How to Build compelling Stories from Your Data sets

How to Build compelling Stories from Your Data sets

Every number has a story. As a data scientist, you had the incredible job of digging in and analyzing massive sets of numbers to find what that sto Ry is. The challenge can be is, and the May has an artistic bent, and you are not know how to turn that beautiful visualization I Nto something more meaningful. Is it even possible?

Even the most mundane datasets can is compelling to an audience; It ' s simply a matter of presentation. This post is aim to the guide, through just how can make a statistical analysis into a compelling narrative.

Visualization is Your Friend

From the start, visualization is already helping a compelling story-so you ' re starting from a winning Standpoi Nt. In fact, one study shows this people who use visual aids in presentations is 43% more persuasive in their argume Nts.

Now, the your job is to take the that visualization and make it something that ' s truly compelling. To does that, we ' re going to focus less on the actual visualization, and more about what's behind it:a well-crafted story.

Create a Narrative

Whatever the dataset you ' re visualizing, there's a story that comes out of it. This can being represented in something as simple as the change over Time-what was important to realize are that it's not just Numbers. The visualization isn ' t simply a representation of the numbers; It ' s representing a point in a larger narrative. You just need-to-figure out exactly, what's narrative is.

Narrative Structure 101:every story Needs Conflict

Based on this interview from the Atlantic, it becomes clear very quickly that a compelling stories hinges on Confli Ct. There needs to being some sort of tension in the story. While the might not play out in terms of ' character development ' or a plot arc, there is still a-to-convey this tensi On-that something is wrong, or broken, or being fixed. There is significance to the data beyond it simply presenting something new.

The Different Types of plots

According to Christopher Booker, there is seven basic plot types:overcoming the monster, rags to Riches, the Qu EST, Voyage and return, comedy, tragedy, and rebirth. Most commonly, we see overcoming the monster-but we don't get the full story. That's the beauty of data visualization:you don ' t have a to-tell-the-story, and you had to present some sort of tension th At compels your audience to dive into your visualization.

In this video, surrounding U.S. gun death statistics, the monster is clearly gun violence. They does not present a solution, but rather simply show us the monster. But it's not just the monster that makes this video compelling, they include several other narrative elements that draw th e audience in.

Identifying the narrative Elements

The five main elements of a narrative are the character, setting, conflict, plot, and theme. In the above example, it's extremely easy to identify every a single one:the characters is the victims of gun violence; The setting is the U.S.; The conflict is that they ' re losing years they could has had; The plot is that every day, someone in the U.S. is losing their life to gun violence; And the theme is that gun violence in the U.S. is stealing lives.

They does not present a solution, that's for the audience to conclude themselves, but rather than simply presenting the stat Istic that 9,595 people were killed because of gun violence, totally 413,342 stolen years, they used a beautiful visual PR Esentation of death and then the years, were stolen to make the numbers both tangible and significant.

Build on Your Stories

The challenge for most data storytellers, however are that they ' re not working with ' compelling ' data. You could is working with cell phone customer data in China, or consumer behavior based on ECommerce search queries. So what do you make, into something persuasive and beautiful?

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 this apply to Data visualization, notably: "Writing improves in direct ratio to the number of things we keep out of it that should N ' t be there.

Here's a great example: Highway Data, and what it ' s costing us.

In this first chart, we see a 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. is 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 would 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 is actually costing individual drivers. This information went from theoretical, and kind of boring, to a totally compelling stories with a real conflict:every day That's 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 do you ' re trying to say. The idea was that you had a kernel, and that kernel becomes a more complicated idea. You have the 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 ' in and of Itsel F may isn't being compelling, but the greater context adds value to the story. That kernel can work in the 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 D Elivery 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 area s of information predicated on the overall. We ll call the "enhance" method.

In this example from Jacob Vigdor over at Tableau, he presents a extremely full picture, and from there, allows the Reade R to explore different enhanced parts of the narrative so can leads them to a number of different, more specific Conclusi ONS based on the initial theme:immigration have boosted the housing wealth per homeowner in many different parts of the CO Untry.

He allows you, after seeing the "full picture," to "Zoom in" and "Find out" how this plays out in specific parts of the country. Done in reverse, it would is much harder to identify the theme and conflict.

Snowball:start Small and Build

The other option was to smart small and build out. By doing the A great effect on the delivery of the conflict, showing "may seemingly Incident is actually affecting a more broader range.

This was 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 in a larger region where there is a few less-or totally not-free countries. Finally, showing the global map, continuing to lower the ratio of the free to not-free countries.

While these numbers might isn't stick out to the ordinary informed citizen as surprising, when put into a sequence that show s The contrast, and presents the reality in a straightforward visual manner, it shows just how startling the reality of th E story can is.

Whatever data It is so you ' re presenting and 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 WH At the conclusions is, but also the implications of the conflict for your audience.

How to Build compelling Stories from Your Data sets