Book Image

Learning Tableau 10 - Second Edition

Book Image

Learning Tableau 10 - Second Edition

Overview of this book

Tableau has for some time been one of the most popular Business Intelligence and data visualization tools available. Why? Because, quite simply, it’s a tool that’s responsive to the needs of modern businesses. But it’s most effective when you know how to get what you want from it – it might make your business intelligent, but it isn’t going to make you intelligent… We’ll make sure you’re well prepared to take full advantage of Tableau 10’s new features. Whether you’re an experienced data analyst that wants to explore 2016’s new Tableau, or you’re a beginner that wants to expand their skillset and bring a more professional and sharper approach to their organization, we’ve got you covered. Beginning with the fundamentals, such as data preparation, you’ll soon learn how to build and customize your own data visualizations and dashboards, essential for high-level visibility and effective data storytelling. You’ll also find out how to so trend analysis and forecasting using clustering and distribution models to inform your analytics. But it’s not just about you – when it comes to data it’s all about availability and access. That’s why we’ll show you how to share your Tableau visualizations. It’s only once insights are shared and communicated that you – and your organization – will start making smarter and informed decisions. And really, that’s exactly what this guide is for.
Table of Contents (17 chapters)
Learning Tableau 10 Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface

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, ODBC, or using 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 ensuring 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 or possibly be at a higher level of detail, but 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 in the class, and the number of students in the school:

School

Classroom

Average GPA

Number of Students

Number of Students (School)

Pickaway...