Last week, a new film featuring Brad Pitt came to theaters, and for a change, I’ll be dragging my wife to see it instead of the other way around.  Moneyball is based on the best-selling book by Michael Lewis (2003) which chronicles the new-age, statistically driven philosophy successfully adopted by Oakland A’s general manager Billy Beane (Pitt) nearly 10 years ago.  From the New York Times:

“Moneyball is an exuberant fictionalized look at how Mr. Beane helped transform the team, one of the poorest in baseball, into serious competition for the wealthiest franchises, mostly by ignoring everything he’d been taught about the game.”

Much of the popular commentary suggests the central story is one in which analytics and objectivity trump instinct and tradition.  This is certainly one theme, but those of us working at SAS are already beyond that, as even the general public accepts that data and computing introduce the possibility for better decision making. (The Power to Know, anyone?)  A less-prominent but equally significant idea emerges if we consider the analysts charged with employing Moneyball tactics in new business scenarios. Specifically, we are reminded that successfully leveraging analytics to win is an art that requires what we’ll call quantitative creativity.

What is quantitative creativity?

Quantitative creativity is the necessary ingredient that allows organizations to use analytics to create a competitive advantage over counterparts with the same intentions. It is looking at the same marketplace and measuring it differently than rivals--in ways that highlight previously obscured inefficiencies and opportunities. Consider baseball teams prior to the Moneyball revolution.  They all used the same traditional statistics, albeit simple ones like batting average and home runs, to value players. These objective measures were complemented by subjective observations like demeanor, build, work ethic, and "look."  

As all teams gained access and visibility to all prospects, payroll became the overwhelming factor in success. Beane’s philosophy was not simply to use objective measures exclusively, but to determine which objective measures were best suited for building a team.  His opportunity, like those of our clients, was to come up with a different and more accurate answer to that question.  Finding new answers to an old question is the promise of quantitative creativity.

There are examples from both the private and public sectors that illustrate the value or potential of quantitative creativity.  The legions of quant-jocks working on Wall Street are not hired to replicate existing models. They are paid to create, test and validate proprietary strategies. Commercial banks spend feverishly to fashion new and better credit scoring models that more accurately price the risk.  In education and student achievement, the primary pursuit of many is to identify the right measure for annual progress.  This is the first step in enacting policy that can improve student outcomes.

Building analytic leadership

Once quantitative creativity is accepted as a prerequisite to competitively successful analytics, the implications are significant. Foremost among them is the need for creative, forward-thinking leadership. The winning organization requires management that not only encourages experimentation, but expects and tolerates some frequency of failed hypothesis. Beane challenged his front office team to look at player evaluation differently. He valued a different answer, and recognized there could be mistakes in his acquisitions.

Next, by its very definition, a competitively advantageous analytic approach will threaten long-standing business processes and challenge traditional authority.  The Oakland A’s on-field manager, the baseball lifer Art Howe, was not always on the same page with Beane’s new Moneyball philosophy.  Overcoming skepticism while preserving experienced human assets requires artful and creative top-down leadership.

Fostering a creative culture

A final challenge for leaders of a quantitatively creative enterprise is fostering a creative culture.  To nurture and capitalize on creativity, organizations must employ people with a diverse set of backgrounds and a broad spectrum of skills.  An expert in stochastic control theory is likely to know very little about the latest estimation models, but together they can solve a wide set of practical problems.  Beyond technical skills, contributors to the creative culture must also have the capacity for collaboration.  Ideas must be shared both amongst the analytical community and between this community and the business. While moments of inspiration can come from any individual, the chances of solving the same problem in new ways increase dramatically when more people contribute. The chances of deploying the new approach are even more dependent upon these skills.

It is tempting to ask whether an organization has the quantitative aptitude to compete on analytics. As these skills are commoditized, though, the real differentiator is something wholly different.  We need to ask whether the company has a front office full of artists to employ their own brand of Moneyball.