Judgment and expertise may be used to adapt models and data from other domains to the current situation. So how can data scientists and leaders make good decisions during this crisis? Clearly, one cannot directly use the same data or model from 2008 and apply it to our current financial predicament, but at the same time there is a lot we can learn from the past. Yes, in terms of business and economics, the question arises how much can we learn from previous financial crises to predict the financial implication of the current pandemic and our ability to recover from it. Are the comparative data similarly challenging in the field of business? This is where we need to pour in some researcher/human judgment and domain acumen.
#SOUND DECISIONS HOW TO#
We cannot expect a model to directly tell us how to adapt previous data to the current situation. These challenges of using the limited data we have doesn’t mean that we should throw the baby out with the bathwater and ignore these possibly useful data altogether. Similarly, given the limited information we have on the COVID-19 virus, it is difficult to assess how similar or different this virus is to previous epidemics. Differences such as population-age distribution can be fairly easily handled in a model, but adjusting for aspects such as political regimes and privacy concerns is much more difficult. Using data from other countries is difficult because countries differ with respect to their political regimes, population-age distribution, health care systems, etc. Obviously, none of these related data sources can be directly applied to the current U.S. Similarly, we use data from related epidemics such as SARS, MERS, and even from the Spanish Flu of 1918. When evaluating the expected pattern of COVID-19 diffusion in the U.S., we often use data from other countries that may be at more advanced stages of the pandemic diffusion (e.g., China, South Korea, or Italy). What about using comparative coronavirus data from other countries or previous epidemics? In a course I teach at Columbia Business School with colleagues Christopher Frank and Paul Magnone, we talk often about quantitative intuition-a combination of data science coupled with a leader’s sound judgment and acumen. Hence, we need to combine the limited data we observe, which are often far from perfect, with a good amount of intuition and domain specific acumen.
In such situations, we have very limited historical or benchmark data to base our decisions on. Unprecedented realities, such as the one we are facing now with the COVID-19 pandemic, provide a challenge to this traditional practice of data science.
How has the coronavirus upended that conventional thinking? We have been encouraging leaders to use rich historical or comparable data to estimate a sound model and identify repeated patterns, and then apply these techniques to guide their decision making. In the past few years, we have been promoting the notion of data-driven decisions and encouraging decision makers to use the wealth of data typically available to them to make better and more informed decisions. How can leaders, regulators, and businesses make informed decisions with scant data on COVID-19?įor those of us with an expertise in data science, the COVID-19 pandemic has been a humbling experience. Here, he discusses how leaders from all fields can make sound decisions with scarce data to guide them. Oded Netzer is a Columbia Business School professor and Data Science Institute affiliate who builds statistical and econometric models to measure consumer behavior that help business leaders make data-driven decisions. Coronavirus presents an unprecedented predicament: Everyday, leaders must make momentous decisions with life or death consequences for many-but there is a dearth of data.
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