Book Image

Learning Tableau 2022 - Fifth Edition

By : Joshua N. Milligan
Book Image

Learning Tableau 2022 - Fifth Edition

By: Joshua N. Milligan

Overview of this book

Learning Tableau 2022 helps you get started with Tableau and data visualization, but it does more than just cover the basic principles. It helps you understand how to analyze and communicate data visually, and articulate data stories using advanced features. This new edition is updated with Tableau’s latest features, such as dashboard extensions, Explain Data, and integration with CRM Analytics (Einstein Analytics), which will help you harness the full potential of artificial intelligence (AI) and predictive modeling in Tableau. After an exploration of the core principles, this book will teach you how to use table and level of detail calculations to extend and alter default visualizations, build interactive dashboards, and master the art of telling stories with data. You’ll learn about visual statistical analytics and create different types of static and animated visualizations and dashboards for rich user experiences. We then move on to interlinking different data sources with Tableau’s Data Model capabilities, along with maps and geospatial visualization. You will further use Tableau Prep Builder’s ability to efficiently clean and structure data. By the end of this book, you will be proficient in implementing the powerful features of Tableau 2022 to improve the business intelligence insights you can extract from your data.
Table of Contents (20 chapters)
18
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19
Index

Structuring data for Tableau

We’ve already seen that Tableau can connect to nearly any data source. Whether it’s a built-in direct connection to a file or database, data obtained through a custom Web Data Connector (WDC), or through the Tableau data extract API to generate an extract, no data is off limits. However, there are certain structures that make data easier to work with in Tableau.

There are two keys to ensure a good data structure that works well with Tableau:

  • Every record of a source data connection should be at a meaningful level of detail
  • Every measure contained in the source should match the level of detail of the data source or possibly be at a higher level of detail, but it should never be at a lower level of detail

For example, let’s say you have a table of test scores with one record per classroom in a school. Within the record, you may have three measures: the average GPA for the classroom, the number of students...