Data Normalisation in Power BI

In this guide, we will explain how to perform data normalisation within Power BI effectively.

Data normalisation is crucial for efficient and well-structured data models, leading to better performance and accurate reporting. By mastering this skill, you can expect improved data integrity and reduced redundancy in your Power BI reports.

Unfortunately, many people struggle to implement data normalisation effectively in Power BI.

The primary reason why: a lack of understanding of database design principles.

Other reasons people struggle with data normalisation in Power BI include:

• Struggle #1: Limited knowledge of Power BI's data modelling capabilities.
• Struggle #2: Difficulty identifying and addressing data anomalies.
• Struggle #3: Inadequate understanding of normalisation levels and their benefits.
• Struggle #4: Balancing normalisation with performance considerations.

Don't worry - we're here to help you overcome these challenges and master data normalisation in Power BI.

Here's how, step by step:

Normalisation (Or Normalization for our US cousins)

Step 1: Understand the principles of normalisation

Ultimately, normalisation is about reducing redundancy, aka duplicate data. First, however, we refer to abstractions of normalisation of forms, namely the First Normal form (1NF), Second Normal form (2NF), and Third Normal form (3NF).

The rules surrounding forms are as follows:

How to reach First Normal Form (1NF):

• Atomic values: each cell contains unique and single values.
• Be able to add data without altering tables.
• Separate different relations into different tables.
• Keep relationships between tables together with foreign keys.

Second Normal Form (2NF):

• Have reached 1NF.
• All columns in the table must rely on the Primary Key.

Third Normal Form (3NF):

• Must be in 2nd Normal Form.
• No transitive dependencies. Remember, the transitive dependencies you are trying to maintain is that to get from A-> C, you want to avoid going through B.When to use 3NF:

When you want to update data, we want to be able to do it in just one place.

It's essential to learn the fundamentals of normalisation to apply them effectively in Power BI. Familiarise yourself with functional dependencies, primary keys, and normal forms like 1NF, 2NF, and 3NF.

Explore resources on database design and normalisation to strengthen your foundation. This knowledge will empower you to make informed decisions when structuring your data models in Power BI.

Step 2: Analyse and prepare your data

Many people fail to thoroughly examine their data before diving into Power BI. Review your datasets for redundancies and inconsistencies and identify areas that require normalisation.

Use Power Query to clean, transform, and shape your data before loading it into Power BI.

By addressing issues in your data upfront, you'll be better prepared to create efficient data models.

This also allows you to load industry-standard data models into Power BI Service for your client to reuse.

Step 3: Apply normalisation techniques in Power BI

Now that you have a solid foundation and have prepared your data, it's time to normalise your data model in Power BI. Use Power BI's relationships and data modelling features to create a structured, efficient data model.

Start by identifying tables that require decomposition and breaking them down into smaller, related tables. Then, establish relationships between these tables to maintain data integrity and reduce redundancy.

By following these steps and overcoming the challenges outlined earlier, you'll be on your way to mastering data normalisation in Power BI.

Your reports will benefit from improved data integrity, reduced redundancy, and better overall performance.

This also makes a data model easier to document and adds another element to your performance-boosting toolkit.

Next up, denormalisation.