For those who couldn't attend, I summarize the discussions that took place during the JSM session, and the talk at EdLab at Columbia's Teachers' College.
At JSM, the panel, led by Professor Mammo Woldie, focused on what business analytics is all about. Dan Coates, who heads up Youth Pulse and previously spent years at SPSS, gave an overview of the types of problems that businesses face, and the companies that have made key investments in developing advanced analytics teams. Nathaniel Derby, an independent consultant and entrepreneur, has lots of advice for statisticians who are interested in developing private practice. I made some comments about how to recruit and manage an analytics team.
Several themes emerged that elicited considerable discussion. One revolves around the analogy to cars. Should schools teach students how to drive a car or how to build a car? My view is that today, almost all statistics courses teach building, and if there is excess time (however scarce), may cover a tip or two on driving (like, pay attention to the cyclists). For most people in business it is not necessary to know how to build a car but essential to understand how to drive one.
This leads to a related point. The decision makers at most corporations are not quantitative thinkers. Is it better to give corporate executives technical training so they understand what statisticians are saying? Or is it better to train statisticians to speak to non-technical people instead? Readers of my blogs won't need to ask where I stand on this. Unfortunately, I sense that outside of this room, my opinion is not in the majority.
PS. An attendee who works at Capital One pointed out that their business leaders typically have quantitative training, and would demand technical details. Those are the cultures that would be most comfortable for statisticians but today are exceptions rather than the rule.
One of my slides received much attention. Nathaniel first asked for it to be re-posted, and it stayed up there for much of the session. The slide points out that analytics professionals do best if they can succeed on three types of skills: technical (obviously), business thinking and "intangibles". Business thinking is often the hardest to develop because quantitative training in college or grad school (outside of business schools) just do not teach it. For me, intangibles are the hardest to find, mainly because it is impossible to gauge in an interview.
Another theme concerns which types of companies are investing in analytics. In his career, Dan has sold analytical solutions to many companies and he told us that only large corporations have the resources to do so. I am sympathetic to this point of view because analytics has prerequisites, the most obvious being an accessible, somewhat accurate database. Nathaniel, however, has been working with many small companies, so what Dan and I see as a gap he sees as opportunities.
EdLab at Teachers' College does a lot of interesting things, like creating technologies for the classroom and libraries, blogging, and analyzing data sets in the education sector. I had a great time speaking to this group, which included summer interns (hello, the one who asked about the difference between observational study and experimental study, perhaps working on a problem set?).
The most intriguing and unexpected question was: to do well in this business, do you have to read a lot? This is where I stumbled into a spaghetti carbonara analogy while mixing metaphors with the gray flannel, with which I have already been associated. Basically, statistics is not pure mathematics, there is not one correct way of doing things, there are many different methodologies, like there are hundreds of recipes for making carbonara. What statisticians do is to try many different recipes (methods), and based on tasting the food (evaluating the outcomes), we determine which recipe to use. Because of this, statisticians need to be well-read, to keep up with what are the new methods being developed.
Another attendee -- a reader of Junk Charts -- asked about why I prefer graphics that are clearer but less "engaging" than the original. By "engaging", he meant telling stories. This gives me an opening to point out that while I have written extensively about the shortcomings of "infographics", one aspect I like a lot is the use of narrative in this type of charts, the use of multiple charts to illustrate complex concepts. Like going from photographs to a movie.