How To Build Compelling Stories From Your Data Sets
Are you done with digging, slicing and aggregating those numbers, your job is not over yet. Presenting your findings is an art itself, find out how by means of visualization you can achieve this.
Keep It Simple, Keep It Safe
The key is in simplicity and patience. Arguably the greatest teacher of non-fiction writing, William Zinsser, had a lot to say about simplicity that apply to data visualization, notably: “writing improves in direct ratio to the number of things we keep out of it that shouldn’t be there.
Here’s a great example: highway data, and what it’s costing us.
In this first chart, we see an easy to read, heatmapped map of the country, setting up the basics of our narrative. We’ve got a plot, a setting, and characters, and we’re even starting to see the beginnings of the conflict and theme: The roads in the U.S. are bad, and a lot of them need serious repairs.
In a basic conversation, highway data isn’t the most compelling thing in the world. And even then, it’s kind of a two-sentence conversation: “Yeah, the roads really suck, huh?” “Yeah, hopefully that damned government will fix them.”
Now enter the real driving point of this data story:
As it turns out, those roads aren’t just bad, they’re costly. Using the same heatmapping format, we now see what those bad roads are actually costing individual drivers. This information went from theoretical, and kind of boring, to a totally compelling story with a real conflict: every day that goes by where the roads aren’t getting fixed, it’s costing you dollars.
Start With a Kernel
Most often, you’re taking complex information and making a compelling presentation, so layer what you’re trying to say. The idea is that you have a kernel, and that kernel becomes a more complicated idea. You have to get people on board with a basic principle—in science, it’s a thesis statement.
From there you can develop the kernel, and begin to focus on “minor plot lines” and other information that in and of itself may not be compelling, but in the greater context adds value to the story. That kernel can work in two different ways.
Enhance: Start Big and Narrow In
Whether you’re using a series of visuals, a graph, a chart, or something completely new and different, you can layer the delivery of your information. The first method of layering is to put all the layers on at once, and then begin to highlight more specific, targeted areas of information predicated on the overall picture. We’ll call this the “enhance” method.
In this example from Jacob Vigdor over at Tableau, he presents an extremely full picture, and from there, allows the reader to explore different enhanced parts of the narrative that can lead them to a number of different, more specific conclusions based on the initial theme: immigration has boosted the housing wealth per homeowner in many different parts of the country.
He allows you, after seeing the full picture, to zoom in and find out how that plays out in specific parts of the country. Done in reverse, it would be much harder to identify the theme and conflict.
Snowball: Start Small and Build
The other option is to smart small and build out. By doing this, you may have a great effect on the delivery of the conflict, showing what may seemingly only be an isolated incident is actually affecting a more broader range.
This is a fantastic example, created by Ben Jones:
The gif here builds in three different stages. It starts by showing the zone in Europe which contains only “free countries.” Building out, it adds on a larger region where there are a few less- or totally not-free countries. Finally, showing the global map, continuing to lower the ratio of Free to Not-Free countries.
While these numbers might not stick out to the ordinary informed citizen as surprising, when put into a sequence that shows the contrast, and presents the reality in a straightforward visual manner, it shows just how startling the reality of the story can be.
Whatever data it is that you’re presenting, you have the ability to make it interesting. It’s a matter of discovering the conflict that’s within the numbers—taking the time in your analysis to decide not just what the conclusions are, but also the implications of the conflict for your audience.
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