In this blog, we’ll learn How to Create a Decomposition tree in PowerBI. Firstly, we have to understand what is it and when should we use it.
Introduction to Decomposition tree in PowerBI
Decomposition tree: It is a visual representation of breaking down complex data into its various factors.
Usually used in project management, data analysis and business intelligence.
Dataset description
The dataset description is given below
| Column Name | Description |
|---|---|
| Date | The date on which the sales transaction occurred. |
| Product | The name or identification of the product sold. |
| Quantity | The number of units of the product sold in the transaction. |
| Price | The unit price of the product at the time of the transaction. |
| Rate | The rate of the product after applying any discounts. |
| Discount | The discount applied to the product during the sale, either in percentage or monetary value. |
| Customer | The name or unique ID of the customer making the purchase. |
| Location | The geographical location (city, region, or country) where the transaction took place. |
| Total Sale | The total sales value for the transaction, calculated as Quantity * Rate. |
| Total Profit | The total profit generated from the transaction, calculated after deducting the cost of the goods sold from the total sales. |
| Target Sale | The sales target set for the transaction, which could be used to assess performance against goals. |
You can get dataset from here.
Sample of Data:
Procedure to create Decomposition in PowerBI:
Step1: Open your PowerBI Desktop on your Device
Step2: Click on Get Data button in Home Ribbon and select Text/CSV.
Step3: Open Prompt box will be open to select your text or csv file.
Step4: After selection of dataset, It will pop up a window for Load OR Transform Data. Click on Load to use data on PowerBI.
Step5: In the following picture, data has been loaded successfully.
Step6: Select Decomposition tree from Visualizations Section (Right Hand Side).
Step7: Here, we’ll put the following data (from Sales) in different fields:
Analyze | Total Profit (Sum of Total Profit) |
Explain by | Product |
Location |
Step8: Select Product as shown in following picture.
Note:
- You can select any options (High value, Low value, Location, Product) according to your requirement.
- According to our dataset, if we select:
- Product – We’ll get Higher sum of Total Profit according to Product.
- Location – We’ll get Higher sum of Total Profit according to Location.
- High value – We’ll get Higher sum of Total Profit according to Location.
- Low value – We’ll get Lower sum of Total Profit according to Product.
Step9: Click on + (as shown in following picture) to get the options to digging up more information and select High Value to get Higher sum of Total Profit according to Location.
Step10: As we can see, the following picture presenting information in the form of hierarchical model.
There are lots of things like Filters and Format, that can be apply on your visual.
In Visual Section
- Tree 🡪 Tree setting 🡪 Density = Sparse
- Values 🡪 Font color = #E6E6E6
- Headers -> Title -> Color = #000000
- Headers 🡪 Subtitle 🡪 Color = #666666
- Headers 🡪 Background 🡪 Color = #118DFF
In General Section,
- Effect 🡪 Background 🡪 color = #000000 (Black)
- Effect 🡪 Visual border 🡪 color = #118DFF (Blue)
- Effect 🡪 Visual border 🡪 Rounded corners = 10 px
- Effect 🡪 Shadow = On
Example:
Insights from the Decomposition tree in PowerBI
From this visual, we are getting the sum of total profit with hierarchical based data which looks like the following data:
Total Profit | Profit (on Product) | Profit (on Location) |
3819.25 | Gaming Laptop (360.00) | Los Angeles (180.00) |
Shanghai (180.00) | ||
—- | —- |
Conclusion
With decomposition tree in PowerBI, we can break down a key metric into its contributing factors. It is useful in displaying the hierarchical relationship between data. Each branch can be analyzed with its expansion technique and more details can be gathered about the data. It helps in uncovering insights, root cause analysis, and data-driven decision making.
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