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The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

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  • Perrons, Robert K.
  • McAuley, Derek

Abstract

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

Suggested Citation

  • Perrons, Robert K. & McAuley, Derek, 2015. "The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector," Resources Policy, Elsevier, vol. 46(P2), pages 234-238.
    • Handle: RePEc:eee:jrpoli:v:46:y:2015:i:p2:p:234-238
    DOI: 10.1016/j.resourpol.2015.10.007
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    References listed on IDEAS

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    Cited by:

    1. Maroufkhani, Parisa & Desouza, Kevin C. & Perrons, Robert K. & Iranmanesh, Mohammad, 2022. "Digital transformation in the resource and energy sectors: A systematic review," Resources Policy, Elsevier, vol. 76(C).
    2. Karakaya, Emrah & Nuur, Cali, 2018. "Social sciences and the mining sector: Some insights into recent research trends," Resources Policy, Elsevier, vol. 58(C), pages 257-267.
    3. Gerassis, S. & Albuquerque, M.T.D. & García, J.F. & Boente, C. & Giráldez, E. & Taboada, J. & Martín, J.E., 2019. "Understanding complex blasting operations: A structural equation model combining Bayesian networks and latent class clustering," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 195-204.
    4. Korayim, Diana & Chotia, Varun & Jain, Girish & Hassan, Sharfa & Paolone, Francesco, 2024. "How big data analytics can create competitive advantage in high-stake decision forecasting? The mediating role of organizational innovation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

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