Data visualization is often described as part art and part science, and it’s true: Visual design and information representation are separate yet highly interrelated concepts that, together, create meaning visually from data. Applied in data visualization, these elements leverage our brain’s cognitive functions to help us better see and understand information, and to interact with, learn from, and reach new insights in our data.
Balancing Art and Science Pre-Attentively
Today’s best data visualizations are designed to capitalize on the science of our brains and take advantage of what are called “pre-attentive features,” a limited set of visual properties that are detected very rapidly (approximately 200 to 250 milliseconds) and accurately by our visual system. Pre-attentive features include the following:
Color and Perceptual Pop-Out: The use of color as a visual beacon to detect items of importance. The shape, size, or color of the item is less important than its ability to “pop out” of a display—although color itself is an important variable.
Patterns and Recognition: Repetitions of shapes, forms, or other visual cues and patterns help us recognize the importance of how data is connected—through categorical cues such as dots, lines, or clusters, or other ordinal visual cues such as color, shapes, and lines.
Counting and Numerosity: Clustering replaces similar data objects with an alternative, smaller data representation and reduces the need for counting. Numerosity is an almost instantaneous intuition pattern that allows us to “see” an amount without counting it.
It’s easy to see how a discussion about human cognition might blur into one about visual design. Regardless, though, of how we approach data visualization, we should work to ensure it utilizes core visual and cognitive design principles to direct viewers’ comprehension of visually encoded information.
Understanding the Picture Superiority Effect
Research has shown that we can remember upward of 10,000 images at one time, and that we can recall these images with about 83% accuracy. Even more importantly, we remember pictures better than words, and we remember images long after we have forgotten the words that go with them. This is the Picture Superiority Effect, which says concepts learned by viewing pictures are more easily and frequently recalled than those learned purely by textual or other word-form equivalents.
Numerous studies have provided empirical evidence that visualizations which blend information with influential features (i.e., color, density, and content themes) significantly and reliably increase learning, memorability, and recall. This is where visualizations shift from being merely communication mechanisms to valuable information assets.
Working Within the Triangle of Forces
Nothing should stand between the visual’s message and its audience. Used wisely, visual cues give life to data, along with context, meaning, and resonance. Data visualizations should use visual enhancements judiciously—for emphasis rather than explanation. Every graphic is shaped by a triangle of constraints: the tools and processes that make it, the materials from which it is made, and the use to which it is to be put.
The idea of design constraints as a triangle of forces comes from Jacob Bronowski. In his essay on aesthetics and industrial design, Bronowski wrote: “The object to be made is held in a triangle of forces. One of these is given by the tools and the processes which go into making it. The second is given by the materials from which it is to be made. And the third is given by the use to which the thing is to be put. If the designer has any freedom, it is within this triangle of forces or constraints.”
This triangle is not fixed. Each of its axes can move and, consequently, adjust the others along with it. However, every move of one axis puts a strain on the other two. Thus, it’s important to not only recognize the importance of the triangle of forces but to strive for balance within it.
Design Principles for Visual Dialogue
Used with purpose, each of these concepts helps us to design data visualizations that provide the opportunity to establish a two-way visual dialogue with our data through which we can glean new information in salient, memorable, and lasting ways. This conversation is incremental: It builds on what came before to construct layers of learning and insight. It’s also expressive, adding meaning, emotion, and understanding to transform information into actionable data.