The Moneyball phenomena may very well have been the mainstream introduction to the tangible benefits of advanced analytics, illustrating the concept that data scientists armed with the right software, storage solutions, and clear objectives posses the ability to reveal the insights hidden deep within raw data. In the context of sports – where games can be won by the slightest of margins – truly knowing which strategic plays actually work and which players perform well together is an invaluable insight. Yet, there are many parallels in the solutions, tools, approaches used by the sporting world that also translate into business scenarios.
Business intelligence can be used to optimize performance and refine strategy in the enterprise – and in the sports world, as well.
Coaches – the business leader-equivalent of their respective teams – aided by traditional statistics and newly available sensor data, now have an unprecedented view of what really works. This article by Bleacher Report illustrates the incredible amount of data available to coaches, where the location of all 10 players, the ball and the officials is captured 25 times a second by a network of SportVU motion-tracking cameras and then translated into insights.
You can see one example of this type of application in a study that provides insights into if, and when, a 3-point shot makes sense. “Live by the Three, Die by the Three? The Price of Risk in the NBA” uses analytics to conclude that teams losing by a significant margin should avoid 3-point shots, while teams only trailing by several points actually become more effective at shooting both 2s and 3s. These insights could be valuable enough to change the way a coach understands and approaches their game strategy!
Another example of the application of sports intelligence can be seen in the recruitment process – which can easily be translated into business recruitment, investment, or financial forecasting. We see it every year: the star college player and early draft pick fizzles in the pro leagues. It seems that the same skills that make a stand-out college player don’t always translate into professional sports. In order to avoid these often-expensive draft mistakes, scouts are creating an algorithm of crucial identified statistics in order to predict a college player’s true pro potential. The same can be done in the enterprise to better ensure an appropriate ROI.
Each of the sport situations above require sophisticated enterprise IT solutions and data scientists that can enable the business to utilize the data sets available to them. Increasingly, these solutions require cost-efficient storage of expanding sources of data, conducting rapid analysis on diversified data sets, and generating meaningful insights that can be used for real-time situational decision-making.
So if the IT solutions are similar, what can business leaders learn from sports-world counterparts? Three things standout to me:
- Competitive differentiation and business value often lies buried in the data.
- Gaining useful insights requires a clear understanding of the problem we are trying to solve.
- For maximum cost efficiency, complete step 2 before step 1, so you capture the right data.
If this blog has sparked your thinking about how best to tackle the business and IT challenges associated with analyzing your own organization’s data, take a start-to-finish look at how Intel IT used predictive analytics to boost company sales.
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Chris Peters is a business strategist with more than 21 years of experience ranging from Information Technology, manufacturing, supply chain, nuclear power and consumer products.
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