As someone who works with and seeks to understand data, you will find yourself working within the cycle of analytics. This cycle might be illustrated as follows:

Tableau allows you to jump to any step of the cycle, move freely between steps, and iterate through the cycle very rapidly. With Tableau, you have the ability to do the following:

• Data discovery: You can very easily explore a dataset using Tableau and begin to understand what data you have visually.
• Data preparation: Tableau allows you to connect to data from many different sources and, if necessary, create a structure that works best for your analysis. Most of the time, this is as easy as pointing Tableau to a database or opening a file, but Tableau gives you the tools to bring together even complex and messy data from multiple sources.

• Data analysis: Tableau makes it easy to visualize the...

Tableau connects to data stored in a wide variety of files and databases. This includes flat files, such as Excel documents, spatial files, and text files; relational databases, such as SQL Server and Oracle; cloud-based data sources, such as Google Analytics and Amazon Redshift; and OLAP data sources, such as Microsoft Analysis Services. With very few exceptions, the process of analysis and creating visualizations will be the same, no matter what data source you use.

We'll cover details of connecting to different types of data sources in Chapter 2, Working with Data in Tableau. And we'll cover data spanning a wide variety of industries in other chapters. For now, we'll connect to a text file, specifically, a comma-separated values file (.csv). The data is a variation of the sample that ships with Tableau: Superstore, a fictional retail chain...

When you first connect to a data source such as the Superstore file, Tableau will display the data connection and the fields in the data pane on the left Side Bar. Fields can be dragged from the data pane onto the canvas area or onto various shelves such as Rows, Columns, Color, or Size. As we'll see, the placement of the fields will result in different encodings of the data based on the type of field.

The fields from the data source are visible in the data pane and are divided into Measures and Dimensions. The difference between measures and dimensions is a fundamental concept to understand when using Tableau:

• Measures are values that are aggregated. For...

A new connection to a data source is an invitation to explore and discover! At times, you may come to the data with very well-defined questions and a strong sense of what you expect to find. Other times, you will come to the data with general questions and very little idea of what you will find. The visual analytics capabilities of Tableau empower you to rapidly and iteratively explore the data, ask new questions, and make new discoveries.

The following visualization examples cover a few of the most foundational visualization types. As you work through the examples, keep in mind that the goal is not simply to learn how to create a specific chart. Rather, the examples are designed to help you think through the process of asking questions of the data and getting answers through iterations of visualization. Tableau is designed to make that process intuitive, rapid...

Often, you'll need more than a single visualization to communicate the full story of the data. In these cases, Tableau makes it very easy for you to use multiple visualizations together on a dashboard. In Tableau, a dashboard is a collection of views, filters, parameters, images, and other objects that work together to communicate a data story. Dashboards are often interactive and allow end users to explore different facets of the data.

Dashboards serve a wide variety of purposes and can be tailored to suit a wide variety of audiences. Consider the following possible dashboards:

• A summary-level view of profit and sales to allow executives to have a quick glimpse into the current status of the company
• An interactive dashboard, allowing sales managers to drill into sales territories to identify threats or opportunities
• A dashboard...

Tableau's visual environment allows for a rapid and iterative process of exploring and analyzing data visually. You've taken your first steps toward understanding how to use the platform. You connected to data and then explored and analyzed the data using some key visualization types such as bar charts, line charts, and geographic visualizations. Along the way, you focused on learning the techniques and understanding key concepts such as the difference between measures and dimensions, and discrete and continuous fields. Finally, you put all the pieces together to create a fully functional dashboard that allows an end user to understand your analysis and make discoveries of their own.

In the next chapter, we'll explore how Tableau works with data. You will be exposed to fundamental concepts and practical examples of how to connect to various data sources...