Thomas Devenport: Those Good at Analytics Not Good at Visualization

IO Impotency, IO Mapping
Thomas Davenport
Thomas Davenport

Q&A: Tom Davenport urges more clarity in data analytics

By Joe McKendrick | March 19, 2013, 4:00 AM PDT

Businesses may be seeking to compete on analytics, but it’s often difficult for business decision-makers to get their heads around data.

I recently had the opportunity to chat with Tom Davenport, visiting professor at Harvard University and co-author of the seminal work Competing on Analytics: The New Science of Winning, about the difficulties of converting to an analytics-driven culture. Davenport, who is also co-founder and research director of the International Institute for Analytics, and a senior advisor to Deloitte Analytics, is working on a new book, dicussing on how analytics need to be better communicated to business decision-makers. He shared some of the thinking behind his forthcoming work:

Q: BI and analytics vendors have been coming out with all sorts of graphic tools — dashboards, balanced scorecards and so on — for years. Do we need more than a nice splashy presentation on the tool to communicate analytics?

TD: We’ve all grown up on pie charts and bar charts or whatever, but there are probably at least tens, if not hundreds of alternative approaches to visual analytics. Narratives are a pretty good way to convey information in the past, so maybe we should be converting our data and analysis into stories. People are starting to do that more. Most analysts were unfortunately not trained in how you communicate effectively about analytics, so we’ve got a long way to go in terms of doing a better job of that.

Q: More and more data is flowing through enterprises. Is it a challenge to get C-level executives interested in turning this data into analytics?

TD: Not for all applications. Because increasingly people are feeding data into computers and the results go into another computer, and the decisions are getting more automated. Any time you have a human involved, it’s important to try to help them extricate the meaning of the data and analysis. And there a variety of ways to do that. Historically, we haven’t been too terribly good at it, the quantitative people among us.

Read full interview.