Big data analytics in turbulent contexts: towards organizational change for enhanced agility

Abstract

This conceptual paper addresses the capabilities offered by big data analytics (BDA), critical in turbulent environments, in the context of the Industry 4.0 revolution. BDA enhances organizational agility, as it is highly valuable for the identification of opportunities and threats (i.e. the sensing process of agility); however, with the exception of aiding decision-making tools, seizing its benefits for reacting (i.e. responding) is more difficult without organizational adaptation. Hence, the changes required to exploit the full capabilities offered by BDA, particularly at the organizational level, are considerable. From a theoretical perspective, through a narrative literature review, this paper highlights several possibilities of organizational changes for enhancing a company’s agility and leveraging BDA to achieve enhanced performance in turbulent contexts. The findings are then related to three organizational theories. This work ends in a conceptual model and several research paths for future studies. From a managerial perspective, this paper highlights, through several examples, the importance of top management’s role in achieving the necessary organizational changes to enhance the companies’ responding capabilities.

Keywords:

• Big data analytics
• agility
• Industry 4.0
• organization
• turbulence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Industry 4.0 combines intelligent sensors and data analytics aiming to optimise manufacturing in real-time (Xu, Xu, and Li 2018).

2 Only journals ranked 2 and better in the 2018 AJG/ABS ranking were included.

3 Internet of things, i.e. connected objects and sensors.

4 The recent concept of ‘edge analytics’ or ‘edge computing’ qualifies automated analytical computations that are conducted on data from sensors or other devices, without waiting for the data to be sent back to a centralized data store; it helps firms make real-time decisions close to the data (Satyanarayanan et al. 2015; Beavers 2017).

5 Chief Information, Executive and Marketing Officers.

6 N + 1 = direct line manager; N + 2 = N + 1’s manager, etc.

Additional information

Funding

The present work was financially supported by Université de Montpellier and Labex Entreprendre 10.13039/501100004100 [ANR-10-Labex-11-01].

Notes on contributors

Yves Barlette

Yves Barlette is Full Professor of Information Systems at Montpellier Business School, France. He is member of the MRM research centre and Entrepreneurship & Innovation Chair, part of LabEx Entrepreneurship. His current research interests include behavioural issues related to information security, and digital transformation of organizations. He has published papers in Systèmes d’Information et Management, International Journal of Information Management, Journal of Global Information Management, Journal of Organizational Change Management. He also authored thirteen books and book chapters. Yves Barlette is the corresponding author and can be contacted at: [email protected]

Paméla Baillette

Paméla Baillette is Associate Professor in Management Science-Information Systems at the University of Bordeaux, France. She is member of the research centre IRGO-Bordeaux. Her current research interests include behavioural issues related to information security, technological and management innovation, and traceability related to agribusiness management. She has published research papers in International Journal of Information Management, Systèmes d’Information et Management, Revue Internationale P.M.E., Journal of Organizational Change Management, and International Small Business Journal.