Data visualization helps us to make sense of complex and difficult topics, but in the process, they can also produce some aesthetically pleasing images. How do we approach its use as a form of storytelling as it becomes more popular?
What is data visualization
In his comprehensive guide “The Art and Science of Data Visualization,” analyst Michael Mahoney defines it simply as “the graphical display of data.” It’s how you take different data points and find ways to differentiate them according to certain variables (such as size, color and shape), and arrange them in a way that can be understood. In his words, “Visualizations are often the main way complicated problems are explained to decision makers.”
In his guide, he provides mantras that underscore four key factors in creating good data visualizations:
- Effective: “A good graphic tells a story.” Because of the size and breadth of modern data sets, the visualization needs to include only the elements that make identifying patterns and trends easier to comprehend.
- Simple: “Everything should be made as simple as possible — but no simpler.” Practices like using only 2D or cutting down on extraneous visual elements keep it lightweight.
- Efficient: “Use the right tool for the job.” Using the right methods to depict the data correctly.
- Digital: “Ink is cheap. Electrons are even cheaper.” Make more than one graph to split data to show difference between different categories and groupings.
And if not to these decision makers, Mahoney says visualizations are used to help identify patterns in a data set or explain those patterns to wider audiences. To this end, they have to be both truthful and easier to decode.
There is no “one chart fits all”
However, Dear Data’s hand drawn approach also challenges the expectation of how a data story should be told. The project is the brainchild of Giorgia Lupi and Stefanie Posavec who began it as a form of visual correspondence between the two, who would use hand-made postcards to share information about their lives over the course of their year. Their first week of their exchange tracked the number of times they checked the time, while subsequent ones tracked everything from smartphone addiction to number of times they saw their reflections..
Looking at them, it’s not always apparent that the drawing is in fact a representation of data points (perhaps more-so to the untrained eye), much less a picture of someone’s life. Still, “data is a way to filter reality in a way that words cannot,” Lupi says. This consideration adds a layer of nuance and complexity to data visualization that is at odds with the transparent science aspect of it.
Does data visualization need to be easy to understand for everyone? After all, the deep perspectives we can gain from examining our own very personal data might, like many codified narratives, be better reserved for the select interpretation of just us or very specific people. Put another way, some stories are told for specific audiences. Could data stories be told in a similar way that is deemed ‘art’ to the initiated but comparatively opaque to others?
Our reaction to data visualization
Not unlike how editorial illustrations contextualize a much longer and larger story, data visualizations offer us the perception-altering perspectives that accompany drawings. However, our treatment of them might differ because they lie at the intersection of our current preoccupation with:
- Metrics: Facts that emanate from the scientific or academic community give us a grounding in reality that helps us pierce through conflicting opinions and misinformation, as well as use that knowledge to help us get ahead in life. For better or worse, metrics and figures give us standards we can use to measure our lives against and improve.
- Visuals: The shift in audience preference for video means visual representations of a topic will rank higher than a few thousand words of even well-written text (and likely even more than charts full of raw data points).
- Digestibility: Like infographics, data visualizations give people with significantly less experience with a subject the ability to digest a much larger, more complex story.
But beyond just using data visualization as a way of understanding topics we want to know more about, they also could pique our curiosity in others that we otherwise wouldn’t. A look at some of the visualizations from Nathan Yau’s Flowingdata likewise could put us onto important topics like saving for retirement (or decidedly less urgent matters like burger rankings).
The point is that the role of data visualizations in the diverse media landscape will become more pronounced so long as audiences recognize their potential to broaden understanding and fight misinformation..
Data visualizations provide a way to contextualize phenomena and make sense of complex and difficult topics. However, they are only as honest as the people who design them. Like all stories, it’s important that we also think critically about the larger themes of the topics themselves and especially simplifications of them. When data sets become condensed into comparatively small but pleasing gold “nuggets” of information, those nuggets become as easy to misconstrue and abuse as they are to share.
In short, the facts might be embedded into the image and one story told through their arrangement, but any truths are still unfortunately up to us to figure out.