Journal Article

Making sense of Big Data – can it transform operations management?

Fouweather, Ian, Vernon, Andy, Gregory, Ian, Matthias, Olga

2017

Overview

Purpose The purpose of this paper is to focus on the application and exploitation of Big Data (BD) to create competitive advantage. It presents a framework of application areas, and how they help the understanding of targeting and scoping specific areas for sustainable improvement. Empirical evidence demonstrates the application of BD in practice and tests the framework. Design/methodology/approach An exploratory approach is adopted to the secondary research which examines vendors’ offerings. The empirical research used the case study method. Findings The findings indicate that there is opportunity to create sustainable competitive advantage through the application of BD. However there are social, technological and human consequences that are only now beginning to emerge which need to be addressed if true long-term advantage is to be achieved. Research limitations/implications The research develops a framework and tests it only in two dimensions. This should be expanded. The vendor analysis limitations lie within the nature of the information available and the difficulties in mitigating against bias. Practical implications The suggested framework can help academics and managers to identify areas of opportunity to do so, setting new levels of performance and new agendas for business. Originality/value This work contributes to service operations management, building on Kranzberg (1986) and the impact of technology and on Fosso Wamba et al. (2015) by developing a systems application framework to further understanding of BD from a practical perspective to extend their research taxonomy insights. The case studies demonstrate how the use of BD enhances operational performance.

• Publisher: Emerald Publishing Limited,Emerald Group Publishing Limited
• Subject: Big Data / Business analytics / Business competition / Competitive advantage / Data analysis / Data management / Data mining / Datasets / Decision making / Empirical analysis / Enterprise resource planning / Information management / Knowledge / Knowledge management / Manufacturing / Operations management / Production management / Social networks / Software / Taxonomy / Test procedures