Are you game for the Moneyball Process?
The importance of data-driven decision making and different aspects of looking at data was much popularized among the civic society by Brad Pitt starred Hollywood movie ‘Moneyball’ which is based on a true story. A quick snapshot of the storyline:
“Oakland Athletics general manager Billy Beane (Brad Pitt) is upset by his team’s loss to the New York Yankees in the 2001 postseason. With the impending departure of star players, Beane attempts to devise a strategy for assembling a competitive team for 2002 but struggles to overcome Oakland’s limited payroll. Billy turns baseball on its ear when he uses statistical data to analyze and place value on the players (not star players though) he picks for the team. This resulted in Oakland’s Athletics (baseball team) set a team record of 20 wins in a row. Similar strategy was adopted by Boston Red Sox’s who won the World Series in 2004 since their first win in 1918”
What Beane had done differently that turned the game around was application of Sabermetrics (A Statistical analytics method of analyzing data points in the game of baseball). The analytics lead application of Sabermetrics helped Beane to question traditional methods of evaluation such as RBI (Runs Batted In) and batting average. It took in-depth analysis to conclude that matches were not won by players with higher batting average but by those with a higher On Base percentage (OBP), Slugging percentage (SLG). Beane formed a team based on these new metrics and other parameters.
An important observation according to Sabermetrics is that a player’s worth or indication of their success is based on desired outcomes and the factors that lead to their creation. Having given this background, let’s draw parallels between the Moneyball scenario and the world of business – Often business/project decisions have:
a) Specific Target/Goals b) Team formation c) Resource evaluation (right metric identification) d) Continuous improvement to achieve the desired outcomes.
One might argue that the data points that are gathered in sports is much referenceable, detailed and substantiated as every game is recorded. But new age technologies such as Big Data, Faceted data models (e.g. Oracle’s Endeca) and data exploration tools (e.g. Qlikview, Tableau etc…) make the application of Moneyball Analytics – ‘Application of analytics to scenarios similar to that of Beane’s’ highly possible. Deciphering Beane’s strategy, following are some of the key steps to establish your Moneyball process:
- Challenge: Question the traditional methods of looking at problems and solutions
- Strategize: Identify hidden new data elements that will directly contribute to the desired outcome
- Relate: Use data mining technique to identify the correlation of these data elements
- Innovate: Apply quantitative creativity (Looking at same problems differently) through data exploration, visualization techniques
- Act: Perform necessary action to implement insights and Optimize your process accordingly
Here are few use cases where Moneyball analytics can bring about difference in the business world:
|Industry||Traditional Way||Moneyball Way|
|Retail||Looking at customer data within enterprise to foster sales||Traditional methods + analyzing different customer touch points, social media – Read Social MDM|
|Wealth Management||Looking at large clients > $10 million. Assets under management, loan volume – indicators of success||Traditional methods + look at new market segments ($1 to $10 million) named by Mckinsey as fastest growing, look beyond allocated costs|
|Banking – Mortgage||Metrics – Number of branches, number of loan officers, total volume||Traditional methods + loan recovery rates, customized pricing points, cross-selling|
|Manufacturing||Analyzing production rates, post-production defect rates||Traditional methods + Machine logs, shop floor records (text mining), co-relation between components to predict failures|
As seen in the above use-cases Moneyball Way/Analytics is all about – Looking at business problems differently, gathering data, application of analytics and gaining insights. Finally for those of you who have not watched this movie, I would strongly recommend to do so to get a feel of analytics in action. Signing off with a short clip of the movie from YouTube