A Vós a Razão
Opportunity in analytics
Por Luís Guimarães*
I am a regular reader of the INFORMS (Institute for Operations Research and Management Science) magazine called Analytics. I have to admit that for long, besides my interest in this topic, I had a hidden objective… understanding if analytics was really a new thing or just a sexed up version of what we used to call Operations Research (OR). Recently, I have found a very interesting study tackling this question [1] (I am sorry for the reference, it seems that I am no longer able to write something without citing a paper). The study was based on a survey done to the members of the INFORMS where they were asked to compare OR and analytics. Thirty percent of the respondents stated, “O.R. is a subset of analytics,” 29 percent stated, “analytics is a subset of OR,” and 28 percent stated, “advanced analytics is the intersection of OR and analytics.” The remaining 13 percent were split between “analytics and OR are separate fields” (7 percent) and “analytics is the same as OR” (6 percent). I was not alone after all.
Probably as an answer to this confusion, INFORMS has developed working definitions for both OR and analytics. OR is defined as the “application of advanced analytical methods to help make better decisions”, while analytics is characterized by the “scientific process of transforming data into insight for better decision-making.” Thus, OR tends to focus on the solution of a specific problem using a defined set of methods and techniques, whereas analytics tends to go beyond solving a single problem and focuses on the overall business impact. Then, analytics is a broader area than OR and on continuing expansion. The emergence of data science only adds to this statement. The Harvard Business Review issue of October 2014 has called data scientist “the sexiest job of the 21st century,” and McKinsey & Company predicted a 50 to 60 percent shortfall in analytic scientists in the United States by 2018. Analytics clearly have a huge potential.
However, analytics are not all the same. Different types exist with distinct objectives and are employed in different settings. Descriptive analytics focus on gaining insight from historical data with reporting, scorecards, clustering, etc. Essentially, they seek to turn data into information. Predictive analytics are based on statistical and machine learning techniques to obtain better predictions. Finally, prescriptive analytics recommend decisions using optimization, simulation; in other words, they try to support decision making. Nevertheless, most real-world problems usually require more than one type of analytics expertise. Many of INESC TEC units have different competences linked with these requirements, and as such we have the opportunity to create a framework to enhance fruitful collaborations in analytics.
*Colaborador do Centro de Engenharia e Gestão Industrial (CEGI)
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[1]. Matthew Liberatore, Wenhong Luo, “INFORMS and the Analytics Movement: The View of the Membership,” Interfaces, Vol. 41, No. 6, November-December 2011, pp. 578–589.
