Decomposition Tree in Power BI

Introduction

• Decomposition Tree visual in Power BI allows visualization of data across multiple dimensions.
• It aggregates data automatically and enables drilling down into dimensions in any order.
• Primarily used for conducting root cause analysis.
• Identified as an AI visualization.

Implementing Decomposition Tree

1. Load Data
   • Use a dataset (e.g., order sheet) for decomposition.
2. Select Decomposition Tree Visual
   • Choose the decomposition tree visual in Power BI.
3. Add Columns for Analysis
   • In the 'Analyze' field, add the column 'Profit'.
   • In the 'Explain By' field, select columns: Category, Subcategory, Sales, and Region.

Analyzing Data

• Start with selecting profit; it will display the sum of profit.
• Click the plus sign for more options, including high value, low value, category, subcategory, etc.
• Example Path: Profit → Category → Region → Sales

AI Splits in Decomposition Tree

• If 'Enable AI Split' is activated:
  • High value and low value options appear.
• If disabled:
  • These AI split options will not be visible.
• Path Example: High value category, followed by high value subcategory.

Analysis Types

• Absolute: Independent value (e.g., profit based on category, subcategory, and region).
• Relative: Dependent value based on fields.

Formatting Decomposition Tree Visual

1. Tree Density
   • Settings: default, dense, or sparse.
2. Connector Shape
   • Change the visual shape of connectors.
3. Bar Settings
   • Options for top node, parent node, and level node.
   • Change colors for positive and negative bars.
4. Category Label
   • Modify font, font color, size, text properties (bold, underlined).
5. Values
   • Adjust font, color, display unit, etc.
6. Header & Title
   • Change title font, add/subtract subtitle.

Summary

• Comprehensive explanation of the decomposition tree in Power BI.
• Steps to implement, configure, and analyze data using this visual tool.
• Formatting options for better visual representation.