This week, the VisualCrew had the opportunity to speak to one of the industry’s most ardent supporters of the data storytelling movement, Ted Cuzzillo.

In July of this year he wrote a compelling article about the current state of data storytelling, in which he states “Fine, we agree that data storytelling is a good thing. But we still have scant observation of the actual practice of data storytelling – hard, direct evidence of what has worked, what hasn’t… I don’t even see consensus yet on what a data story is.”

What makes Cuzzillo interesting is that he, unlike most writers, is not content to stop at merely noticing the problem. He asks those of us in the world of data, business intelligence and visualization a few questions to get the ball rolling. His first question we found to be the most important because from it spring the rest: “What is the definition of ‘data story?’ How is data storytelling different from traditional storytelling?”

Well, we’ve got some thoughts on that.

Begin at the Beginning

As we were thinking about the definition of a ‘data story’ we thought it wise to not reinvent the wheel, but rather look to other disciplines who have been debating about the nature of story for generations: literature. Our research lead us to the 2014 text “Entranced by Story” by Hugh Crago. In it, he defines ‘story’ as “a particular literary form that reliably evokes particular experiences in readers, listeners or viewers.”

Looking at that definition you may be tempted to decry it as too minimalist and broad, and you wouldn’t be wrong, it’s not very specific. But that’s precisely why we chose it. By taking story down to it’s most basic roots he has struck the heart of it, and from it we can not only understand what a data story is but get clues on how to do it effectively.

Crago’s definition begins with ‘literary’- which is any use of language, written in some form, to communicate ideas. Under that banner, data visualizations of all kinds are a form of literature: they extrapolate analysis from data and write it, usually in the form of charts, graphs, or some other obfuscating tongue.

The next part of Crago’s definition is crucial, and also gives us a key to answering Cuzzillo’s next question: what characteristics do successful storytellers have? According to Crago, stories evoke particular experiences. For fiction literature or movies, which is where most of encounter stories, those experiences are often pleasurable: surprise, disgust, fear, joy, excitement.

The goal of data storytelling should be the same- data stories, if they are to be effective, should reliably evoke an experience, but of a different kind than traditional storytelling- data stories should reliably evoke understanding.

And not just understanding for the 20% of business users with a background and understanding in data. Effective data stories should communicate to anyone regardless of background or training.

Cargo talks about how necessary clear, effective communication is to a successful storyteller when he writes “Words must bridge the gap between individual tellers and their audience, otherwise the tale would not survive and flourish in the wider world.” If our data visualizations are not bridging the gap between the business user and the data then it is not a story at all. Cuzzillo also points out this principle when he writes “More people understand stories than understand data.”

Data storytelling, at it’s most basic, is a literary form that reliably evokes understanding. Effective storytelling does the same in a wide range of users.


So what?

Alright, so we have a definition of data storytelling that should work for everyone and even carries some hints in it for how to tell data stories effectively.

Why do we care?

We care because the future of data is in storytelling. According to Ventana Research, “In 2014, IBM announced Watson Analytics, which uses machine learning and natural language processing to unify and simplify the s her experience in each step of the analytic processing: data acquisition, data preparation, analysis, dashboarding and storytelling.”

Did everyone catch that? The end of the analytic process is storytelling, and IBM is pumping tons of money into making sure that data storytelling is the future.

The VisualCrew commends Cuzzillo for his quest to gather information to define and discuss data storytelling. Be sure to add your own thoughts on his blog and join the conversation.

Until next time,

The VisualCrew