[MUSIC] There's a good chance you may have heard the story of baseball manager Billy Beane. The story's well known thanks to Michael Lewis's best selling book Moneyball. And also because Brad Pitt played Billy Beane in the movie of the same name. But the story is almost as widely misunderstood as it is well known. To many executives, the Moneyball story begins the era of big data and analytics. This is an era in which organizations that are not driven by big data and analytics will lose out. Now, it is true that data and analytics play a big role in the Moneyball story. In 2002 the Oakland A's had one of the smallest budgets for player salaries of any team in baseball. And because he was frustrated that he couldn't outbid other teams for good players, Billy Beane hires Paul DePodesta. Now Paul DePodesta is an economics major from Harvard and he's very good at analytics and at baseball statistics. When Beane and DePodesta started doing analytics, they discovered two statistics that are predictive of how many runs a player scores, which is how you win games in baseball, and they are on-base percentage and slugging. Where things get interesting is that these statistics are not what baseball scouts had traditionally valued. What Beane and DePodesta realized, is that this meant that players who ranked highly on the statistics were likely to be undervalued by the market. And so Billy Beane began looking for player bargains, players whose statistics suggested they would score runs but who were under the radar of other teams. When Beane began to hire such players, the A's started to win a lot. As an aside, there's an interesting story what happens in the years that follow what the Oakland A's did, but that is not important for the point I want to make today. Now many executives interpret this story as baseball's big data and analytics moment, but that really misses its key message. You see, none of the data analytics elements of the Moneyball story, were new in 2002. Detailed data on baseball players have been available for over 100 years. Regression analysis, a statistical method that the Oakland A's used, was invented more than 200 years ago, and could have been implemented with 1980s computer power. Even the idea of applying analytics to baseball wasn't new. There is a famous baseball statistician, Bill James, who coined this approach sabermetrics, and who published yearly books since 1977 about data analytics in baseball. And so the question is, why did the Moneyball story happen in 2002? Why did it not happen 1992 or even 1982? What was new in 2002 was that a leader, Billy Beane, had the courage to use the insights from data analytics to drive the way he ran his business. It took courage both because what the analytic said went against the conventional wisdom of how to run a successful baseball team and, because Beane had to overcome a lot of reluctance inside the organization to change to this new approach. The reason Moneyball succeeded for the Oakland A's was not because of the success of the data analytics themselves. It succeeded because the leader understood the potential of analytics for his business, and because he changed the organization so that it could deliver on that potential. You see just as many executives misunderstand Moneyball, they also misunderstand the key to transforming into an analytics driven organization. Too often big data and analytics is seen as a data science and a technology problem. Of course analytics requires investments in technology, in infrastructure, and in data scientists but putting analytics to work for the business is mostly a leadership problem. You cannot simply hire a room of data scientists and hope to be successful. They can't leverage analytics in a vacuum. And so, in this module I want to explain in more detail the three reasons why analytics is a leadership problem. [MUSIC]
