A key part of telling a story with data, says a Schwab executive I talked to last week, is thinking like a business person.
“You want to go for the high-level ‘aha!’” says John F. Carter, Schwab senior vice president of analytics and business insight, in San Francisco. Start the main conclusion, similar to news reports. “Less is more, but the right less. Executives don’t have time to get into the weeds.”
“The right less” has a long tail, where most of the data should go.
Less is also hard. The monthly summary comes after a long process of finding the story. Carter begins by meeting with a data analyst who has already sliced and diced a lot of data, such as new clients this month, new assets, new-client acquisitions. He’s turned it into preliminary conclusions and questions. A second meeting calls in units from marketing, business, and the field.
“One person doesn’t always have the answers,” says Carter. Whether business has been trending up, down, or even, many factors have effects: competition’s activity, Schwab’s own program, the economy, tax events, interest rates, and others. “The stories usually are a little complicated,” he says, “with multiple reasons impacting a result.”
Reducing data
The group brainstorms and throw hypotheses onto the table. If, for example, new account openings were up, someone might point out that that a new call center interface gave reps better information. Another one might reason that a marketing program might have finally hit its stride. Or was it the unseasonal warmth?
Each unit contributes its view on the month’s events and reasons for them. The context builds, and answers begin firming up. Was this or that event or trend a fluke? What drove it? Should the company respond? Out of such triangulation comes a cohesive story that’s substantiated by the data.
Finally, the story’s ready. Carter finds that the traditional story structure — exposition (beginning situation), complication (whatever happened to disrupt the situation), resolution (insight, discovery, or resolution for action) — generally works well.
The audience can be a single executive or a group of five to 10 people. The story goes fast. Carter says, “Usually we keep stories to 60 seconds.”
Operationalizing intelligence
Stories help everyone, even the data-shy, understand the insights coming from data scientists. “Operationalizing the intelligence” is critical. “If you can’t get the less technical people to understand,” and to drive the insights into the organization, he says, “it all kind of sits on a shelf.”
Three things new storytellers miss
He lists three things many new data storytellers have a hard time understanding.
1. Less is more. Start with the high-level “aha!” Give the most relevant
finding. Drop the data and just say it in English. Sales went down…why? Put the
visualizations in the appendix.“ We see this all the time,” he says.
Information is presented without awareness of the main point. Ineffective stories say too
much.
2. Think like a business person. Put yourself in the audience’s shoes. Use
storytelling to drive insights into the organization.
3. Storytelling is for everyone. Carter says, “I think we need to be storytellers at
all levels of the organization.”
Do you tell stories in business? I want to hear about it. Email me at teddatastories@gmail.com.
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