Data Science, Decision Theory and Strategic Management: A Tale of Disruption and Some Thoughts About What Could Come Next

Federico Brunetti ()
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Federico Brunetti: University of Verona

A chapter in Strategic Management, Decision Theory, and Decision Science, 2021, pp 271-280 from Springer

Abstract: Abstract This paper intends to assume a comprehensive and far-sighted enough perspective on the pillar themes of the Conference. Data Science (DS), Decision Theory (DT) and Strategic Management (SM) are three relevant and interrelated fields that have undergone substantive changes in recent years. The purpose of the paper is, first, to examine the revolution that occurred in DS, DT and MS and, then, to discuss the leap that happened in the process data are linked to decisions and these, in turn, to strategies. In short, data moved from a situation of scarcity, fragmentation and poor quality to a situation of abundance, structure and extreme richness; Decision Theory moved from Olympic rationality and certainty to bounded rationality, uncertainty and cognitive biases; Strategic Management moved from clear and straightforward course of action to an open, incremental and “many best ways” process. So, once upon a time, (few) data were the input for decisions based on sound criteria and led to a definite route to be followed by the organization. Nowadays, a multitude of data are available to decision-makers who are more or less aware of the flaws affecting their behavior with the whole process ending in simple claims of strategic agility and flexibility. Finally, some reflections are introduced about the consequences of such changes, the challenges digital technology poses to companies and society in the coming years and about a tentative logic to cope with such complexity.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-1368-5_17

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DOI: 10.1007/978-981-16-1368-5_17

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