Hello and welcome to this course on Designing a Data Model in Power BI. Just as important as building a data model is knowing how to deliberately design the data model for best performance, high-quality insights, and user-friendliness. My name is Chelsea Dohemann and I am a Certified Microsoft Office Master and have been teaching about Power BI since before its inception, when it was a suite of Excel add-ins. Feel free to connect with me at the link on the screen, or if you have any questions about this course or any other Cloud Academy courses, direct these queries to support@cloudacademy.com.

The only prerequisite for this module is a familiarity with preparing data using Power BI. When you have completed this course, please do take a moment to rate it as this valuable feedback helps us deliver high-quality training to you.

Let's start by defining a data model. A Power BI data model describes a collection of data brought together and made relatable so that analysis can be performed. Data models often include data from various different sources. Because that data often comes from different sources, most data models require logical relationships to be established between the data sources so that data from those different sources can be analyzed together.

Most of the time, we will need to define or program the relationships between the data ourselves, and the way we define these relationships is extremely important as it has various downstream impacts. Furthermore, not all data will automatically play nice with the other data in the model. So, much of data modeling competency involves being aware of common problems, recognizing problem indicators within our datasets, and having the tools to resolve those items before they manifest as issues downstream for the user. Which is why this module is so important.

Now, all data is different. So one size does not fit all when it comes to data models, but all data models do seek to have a design that enables fast navigation through the data, the design of simple measures and calculations, aggregation accuracy, of course, ease of report creation, and simple report maintenance.

In this course, we will begin by defining the elements of an ideal data model. We will approach data model's various components starting with tables, relationships, and explore the impact different relationships will have on our reports. We will learn how and when to adjust table and field settings to ease information flow between the tables, and create some quick measures to help us get started building reports. We will talk about the natural occurrence of hierarchies in our data, how they can be used, and how to flatten them when needed.

Date tables are also covered as a solution to common date analysis issues. The important concept of data granularity is discussed so that we can make the best performance decisions for our data models. And we will round all this out by covering other performance considerations and how to set ourselves up for success. Let's begin.