Families, Types, and Importance
Visual analytics is an evolving discipline. Over the past decades several schools of thought evolved on when and how to use visuals to analyze data. There are many books on the subject from experts and scholars. The best of which contain the pros and cons to using any given type of data visualization. For those with a deep interest in learning all about visual analytics they represent the best way to gain a comprehensive foundation. Yet, they are not designed to be accessible to the general public, business users, or non-analysts.
The world is full of data. Data can be beautiful, powerful, and deceitful. Making sense of it requires a mental framework that most people are not trained in. And yet, data visualizations are everywhere. We see them in our apps, on websites, in publications, and throughout our interactions with the media. But how do we know if the visualization is good? How do we know if we're reading it correctly?
At Tableau we recognize the complexities of learning visual analytics. It’s part of why we have the mission: "to help people see and understand data." We know visual analytics is a critical skill. Visualizations provide faster insight. They show us how a business operates or how environmental factors affect the way people live. They provide us with ways to interrogate biases, demonstrate value, evaluate the success of programs and initiatives. And with these insights, we can make thoughtful decisions to solve problems big and small.
This section of the Reference Library focuses on providing foundational skills around reading and using data visualization for analysis. Many topics and types of visualizations are not covered in it at the moment. This is somewhat intentional. We started with the most fundamental visuals people use to interact with data. These are the ones we baked into our product, because they support the most meaningful types of analysis. We will refine this content and add to it to help fulfill our mission.
Families of visualizations
Charts display data in graphs, plots, and diagrams, organized along two axes. The horizontal line is the x-axis, and the y-axis is the vertical line.
Geospatial visualizations display data in map form, using colors, shapes, and other visual elements to show the relationship between location and data.
Tables display data in rows and columns as you've likely encountered in a spreadsheet. They represent exact numbers and categories with less focus on preattentive attributes.
Why you want to use the right visualization
Visualization provides a set of tools for analyzing data. And, like all tools, some serve specialized roles while others are more general. A bar chart can address a significant number of needs but a word cloud is only useful in limited situations. Choosing the right type of visualization is critical to your analysis. It relies on considering many things: the purpose of your analysis, the data you want to display, and the needs of your audience. It’s not always possible to define all three before you build, but it's a good practice to try.
Of the considerations you make, knowing the needs of your audience is most important. Good visualizations allow people to gain insight from complex data at a glance. They highlight the relationships between measures. They explain concepts and tell stories. They engage the mind in ways looking at the raw data will not. Yet, using the wrong visualization will leave the viewer in a state of confusion or indifference. Be aware of your audience’s needs to ensure your visualization is effective.
Declaring a specific purpose helps you use the audience’s needs to narrow down your choices. Some visualizations speed up analysis. Others present information in a beautiful way to affect the viewer's emotions. A few types depict concepts, processes, or strategies in a digestible manner. Every analytical purpose is best served by a specific set of visualizations.
Know your data well to understand what types of visualizations it can support. Certain charts support limited dimensionality. Others excel at highlighting the relationships between many categories. You can use the structure and nature of the data you have to guide you towards making a final selection. When in doubt, experiment. Then you can create a visualization balancing your purpose with the data you have and the needs of your audience. This will allow you and your viewers to perform great visual analysis.