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Data Modeling with Tableau

You're reading from   Data Modeling with Tableau A practical guide to building data models using Tableau Prep and Tableau Desktop

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803248028
Length 356 pages
Edition 1st Edition
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Author (1):
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Kirk Munroe Kirk Munroe
Author Profile Icon Kirk Munroe
Kirk Munroe
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Table of Contents (22) Chapters Close

Preface 1. Part 1: Data Modeling on the Tableau Platform
2. Chapter 1: Introducing Data Modeling in Tableau FREE CHAPTER 3. Chapter 2: Licensing Considerations and Types of Data Models 4. Part 2: Tableau Prep Builder for Data Modeling
5. Chapter 3: Data Preparation with Tableau Prep Builder 6. Chapter 4: Data Modeling Functions with Tableau Prep Builder 7. Chapter 5: Advanced Modeling Functions in Tableau Prep Builder 8. Chapter 6: Data Output from Tableau Prep Builder 9. Part 3: Tableau Desktop for Data Modeling
10. Chapter 7: Connecting to Data in Tableau Desktop 11. Chapter 8: Building Data Models Using Relationships 12. Chapter 9: Building Data Models at the Physical Level 13. Chapter 10: Sharing and Extending Tableau Data Models 14. Part 4: Data Modeling with Tableau Server and Online
15. Chapter 11: Securing Data 16. Chapter 12: Data Modeling Considerations for Ask Data and Explain Data 17. Chapter 13: Data Management with Tableau Prep Conductor 18. Chapter 14: Scheduling Extract Refreshes 19. Chapter 15: Data Modeling Strategies by Audience and Use Case 20. Index 21. Other Books You May Enjoy

Use case 2 – sales performance management dashboards

Scenario:

We have a request from sales to create a data model for their analyst team to create interactive dashboards for the worldwide sales organization. The dashboards contain sales of all products and services for every customer, in every channel, and every region. The range of users goes from sophisticated sales analysts to sales representatives who aren’t always tech-savvy.

The data comes from many sources, but the data engineering team has consolidated all sources into a Snowflake database. The data is messy as the company uses different customer relationship management software in different regions of the world. The rules for data entry validation vary wildly, resulting in messy data in some regions and cleaner data in others. The data engineering team does not have the mandate, resources, and time to clean the data. They will leave this cleaning task to a data steward who works with the business. Finally...

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