DATA-DRIVEN ANALYTICS IN THE DECISION-MAKING PROCESS: DO WE STILL NEED INTUITION?

Abstract

When people discuss how machines will replace humans in the future, it is no longer a new phenomenon. Numerous studies, both old and new, have shown that machines are far more reliable than humans and that machines outperform humans in every way possible. However, these facts do not correspond to reality, in which leaders are forced to think beyond what they can see during the business decision-making process. The future necessitates ingenuity and, at times, absurd decisions. What we see in the markets these days is stuff we cannot even imagine. Machines are powerless because they can only predict when data sources are available, and the future requires more than data. Future decision-making, on the other hand, necessitates intuition. This study emphasizes the importance of incorporating intuition capabilities into the business decision-making process and a specific context in which intuition works best. This study uses a qualitative method by choosing five prominent and successful Indonesian business leaders as key information sources from 15 companies interviewed. The findings indicate that both analytics and intuition are essential in business decision-making. To use them effectively, we must understand when to use them separately and when to combine them. This research proves that in the recent big data world where data are considered the new gold mining, data-driven analytics alone cannot provide sufficient sources for the business decision-making process unless intuition is added. Therefore, business leaders should be equipped with intuitive skills to supplement their analytic skills when they do not have supporting data. This study has several limitations. First, this research is conducted in the context of a business process, which may yield different results in the context of a service, which necessitates a more social approach. Second, there is no precise proportion for defining a successful combination of intuition and analytics.