Abstract:
Decision making requires managers to carefully analyse the business environment and make sense of existing information in a bid to direct and influence particular courses of action for organisations. However, there is complexity of this process in uncertainty, such as that exemplified by the year 2020 due to the effects of the global COVID-19 pandemic. Within this context of uncertainty, and given the proliferation of big data, the role of data-driven decision-making under uncertainty is yet to be established.
This research explored the role of data-driven decision-making under uncertainty, including the preconditions for, enablers and functional benefits thereof. This research was a qualitative study through 10 in-depth interviews with South African senior managers, who made use of data to support their decision-making processes. The understanding of the role of data-driven decision-making was explored using thematic analysis.
The researcher presents an integrated model of the data-driven decision-making process under uncertainty, that can be adopted by organisations and decision-makers faced with uncertainty, in need of improved rationality, enhanced objectivity and more accurate probability modelling under uncertainty. This integrated model outlines key preconditions for data-driven decisioning under uncertainty and the challenges categorised as organisation specific, external to the organisation, inherent to the data and data management practices. The integrated model also outlines key enablers for data-driven decisioning under uncertainty as well as the perceived benefits, pivoting between strategic and application benefits.
