BI documentation package, how to structure it so it's useful (not just a checkbox), and how to make the process less painful.

What should a documentation package cover?

Good documentation answers two questions: "What does this report do?" and "How is it built?" That breaks down into a few layers.

The model layer

This is the foundation. You need to record:

• Every table in the semantic model — its source type (Import, DirectQuery, or calculated), where it pulls from, and what role it plays in the report
• Every relationship — which tables connect, on which columns, and what the cardinality and cross-filter direction are
• Columns and their types — especially calculated columns, which have DAX logic that someone needs to understand to modify safely

If you're using Import mode, note which tables are large. A table with 50 million rows that could be filtered at the source is a common performance issue that's invisible without documentation.

The measure layer

Measures are where most of the complexity lives. For each measure, capture:

• The DAX expression (formatted, not a one-liner)
• Which other measures it references (the dependency chain)
• Where it's used — on which pages, in which visuals

That last point matters more than people think. "Unused measures" is one of the most common findings in any model audit. They accumulate over time as pages get redesigned and requirements change, but the old measures never get deleted.

The visual layer

For consulting deliveries especially, you need to record what the end user actually sees:

• Each page, its purpose, and whether it's visible or hidden
• Which visuals live on each page and what fields they use
• Page-level, visual-level, and report-level filters
• Drillthrough configurations
• Bookmarks and their saved states

The "why" layer

This one gets skipped almost always, but it's the most valuable. Brief notes on design decisions:

• "We use a star schema with a shared Date table because the source system uses different date columns per fact table"
• "The YTD calculation uses DATESYTD instead of TOTALYTD because the fiscal year starts in April"
• "Page 3 is hidden — it's used as a drillthrough target from the executive summary"

These explanations are the first thing lost when someone leaves a project, and they're impossible to reconstruct from the model alone.

How to structure documentation for different audiences

A single monolithic document doesn't work well. Different people need different things.

For the business stakeholder: A summary of what each page shows, what filters are available, and how to interpret the KPIs. Don't mention DAX or table names.

For the next developer: The complete model inventory — tables, relationships, measures with DAX, column lineage. This is the technical handover package.

For support/ops: A troubleshooting section — what to check if data looks wrong, refresh schedules, known quirks ("the CRM source updates overnight, so morning numbers may lag by a day").

The manual approach vs automation

You can build documentation manually in Word or Confluence. Open the report, click through every object, copy-paste. For a simple 5-page report, it takes an hour or two. For a mature report with 30 pages and 200 measures, it takes a full day — and you'll miss things.

The alternative is to let a tool read the PBIP project files and extract everything programmatically. Items like table inventories, relationship maps, measure DAX, visual field usage, and unused-measure detection are straightforward to automate because the data is structured in the TMDL and PBIR files.

Dummy BI Automate's Document Report wizard does exactly this — reads your project folder and generates a structured breakdown in seconds. You still need to write the "why" notes by hand (no tool can infer intent), but the mechanical inventory work is handled.

A practical starting point

If you're reading this and thinking "I should document my reports but I won't," here's a minimal approach that takes five minutes:

1. Create a README.md in your PBIP project root
2. Write three things: what the report is for, who the audience is, and any non-obvious design decisions
3. Commit it alongside your report files

That's it. It's not comprehensive, but it's infinitely better than nothing. And when you eventually do a full documentation pass — manually or with a tool — you'll have the context you need to make sense of what you built three months ago.

Documentation isn't exciting. But the moment you need it and it's there, you'll be glad you spent the time.