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Diana Wachenje
Diana Wachenje

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# UNDERSTANDING DATA MODELLING IN POWER BI

Introduction

Power BI is a business analytic platform that transforms raw data into actionable insights through visualizations and reports that helps users to analyze data, identify trends securely across organizations to make data-driven decisions.
Data modelling is the process to connect, structure, optimize data tables to work efficiently together.

Concepts to Understand

  • Joins
    Joins are performed in Power Query where you combine two columns by selecting the first and the second table and join them by clicking on the headers. Also ensure they have same data type for accurate matching.

  • Relationships
    Relationships define how table connect. These are,

  • One-to-Many (Most common)

  • Many-to-One

  • Many-to- Many (Used carefully)
    Example:

  • One customer- Many Sales

  • Schemas
    Schemas refers to the way your data is structured either in facts tables or dimensions and organized in the data model.

  • One Central fact table

  • Multiple surrounding dimension tables

Power BI Relationships

  • Cardinality Cardinality is how rows relate:
  • One-to- One
  • One-to- Many
  • Many-to- Many

  • Cross Filter Direction
    It controls how filters move between tables:

  • Single direction

  • Both directions

Commonly modelling issues
  • Not using proper Data Table
  • Mixing fact and dimensional data
  • Creating too many many-to-many relationships
Steps-by-steps Data modeling process

Step 1: Load Data
Import from Excel, SQL etc.
Step 2: Clean Data (Power Query)
Fix structure before modelling
Step 3: Create Relationships
Go to model view and connect tables
Step 4: Build Star Schema
Organize tables properly
Step 5: Create measures (DAX)
Add business logic
Step 6: Optimize Model

  • Remove unused columns
  • Use correct data types
  • Avoid unnecessary relationships
# Simple Real-life Examples

In a mini mart setup:

  • Sales table- transactions
  • Product table- item details
  • Customer table- buyer information
## Why Data Modelling is important
  • Faster reports
  • Accurate Insights -Easier Dashboards

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