Tension in the data environment: How organisations can meet the challenge

Meadows, Maureen; Merendino, Alessandro; Dibb, Sally; Garcia-Perez, Alexeis; Hinton, Matthew; Papagiannidis, Savvas; Pappas, Ilias; Wang, Huamao

Abstract

Big Data is becoming ubiquitous - widely applied across organisations, industry sectors and society. However, the opportunities and risks it presents are not yet fully understood. In this paper we identify and explore the tensions that Big Data can create at multiple levels, focusing on the need for organisations to meet the challenges that can arise. We draw on insights from twelve papers published in the Special Issue of Technological Forecasting & Social Change entitled “Tension in the Data Environment: Can Organisations Meet the Challenge?” in order to build a ‘Multi-Layer Tensions Model’ that highlights key pressures and challenges in the BD environment. We find evidence of tensions of three types, which we summarise as “Organisational Learning”, “Organisational Leadership” and “Societal” tensions. We contribute, first, by identifying and developing a nuanced understanding of the tensions faced in the Big Data environment; and second, by elaborating on the capabilities that can be developed and the actions taken to maximise the benefits of Big Data. We end with a “Learning, Leading, Linking” framework, which points to implications for practice and a future research agenda.

Citation

Meadows, M., Merendino, A., Dibb, S., Garcia-Perez, A., Hinton, M., Papagiannidis, S., Pappas, I., & Wang, H. (2022). Tension in the data environment: How organisations can meet the challenge. Technological Forecasting and Social Change, 175, Article 121315. https://doi.org/10.1016/j.techfore.2021.121315

• Journal Article Type: Article
• Acceptance Date: Oct 23, 2021
• Online Publication Date: Oct 30, 2021
• Publication Date: 2022-02
• Deposit Date: Nov 1, 2021
• Publicly Available Date: May 1, 2023
• Journal: Technological Forecasting and Social Change
• Print ISSN: 0040-1625
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 175
• Article Number: 121315
• DOI: https://doi.org/10.1016/j.techfore.2021.121315
• Public URL: https://nottingham-repository.worktribe.com/output/6605541
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0040162521007460