Using Specialization to Analyze and Apply Data

Unimaginable amounts of data are collected about just about everything (ex. Patchoplis has had 175 views since the last post); however, in many cases there are not established controls to filter, analyze, and apply results in a meaningful and reliable manner. As an avid fantasy baseball player I’m accustomed to poring through data to justify any decision I want to make. ESPN’s Matthew Berry annually makes this point in his book Fantasy Life (Berry, 2014). Simply having data and knowing how to manipulate it is useful for theoretical conversations and charting a path, but in execution you must also have have a certain touch – a specialization – to know how to correctly apply the data.

In baseball Alfredo Simon, starting pitcher for the Detroit Tigers, is a prime example of a specialist using highly developed sense of touch to master his craft. Prior to a start, a data manager certainly shares with him a method to attack each a hitter; however, on the mound he isn’t using that data to make pitch selections. He pitches by feel, judging when to pitch to his own personal strength or against his opponent’s weakness (Tiger’s Broadcast, 4/25/2015). Being a specialist in his trade, he knows something the data can not; he knows when he is at his best and when he is slightly off. While on the mound he assesses when present conditions and his performance that day will allow him to break off his best slider and when he is throwing a lesser version. He is a specialist at what he does and uses that unfair advantage to win his battles on the mound.

Everyone develops an unfair advantage, a specialty they can have that others do not (Peter Thiel, Zero to One). Data and a data driven machine will never possess the ability to apply this specialization because it can not adjust on the fly by feel and experience. Watson, IBM’s world famous computer, recently created the cookbook Cognitive Cooking with Chef Watson (IBM and Institute of Culinary Education, 2015). As Mark Wilson discovered, the recipe’s produced by Watson lack a palette pleasing touch (Wilson, Fast Company, April 20, 2015). While the dishes the recipes contributed to were not completely inedible and had a logic, they created a dish that was not enjoyable. Watson understood how to take the parts of the recipe and explain how to prepare and assemble into a dish, but not the flair that would convert them to an enjoyable meal.

Continuing to use food as an example, how many of us can truly say we have that touch? Most have no real training in cooking and less background in nutrition. While 3D printers, refined computer algorithms, and local high quality ingredients can improve our creation of a dish, we do not posses the specialization to make an outstanding and nutritional meal, much less do it with a pleasing daily variety. Add in the factor of where we consume these less than satisfying meals. At the end of a rough day a less than satisfying meal alone in your kitchen is not the best method to replenish and prepare for a fresh tomorrow. Instead look to professionals to prepare meals; those with training in food preparation and nutrition and meet the Malcolm Gladwell 10,000 hours of rule of enough practice to have honed their craft and are now specialists (Gladwell, Outliers, 2008).

Patchopolis envisions a modernized model of community meals outlined in Thomas More’s Utopia.
“…for though any that will may eat at home, yet none does it willingly, since it is both ridiculous and foolish for any to give themselves the trouble to make ready an ill dinner at home when there is a much more plentiful one made ready for him so near hand” (53). Patchopolis envisions a format where individuals order through an app that feeds information to a chef — a specialist in culinary and nutritional arts – who, based on data from the app, knows your personal dietary allergies, general food preferences, and daily consumption in combination with their refined touch to prepare a meal designed for the individual.

Patchopolis envisions a society where specialists use their unfair advantage and data provided to strengthen and contribute to the betterment of the community. Rather than expecting each individual to adequately perform the same tasks, the individual is accountable to appropriately utilize data in combination with their practiced skill and truly shine in service. The community relies on each individual to utilize their unfair advantage as the individual’s right of membership.