Not for lack of trying. That’s item one.
I don’t recall who, but a gentleman once commented on a post here that business knowledge and expertise was entirely different from scientific and engineering knowledge and expertise, and that scientists, bright fellows all, assume that because they have mastered group theory and Feynman diagrams that operating a business is a cinch.
Item two is this:
Monsanto just bought The Climate Corp., née WeatherBill, for a cool nine-hundred-and-thirty million-with-an-M bucks. If you’re like me—and you may thank the good Lord you are not—your immediate thought was…those cheap so-and-sos couldn’t come up with a lousy extra seventy million?
Climate Corp. specializes in “Using ‘hyper-local weather monitoring,’ predictive models and other data to help farmers make optimal growing decisions”. In other words, taking the output from skillful climate models—those out a month or two have skill (in the technical sense)—and blending it with weather to produce better weather forecasts.
I had the same idea years back and published the general idea in Journal of Climate. (The difference between skillful and non-skillful forecasts is what convinced me climatologists ought not to cherish their creations as much as they do, incidentally.)
This idea—better, more precise and tailored weather forecasts—was so good that I and a couple of friends set up a company to sell them back in the 2000s. Called ourselves Gotham Risk Management. The technical side of the biz wasn’t difficult. Creating a database, building automatic models, a website of course and all that held no mystery for us.
But then came phone calls with potential clients. I’d open with, “Gbbbb, uh, bwwwpth, you see, um.” After confirming that I wasn’t a patient from the Speech Pathology Lab, my listener would beg to be told what the heck I wanted. I had an answer: “Buy our forecasts. You won’t be sorry.”
Shyness, as anybody who has ever been within one hundred standard English yards of me will confirm, is not my problem. Knowing how to convince people to part with their money is.
Here I had this tool that would let people price their weather insurance and climate derivatives more correctly, that would make them more money than it would cost, but I couldn’t convince anybody to try it. It didn’t help that the futures market in heating and cooling degree days in Chicago had froze solid—literally, trading ceased entirely because of the naughtiness originating at Enron. But that’s no excuse.
Item three. So I had another terrific idea that would track people’s predictions and rate them in an optimal way. Doesn’t sound much, until you tie it to something practical. How about sports betting? With a company called (at first) EdgeHogs, we tried.
It’s easy to make accurate sports predictions, but only for games which any clod can guess. University of Michigan v. St Peter’s Boys Junior Academy? Better get that one right. Tight games are hard. The optimal rating takes these kinds of things into account. We did all the big sports.
Everybody, including pros, entered guesses into the system and were rated, using a bevy of statistics about moneylines, Vegas odds, and all that.
So what? Well, exactly. The idea the guys with the money had (Yours Truly has an amount close to the subscript on the first kind of infinity mentioned above) was to make the business into a social media phenomenon. Get rated for bragging rights, win prizes. Didn’t fly. Social media is saturated.
Idea is still good, though, and should be applied in a way such that better predictions are produced from knowledge of how old ones fared. These superior ones can be sold. Money, baby. And it doesn’t have to be sports, but how about stocks? Or even weather predictions again?
Couldn’t sell this, either.
Item four. My latest mission has been to tell people who routinely use statistics, mainly marketing companies, that the results on which they rely are too certain. Being too certain means suboptimal and even bad decisions are made. And don’t people want to know that?
Well, my selling talent has proved itself again. Either that, or it turns out people really do want to be too certain.
Gist? There’s much truth in the old adage that those that can’t do, teach.