Using data visualization to support visual data storytelling is a craft, and one that takes practice, expertise, and a good bit of drafting and rewriting. Strong visual narratives that make data easier to understand, according to The Economist, “meld the skills of computer science, statistics, artistic design, and storytelling.”
Here are 5 steps to help you build that perfect data story.
1. Find data that supports your story. The first step in telling a data narrative depends on finding data that supports the story. Part of the revelation of storytelling is akin to the scientific process—asking questions, performing background research, constructing and testing one or many hypotheses, and analyzing results to draw a conclusion.
However, finding data to support a story doesn’t necessarily require scientific data. Ultimately, the data chosen to tell a story should support the story it is telling in context, complexity, and depth. In other words, find a story you’re interested in telling. Then, make sure you understand your data.
2. Layer information for understanding. Once you have your story in mind and your data in hand, next script your story by layering information to build a framework around a narrative. In writing terms, think of this as constructing your story’s outline.
Knowledge is incremental. Every piece of information we learn is founded on something we have already learned before. Thus, layering information is critical. It is a tool you can use to guide your audience through a complex story. In data storytelling, we can achieve this by compounding builds in visualization or by sequencing different types of visualizations, drilling deeper into a single visualization, etc.
3. Design to reveal. As tools, charts can’t do it all—data visualizations can’t be relied upon to tell the story for you. Likewise, various types of visualization can present the data properly, but still fail to tell a story. Thus, choose your data and your visual form carefully so that the two work in tandem toward communicating one accurate and meaningful message. Then, put the right dialogue into place to guide your audience through a story.
Start by stripping out unnecessary information and design the data story in a way that leaves the audience with a single, very potent, message. Focus on the most powerful elements; however, understand that these aren’t always the most obvious trends or elements. And remember: There is not always one truth in data, and this is where context becomes a critical element of a data story.
4. Beware the false reveal. A false reveal can be a dangerous thing. It can incite the audience to draw the wrong conclusions or take an incorrect action. It can also damage the effect of the data itself. As a visual data documentary, data stories should be engaging and entertaining but should focus foremost on sharing truth.
Whether we do it intentionally or inadvertently, we can force the data to tell the story we want it to, even if it’s the wrong one. With visual narratives, we are tasked not only with telling a story but also with making it interesting, engaging, and inspiring storytelling. But data stories aren’t works of fiction. Think of a visual story as a documentary: a nonfiction work, based on a collection of data, told in a visually compelling way.
5. Tell it fast. Stories have an inherent amount of entropy, and have the most potency when they are happening. Data journalists are taking this to heart in models that keep track of events as they have in real time (such as political elections or disaster scenarios). The time stamp on when data is reported—or a visualization story released—can be a big difference in how the story is interpreted or the impact it makes
One way to tell a data story fast is by sharing with mobile. Mobile has been a game changer for data visualization in many ways and will be even more so in the years ahead. However, mobile requires wise editing. Be aware of form factor limitations and rethink the way storytelling via mobile devices happens.
Image courtesy of Shutterstock.
This article first appeared in the Summer issue of Big Data Quarterly Magazine.