Baseball is probably one of the most well-known businesses that uses everyday Big Data to uncover value. Sabermetrics, as it is known, comes from an acronym of the Society of American Baseball Research and represents an analysis of the game of baseball using detailed performance data like on-base percentage, rather than qualitative methods such as how fast a pitcher can throw. It has been pioneered by non-baseball people who saw the value of Big Data.
The use of sabermetric data became mainstream in 2003 when the low-budget Oakland A’s had to find a way to compete with larger market teams. As a subject of the movie “Moneyball,” the rest of the baseball world found out that by using performance metric data, a pro-baseball team can assemble a roster that can compete with other teams that are comprised of high-priced stars. General Manager Billy Beane was able to “level the playing field” so to speak and look inside the numbers to find players of value. A century and a half old business model was turned on its head when the Oakland A’s won 103 ballgames – about as many as the New York Yankees, who spent over twice as much.
The goal of baseball is to score more runs than the opposing team before recording 27 outs. So, sabermetricians look at players who contribute to scoring runs and/or do not contribute to outs or pitchers who do not allow base-runners. A traditional qualitative method of evaluating a pitcher might be how hard they throw – a fastball in the mid-90 miles per hour range is considered above average. Instead of a pitcher’s mid-90’s fastball, sabermetrics might look at how many base-runners per innings pitched they allow, known as walks and hits per innings pitched (WHIP). The data shows the fewer base-runners allowed, the less often the other team scores. The less the other team scores, the more your chances of winning. So, a pitcher with a low WHIP has more value than one with a 95 mile per hour fastball. Yet, the pitcher with a 95 mile-per-hour fastball commands much more salary. The ability of sabermetrics to catch on in a very traditional business model is the result of not only innovative thinkers, but the volume, velocity, variety, and the computer intelligence and sophistication of the digital age to process the data.
Making Sense of the Data
In the digital era, organizations are using this same model. Retailers, like Walmart and Kohl’s, analyze sales, pricing and economic, demographic, social networking data, and even weather data to tailor product selections at particular stores. In addition to a good deal of data becoming available through social networking, in 2009, Washington opened the data doors further by starting Data.gov that makes all kinds of government data accessible to the public.
If you take advantage of a retailer’s “free” WiFi, you are supplying valuable data to help determine the timing of price markdowns. Your seeming innocent use of the internet to search other stores or do comparative pricing enables these retailers to capture consumer trends simply because you agreed to use their internet service provider. You are also supplying data on product placement – your actual in-store movement helps determine why one product should be next to another. This is the functional equivalent of the online experience where the retailer website shows you “people also viewed this.” Shipping companies mine data on truck delivery times and traffic patterns to fine-tune routing to save delivery time. More efficient delivery times means fuel savings and, yes, more deliveries. A human dispatcher at a loading dock could not possibly compete.
Just as the “Moneyball” experience has put professional baseball talent evaluators on notice that there may be another way, so too has business use of Big Data to augment or even replace management for decision-making. Some retailers use “sentiment analysis” techniques to mine the huge streams of data generated by consumers using social media and adjust strategies accordingly. And like the traditional baseball talent evaluator, the marketing analyst is left to, at most, interpret the data. HR departments that segment employees by task and performance can change work conditions and implement incentives that improve both satisfaction and productivity. It is not a stretch to imagine a company going beyond the resume to fill vacant positions based on more than just education and experience.
Big Data also uncovers the value a business could achieve by actually spawning new business models. One company learned so much from analyzing its own data that it decided to create a business to do similar work for other firms. Big data also provides additional opportunities for data aggregators who combine and analyze information to generate value for clients. For example, the sabermetric revolution has launched several Big Data web sites that baseball analysts, talent evaluators, and fantasy sports patrons alike use all the time. Not to mention the fantasy sports business itself that has thrived because of Big Data.
The digital age that spawned this data surge also spawned the tools to categorize it. All the unstructured data like words, images and video, those streams of sensor data, and real-time customer interactions can now be processed, interpreted, and reacted to in seconds. Artificial intelligence like natural-language processing, pattern recognition and machine learning enable us to uncover the real value of Big Data.
Analytics is about using historical data to make inferences of the future performance on past performance. Big Data is about volume, velocity, variety, and the computer intelligence and sophistication of the digital age to process it:
- Volume – More data cross the internet every second than were stored in the entire internet just 20 years ago
- Velocity – Real-time or nearly real-time information makes it possible for a company to be much more agile than its competitors
- Variety – Most of the important sources of big data are relatively new. Information from social networks are only as old as the networks themselves.
Big Data is not only becoming more available but also more understandable. Big Data is not just about volume, velocity, and variety. It is about the computer intelligence and sophistication of the digital age providing the ability to manage and categorize that data to uncover value. To quote the great baseball movie Bull Durham, “You can look it up.”
- Beneventano, P., Berger, P. D., & Weinberg, B. D. (2012). Predicting run production and run prevention in baseball: the impact of Sabermetrics. Int J Bus Humanit Technol, 2(4), 67-75.
- Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’? (cover story). Mckinsey Quarterly, (4), 24-35.
- Lohr, S. (2012, February 12). The Age of Big Data. New York Times. p. 1.
- McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. (cover story). Harvard Business Review, 90(10), 60-68.
About the Author:
Chief Scientist & Institute Fellow
Dr. Frank Granito is Chief Scientist and Lead Fellow at the Institute for Digital Transformation. He has over 40 years of experience in the Information Technology field and has successfully implemented IT Service Management transformation solutions for Government and Commercial clients. Dr. Granito holds a Doctor of Management from the University of Maryland University College with a thesis on Organizational Culture.
As Lead Fellow, Frank selects current transformational topics and leads the monthly discussions with all of the Institute Fellows, parts of which are available on our YouTube Show “Digital Transformation Unplugged“. In his role as Chief Scientist, Dr. Granito created the analytical model that is the basis of the Digital Enterprise Readiness Framework. He designs, creates and produces the analysis of our Rapid Research. He is also the principal author the book Digital Transformation Demystified.
In his spare time, Frank is also a Professional Chef and you can follow his culinary exploits on Instagram @pasticciogranito.
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