MIT Sloan Management Review Article on The Recession's Impact on Analytics and Data Science

The outbreak of the COVID-19 pandemic is having a dramatic negative impact on economies in the U.S. and worldwide, and unemployment rates are soaring. Given the economic disruptions, it seems likely that many countries in the global economy will experience a recession.

Organizations are beginning to grapple with how the economic slowdown will influence investments they are making across the board. One question we wonder about is whether the demand for analytics and data science resources will remain heavy or slow down. You don’t have to look far to find evidence that the focus in this area has been strong: A 2017 report by IBM, for instance, predicted that the number of analytics and data science positions in the U.S. alone would increase by 364,000, to 2,729,000 by 2020. In 2019, LinkedIn ranked “data scientist” the No. 1 most promising job in the U.S. based on job openings, salary, and career advancement opportunities and reported a 56% rise in job openings for data scientists over the previous year. The exponential growth of data — and industry’s desire to use that data for better business outcomes — has been widely cited as a reason for the increasing demand for analytical talent.

Will the current recession slow the growth in demand for analytics and data science? Will changes in organizational goals and focus make job losses in these fields likely? Any lessening of general demand would be good news for aggressive AI adopters and AI-focused vendors, many of which are already hiring laid-off data scientists and engineers. But for the average company, lower demand for data scientists will be a signal that less data science is going on within their organizations, meaning there will be a continued reliance on intuition and other less-powerful guides to decision-making and action.