Book Image

Microsoft Power BI Quick Start Guide - Third Edition

By : Devin Knight, Erin Ostrowsky, Mitchell Pearson, Bradley Schacht
Book Image

Microsoft Power BI Quick Start Guide - Third Edition

By: Devin Knight, Erin Ostrowsky, Mitchell Pearson, Bradley Schacht

Overview of this book

Updated with the latest features and improvements in Power BI, this fast-paced yet comprehensive guide will help you master the core concepts of data visualization quickly. You’ll learn how to install Power BI, design effective data models, and build basic dashboards and visualizations to help you make better business decisions. This new edition will also help you bridge the gap between MS Excel and Power BI. Throughout this book, you’ll learn how to obtain data from a variety of sources and clean it using the Power Query Editor. You’ll also start designing data models to navigate and explore relationships within your data and building DAX formulas to make data easier to work with. Visualizing data is a key element of this book, so there’s an emphasis on helping you get to grips with data visualization styles and enhanced digital storytelling. As you progress, you’ll start building your own dataflows, gain an understanding of the Common Data Model, and automate dataflow refreshes to eradicate data cleaning inefficiency. You’ll learn how to administer your organization's Power BI environment so that deployment can be made seamless, data refreshes can run properly, and security can be fully implemented. By the end of this Power BI book, you’ll know how to get the most out of Power BI for better business intelligence.
Table of Contents (13 chapters)
11
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12
Index

Building the Data Model

In this chapter, you are now going to create a coherent and intelligent data model. Creating a data model is primarily the process of creating necessary relationships between the different data sources that are leveraged in your model.

Self-service BI would not be possible without a functional data model. Historically, BI projects focused on building data models could take months and even years to develop when working within the rigid structure and constraints of a corporate business intelligence environment. Unfortunately, studies show that about 50 percent of all enterprise BI projects fail. These projects fail because the costs are often too high; these projects can cost anywhere from hundreds of thousands of dollars to millions of dollars due to the costs associated with the infrastructure, licensing, and labor. Another reason for the low success rate is that the business and end users often won’t see any results for many months and can grow...