How to Automate Power BI Documentation?

This is the first Analyst in Action post of 2026, and it starts with a problem most Power BI teams know well and still tend to avoid. Documenting a Power BI model often feels like labeling boxes while the warehouse is still being rebuilt. You might get everything neatly written down today, but by next week, half of it no longer matches what’s actually on the floor. Before long, documentation starts to feel like an admin task, and it quietly drops to the bottom of the list.

In real projects, things move quickly. Measures get added late, business logic changes after stakeholders review the numbers, and datasets end up feeding far more reports than originally planned. Documentation struggles to keep up with that pace. When it exists, it’s usually manual, spread across multiple files, and already outdated by the time someone looks at it.

The impact is very real. New BI developers take longer to become productive, small changes break reports in unexpected places, and important logic lives in people’s heads instead of in the model. This usually isn’t because teams don’t care about documentation; it’s because manual documentation doesn’t scale with Power BI development. The only approach that holds up over time is documentation generated directly from the model and the Power BI platform itself, where the real source of truth already lives.

Okay, but before jumping into tools and automation, it helps to be clear about what “Power BI documentation” actually means in practice. For some teams, it’s a data dictionary, for others, it’s a list of measures, dependencies, or report usage. In reality, documentation in Power BI isn’t a single thing; it’s a set of different views on the same model, each answering a different question. Getting that clarity first makes it much easier to choose the right tools and avoid documenting things that don’t add real value. Be wise and decide what exactly you want to document.

What does Power BI documentation mean?

As mentioned above, we need to first decide on the type of documentation you need. Power BI documentation can mainly cover: you can document the dataset, data model, and usage of data across different platforms.

• Documenting Dataset - This is the document where the core information lives about your dashboard. It covers almost everything that you need to onboard new team members or make some changes in the core logic of the dashboard. That being said, let's uncover what exactly you can document with it. Tables and columns, including hidden fields, Data types, and basic metadata, Relationships (cardinality, direction, active vs inactive), Measures and their full DAX expressions, Dependencies between measures, columns, and tables.
• Documenting the Data Model - This is the extra layer to the Documenting Dataset, which will make the data model easier to maintain in the future and provide contextual information to anyone who will be managing your dashboard. It basically covers- Clear and consistent naming conventions, Measure folders and logical organization, Descriptions that explain intent, not implementation, Calculation groups and shared logic patterns
• Documenting the usage of data across platforms - This will be the biggest enhancement that you can do to your dashboard. Such documents create a big impact when there is a change in the dataset, and you don't know which dashboards will be impacted. You can cover - which reports are connected to which datasets, how widely a dataset is reused, who owns each dataset and report, refresh history, failures, and reliability.

Tools to Document

Now that we know the things that you can document, it is time to see how we can create the documents. For a BI developer, the obvious choice is to give me one tool, and I will document everything there. But unfortunately, that is not the case with documentation. For every scenario, there is a separate choice of tools.

• DAX Studio for extracting model metadata and logic (what the model is)
• Tabular Editor for enforcing standards and improving metadata quality (how the model reads)
• Power BI REST APIs for platform lineage and governance (how the model is used)

DAX Studio - One of my go-to external tools to explore the data model. There is a lot you can do with DAX Studio, and we have covered the most common use cases in this 2-part blog.

• Ultimate beginners guide to DAX Studio
• Go a level beyond with DAX Studio

What you typically document with DAX Studio: Tables and columns (including hidden fields), Measures and full DAX expressions, Data types and formatting, Relationships (depending on DMV used), Dependencies (impact analysis)

Let's see a couple of scenarios to start the documentation journey 

• Creating a Measure Dictionary- First, let's understand what a measure dictionary actually means. For me, such a dictionary should have everything about all the measures in your dashboard. It can be measure definitions, descriptions, format strings, and many more. Here is the query you can run and try by yourself. I have mentioned TMSCHEMA_MEASURES, but you can achieve the same with MDSCHEMA_MEASURES 
• Creating a dependency lineage - This is a big help when you have decided to fix one of the base calculations, and you see that everything turns to error. You can track it easily with the help of DAX Studio. You can check for measures, calculated columns, 

Query: List measures from TMSCHEMA_MEASURES

SELECT [MEASURE_NAME], [MEASUREGROUP_NAME], [EXPRESSION], [DESCRIPTION], [FORMAT_STRING]
FROM $SYSTEM.TMSCHEMA_MEASURES
ORDER BY [MEASUREGROUP_NAME], [MEASURE_NAME]

Tip: This DMV works for Tabular models / Power BI XMLA endpoints.

Query: DISCOVER_CALC_DEPENDENCY

SELECT *
FROM $SYSTEM.DISCOVER_CALC_DEPENDENCY

Tabular Editor (TE) - TE is a great addition to your external tool library because it will help to make your data model understandable, and it is the best place to make your data model more maintainable for the future. 

This is what you can document in TE: Consistent naming conventions, Descriptions that explain intent, Measure folders/display organization, and Calculation group structure

Here are a few examples you can try on

• Add a description to measures, and it should appear uniformly in all measures. You can add the descriptions in your measures, but if you are creating tons of them, you won't be able to maintain the uniformity. To do so, in a matter of a click, you can try this C# script.
• Provide a standard naming for measures- It is normal to create multiple test measures, but when it comes to documenting them effectively, your data model contains multiple versions of the same measure. To cut that out, try the second C# script.

C# Script: Fill missing measure descriptions

foreach (var m in Model.AllMeasures)
{
    if (string.IsNullOrWhiteSpace(m.Description))
        m.Description = "TODO: Add business definition (what this measure means and when to use it).";
}

C# Script: Normalize and prefix measure names

foreach (var m in Model.AllMeasures)
{
    m.Name = m.Name.Trim();

    // Example rule: prefix base measures with "M - "
    if (!m.Name.StartsWith("M - "))
        m.Name = "M - " + m.Name;
}

We can answer everything related to the platform. Questions like which reports are using this dataset, how many workspaces depend on it, who owns it, and how often the refresh fails. This becomes part of governance and operational visibility.

Power BI REST APIs

Final advice is to start with the core, document with the help of DAX Studio, and develop it as a habit. Slowly improve this with Tabular Editor and REST API layers instead of trying to perfect everything on day one.

Can I automate this completely?

Yes, this is a tricky one. In most cases, documentation can only be automated up to a point. The parts that work well are the ones Power BI already stores for you, such as tables, columns, measures, relationships, and which reports use a dataset. Instead of writing these down manually, you can simply pull them from the model and the Power BI service.

Automation comes from doing this the same way every time. The queries or scripts used to extract this information are saved once and then run again whenever needed, for example, after a deployment or on a schedule. Each time they run, they generate a fresh version of the documentation based on the current state of the model. If something changes in Power BI, the next run reflects that change automatically.

This can be done in a few common ways. Some teams use a simple PowerShell script to run the extraction and save the output to a shared folder or repository. Others trigger it through deployment pipelines, scheduled tasks, or lightweight workflows built with the Power Platform. The exact tooling matters less than the idea: the documentation is regenerated from the source instead of being updated by hand. If you need a detailed article on any of the approaches, do mention it in the comments

What can’t be automated is the quality of the metadata itself. Descriptions, naming, and model structure still come down to design choices made by developers. Automation will publish whatever exists in the model, whether it’s clear or messy, so basic standards and cleanup still matter.

The result is documentation that stays accurate but doesn’t fix itself. Automation keeps it up to date and removes manual effort, while human input is what makes it useful in the first place.

Practical Setup from Scratch 

This looks like a big task, and a literal add-on, but you will realise what exactly you are missing if you haven't started documenting yet. The biggest mistake is with such things are we are looking to perfect this from day 1 itself, but documentation is a thing that will improve day by day. You can start with something very basic and get in the habit of doing this. At this point, don't think about the automation.

• Use existing DAX Studio queries to extract model metadata
• Export results to the linked Excel
• Store them somewhere easy to access, like SharePoint or Teams
• Refresh the documentation manually before major changes or releases

Once you are doing the basic setup correctly, you can slowly scale this up by doing this. This is the setup where you are not doing this ad hoc. Slowly, you get rid of the manual runs, and this transforms into scripted runs, and you run them based on a schedule rather than occasionally.

• Save metadata queries and scripts instead of running them ad hoc
• Trigger them automatically after deployments or on a schedule
• Store outputs in a central location

Most documentation efforts fail for the same reasons. Teams try to document too much, automate poor metadata, or treat documentation as a one-time task. The result is usually documentation that exists but isn’t trusted or used. Focusing on minor details while missing the core information, such as measures, dependencies, or usage patterns, adds noise without reducing risk.

The biggest gains come when documentation is generated directly from the model and refreshed regularly. When that happens, changes become easier to reason about, onboarding speeds up, and developers spend less time guessing. 

Power BI documentation works best when it comes from the model itself instead of from someone’s experience or memory. When it’s generated from the dataset and the Power BI service, it stays current as things change and doesn’t rely on constant upkeep. That makes it easier to understand what’s in a model, safer to make changes, and much less likely that important details get lost over time.