Introducing Data Modeling in Tableau

Welcome to data modeling in Tableau. You might know Tableau as a great self-service analytics tool that provides both powerful analytics and is also easy to use. You might also think that Tableau is light on the key enterprise analytics requirement of data security, data model robustness, and data maintainability. In this book, you will learn that Tableau has all these key data requirements covered. You will learn how data is best structured for Tableau analysis and performance, and understand the functionality of Tableau Prep Builder and Tableau Desktop and the role each plays in building data models. You’ll then publish these data models to Tableau Server or Online and optimize them for performance, governance, and security.

By the end of this book, you will have all the strategies and techniques needed to enable individuals in your organization to answer their own questions with data, regardless of their level of expertise. You will also drastically reduce the calls you receive from these same individuals about confusing data and dashboards that are slow to load.

Tableau is very different from most other BI tools in that the model can be either implicit or explicit. For instance, many analysts open Tableau Desktop, connect to data, and immediately begin creating visuals. In this instance, Tableau implicitly created a data model (that is, made a connection, executed a query, and created metadata) without an analyst having to do anything to create the model.

This implicit data modeling works well when your data source has already been prepared for analysis and you are the person creating charts and dashboards. Often, our data is not structured this way. It comes from different sources and needs to be combined and defined in meaningful ways. In these instances, Tableau provides the tools for you to create data models that are scalable, secure, and targeted to the different skills of a broad class of developers and consumers.

Tableau uses a data model as the foundation for the creation of all analyses. A Tableau data model contains the following:

• Connection information to the underlying data source.
• The queries required to retrieve the data.
• Additional metadata, or data about the data, added to the underlying data. Metadata can include more readable field names, field types, the grouping of data into hierarchies, and calculations not in the underlying data.

Tableau works best when your data is in a traditional spreadsheet table format – that is, Tableau assumes that the first row of your data consists of column headers and each column header maps 1:1 to a field name, with additional rows of data each containing one record of data. If the underlying data is not formatted in this way, analysis within Tableau becomes very difficult and performance will suffer. To address this, you can model your data in a format that works best with Tableau. The best practices to model data properly are the primary content of this book.

This chapter demonstrates how Tableau automatically creates a data model when you connect to a data source, how it interprets rows and columns in your data, and how you can shape and combine additional data into your data model.

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

• What happens when you connect to data in Tableau Desktop
• The ideal data structure for Tableau
• Shaping data for Tableau
• Connecting multiple tables