One insurance company’s chief data scientist is “terrific at what he does,” says his boss, even “brilliant.” The company is lucky to have him. But he never sees the inside of the executive suite.
“He is a quirky, quirky guy,” says the boss, “and he is a super powerful dude in what he’s doing for us.” But upper executives would judge him harshly. “There would be a terrible outcome.”
One insurance company’s chief data scientist is “terrific at what he does,” says his boss, even “brilliant.” The company is lucky to have him. But he never sees the inside of the executive suite.
“He is a quirky, quirky guy,” says the boss, “and he is a super powerful dude in what he’s doing for us.” But upper executives would judge him harshly. “There would be a terrible outcome.”
“These [analysts], including me,” says the analyst’s boss, “we’re different.” The brilliant data scientist — who as a hobby builds computers from video cards because “‘there’s a lot of power in video cards’” — makes a bad impression in person. He wanders off topic and, for example, has been known to digress into world politics and weird analogies.
“When I hear something come out his mouth that’s a little off color,” the boss says, “I chalk it up to his brilliance.” The company’s top executives, on the other hand, are “a harsh crowd.”
This crowd judges the off-color analyst harshly and fails, for example, to appreciate the real meaning of his risk models and other aspects of advanced analytics. They isolate themselves from the abstract evidence in data analysis that could correct or at least be reminded of their own biases.
The executives tell the analyst’s boss they want that. “I say that if you want that, you have to realize you’re dealing with people whose skill sets are completely different from anything you’ve dealt with. In a perfect world, my chief executive officer would say, ‘I understand how his analysis is applicable.’ No. Nothing.” They just don’t respect what he has to offer.
We’ve seen the work of harsh crowds before. Didn’t we just witness something like this on Wall Street? Those who knew the data saw the mortgage meltdown coming. But those who could have prevented it either dismissed the warnings or misunderstood them. What idiots, we say now. They should be jailed or made to work at Panda Express the rest of their working lives.
Potential consequences of the Analytics Gap are mostly minor by comparison but more widespread and chronic.
“I don’t think it’s a stretch to suggest that micro-versions of [the meltdown] could happen in product development, pricing, marketing, risk, and other domains” — unless data analysts and executive leadership come to understand each other.
Why not train more quants? It won’t work, he says. Generally, those interested in data analysis tend to be too technically minded for management.
(The Analytics Gap / shutterstock)
