Tableau Your Data!

Fast and Easy Visual Analysis with Tableau Software

Daniel G. Murray

• Verlag: Wiley
• Jahr: 2016
• ISBN:
  9781119001201
• Auflage: 2
• Thema: Computer Science
• Thema: Computer Science General

Transform your organization's data into actionable insights with Tableau

Tableau is designed specifically to provide fast and easy visual analytics. The intuitive drag-and-drop interface helps you create interactive reports, dashboards, and visualizations, all without any special or advanced training. This all new edition of Tableau Your Data! is your Tableau companion, helping you get the most out of this invaluable business toolset. Tableau Your Data! shows you how to build dynamic, best of breed visualizations using the Tableau Software toolset. This comprehensive guide covers the core feature set for data analytics, and provides clear step-by-step guidance toward best practices and advanced techniques that go way beyond the user manual. You'll learn how Tableau is different from traditional business information analysis tools, and how to navigate your way around the Tableau 9.0 desktop before delving into functions and calculations, as well as sharing with the Tableau Server.
Analyze data more effectively with Tableau Desktop

• Customize Tableau's settings for your organization's needs with detailed real-world examples on data security, scaling, syntax, and more
• Deploy visualizations to consumers throughout the enterprise - from sales to marketing, operations to finance, and beyond
• Understand Tableau functions and calculations and leverage Tableau across every link in the value chain
• Learn from actual working models of the book's visualizations and other web-based resources via a companion website

Tableau helps you unlock the stories within the numbers, and Tableau Your Data! puts the software's full functionality right at your fingertips.

Part I
Desktop

In this part

• Chapter 1: Creating Visual Analytics with Tableau Desktop
• Chapter 2: Connecting to Your Data
• Chapter 3: Building Your First Visualization
• Chapter 4: Creating Calculations to Enhance Data
• Chapter 5: Using Maps to Improve Insight
• Chapter 6: Developing an Ad Hoc Analysis Environment
• Chapter 7: Tips, Tricks, and Timesavers
• Chapter 8: Bringing It All Together with Dashboards
• Chapter 9: Designing for Mobile
• Chapter 10: Conveying Your Findings with Stories

Chapter 1
Creating Visual Analytics with Tableau Desktop

Data graphics should draw the view’s attention to the sense and substance of the data, not to something else.

—Edward R. Tufte¹

The seeds for Tableau were planted in the early 1970s when IBM invented Structured Query Language (SQL) and later in 1981 when the spreadsheet became the killer application of the personal computer. Data creation and analysis fundamentally changed for the better. Our ability to create and store data increased exponentially.

The business intelligence (BI) industry was created with this wave, each vendor providing a product “stack” based on some variant of SQL. The pioneering companies invented foundational technologies and developed sound methods for collecting and storing data. Recently, a new generation of NoSQL² (Not Only SQL) databases are enabling web properties like Facebook to mine massive, multi-petabyte³ data streams.

Deploying these systems can take years. Data today resides in many different databases and may also need to be collected from external sources. The traditional leaders in the BI industry have created reporting tools that focus on rendering data from their proprietary products. Performing analysis and building reports with these tools require technical expertise and time. The people with the technical chops to master them are product specialists who don’t always know the best way to present the information.

The scale, velocity, and scope of data today demand reporting tools that deploy quickly. They must be suitable for non-technical users to master. They should connect to a wide variety of data sources. And, the tools need to guide us to use the best techniques known for rendering the data into information.

The Shortcomings of Traditional Information Analysis

Entities are having difficulty getting widespread usage of traditional BI tools. A recent study by the Business Application Research Center (BARC, 2009) reported adoption rates are surprisingly low.⁴

In any given BI using organization just over 8 percent of employees are actually using BI tools. Even in industries that have aggressively adopted BI tools (e.g., wholesales, banking, and retail), usage barely exceeds 11 percent.

Nigel Pendse, BARC

In other words, 92 percent of the people who have access to traditional BI tools don’t use them. The BARC survey noted these causes:

• The tools are too difficult to learn and use.
• Technical experts were needed to create reports.
• The turnaround time for reports is too long.

Companies that have invested millions of dollars in BI systems are using spreadsheets for data analysis and reporting. When BI system reports are received, traditional tools often employ inappropriate visualization methods. Stephen Few has written several books that illuminate the problem and provide examples of data visualization techniques that adhere to best practices. Stephen also provides examples of inappropriate visualizations provided by legacy vendor tools.⁵ It turns out that the skills required to design and build database products are different from the skills needed to create dashboards that effectively communicate. The BARC study clearly indicates this IT-centric control model has failed to deliver compelling answers that attract users.

You want to make informed decisions with reliable information. You have to connect with a variety of data sources and may not know the best ways to visualize the data. Ideally, the tool used should automatically present the information using the best practices. Tableau has become a popular choice because it makes industrial-strength reporting, analysis, and discovery accessible to less-technical staff. During the last few years, information technology teams have started to embrace end-user empowerment because it provides a more efficient way to provide information, reduces request backlogs, and provides a toolset for leveraging the knowledge of constrained technical human resources.

The Business Case for Visual Analysis

Whether your entity seeks profits or engages in non-profit activities, all enterprises use data to monitor operations and perform analysis. Insights gleaned from the reports and analyses are then used to maintain efficiency, pursue opportunity, and prevent negative outcomes. Supporting this infrastructure (from the perspective of the information consumer) are three kinds of data.

Three Kinds of Data That Exist in Every Entity

Reports, analysis, and ad hoc discovery are used to express three basics kinds of data.

Known Data (Type 1)

Encompassed in daily, weekly, and monthly reports that are used for monitoring activity, these reports provide the basic context used to inform discussion and frame questions. Type 1 reports aren’t intended to answer questions. Their purpose is to provide visibility of operations.

Data You Know You Need t...