Abstract
AbstractAlthough scholars in management recognize the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques can be harnessed to provide the basis for guiding scholars in studying contemporary management topics and global grand challenges raised by the COVID‐19 pandemic. In this Methodology Corner, we present a review of the methodological innovations in studying big data analytics and how they can be better utilized to examine contemporary organizational issues. We provide insights on methods in descriptive/diagnostic, predictive and prescriptive analytics, and how they can be leveraged to study ‘black swan’ events such as the COVID‐19‐related global crisis and its aftermath's implications for managers and policymakers.
Highlights
The spread of the COVID-19 global pandemic has generated an exponentially mounting and extraordinary volume of data that can be harnessed to improve our understanding of big data management research as well as exemplifying the necessity among scholars, practitioners and policymakers for a better and deeper understanding of a range of analytical tools that could be utilized to better anticipate and respond to such unforeseen ‘black swan’ events and risks
This Methodology Corner piece sought to present a review of the methodological innovations in studying big data analytics and how they can be utilized to examine contemporary organizational and management issues arising from the global pandemic caused by COVID-19, as well as other grand challenges of modern times
Given that COVID-19 is a ‘black swan’ event in that it is difficult to predict and assess its full effects (Yarovaya, Matkovskyy and Jalan, 2020), we contend that data analytics offers an effective pathway to better make sense of the event and how organizations can strategize and respond in this new era through the use of various big data analytic methods
Summary
The spread of the COVID-19 global pandemic has generated an exponentially mounting and extraordinary volume of data that can be harnessed to improve our understanding of big data management research as well as exemplifying the necessity among scholars, practitioners and policymakers for a better and deeper understanding of a range of analytical tools that could be utilized to better anticipate and respond to such unforeseen ‘black swan’ events and risks (see Ienca and Vayena, 2020; Wang, Ng and Brook, 2020; World Health Organisation, 2020a). In the face of COVID-19, which has led to more than 26 million cases and over 800,000 fatalities, impacting over 200 nations at the time of writing (Worldometers, 2020), many governments have been forced to forge a closer relationship with science and lean towards datadriven decisions for effectively responding to the unprecedented challenges caused by COVID-19. Henke, Puri and Saleh (2020), in a McKinsey’s report, suggest that such data analytics capabilities could offer between $9.5 trillion and $15.4 trillion in annual economic value to organizations. These trends show that big data analytics potentially offers important insights to managers and policymakers alike. From the 1800s to modern times, the time for new technologies to diffuse has shrunk from around 100 years to within a decade for multiple technologies (Comin and Hobijn, 2004; World Bank, 2008), thereby ushering in a new environment where access to technologies, information and data has become increasingly common across the globe
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