Chapter 1

Learning Tableau Lingo

IN THIS CHAPTER

Differentiating among business intelligence, data analytics, and data visualization

Grasping key Tableau terminology

Discovering Tableau Desktop and Tableau Prep installation prerequisites

Tackling data selection fundamentals

There is much hype about data, and the use of business intelligence, data analytics, and data visualization tooling gets plenty of hype as well. Although there are many enterprise business intelligence tools on the market, Tableau stands out among the leaders for being bundled as a single platform for business intelligence, analytics, and visualization.

In this chapter, you start exploring the Tableau landscape by discovering the main Tableau terminology you need to familiarize yourself with regarding business intelligence, data analytics, and data visualization functionality. In addition, you can dip your toes into what it takes to install Tableau applications and the various file-based output types produced depending on the Tableau product.

What Is Tableau?

Tableau is a business intelligence platform that helps users see and understand their data using highly visual representations. Unlike other enterprise business intelligence platforms, Tableau incorporates business intelligence, data analytics, data science, data mining, and data visualization into a single solution. As a result, its capabilities are considered the broadest and deepest for data evaluation on the market.

In 2019, Salesforce acquired Tableau. At the time, Tableau's focus on data was big but not all-encompassing. It included enterprise data applications, data management and governance, visual analytics, and end-to-end storytelling. As with every other platform on the market, machine learning (ML) and artificial intelligence (AI) have become entrenched in the platform. Salesforce's Einstein AI engine is built into Tableau to help accelerate data analytics predictions, provide a strong recommendation engine, and afford an advanced workflow while touting a low-code development environment.

Tableau is not a single product but is rather a suite of products that includes Tableau Desktop, Tableau Prep, and Tableau Server or Tableau Cloud. Chapter 2 describes the purpose of each in more detail, but in brief, people use Tableau Desktop to create their data models. In contrast, Tableau Prep facilitates data preparation. And when users are ready to collaborate with others, they must publish their outputs from Desktop and Prep to Tableau Server or Tableau Cloud.

Tableau and Business Intelligence

The term business intelligence refers to taking in the big picture of an organization's activities and goals, from the collection and analysis of data to the presentation and dissemination of the data using a single platform. A look at the big picture is precisely what you get with Tableau. This best-in-breed platform allows users like yourself to customize views of their data to make data-driven decisions at speed and scale.

Why do folks like you and me need an enterprise business intelligence (BI) solution to organize data? The more data you have, the more difficult it is to dig in and get the information quickly. Making informed decisions requires various capabilities, from data mining to visualizations and analytics. With business intelligence solutions, you get everything under a single umbrella. The key benefits of business intelligence are plentiful, but here are the main ones:

• Provide a platform for faster analysis: BI platforms perform heavy-duty data processing, leading to quick calculations and the creation of stunning visualizations. Assuming that you've connected to your data source and you have already gone about prepping the data with a robust data model, Tableau can accelerate the visualization and analysis process by as much as 100 times as conducting data analysis and business intelligence activities manually, especially when integrating many data sources into a single repository.

• Create business efficiency and driving decisions: Leaders can benchmark results with speed and agility when a business intelligence platform offers a holistic view of operations. It's easy to spot opportunities and find those needle-in-a-haystack moments. Instead of spending hours poring through datasets, users can filter, aggregate, and forecast using Tableau data analytics and visualization options, thereby cutting down the time to make decisions from months, weeks, or days to perhaps even minutes. Talk about saving time!
• Drive customer and employee experience satisfaction: What is the worst possible thing for an organization to experience? Sure, most say financial loss. But financial loss results from two factors: lack of customer satisfaction and low employee morale. A primary culprit is the inability of customers and employees to access data quickly; it impacts their entire experience of interacting with the organization, internally and externally. Investing in business intelligence solutions that present a 360-degree view from all data sources can lead to less time worrying about analysis paralysis and more time innovating. The opportunity costs are often measurable in loyalty and, yes, financial rewards.
• Have data you trust: When you have many data sources, organizations try to figure out ways to control the disorganized chaos. When you have thousands of Excel or CSV files, a good tactic is to centralize them in a single data repository. But wait a moment: How do you connect the dots - that is, discover the relationships between the data in those files? The answer is to use a business intelligence solution. Relationships exist if the data is like-kind, and you can create potential single-point data sources, hence the use of governed data repositories in a business intelligence platform such as Tableau. Trusted data is not limited to the one-off files; engagement rules apply to relational and nonrelational database stores with tens of millions of records.

Connecting Big Data with Business Intelligence

Make no mistake: The term big data is undoubtedly a catch-all buzzword. It pops up a lot in this book. It's meant to encompass five aspects of a business intelligence activity: data volume, data velocity, data veracity, data value, and data variety. Big data brings together unstructured data (data with no organized convention), semi-structured data (data that has some logical order but isn't necessarily formalized), and structured data (data that is formalized or organized). Each of these data types maintains some level of these five attributes:

• Volume: The amount of data that exists
• Velocity: The speed at which data is generated and moves
• Veracity: The quality and accuracy of data available
• Value: The credibility, in monetary and nonmonetary terms, that the data provides
• Variety: The diversity of data types available within the dataset

Big data is paramount for business intelligence solutions such as Tableau because businesses constantly create more data, practically by the minute. These businesses must keep up with the data deluge. A good business intelligence platform such as Tableau grows with the increasing demands; however, if the data is not maintained, your ability to handle data visualizations and the associated data sources also becomes impaired. Therefore, it's essential to implement good data hygiene and maintenance practices.

Analyzing Data with Tableau

Don't get business intelligence confused with data analytics. Business intelligence platforms use data analytics as a building block to tell the complete story. A data analyst or scientist evaluates the data using the treasure trove of tools built into Tableau, from advanced statistics to predictive analytics or machine learning solutions to identify patterns and trends.

Tableau offers that end-to-end data analytics experience so that the analyst, scientist, and collaborator can complete the entire data life cycle, from gathering, prepping, analyzing, collaborating, and sharing data insights. The big difference between Tableau and its competitors is the self-service nature of the offering, allowing users to ask questions or predict the kind of visualizations the user may require without manually completing the work, thanks to the predictive Einstein AI engine.

Like the three-year-old child asking "Why?" all the time, as you ask more questions and the platform learns, Tableau builds an analysis output while simultaneously learning from the output. The result is an opportunity for the system to understand why something happens and what can happen next. Business intelligence platforms take the resulting models and algorithms and break these results into actionable language insights for data mining, predictive analytics, and statistics. The final product is data analytics, the byproduct of answering a specific question (or set of questions). The collection of questions helps the organization move forward with its business agenda.

Visualizing Data

Raw data that is transformed into useful information can only go so far. Assume for a moment that you were able to aggregate ten data sources whose total record count exceeded 5 million records. As a data analyst, your job was to try to explain to your target audience what the demographics study dataset...