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Big Data: A Normal Accident Waiting to Happen?

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  • Daniel Nunan

    (University of Reading)

  • Marialaura Di Domenico

    (University of Surrey)

Abstract

Widespread commercial use of the internet has significantly increased the volume and scope of data being collected by organisations. ‘Big data’ has emerged as a term to encapsulate both the technical and commercial aspects of this growing data collection activity. To date, much of the discussion of big data has centred upon its transformational potential for innovation and efficiency, yet there has been less reflection on its wider implications beyond commercial value creation. This paper builds upon normal accident theory (NAT) to analyse the broader ethical implications of big data. It argues that the strategies behind big data require organisational systems that leave them vulnerable to normal accidents, that is to say some form of accident or disaster that is both unanticipated and inevitable. Whilst NAT has previously focused on the consequences of physical accidents, this paper suggests a new form of system accident that we label data accidents. These have distinct, less tangible and more complex characteristics and raise significant questions over the role of individual privacy in a ‘data society’. The paper concludes by considering the ways in which the risks of such data accidents might be managed or mitigated.

Suggested Citation

  • Daniel Nunan & Marialaura Di Domenico, 2017. "Big Data: A Normal Accident Waiting to Happen?," Journal of Business Ethics, Springer, vol. 145(3), pages 481-491, October.
    • Handle: RePEc:kap:jbuset:v:145:y:2017:i:3:d:10.1007_s10551-015-2904-x
    DOI: 10.1007/s10551-015-2904-x
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    References listed on IDEAS

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    Cited by:

    1. Keren Naa Abeka Arthur & Richard Owen, 2019. "A Micro-ethnographic Study of Big Data-Based Innovation in the Financial Services Sector: Governance, Ethics and Organisational Practices," Journal of Business Ethics, Springer, vol. 160(2), pages 363-375, December.
    2. Raturi, Varun & Hong, Jinhyun & McArthur, David Philip & Livingston, Mark, 2021. "The impact of privacy protection measures on the utility of crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 92(C).
    3. Christopher Baird & Thomas S. Calvard, 2019. "Epistemic Vices in Organizations: Knowledge, Truth, and Unethical Conduct," Journal of Business Ethics, Springer, vol. 160(1), pages 263-276, November.
    4. Calic, Goran & Ghasemaghaei, Maryam, 2021. "Big data for social benefits: Innovation as a mediator of the relationship between big data and corporate social performance," Journal of Business Research, Elsevier, vol. 131(C), pages 391-401.
    5. Choi, Hyoung-Yong & Park, Junyoung, 2022. "Do data-driven CSR initiatives improve CSR performance? The importance of big data analytics capability," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    6. Andrea North-Samardzic, 2020. "Biometric Technology and Ethics: Beyond Security Applications," Journal of Business Ethics, Springer, vol. 167(3), pages 433-450, December.
    7. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    8. Chanhee Kwak & Junyeong Lee & Heeseok Lee, 2022. "Could You Ever Forget Me? Why People Want to be Forgotten Online," Journal of Business Ethics, Springer, vol. 179(1), pages 25-42, August.

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