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Data Modeling with Tableau

Data Modeling with Tableau

By : Kirk Munroe
4.5 (14)
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Data Modeling with Tableau

Data Modeling with Tableau

4.5 (14)
By: Kirk Munroe

Overview of this book

Tableau is unlike most other BI platforms that have a single data modeling tool and enterprise data model (for example, LookML from Google’s Looker). That doesn’t mean Tableau doesn’t have enterprise data governance; it is both robust and highly flexible. This book will help you effectively use Tableau governance models to build a data-driven organization. Data Modeling with Tableau is an extensive guide, complete with step-by-step explanations of essential concepts, practical examples, and hands-on exercises. As you progress through the chapters, you’ll learn the role that Tableau Prep Builder and Tableau Desktop each play in data modeling. You’ll also explore the components of Tableau Server and Tableau Cloud that make data modeling more robust, secure, and performant. Moreover, by extending data models for Ask and Explain Data, you’ll gain the knowledge required to extend analytics to more people in their organizations, leading to better data-driven decisions. Finally, this book will guide you through the entire Tableau stack and the techniques required to build the right level of governance into Tableau data models for the correct use cases. By the end of this Tableau book, you’ll have a firm understanding of how to leverage data modeling in Tableau to benefit your organization.
Table of Contents (22 chapters)
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1
Part 1: Data Modeling on the Tableau Platform
4
Part 2: Tableau Prep Builder for Data Modeling
9
Part 3: Tableau Desktop for Data Modeling
14
Part 4: Data Modeling with Tableau Server and Online

Data Modeling Considerations for Ask Data and Explain Data

Tableau Ask Data and Explain Data are powerful machine learning features that put analysis in the hands of casual users – those who would struggle to create their own analysis through the drag-and-drop features of Tableau Desktop and web authoring. For these casual users to get answers to their questions, the data models and available fields supporting them must be well thought-out; otherwise, the users may end up frustrated with answers that don’t make sense. This chapter explores these considerations.

In this chapter, we’re going to cover the following topics:

  • Visual analytics through natural language search with Ask Data
  • Creating a lens for Ask Data, including field exclusions, renaming, and creating aliases
  • Uncovering outliers in your data with Explain Data
  • Curating data sources for Explain Data by telling the model which columns to use and ignore
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Data Modeling with Tableau
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