Originally published on September 22, 2015 in BI This Week, a TDWI publication.
A “data story” sounds like such a great idea. You just mix data with storytelling and you’re done — except that most data storytellers get one thing wrong: they drown out the story with data.
Such storytellers, I believe, assume that audiences enjoy data as much as they themselves do. For them, data ranks with love, food, and air. They throw visualization upon visualization at audiences because in the belief that data is the new language of business.
The trouble is that many of us in business — roughly 80 percent of business people, according to business intelligence industry studies — don’t embrace data with such warmth. We just want its meaning. We’re perfectly happy with business’s first language: stories.
In almost a year of talking to data storytellers one-to-one and co-teaching a data storytelling class at TDWI conferences, I find that most nascent data storytellers need help finding the right data-to-story balance, and they miss a few other critical points, too.
A good data storyteller’s first priority is context and meaning, supported by data. The most effective stories use the native human grammar: a beginning, middle, and end that presents and then resolves a problem or question.
Here’s my best advice for would-be data storytellers.
Organize facts into a narrative, and include a protagonist if you have one. Classic story structure organizes facts into a narrative. It starts with a problem that upsets life as people have known it, follows with attempts to resolve the problem, and ends with the resolution. Another useful element is the protagonist: a person whose goals drive the story for whom readers or listeners have empathy, which is easier felt for an individual than for a committee.
Judge your audience’s appetite for depth of data. Max Galka, founder of a New York City-based apartment-rating site Revaluate and a data analyst by trade, had dozens of factors available for renters to judge prospective housing. He quickly realized it was overwhelming. Revaluate’s score used many factors, including complaints about restaurants on the ground floor, neighbor complaints about the building, and statistics on the neighborhood overall. It was all verified with subjective observations. Meanwhile, a competing site offered just one score for each apartment building — and it won more traffic.
Galka said, “If that’s how I like to see data. I had a difficult time imagining how other people wanted to see it.” He learned to focus on the high level. What to a data analyst looks like uncrossed T’s is just fine, he said. “If I were to look at a building’s overall score but saw that there wasn’t any detail behind it, I wouldn’t put much credence in it, but consumers do like it.”
The more tech-savvy the audience, though, the more likely they’ll want details. He found that one group from a large tech company known for its “big data” liked detailed data.
Stories can elicit conversation better than naked data can. “BI is a social problem,” says Juice Analytics founder and CEO Zach Gemignani. “Ultimately it is about people trying to understand their environment and do something about it.” Most people, even sophisticated business people, talk about the tangible and meaningful more readily than the abstract.
Storytelling with data has skeptics. Get used to it. The most common objection seems to be from a weariness of trends; the data industry always has some new shiny thing up its sleeve, they say, and storytelling must be one of them. These skeptics should separate the hype from the fact. Storytelling is a basic method for conveying information, revealing insight, and inspiring action.
Other, more thoughtful skeptics worry that stories tend to warp data. They forget that humans warp everything we touch.
New methods almost never escape ridicule. When visualized data first gained popularity, skeptics called it “pretty pictures” and scoffed that spreadsheets were good enough. When data became widely available for decision support, some insisted their “gut” told them everything they needed to know. Ignore the critics.
Take off your analyst’s hat and put on a journalist’s hat. To the data-driven, this may be heresy, but let’s be rational. Data stories and data journalism stitch facts into a coherent, memorable whole. They help people organize data in their minds so that they can rake over each story’s assumptions and facts. Data storytelling can learn a lot from journalism. Look for inspiration in journalism’s variety: the news story written in pyramid style, the sensual feature story, or the passionate and articulate online comment. Study the use of text, video, and images. In story after story, people see a tapestry of insights coalesce that goes higher, wider, and deeper than data could ever go by itself.
If you want to show data, show data and don’t bother calling it a data story. Otherwise, put the story out in front. Don’t leave your audience asking, “Where’s the story?”