Having just seen the movie myself, I was delighted to receive this guest post from my colleague Rita Sallam, a Research Director here who focuses on Analytics, BI, and Performance Management. It’s a good read.
As demonstrated in movie “Moneyball”, starring Brad Pitt and opening in theaters today (http://www.moneyball-movie.com/), professional sports teams are increasingly using data mining and statistical analysis to find the players that best correlate to success.
This approach has resulted in the displacement of many long-held, but less relevant, performance statistics and “gut feel” recruiting approaches. Many successful teams are building on – and supplementing – this fact-based approach to winning by using collaborative decision making (CDM) platforms that enable key team decision makers to assess, weight and optimize a combination of quantitative and qualitative measures used to select the best players at any one time to meet their specific team needs.
CDM platforms combine business intelligence (BI) and other sources of information used for decision making, with social networking and collaboration capabilities, decision support tools, and analytic processes such as data mining and statistics, methodologies and models — to improve and capture the decision process.
Of course, professional sports teams are not new to BI; they have one of the longest histories of any industry for using player and game statistics to report on, assess and value players and team performance.
Advanced analytical and statistical approaches, used by teams such as the Oakland Athletics, Green Bay Packers, and the Calgary Flames shift the odds of winning and have forced coaches and management to rethink the statistics and player attributes that matter most in player selection and to remix the formula for winning. These new analytical techniques are resulting in new correlations between previously ignored and undervalued statistics and player performance and winning.
This approach doesn’t just stop with movies and sports teams. Statistical analysis, combined with CDM, can help any organization, to rethink and then optimize the business processes that drive competing and succeeding. CDM can also highlight and resolve differences of opinion in the decision-making process in any organization relating to judgment and weighting of decision drivers.
Taking the view that player selection is similar to the key decision processes – such as vendor selection and portfolio optimization – which most companies must leverage; lessons can be learned from early adopters of CDM in the professional sports world to find opportunities to improve the quality of decision making in any organization.
Like professional sports teams, traditional companies must rethink how new measures that drive outcomes can also drive changes in business processes and look for ways to modify and optimize processes based on new CDM insights. Given that the potential competitive advantage from finding new statistical correlations for success can be short lived once other teams learn of the advantage, what is needed is a new way to assess performance and identify the ‘best fit’ players: one that could combine a range of quantitative and subjective measures, and that is customized to a particular team and their specific needs at any time, given the full picture of team dynamics, strengths and weaknesses. CDM can play this role by directly linking analytics to the decision-making process, and literally puts all decision makers on the same page.
If you’re interested in additional information, I have published a report titled “Beyond Moneyball: How Professional Sports Teams Are Using Collaborative Decision Making to Win” at http://www.gartner.com/resId=1800819 (a client subscription is required).
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