Swing and a Miss – How to Avoid Striking Out with the Wrong Data

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In this special guest feature, Mark Budzinski, CEO at WhereScape, discusses how baseball can teach valuable data management lessons for enterprise by outlining how to get the right mix of automation and intelligent human analytical input. Context with any data set is just as critical to the result as is the collection of such data. As CEO of WhereScape, Mark sees firsthand the challenges customers face in designing, developing, deploying and operating data infrastructures for effective analytics and reporting. Prior to his CEO appointment in January 2017, Mark served as president of WhereScape running worldwide field operations. He has held a variety of sales and marketing roles throughout his 30-year career at notable technology companies such as Intel, Sequent Computer Systems, RadiSys and Applied Microsystems. Mark holds an MBA from the University of Oregon and a Master’s degree in Computer Science from the University of Southern California.

When you think about data analysis, it’s likely an image of a learned scientist springs to mind, laboriously poring over columns of figures on reams of spreadsheets, trying to figure out what to do with those numbers.  Who’d get excited about that? Well, when you add in some situational context, that’s when the analysis starts to get interesting.  How many sports fans are data analysts without even realizing?  Ask any ardent fan of a particular team and they’ll likely give you a pretty accurate prediction about the possible outcome of an upcoming game based on performance data.  Baseball is no exception to this rule.

In the book, Big Data Baseball, Travis Sawchik details how, in 2013, the Pirates used big data to overcome the longest losing streak in the history of North American professional sports, proving how important data analysis can be. A pastime as old and traditional as baseball takes into account dozens or hundreds of historical and current statistics quickly computed and flashed across the TV screen, covering individual player records, the type of pitch, hit angle or speed to base. Within all this data lies key information that enables teams to decide player lineups, batting orders or who is pitching from the mound.

During an interview in Big Data Baseball, Travis Sawchick described the type of data the team wanted to help improve the team’s performance on the field and pitching rotations. They looked at the pitch types, and locations of the strike zone and how it relates to the chances of a batter hitting a ground ball. “They also wanted to see the psychological impact of pitching inside in an at-bat and then going outside later in the at-bat as it related to inducing groundballs,” he said. “It’s an example that not every data-based theory or idea was driven from stat crunchers.”

Sifting through the influx of data

In the same way smart data analysis can help a team recognize and use its strengths and weaknesses to achieve a competitive advantage, it can help corporations make savvy business decisions to propel them to the top of their market.

The key here, however, is to recognize which data is helpful and which should be ignored. Too much data can lead to an information overload, prompting decisions to be over analyzed and delayed.  In the case of the Pirates, team owners and coaches had specific ideas about the type of data needed to improve the team’s performance.

Frequently, C-level executives are presented with immense amounts of data all at once from internal teams with the intention of delivering data as quickly as possible. But decision-makers also need the context of the data and its meaning to make informed choices. The CEO of one of the big 3 auto manufacturers once commented: “We have all the data in the world, but we still do not have information. How can this be?”

Automating the data management process and the speed of data delivery drives value. But only when the data is useful. Imagine having volumes of information about various team members with no relation to their purpose or function.

Data becomes truly valuable when it is presented with relevance and context. Success is achieved when data is automated and analyzed to ensure it is relevant to an organization and its goals.

In baseball, data is gathered and analyzed to help eliminate the randomness and “luck” on the field. Team and player performance, player combinations, field conditions, weather and location are all analyzed to predict future performance.

The quality of the game and the competition will only continue to improve as teams and players use data to enhance their skills and take advantage of their opponent’s weaknesses. We can all thank technology and big data for making it happen.

In this same sense, to achieve business goals, an organization’s data strategy needs to be founded on the right mix of automation and intelligent analytical input.

The combination of automating data management processes with investments to find data context will help leaders make intelligent decisions that move their organizations forward.


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