Cause-alities

Big Data, Analytics, And Storytelling

“I think storytelling is becoming one of the new frontiers,” said Luke Lonergan, co-founder of Greenplum, now part of EMC Corp. But beyond that, “it really matters a lot to bring the brain to the problem in a way that you can untangle the complexities.”  "Social Media, Genomics Driving Data Tsunami" Wall Street Journal 18 Feb 2011 http://on.wsj.com/g9Lt5A

I've found many times that it is very difficult for audiences to use and consume analysis -- no matter how insightful it might be.  I suspect this is why effective analysts always find and present "the story" that the data tells. Audiences, especially many decision makers, simply glaze over when presented with the details of a complex analysis.  But presented as a story they can interact, explore, test, and consume the analysis.   Used correctly, storytelling can be the common language for both consumers of analytics and the those that truly revel in the abstractions of the analysis process.

There is a measure of irony in Lonergan's comment about storytelling being a "new frontier" since it has to be one of the most ancient and powerful modes of human thinking and communication.  I'm guessing he means storytelling as a means to facilitate the application of big data (I don't know Lonergan, although I'd like to, so all I can do is guess) and that would be a new, but not unprecedented, application for storytelling.

I think that storytelling is more than a communication mechanism -- something that we think about after the analysis is complete.  Storytelling can provide an analytic framework.  As I read the interesting WSJ blog post and got to Lonergan's quote at the end I was prompted to describe some of my thinking about the relationship of storytelling and analytics and explore some ideas about how it might be relevant to the promise of "Big Data".

Why Model?

I recently ran onto a short paper (speakers notes, really) by Joshua M. Epstein titled "Why Model?"  I spend a lot of my life answering that question and I am excited by Epstein's concise, reasoned explanation.  He boils it right down to the basics:

1. We're all modelers, but most of our models are implicit, not explicit.
2. Sometimes we model to predict.
3. Sometimes we model to explain.
4. And there are at least 15 other good reasons to build explicit models . . .

In some sense Epstein's position on modeling is a presentation of the scientific worldview and its moral advantages.  So, mixed in with some really concrete reasons (eg: #2 -- Guide data collection) are some seemingly more esoteric objectives (eg: #6 -- Promote a scientific habit of mind).  

Alas, business, financial, and other organizational leaders are mostly not swayed by a "scientific approach".  I find that business and organizational clients generally need an additional level of motivation to justify an investment in explicit modeling.  Usually, for the business person it is not enough to believe that an investment in explicit modeling will accomplish any of Epstein's 16 reasons.  The business person wants to know What Then?  Often phrased as a somewhat derisive "So What?"

For most business leaders explicit modeling has to be linked to some decision making or problem solving process.  And, unfortunately, this often boils down to a focus on prediction at the expense of the other 16 reasons that are also part of excellent decision making and problem solving.

If you like this paper by Epstein try his book Generative Social Science: Studies in Agent-Based Computational Modeling.  It's one of my favorites.