Data Visualization and Data Storytelling: A Visual Revolution

There is a data revolution happening across the globe. From academics to politics, and everywhere in between, the world’s stories are being told through their datapoints. And, while using visualization to tell stories about data is by no means new, we are now telling them in more influential and impactful ways than ever before. Charts and graphs created 5 years ago in Excel are far different than the incredible visuals we are now producing with best-of-breed tools such as Tableau, or scripting with dynamic JavaScript libraries such as D3.js.

However, all of these visualizations, from the most dynamic to the most static, need more than just data to make the leap from information representation to resonation. They need a story—something to show, or, more aptly, to “tell” visually—and finding this tale isn’t always obvious when digging through data. It takes exploration, curiosity, and a shift in mindset to move from creating a data visualization to scripting a data narrative. They are similar, but not identical, skill sets. Once mastered, they can open up an entirely new world of data communication.

Scripting a data narrative may sound like a vague or overwhelming process. After all, many of us would more comfortably describe ourselves as “data people” before we tried on the hat of storyteller. Nevertheless, the two are fundamentally intertwined; we must know our data, its context, and the results of analytics in order to extrapolate these into meaning for an audience who doesn’t. That’s all a story is: one person sharing something new and unknown to another in a way that is easily understandable and relatable. The good news? There’s no one single way to do it. For each of the proven narrative frameworks we can use to design a data storyboard, there are just as many quintessential examples where a data storyteller has exercised a generous amount of creative liberty and done something entirely new (a classic example, Minard’s 1869 visualization of Napoleon’s March, comes to mind). After all, like any kind of story, data stories require a certain amount of creativity—and while tools and technology can do much with our data for us, creativity is a uniquely human contribution to any narrative.

It’s true: Data visualization and data storytelling aren’t interchangeable—there are lots of nuances between them—but they are two halves of the same coin. A true data story utilizes data visualizations as a kind of literary device—proof points to support the narrative. However, while data visualizations provide the “what” in the story, it’s the narrative that answers the “why.” It’s important here to point out that visualizations for analysis and visualizations for presentation are not always the same thing. Rather, the two work together to translate data into something meaningful for its audience. Consider, for example, Nigel Holmes’ Monstrous Costs chart. Analytically perfect? No. Visually engaging and memorable? Absolutely.

Any meaningful visualization is a two-pronged one. It requires analytical perfection and correct rendering of statistical information, as well as a well-orchestrated balance of visual design cues (color, shape, size, etc.) to encode that data with meaning. So, what’s the key tenet that ties data visualization and visual data storytelling together? I think it’s this: Data visualizations are only as effective as the insights they reveal and how long we remember them. In this context, effectiveness is a function of careful planning.



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