Decision support model for big data analytics tools

Abstract

Background: Despite the increasing interest and investment in big data analytics (BDA), many organisations find the implementation and use of the tools challenging. This is attributed to the cumbersome nature of some of the tools.

Objectives: From both business and academic domains, this study sets out to provide a model that enables, supports, and makes the selection and use of BDA tools easier.

Method: The qualitative methods from the perspective of an interpretive approach were employed in the study. The actor-network theory (ANT) was applied as a lens to underpin the phenomenon being studied and gain a deeper understanding of why things happen in the way that they confusedly do, in the selection and subsequent use of BDA tools.

Results: The research revealed that five factors, organisational requirements, top-down versus bottom-up approach, the role of stakeholders, the usefulness of BDA, and organisational structure, primarily influence the selection and use of BDA tools in organisations.

Conclusion: Empirically, the factors bring fresh perspectives to support the decision in appropriately managing BDA deployment for organisational purposes.

Contribution: The main contribution of this study lies in the use of the decisions support model, to practically and theoretically provide a guide for managers in the organisation, in selecting BDA for decision support purposes. From an academic perspective, the study contributes to the advancement in the use of ANT for analysis in information system (IS) research.