To Get Your Team to Use Data, Demystify It
Billy Beane, front office executive for the Oakland Athletics and the subject of Michael Lewis’s book Moneyball, transformed baseball using data. He didn’t do it using complicated algorithms. He did it by asking an important question: What kinds of players in the Major League draft typically go on to have the most successful professional careers? He used years of data to answer that question, and then drafted players with those attributes (e.g. those who were playing in college, drawing lots of walks, and so on). Beane’s insight was not some kind of arcane statistical manipulation. It was much simpler. He recognized that he could predict who would succeed in the Major Leagues by studying who had succeeded in the Major Leagues. That’s just exploiting a pattern. In the same vein, the logic behind Moneyball can be applied to any business: Ask an important question, find the data that might offer an answer, and figure out the pattern.
For some of your team members, the idea of using data to inform decision-making can feel intimidating. Maybe they don’t consider themselves to have strong analytical skills. Maybe they felt overwhelmed by their statistics course in college. Maybe they like to “go with their gut,” or simply dread the idea of wading through a ton of data. But it doesn’t have to be that way. If you can show your team that there are simple, straightforward ways to make a big impact with data, it will go a long way toward getting your employees to use data more often in their day-to-day decision-making.
Consider three examples. The first involves Billy Beane, front office executive for the Oakland Athletics and the subject of Michael Lewis’s book Moneyball, who transformed baseball using data. He didn’t do it using fancy new math, or even sophisticated statistical work. He did it by asking an important question: What kinds of players in the Major League draft typically go on to have the most successful professional careers? He used years of data to answer that question, and then drafted players with those attributes (e.g. those who were playing in college, drawing lots of walks, and so on).
Beane’s insight was not some kind of arcane statistical manipulation. It was much simpler. He recognized that he could predict who would succeed in the Major Leagues by studying who had succeeded in the Major Leagues. That’s just exploiting a pattern. In the same vein, the logic behind Moneyball can be applied to any business — there’s enormous potential to use data more powerfully without spending years studying statistics or using complicated algorithms. The essence of “big data” is much simpler: Ask an important question, find the data that might offer an answer, and figure out the pattern.
Example two is out of law enforcement. When police in Santa Cruz, California, claimed that they had “solved a crime before it happened,” it was not some futuristic, Orwellian crime fighting strategy. It was just a pattern. The Santa Cruz police used crime data to determine when and where crimes were happening most often. Then they sent more officers to these locations. One of these spots was a parking garage where there had been a large number of break-ins. Officers spotted two suspicious-looking women lurking near a car. One of the women was wanted on an outstanding warrant; the other was carrying drugs. Police arrested them both — ostensibly before they broke into the car.
Did the police really solve a crime before it happened? The question misses the point. The Santa Cruz police used data to spot crime patterns and then sent officers to the places where they would have the most impact. That’s not mathematical genius; it’s just clever use of data.
Lastly, when the retailer Target wanted a tool for reaching pregnant shoppers (who tend to develop strong retail loyalties during pregnancy), analysts developed a “pregnancy prediction index.” This was neither as hard nor as intrusive as it would appear. Target already had the relevant data.
