Telling a Great Data Story: A Visualization Decision Tree
Have you ever seen a great-looking dashboard or report that doesn’t do much more than just look good? You can’t really figure out the story. It’s usually because the developers haven’t picked the right visualizations and organization. Picking the right visualization will tell the story that you may never have the time or opportunity to tell.
Imagine presenting a chart in which you hope to highlight an important trend over the past twelve months. After observing it, the executives come out of the meeting conversing about this month’s record high measure. Your point has been missed. You didn’t tell a story. This year when virtual meetings are creating a bigger challenge to capture and hold the attention of attendees, no one can afford to waste time trying to decode the meaning of a visualization.
Great stories have compelling characters, a looming conflict, and well-organized narrative arc. Each of your key metrics is a character in the story. Conflict arises when one metric threatens to undermine another. In our banking case study, a great presentation could begin at the climax of the conflict, drawing attention to the pressing problems with liquidity and looming loan defaults with scorecard visualizations. Then the narrative arc could loop back to the beginning of the trouble showing time trend visualizations of how individual metrics began to change as the pandemic unfolded.
Next, you would compare categories to add nuance to the metrics, showing, for example, that loan risk has not increased evenly across industries. This type of character development helps build more anticipation to the looming conflict as multiple metrics interact, as shown in a scatter plot type visualization. Finally, you arrive back at the climax of the conflict, with the audience in heavy anticipation to see what you will propose to resolve the situation. The overall presentation will leave a powerful effect on the audience due to the combined impact of well-crafted narration backed by visual pictures.
A visualization should speak for itself. You should not need to spend time trying to tell its story or do its job. Stephen Few writes that “An effective [visualization] is the product not of cute gauges, meters, and traffic lights, but rather of informed design: more science than art, more simplicity than dazzle. It is above all else, communication.”
How can a visualization present YOUR story on its own?
Visualization type selection is key. The decision tree above explains how to choose which type of visualization to employ depending on the story you want to tell.
In our taxonomy there are four main story narratives. Let’s walk through each of them using a case study of a bank working its way through the turbulence of a pandemic.
If we imagine the presentation of a banking executive to employees, peers or the board of directors during economic turbulence, she would need to use time series analysis to set the initial context for the change.
Line charts would be the key player in this presentation. They could show the unprecedented change in a single financial metric on a daily, monthly or annual granularity.
Multiple line charts are for multiple data sets that all share common units of measure, such as showing the decline in revenue, expense, and income over the same time period.
Stacked area charts would come into play show change over time for multiple data sets that together make up a whole. In the case of the banker, she could show how each of the regions changed over time, while showing how the combination of the regional totals added up to the corporate total.
