What do baseball, Wall Street and supply chains have in common? A fixation with data analysis, to start.
Once upon a time there used to be data… now it’s all about Big Data. Data collection and its high-tech partner, data analytics, is, according to Wikipedia a “process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.” At the end of the day it is the quality of the data that reigns supremely.
Evidently, data analytics is embraced by an incalculable array of practitioners. But let’s get back to baseball first.
Michael Lewis’s 2003 business best seller, Moneyball: The Art of Winning an Unfair Game, persuasively explains how Oakland Athletics General Manager Billy Beane, using judiciously selected and interpreted statistical data, was able to assemble a very formidable baseball team on a budget far less than most other Major League Baseball teams. In the 2011 film version of the book, Moneyball, starring Brad Pitt as the data hero, (spoiler alert) the A’s don’t win the World Series. However the Boston Red Sox triumph a scant 2 seasons later by implementing the same statistical model established by the Athletics.
Additional variations of moneyball are played on Wall Street. Hungry to identify the trending prices of stocks and commodities, companies leverage the attendance at large business conferences and deploy data hunters to search for contacts that may have data valuable for forecasting the stock prices of other companies.
Hedge funds have gotten into the act, too. According to the Wall Street Journal article, Wall Street’s insatiable lust: Data, data, data, “WorldQuant, a quantitative hedge fund based in Connecticut, has a team that reviews hundreds of data sets a year and works to bring online as many as possible that provide some value. Its staff of scientists and mathematicians then go to work on the data to see if it helps predict revenues at companies or other market phenomena.”
Comparable to the interpreted statistical data used in the baseball scenario, Wall Street relies on correctly interpreted data (quality data) to make decisions on investments and risk management. Incorrect or missing data arose as a key contributor to the mortgage crisis in 2008.
Who is else is using data analytics? Although, a better question might be, “who isn’t?” The accompanying graphic, from McKinsey Consulting, paints a picture of which industries are engaged and the benefits they reap from using analytics associated with Big Data.
Not an industry itself, supply chain management is a key corporate service integral to a diverse range of industries. Not surprisingly SCM relies heavily on data analytics to optimize their supply chain, mitigate risk and perform demand/ supply forecasting.
In our procurement organization, with high volume transactions, we use analytics to consolidate and cleanse our volumes of data. Company spend and supplier data are linked to allow greater insight into rising / falling commodity prices and sourcing options. This heightened visibility allows us to reduce costs, mitigate risks, and tap into new suppliers – for ourselves and our clients.
More information on how we can help can be found here.