Over the past 30 years, and increasingly the last 10 years, we have seen the rise of the quants, which Felix Salmon defined in his article Why Quants Don’t Know Everything as “people whose native tongue is numbers and algorithms and systems rather than personal relationships or human intuition. They find numerical patterns or invent ingenious algorithms that increase profits or solve problems in ways that no amount of subjective experience can match.”
Quants have risen to prominence in fields as diverse as sports, politics, stock trading, and dating. However, after a field has become quantified, it doesn’t become a data-driven paradise, Salmon cautioned. “The more a field is run by a system, the more that system creates incentives for everyone (employees, customers, competitors) to change their behavior in perverse ways, providing more of whatever the system is designed to measure and produce, whether that actually creates any value or not.”
For all the praise we heap on those who have become data-driven, sometimes we become too driven by data. As soon as people “pick a numerical metric as a way to measure whether they’re achieving their desired outcome,” Salmon explained, “everybody starts maximizing that metric rather than doing the rest of their job.” A few of the examples he cited were police departments focusing on arrests while ignoring conviction rates, public schools focusing on standardized test scores while ignoring graduation rates, and snowplow operators focusing on how much snow they cleared while ignoring patches of dangerous black ice.
Of course the best example of what can happen when we become too data-driven was the recent global financial crisis.
As Salmon explained, the rise of quantification concentrated decision-making—and moneymaking—within a relatively small group of people at the headquarters of global banks. “Soon they were trying to optimize their algorithms to maximize profit, minimize risk, and make millions of dollars for themselves. Global regulators didn’t help: In 2004, in sympathy with the over-leveraged, hyper-quantified banking system, the Basel Committee—the Switzerland-based body that oversees world finance—put out the Basel II accord, more than 250 pages of regulations that effectively placed individual banks in the driver’s seat. The accord essentially embraced all of the quantitative techniques used by the wizards who would end up blowing up Wall Street, and it allowed banks to operate with astonishingly high levels of debt. As everybody knows, all of that ended in catastrophe in 2008.”
Balance can be restored, Salmon suggested, by synthesizing the quantitative insights of big data with some of the human intuition that it displaced. For example, “in September 2010, the Basel Committee came out with Basel III, and while it doesn’t fully dismantle Basel II, it does add layers of common sense on top of all the rocket science. Essentially, while the algorithms were given free rein under Basel II, there’s a host of overrides in Basel III that put power back where it belongs, in the hands of experienced regulators. Basel III isn’t perfect, but no international system of bank regulation could ever hope to be. In a few years’ time, if and when it gets fully implemented, it’s going to be a vast improvement on what preceded it. That’s what a good synthesis of big data and human intuition tends to look like.”
While becoming data-driven is a laudable goal, as Salmon concluded, “let’s not forget that data isn’t everything.”