Data Storytelling

What is meant by “data storytelling?” The two words do not seem to share any similarities. In fact, the terms “data” and “storytelling” are often associated with polar opposite disciplines in academia. Data is typically associated with fields in mathematics or science. Storytelling traces its roots back to literature. How can two seemingly oxymoronic terms combine to represent a discipline that actually enhances data representation?

It turns out that data storytelling works exactly how you would expect it to. The discipline is best described as combining data representation with storytelling elements. These elements include data, visuals, and narrative (Engebretsen and Kennedy 296). In short, data storytelling ultimately seeks to transform data representation into a story. This concept might seem vague at first, but it will begin to take shape after looking at an example.

The “Flatten the Curve” infographic, which grew in popularity during the initial outbreak of COVID-19, perfectly illustrates the effectiveness of data storytelling in journalism. Below is the original infographic, which originated in an article published by The Spinoff:

The layout of this infographic is very simplistic. What immediately stands out is the caricature of two individuals discussing the pandemic at the bottom of the graphic. However, this cartoonish style carries over to the graph that is presented beneath the title. Upon first glance at the graph, the viewer might notice that there are no tick marks on either of the axes. In other words, there are no true numbers presented in this graph. But presenting the actual numbers in this case would be unnecessary, or even harmful to the overall infographic. This is mainly because the infographic is made to send the same message to the viewer, no matter when he or she is viewing it. In the case of “flatten the curve”, the number of cases of COVID-19 is constantly changing, which means that the y-axis of this “flatten the curve” graph is constantly shifting as time goes on. So, the designers of this infographic made the decision to leave the numbers out of this representation, which makes their overall message applicable to any time period.

Something else to note about this infographic is how the caricatures create this illusion of narrative structure (Miller and Jarvis). After interpreting the data, the viewer’s attention naturally shifts to the cartoon at the bottom, where two individuals are engaging in dialogue. One of these individuals seems to be concerned about the outbreak. The other individual states, “Don’t panic, but be careful.” Finally, the viewer’s eyes are drawn to a bulleted list of precautions to take in order to stop the spread of COVID-19. “Washing hands, not touching face, stay home when sick”, the list reads.

This infographic sneaks a narrative into the reader’s mind. What is the reader’s attention first drawn toward? The graph. What does the graph represent? A problem: COVID-19 patients could exceed healthcare system capacities if infections spread too quickly. Next, the reader asks, “What can I do to stop this problem? The answer to this question: Flatten the curve. How can we flatten the curve? Wash hands, keep hands away from face, and stay at home when sick.

This infographic does much more than simply present the data. It takes the viewer on a mini journey. Within about 30 seconds, the viewer has made a connection with the data. The first half of the infographic (i.e., the graph) presents the problem, while the second half of the infographic (the pictures and the list) presents solutions to the problem (Miller and Jarvis). The caricatures even add an element of character to the infographic. After seeing two people discuss the spread of COVID-19, the viewer is left thinking, “maybe I should do the same.” As we can see, simply incorporating visual elements into this infographic allows the viewer to relate to the cause even further.

The “flatten the curve” infographic demonstrates data storytelling at its finest. By incorporating narrative elements into data representation, the viewer forms a much deeper connection with the data than ever before. Rather than simply interpreting the data, the viewer is forced to come to terms with every ounce of the data’s meaning. The viewer is truly left to wonder what the data means in the context of his or her own life.