Specificity is the Soul of Data Narrative

The folks in the front of the room stared with a forced intensity at (what must have been) the 23rd straight slide showing data about website performance. Their glazed eyes would have been entirely evident if the speaker wasn’t so intently focused on pointing out the change in bounce rate between August and July. In the back of the room, Brian wasn’t able to summon the energy to care. The gentle hum of laptops, dim lighting, and endless onslaught of data practically begged his mind to wander...

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Specificity is the soul of narrative

This is a frequently-repeated lesson from John Hodgman's excellent podcast Judge John Hodgman. His fake Internet courtroom demands that its litigants share specific information and stories to bring their arguments to life.

Unfortunately, this lesson is often lost when people use data to communicate. Which is not to confuse detail for specificity. Detail — at least in the data communication context — simply means the access to more and more granular data. Specificity requires something more: delivering information that is familiar to your audience, letting them connect with the subject matter at a more personal level. The data is no longer an abstraction, it is something tangible and real.

How do we deliver more specificity in our data stories? Here are three ideas:

  1. Remind your audience of the people behind the data

  2. Begin with an individual story

  3. Explore individual patterns and behaviors

1. Remind your audience that we are talking about individual people or things.

Data is an imperfect reflection of activity in the real world. You want to find ways to emphasize the connection between real people and the data points shown on the screen. A few examples:

 Use icons as a subtle reminder that we are talking about people

Use icons as a subtle reminder that we are talking about people

 Use images of people to humanize the data

Use images of people to humanize the data

 Use individual components (people) to compose the visualizations. A tradition bar chart is transformed into a stack of the individual units.

Use individual components (people) to compose the visualizations. A tradition bar chart is transformed into a stack of the individual units.

In one memorable meeting, I was demonstrating our workforce analytics solution to a prospective client. I was showing the distribution visualization (above) and was careful to roll over individual people to help explain its meaning. As I was highlighting an employee with 40 years of experience at their company, an executive burst out: “Wait a second, that woman was my elementary school teacher.” The data came to life for him that day.

2. Begin with individual stories before showing the big picture.

One of the all-time best specificity-is-the-soul-of-narrative visualizations is the Gun Deaths visual created by Periscope. Take a moment to experience it.

 To create emotional impact from the data, the designer starts this visual by showing one gun death at a time.

To create emotional impact from the data, the designer starts this visual by showing one gun death at a time.

 Gradually the animation speeds up until the viewer understands the terrifying weight of the many lives cut short.

Gradually the animation speeds up until the viewer understands the terrifying weight of the many lives cut short.

Your data story may be on a more banal topic, but there are still ways to show the individual stories. What does a prototypical conversion in your sales pipeline look like? What is the financial impact of an individual patient going to an abnormally expensive healthcare provider?

3. Provide your audience with the ability to dive into many individual patterns and behaviors.

One compelling anecdote may hook your reader; the ability to see many stories can provide a powerful tool for analysis.

A long time ago we introduced the concept of customer flashcards — visualizations that tell the story of individual people or things, create a language for reading behavior patterns, and the opportunity to flip through many of these visuals. Finding patterns doesn’t have to be the exclusive domain of machine learning — as humans, we are pretty good at seeing and interpreting patterns ourselves. 

Here’s an example from a project we did to see patterns of online learning. Once we found an effective way to show how students took courses, we quickly identified common behaviors that would have been lost in the typical summarization of data. 

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Data storytelling is still finding its fundamental principles and discovering how effectively impact readers. Bringing specificity into these data stories may just be a bedrock principle that we can adopt from a wise Internet judge.