The Three Elements of Successful Data Visualizations

Now that we've discussed when data visualization works — and when it doesn't, let's delve into what makes a successful data visualization. Although there are a number of criteria, including ease of comprehension and aesthetics, I'd like to explore the three that designers most often overlook.

1. It understands the audience.

Before you throw up (pun intended) data in your visualization, start with the goal, which is to convey great quantities of information in a format that is easily assimilated by the consumers of this information — decision-makers. A successful visualization is based upon the designer understanding whom the visualization is targeting, and executing on three key points:

• Who is the audience, and how will it read and interpret the information? Can you assume it has knowledge of the terminology and concepts you'll use, or do you need to guide it with clues in the visualization (e.g., indicated good is up with a green arrow)? An audience of experts will have different expectations than a general audience.
  
  
• What are viewers' expectations, and what type of information is most useful to them?
  
  
• What is the visualization's functional role, and how can viewers take action from it? An exploratory visualization should leave viewers with questions to pursue; educational or confirmational visualizations should not.
  
  

2. It sets up a clear framework.
The designer needs to ensure that everyone viewing the visualization is on common ground about what it is representing. In order to do so, the designer needs to set up a clear framework, which involves the semantics and syntax under which the data information is designed to be interpreted. The semantics involve the meaning of the words and graphics used, and the syntax involves the structure of the communication. For example, when using an icon, the element should bear resemblance to the thing it represents, with size, color and position all communicating meaning to the viewer.

Lines and bars are simple, schematic geometric figures that are an integral component of many kinds of visualizations: lines connect, suggesting a relationship. Bars, on the other hand, contain and separate. In studies, when people have been asked to interpret an unlabeled line or bar graph, people overwhelmingly interpreted lines as trends and bars as discrete relations — even when conflicting with the nature of the underlying data.

There is one other element to the framework: Before everything else, make sure your data is clean and you understand its nuances. Does your data set have outliers? How is it distributed? Where does your data have holes? Are you making pre-judgments about the data? Real-world data is often complex, of diverse types from diverse sources, and not always reliable. Getting to know your data will help you select and appropriately use a framework.

3. It tells a story.
Visualization in its educational or confirmational role is really a dynamic form of persuasion. Few forms of communication are as persuasive as a compelling narrative. To this end, the visualization needs to tell a story to the audience. Stories package information into a structure that is easily remembered which is important in many collaborative scenarios when an analyst is not the same person as the one who makes decisions, or simply needs to share information with peers. Data visualization lends itself well to being a communication medium for storytelling, in particular when the story also contains a lot of data. Minard's graphic of Napoleon's march on Moscow in 1812 is an exemplar. With newer technology freeing designers from the paper-based paradigm of images, even more compelling narratives can be constructed.

Storytelling helps the viewer gain insight from the data. Information visualization is a process that transforms data and knowledge into a form that relies on the human visual system to perceive its embedded information. The goal is to enable the viewer to observe, understand and make sense of the information. The difference between information visualization and traditional storytelling in film, theater or television is that the information and story conveyed in information visualization environments are much more complicated. Design techniques that prioritize particular interpretations in visualizations that "tell a story" can significantly affect end-user interpretation.

Visualization designers need to dig into the data in order to gain an understanding of it, and also to connect with the visualization's audience. Good designers know not just how to pick the right graph and data range, but how to be a compelling storyteller through the visualization.

This blog first appeared on Harvard Business Review on 04/19/2013.

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