This blog helps us to understand the details, advantages, and how to use the Microsoft Power BI Decomposition tree.

What is a Decomposition tree?

The Microsoft Power BI Decomposition Tree visual allows us to visualize data across various dimensions, fields, and categories. It automatically aggregates data while also allowing us to drill down into the data for analysis. It is a powerful tool that tells a story with just a few clicks. An update released by Microsoft in May 2020 has added more features to this visual and included it in the default visuals for Power BI.


  1. This visual is mainly used for root cause analysis.
  2. It is also a valuable tool for ad-hoc exploration.
  3. It has AI (Artificial Intelligence) visualization capabilities.
  4. We can lock a particular drill down.
  5. All users can explore it through the Power BI Service.
  6. We can rename the tabs. It displays the selected tab below the category.
  7. It supports bookmarks.
  8. It can handle any column irrespective of parent and child relations.
  9. This visual helps to analyze the flow of customer interaction within the optimization process.

How to use the Decomposition tree?

Once we select the Decomposition Tree from the visual, we have two buckets which help us to find the insights of the data and an optional tooltip bucket:

  1. Analyze – measure or dimension that needs to be analyzed, mainly an aggregation.
  2. Explained by – dimensions by which we want to explore the above-selected dimension.

Here let us consider an easily available Super Store dataset,

For the Decomposition Tree, once we have added a measure into the Analyze bucket, it is displayed in the visual with a plus sign. Once you click on that, you will see all the dimensions added to the Explained by bucket, along with the AI capabilities.


Power BI Decomposition Tree option

Here we analyze Sales according to their Region and City. The decomposition tree has conditional formatting capabilities as well, which helps to get our user’s attention.


Power BI Decomposition Tree visual

We are free to choose any dimension order in the visual to get insights into the data, which is also possible for the Power BI Service users.

We can also lock the dimension. If I lock the Region dimension, I cannot delete Region, unless I unlock it. But the other dimensions can be changed or deleted. Even the customer cannot change or delete the dimension when it is locked.

When using this visual, dimensions need not have a parent and child relationship. This visual has cross-filtering and cross-highlighting capabilities.

AI Capabilities

Once we have the Decomposition tree, we add a measure into the Analyze bucket and can now see a plus sign next to it in the visual. Clicking on it, we get a dropdown menu with high value and low-value options with a lamp symbol. These have Embedded AI capabilities.


Decomposition Tree AI options

High value – here the Decomposition Tree AI considers all the dimensions in the Explained by bucket and picks the field with the highest value

Low value – here the AI considers all the dimensions in the Explained by bucket and picks the field with the lowest value

Here, I have added the Country.


Country dimension added to the Decomposition Tree

Next, I want to analyze high value in that Country, So I have clicked on the AI “High value” option. AI analyzes the other dimensions in the Explained by bucket and displays the dimension with the highest value.


High value in the country

Now that sales are high in the West Region, I want to know the lowest sales in the West region. So, I click on the “Low value” option, which gives me the city of San Luis Obispo has the lowest sales rate in the west region. Thus, we can play around with the data and get detailed insights.


Low value in the region

Let us consider when we have a filter/slicer. I have added a Category Slicer.  I have selected furniture; we can see the West region to have the highest sales and the South to have the lowest. In the city node, Los Angeles to have the highest and San Luis to have the lowest sales value. In each node, we can see the highest values to be at the top, and the lower values to be at the bottom.


Adding a slicer

When I change my selection to Office supplies in the category Slicer, we can see the AI nodes change accordingly. In the city node, we can see Boise has the lowest sales value.


Changing values in slicer

Thus, this Decomposition Tree visual gives the user a strong understanding of the data and its complete details. Decomposition Tree with AI capabilities/features enables the Power BI tool to stand apart in the Data Analysis industry.

Visual BI also offers a set of Powerful Advanced Visuals for Power BI – Check the visuals here. Learn about the hierarchy tree for Power BI here. Learn more about Microsoft Power BI services offerings from Visual BI solutions here.

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