The Best Practices of Memorably Visualizing Data


Although we are deluged with information, visualizing data and research “cuts through the noise, helping us quickly understand, navigate and find meaning in a complex world.” It reveals patterns and connections submerged in “the seas of data around us” and often becomes “memorable, impactful, enduring” (McCandless 41). Given these strengths, the humanities could use data visualization to communicate its scholarship to new audiences and/or with greater impact. This proposition begs the question: how can we better use data and/or research visualization in the humanities? Or more precisely, what are the best practices of representing data?

This essay cannot explore every best practice, but Edward Tufte’s influential ideas provide a good start. He defines “graphical excellence” as communicating complex ideas with “clarity, precision, and efficiency.” In other words, effective visuals give the audience “the greatest number of ideas in the shortest time with the least ink in the smallest space” (51). Design principles like contrast, repetition, alignment, and proximity, if used well, can achieve Tufte’s ideal. By winnowing data down to its essentials and clearly organizing those essentials, well-designed visuals enable readers to comprehend complex ideas better, faster, and easier.

Ease-of-processing is crucial. It is how data visualization “cuts through the noise” surrounding us. People gravitate to concise, well-organized visuals because they facilitate fast learning. Moreover, the easier something can be understood, the more likely it will be effectively encoded into long-term memory, where it can have a lasting presence in one’s knowledge, attitudes, or beliefs. The more memorable a visual, the more effective its communication.

But visuals need more than ease-of-processing to be memorable. Clarity, precision, and efficiency aid memorableness, as explained above, but they do not necessarily create it. Since our attention, or lack thereof, influences how much or how well we remember, data and research visualization can be more memorable – more effective – if it uses design principles to attract, direct, and allocate a viewer’s attention. But to fully engage that attention in encoding the information into memory, the data visualization also needs the qualities of a good narrative.

As leading visual designer David McCandless asserts, “The art of visualisation is combining and harmonising design thinking, statistical rigour and storytelling without compromising any element” (41). Using design principles can guide attention. Consider, for example, the use of contrast. Since people naturally notice distinctive stimuli, bolding certain data in a table draws attention to that data, signaling its importance and making it more memorable both visually and semantically. Statistical rigor facilitates winnowing data to its essence. And storytelling can contextualize data and give it the memorable qualities of a narrative.

This data need not be limited to numbers. In an art education article, Robert Sweeny presents a list of canonical paintings in a visual continuum of dates, styles, and influences (223, see Figure 3). He uses contrast, repetition, alignment, and proximity to clearly organize the information and to create an obvious cluster within a certain date range. This visualization tells a story: many of the most well-known and influential artworks were created around the same time. Sweeny could have conveyed this knowledge in writing and/or a chronological list, but then it would not have been as noticeable, impactful, or memorable.

As this example demonstrates, the humanities can use the best practices of design and storytelling to visualize either quantitative or qualitative data more clearly and memorably, increasing its communicative power and broadening its potential audience. Though this essay cannot explore every best practice of representing data, hopefully it will inspire further investigations into design’s contributions to our data’s stories and how the stories our data tell influence our design.


McCandless, David. “Who Doesn’t like a Good Data Visualisation?” Creative Review 34.1 (Jan. 2014): 41-4. Print.

Tufte, Edward. The Visual Display of Quantitative Information. Chesire, CT: Graphics Press, 1983. Print.

Sweeny, Robert. “Complex Digital Visual Systems.” Studies in Art Education: A Journal of Issues and Research 54.3 (2013): 216-231. Print. 

Image on front page by Ran Yaniv Harstein and available on Flickr


"Our attention-or lack thereof" is a perfect way to introduce this best practice for data visualization. By implementing principles of design like contrast, repetition, etc. as used in more traditional art practices, I think we really can capture the interest of the viewer and help them to better understand the data presented.  This is a really interesting way to look at this process, and to conceptualize how we can mix research with art to be "noticeable, impactful, or memorable." This post has certainly inspired me to look at my data from a narrative view, and to find better ways to catch attention rather than just displaying research. 

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