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A brief disclaimer: the elements of data storytelling do not always line up perfectly with the elements of literary storytelling. For example, the presence of a narrator in a piece of literature is very different from the presence of a narrator in a piece of data storytelling. This is because literary storytelling is not limited by data. Literature is theoretically limitless. Data storytelling, on the other hand, must remain within the limits of the data in question. It would get quite confusing if a data journalist began writing a full-blown story within a piece that was originally intended to communicate data. Rather than making direct comparisons, the following section is meant to draw loose connections between the elements of data storytelling and the elements of literary storytelling.
Elements of Narrativity
Elements of Narrativity
As mentioned in the previous section (see Data Storytelling), data storytelling can be partitioned into three components: data, visuals, and narrative. So far within this e-text, we have analyzed the components of data and visuals. For the remainder of this section, we will focus on narrative.
We often think of the term “narrative” as being synonymous with a story. Narrative structure, in a literary sense, implies a series of events that together form a story. Some elements of literary narrativity include plot, setting, and characters, for example. However, narrativity in data visualization differs in terms of the presence of these elements. For the most part, data visualization will rarely have direct connections to the elements of literary narrative. In other words, it might be difficult to find characters within a piece of data visualization. However, data visualization does have its own elements of narrativity.
The presence of a narrator in data visualization often appears in textual components of the infographic, including captions, explanatory text, titles, and labels (Engebretsen and Kennedy 267-68). To demonstrate the usefulness of adding a narrator to your data visualization, let us take a look at “The Carbon Budget“, created by the World Resources Institute (see on right).
It is first important to note the use of the first person perspective throughout this infographic. Notice the use of the personal pronoun “we” in the opening explanatory text. “We’re now living on a planet where global temperatures are warmer than most of the past 11,000 years”, the text reads. Simply using “we” incorporates the viewer into the infographic. It not only establishes a narrating voice for the data story to come in the following infographic, but it also forms a connection with the viewer. Especially in this case, where the data is meant to apply to all humans living on this rapidly warming earth, the use of the personal pronoun “we” implies a collective goal shared between the authors and the viewer. In short, establishing a narrator forms the foundation for a compelling and relatable story.
Besides textual cues, authors can establish the presence of a narrator by using tooltips. You have probably seen tooltips before on interactive infographics, or even websites. Basically, tooltips allow the viewer to ‘hover’ over an element within the infographic in order to gain further context about that element (Engebretsen and Kennedy 267). An excellent example can be found in this “Hungry Tech Giants” infographic. Notice how tooltips allow the visuals to speak for themselves. It isn’t until you hover your mouse over the data points that the context is provided, and the narrator is established.
Sequentiality refers to a series of events in which each event is temporally related. One important note about sequentiality in data visualization: it is often portrayed by the act of scrolling, or the act of clicking from one tab to the next (Engebretsen and Kennedy 299). Looking back at the Carbon Budget example, it is clear that simply scrolling through the entirety of this infographic puts each of the data points into sequence. Allowing the viewer to develop this sense of sequentiality puts your data into a narrative structure. Furthermore, it gives the viewer a frame of reference as he or she progresses through your infographic (Engebretsen and Kennedy 300). In the Carbon Budget graphic, the viewer is initially presented with basic information: What is the carbon budget? How can we tell what the effects of our carbon emission are on the environment? Simply scrolling through these questions and their accompanying data emulates a narrative structure in the viewer’s mind, which is what makes their connection to the infographic so strong.
The third element of narrativity in data visualization– temporal dimension– is similar to sequentiality in that the events portrayed in the infographic are temporally related. However, temporal dimension implies that the various components of an infographic are occurring due to the passage of time (Engebretsen and Kennedy 289). A classic example of incorporating a temporal dimension into an infographic is through the use of a timeline. Take this graphic on drone killings as an example of a timeline. Giving your data a temporal order will instantly give off a sense of narrative to your viewers.
Finally, one must consider the element of tellability in data visualization. Tellability addresses the question of why your infographic is worth spreading (Engebretsen and Kennedy 306). Why is it so important to understand the data in question? What is the point of the story?
Perhaps one of the best examples of tellability is included in the “Flatten the Curve” infographic mentioned in Data Storytelling. To recap, the overall message was to encourage hand washing, social distancing, and mask-wearing. In encouraging these actions, the authors make viewers want to spread the message of the data. Increasing tellability gives viewers the incentive to spread the information that they have learned throughout your infographic.
Applying these elements of narrative to data visualization is what truly enhances the connection between the viewer and the data.
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