| | DECEMBER 20238CIOReviewIN MY OPINIONBy Roman Remora, Executive Director, Data Science - Applied Economic Research for Operations, ChewyOver the past five years, I have been asked a lot about unlocking the value of data science and big data programs for a variety of applications in finance and supply chains. This question became even more important as economic growth slowed down (with COVID in the middle as an unpredictable, idiosyncratic event).For companies wishing to make the most of their data science programs, there are two critical aspects you need to keep in mind related to your data science programs: (1) The functional component divided between operational and strategic activities (2) The technical component is divided between infrastructure and science.Operational vs. Strategic' Where and when should I invest? Functional areas for data science programs are often categorized into pure operational activities or strategic activities. Though they are not necessarily mutually exclusive, they often confront each other when it comes time to assign and fund initiatives for the year to come. Data science activities revolving around operations are necessary to conduct day-to-day business. They involve building automated decision systems along with the proper calibration of inputs for these systems. In supply chains, for instance, such systems can be forecasting systems relying on machine learning algorithms or Inventory management systems relying on operations research techniques. Data science activities revolving around strategy are used either to identify new opportunities from the data or potential risks that need to be mitigated. A few examples of applications include pricing strategy, long-term scenario planning, or product lifecycle strategy.When deciding upon funding your data science programs, look where you are in the economic cycle. What I define as an economic cycle is not solely based on real GDP growth but the state of consumer spending, labor market, inflation, and how easy it is to access money through debt/credit for businesses to fund innovation and for households to fund consumption. When at the trough of the economic cycle, leadership traditionally emphasizes operational considerations at the expense of strategic considerations. This necessity is mainly driven by the maximization of shareholders' short-term value `one year ahead.' I want to argue that this strategy will yield suboptimal results in CHASING GROWTH UNDER ECONOMIC UNCERTAINTY WITH DATA SCIENCE AS A DRIVERKeep in mind that your ROI profile on infrastructure and science capabilities will vary as a function of time/sophistication and how mature your organization is
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