Book Image

Data Modeling with Tableau

By : Kirk Munroe
Book Image

Data Modeling with Tableau

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)
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

Use case 4 – marketing analytics of social media campaigns

Scenario:

We have a request from marketing to create a data model for their analyst team to create interactive dashboards for the worldwide marketing organization. The dashboards contain information on social media click-through campaigns.

The data comes from the social media companies where our campaigns are running, but the data engineering team has consolidated all sources into Google BigQuery. The data is clean and stored at the level of granularity of the day – that is, the data is rolled up to the day and broken down by social media site and campaign name. Sometimes, there are days with no data because the campaigns don’t always generate a click-through each day. Finally, everyone in marketing can see all campaign information, regardless of where they are located.

The company uses Tableau Cloud and has groups set up in Azure Directory Services that are synced to Tableau Cloud.

Tableau...