In this age of data and science, it’s easy to underestimate the role of storytelling in human history. But the importance of storytelling is demonstrated by science, according to Louis Cozolino, a psychologist and professor of psychology at Pepperdine University.
In his book, “The Social Neuroscience of Education,” Cozolino says we’ve evolved to remember and tell stories as a way of retaining important information about our community, our world and ourselves.
In fact, a well-told story creates a “nexus of neural network integration” in all parts of the brain, Cozolino states.
Analytics experts also recognize the narrative as a meaningful way to express data, particularly to non-analytic thinkers. The question is: How do you responsibly use the power of storytelling to convey analytical findings?
“Very few people would question the value of such stories, but just knowing that they work is not much help to anyone trying to master the art of analytical storytelling,” writes analytics expert Tom Davenport in a recent Harvard Business Review blog post. “What’s needed is a framework for understanding the different kinds of stories that data and analytics can tell. If you don’t know what kind of story you want to tell, you probably won’t tell a good one.”
Davenport holds the position of President’s Distinguished Professor of IT and Management at Babson College. He is also a research fellow at the MIT Center for Digital Business, co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.
In “Keeping Up with the Quants,” a book he co-authored with Jinho Kim, Davenport identifies a sort of first draft on the typology of analytical stories. In this blog post, he elaborates on that theme.
Davenport outlines the four key dimensions that help decide what type of story you’ll tell. Taken as a whole, the four dimensions identify 10 types of stories:
- Time: Does the data look at the past, the present (via a survey) or the future (predictive analytics)?
- Focus: Davenport defines focus as either telling a “what story, a why story or a how to address the issue story.” Obviously, what tells you what happened, why explains underlying causes, and how looks at ways to improve the situation. Now, any reporter can tell you that a good story will address all of those points (as well as “who”), and Davenport makes that clear as well.
- Depth: Is the solution quick and easy to discover, or does it require in-depth exploration of a complex problem? The more complex the issue, the more you’ll need stakeholder buy-in, since complex can be long, laborious and expensive.
- Methods: Are you telling a correlation or a causation story? “In most cases, doing some sort of controlled experiment is really the only way to establish causation,” he adds.
Davenport is a regular contributor to the Harvard Business Review’s blog section and many of his posts discuss the use and communication of data.
In another HBR post, Jim Stikeleather, executive strategist at Innovation for Dell Services, offers a list of other issues you should consider, such as audience, balance and censorship. Honestly, I never thought I’d see the day when I’d be referencing IT people for story advice, and I have to tell you, it’s story 101. But, these are the fundamentals you have to think about if you’re new to storytelling.
If you’re ready to move beyond the basics, read Data and Storytelling: 6 Ways to Use Data to Move Your Mission. Kurt Voelker, the CTO of digital communication firm Forum One, offers six examples of unique stories told with data.