Introduction – Why Data Modeling Matters

Have you ever wondered why some dashboards are lightning-fast, accurate, and easy to explore, while others
feel slow and confusing? The secret lies in data modeling. In Power BI, data modeling is not just about
connecting tables—it’s about shaping raw data into a structure that delivers clear, actionable insights.
Let’s break it down step by step.

What is Data Modeling?

In simple terms, data modeling is the process of organizing and structuring data to make it easier for analysis and reporting. In Power BI, this means creating logical relationships between tables, ensuring the model is efficient, and setting it up for faster queries and better performance.

Normalization vs. Denormalization

• Normalization
o Data is split into multiple smaller tables to remove redundancy.
o Example: Storing customer details in a separate table rather than repeating them in every sales
record.
o Use Case: Efficient storage, consistency, and reduced duplication.
• Denormalization
o Data is combined into fewer, larger tables.
o Example: Having a single sales table that includes customer, product, and region details
together.
o Use Case: Faster reporting and simplified queries.

Fact and Dimension Tables

In a Power BI model, tables usually fall into two categories:
• Fact Table → Contains quantitative data (e.g., sales amount, order quantity, revenue).
• Dimension Table → Contains descriptive information to slice and filter facts (e.g., product details,
customer data, time periods).
These are connected using Primary Keys (unique identifier in dimension table) and Foreign Keys (matching field in fact table).

Schemas in Data Modeling

1. Star Schema
o Structure: A central fact table connected to multiple dimension tables.
o Use Case: Simple dashboards like sales reporting, finance KPIs.
o Advantage: Easy to understand, fast query performance.
2. Snowflake Schema
o Structure: Dimensions are further normalized into sub-dimensions.
o Use Case: Complex hierarchies (e.g., product category → subcategory → supplier).
o Advantage: Storage efficiency, reduced redundancy.
3. Galaxy Schema (Fact Constellation)
o Structure: Multiple fact tables share dimension tables.
o Use Case: Large enterprises (e.g., an airline analyzing reservations, loyalty programs, and crew
data together).
o Advantage: Handles multiple business processes with shared dimensions.

Cardinality in Data Modeling

Cardinality defines the relationship type between tables:
• One-to-Many (1:*) – Most common (e.g., one customer → many orders).
• Many-to-One (*:1) – Reverse direction of above.
• One-to-One (1:1) – Rare, used for matching unique records.
• Many-to-Many (:) – Complex, usually avoided unless required.
Choosing the right cardinality ensures accurate results and prevents wrong aggregations.

A great Power BI dashboard is not built on visuals—it’s built on the data model behind them. By
understanding normalization vs. denormalization, fact and dimension tables, schemas, and cardinality, you set the stage for accurate, scalable, and lightning-fast analytics.