Looks like the article might provide some 'luster' to IBM; they are going to put 4000 people on this. Hmm ....

What the article predicts would, could, and should happen but won't. Here's the problem:

Let's start with the 'status' of statistics:

Academic Teaching: In academics, the courses available rarely go beyond just some Stat 101, experimental design, or applied regression analysis. The teachers rarely have much expertise in statistics, e.g., rarely understand the strong law of large numbers, the Radon-Nikodym theorem and its connection with sufficient statistics, or the Lindeberg-Feller version of the central limit theorem. Net, the teaching sucks.

Academic Research: The quantity of good academic research in statistics is meager. The applied statistics research such as in the article would not be regarded as solid research. The grant support is far behind that for physics (theory, particle, applied), biomedical, computer science, engineering, or pure math. Net, the research sucks.

Ph.D. Programs. One can count with shoes on all the good Ph.D. programs in statistics. So, over the past 40 years might count Berkeley, Stanford, Chicago, Cornell, Yale, Hopkins, and UNC.

Computer Science. Yup, to do much in statistics, need computing. So, much of the public and academic computer science swallows the idea that computer science has expertise in statistics. No it doesn't, not while they can't state the strong law of large numbers, and nearly no one in computer science can; for that they just didn't take the right courses in grad school. About all CS can do is pull equations they don't really understand from cookbook statistics and try intuitive heuristics, and that is similar to medicine in the days of snake oil cooked up on wood stoves. Suckage.

Professionalism. Law, medicine, and parts of engineering are 'professions' with certifications, licensing, liability, and strong professional societies. Statistics isn't a profession in this sense. Uh, such 'professionalism' is from important up to crucial 'branding' and credibility for customers outside the profession. Medicine has it; statistics doesn't. Indeed, in academics, a suggestion that statistics should be 'professional' is an anathema. Students who want to get their fellowships renewed will keep their mouths SHUT and never say such things. Suckage.

So, net, the status of the field sucks.

Okay, now we can move on to why the field won't catch on in business:

We have to notice that nearly no one high in business now or on the way to being high in business knows more than just some elementary applied statistics, from long ago, that they never understood very well, never really used, and was likely poorly taught. Also they have not seen much of significance in business from anything at all serious in statistics. They know about the importance of computing, the Internet, and maybe some of assembly line robots, supply chain optimization, comparisons among planes, trains, trucks, biomedical research, even efforts in applied nuclear fusion, but they nearly never attribute significant importance to statistics.

So, suppose there is a good statistician, in a business, with some good data and with some powerful techniques in statistics that can convert that data into new information valuable for the business. Suppose this statistician writes an internal memo to his supervisor and proposes that the company fund the statistician to work on delivering the value to the business.

Here's what happens: The memo goes up the management chain of the statistician to the first manager who doesn't have much respect for statistics. Given the status of statistics, don't expect the memo to go up very far.

Then this manager sees two cases:

(1) The project fails. Then the manager will have a black mark on his record for sponsoring some contemptible, risky, wasteful, 'blue sky, far out, ivory tower, intellectual self-abuse, academic research project'. Bummer.

(2) The project is successful. Quickly everyone in the management chain who does not understand statistics will feel threatened. There is a rumor that a women in the office complained that once from 100 feet away the statistician looked at her in a way that made her feel "uncomfortable", and the statistician is GONE.

So, the manager sees only disaster whether the project is successful or not, and the project doesn't get funded. If the statistician proposes a second such project, then he's a 'loose cannon on the deck', out of control, insubordinate, not a 'team player', and gone.

Or a big organization middle manager can fund big projects in computing, supply chain optimization, assembly line robots, etc. he doesn't understand, but, due to the status of the field of statistics he can't fund a project in statistics.

There is really only one way for statistics to come forward in business now:

The guy with the valuable work in statistics starts his own business and sells just the results. The customers like the value of the results for their businesses and don't have to address anything else.

But, for this business the statistician is totally on his own: There isn't an 'information technology' venture partner anywhere in the US who would touch his project with a 10 foot pole, again, for much the same reason as the business manager.

The statistician MIGHT get some seed funding if he shows a good user interface or Series A funding if he shows good ComScore or revenue numbers, but the role of 'statistics' he can be advised to keep quiet.

Or, the venture partners believe in Markov processes: The future of the business given ComScore numbers is conditionally independent of the statistics in the 'secret sauce'! So, look at the ComScore numbers and f'get about any 'statistics' in the 'secret sauce'. This Markov assumption is not fully justified, and likely not a single venture partner in the country could give a solid definition of conditional independence, but this is still the situation.

And that's the way it is.

So, it's tough to make statistics applied; call this situation a 'problem': Then, for someone with some new, powerful, difficult to duplicate or equal work in statistics that can take some of the oceans of data available now and deliver valuable results and sees their way clear with just a bootstrapped company to high profit margins and rapid organic growth, the flip side of this 'problem' is an opportunity.