Business Intelligence: Science, or Game?

 

There is a legend that, in 1815, in the immediate aftermath of the Battle of Waterloo, Nathan Rothschild had learned of its outcome via carrier pigeon. But it’s what he did with that intelligence that fascinates: the morning after the battle, Rothschild attended to his usual station at the London Stock Exchange, looking anxious. He immediately began selling off his English holdings, assets that were sound and provided returns only as long as the government that backed them was solid. Instantly the rumor spread, “Wellington lost! Rothschild knows!”, and a sell-off commenced. Once these futures had plummeted in price, Rothschild had his agents quickly buy them back, at dramatically reduced rates. He made a killing that day.

Again, note his tactic – he didn’t use his advanced intelligence to buy up English futures, which were sure to increase in value once the Brits knew that Napoleon was no longer a threat. Instead, he behaved as if the English futures were worthless, inspiring panic in those who were watching him for advanced intel. It was their reaction which led to Rothschild’s ability to make far, far more than had he acted on his intelligence directly.

I said that this is a legend, and more than a few historians have rejected the story in its entirety. But it does go to illustrate not only advanced use of business intelligence, but out-and-out manipulation of the same. Rothschild broke no laws, and would not have even if he pulled this stunt in today’s far more stringent business law environment.

Such may or may not have been the case with Enron’s behavior during the California Energy Crisis of 2000 – 2001. As I discussed in my must-have book, Enron’s advanced use of business intelligence, coupled with their ability to manipulate the same, led to eye-popping profits in a relatively short period of time. Much of the analysis of the crisis places the blame for the difficulties solely on the malfeasance of Enron, but I tend to disagree with those analysts. I believe the clumsy attempts from the California legislature to pass laws to control the way the power companies operated within the state provided the perfect environment for organizations that knew how to handle business intelligence, as well as manipulate it, to thrive. The laws were byzantine in nature, but Enron quickly found formulaic approaches to maximize their profits within that regulatory environment. These formulaic approaches, or “manipulation strategies” as the Californians liked to call them, were essentially cartage schemes that could be invoked almost instantaneously whenever a certain set of parameters – current load, power line availability, prices on the out-of-state market – manifest. And then, well, it was just a matter of watching the profit meter spin at dizzying speeds. Yes, Enron did play fast and loose with the untimely shut-down of plants for “maintenance,” or the priority renting/leasing of major power lines. But I still blame Sacramento for the fiasco – they set up a business intelligence game, and simply couldn’t play at the same level as their private sector counterparts.

The common thread in these two stories is that a certain level of deception was involved. I would venture to say that in virtually every instance of an advantage in business intelligence leading to dramatic managerial success, the element of deception is present. Now, I understand that that makes many people uncomfortable, but such ones need to understand that the business intelligence component of management science more resembles the game of poker than it does chess. In chess, all of the pieces are visible to both players, as well as their possible moves. There is no deception in chess. Ah, but in poker, there’s deception all over the place. The poker player who bids high on good hands and low (or even folds) on all bad hands will not win very much money at the game. There simply have to be times where he bids up losing hands (“bluffing”), or bids down winning hands for the express purpose of preventing his opponents from realizing and then acting on a known pattern of bidding behavior, and taking advantage of it.

Science, on the other hand, can brook no deceit. By definition, science seeks to test hypotheses in an empirical setting in order to discover truth. In those instances where any form of deception does enter in to the evaluation of a theory, it renders the science fraudulent, as in the behavior of University of East Anglia Climactic Research Unit in its support of the anthropomorphic global warming hypothesis.

Since the pursuit of advanced business intelligence contains an element of deceit, it more closely resembles a game than it does management science, and should be approached on that basis. If only someone out there had written a book that deals with game theory in management – oh, wait, I did.