Data Modelling

Introduction

Creating a great data model is one of the most important tasks that a data analyst can perform in Microsoft Power BI. The following image is an example data model. The boxes contain tables of data, where each line item within the box is a column.

The lines that connect the boxes represent relationships between the tables. These relationships can be complex, even in such a simplistic model. The data model can become easily disorganised, and the total table count in the model can gradually increase. Keeping your data model simple, comprehensive, and accurate requires constant effort.

Star Schemas

You can design a star schema to simplify your data. It’s not the only way to simplify your data, but it is a popular method; therefore, every Power BI data analyst should understand it. In a star schema, each table within your dataset is defined as a dimension or a fact table, as shown in the following visual.

Fact tables
Fact tables contain observational or event data values: sales orders, product counts, prices, transactional dates and times, and quantities.

Dimension Tables
Dimension tables contain the details about the data in fact tables: products, locations, employees, and order types.

Considering this information about fact tables and dimension tables, you might wonder how you can build this visual in Power BI.

The pertinent data resides in two tables, Employee and Sales, as shown in the following data model. Because the Sales table contains the sales order values, which can be aggregated, it is considered a fact table.