Geared at advancing prescriptive analytics, a new research programme by NUS’ Institute of Operations Research and Analytics (IORA) might make it possible to pinpoint ‘perfect’ solutions to everyday problems.

Prescribed by Math

A term first coined by IBM in 2010, prescriptive analytics is a hot topic today. Considered the final stage in the analytics evolutionary path, it aims to optimise decision-making by analysing past data and predicting unknowns to determine the best course of action forward. A research programme spearheaded by the Institute of Operations Research and Analytics at NUS seeks to advance this process further. “Intelligence consists not only in knowledge, but also in the ability to apply knowledge in practice.” The Greek philosopher Aristotle might have lived 2,000 years before the age of big data, but his words ring especially true today. Data-mining technology has placed a wealth of information at the fingertips of many – from corporations and individuals to policymakers and business leaders. But what does one do with all that knowledge? Enter prescriptive analytics. To trace its roots, one might have to go back to 1950, when the ANIAC computer generated the first weather forecast models. By the 1980s, Decision Support Systems that gather and analyse data were already being applied to operations, financial management and strategic decision-making at multiple levels. Fast forward to 2020, and analytics has evolved beyond the descriptive – seen as the first step, which focuses on using historical data to provide a context for understanding information and numbers – and predictive, which uses current and past data to project possible outcomes of the unknown. “Prescriptive analytics goes a step beyond,” says Professor Teo Chung Piaw (Science ’90), Director of IORA, and the lead principal investigator (PI) for the new project. “While predictive analytics tells you if it might rain tomorrow, prescriptive analytics tells you to bring an umbrella.”

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