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Awaiting the Second Big Data Revolution: From Digital Noise to Value Creation

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  • Mark Huberty

Abstract

“Big data”—the collection of vast quantities of data about individual behavior via online, mobile, and other data-driven services—has been heralded as the agent of a third industrial revolution—one with raw materials measured in bits, rather than tons of steel or barrels of oil. Yet the industrial revolution transformed not just how firms made things, but the fundamental approach to value creation in industrial economies. To date, big data has not achieved this distinction. Instead, today’s successful big data business models largely use data to scale old modes of value creation, rather than invent new ones altogether. Moreover, today’s big data cannot deliver the promised revolution. In this way, today’s big data landscape resembles the early phases of the first industrial revolution, rather than the culmination of the second a century later. Realizing the second big data revolution will require fundamentally different kinds of data, different innovations, and different business models than those seen to date. That fact has profound consequences for the kinds of investments and innovations firms must seek, and the economic, political, and social consequences that those innovations portend. Copyright The Author(s) 2015

Suggested Citation

  • Mark Huberty, 2015. "Awaiting the Second Big Data Revolution: From Digital Noise to Value Creation," Journal of Industry, Competition and Trade, Springer, vol. 15(1), pages 35-47, March.
    • Handle: RePEc:kap:jincot:v:15:y:2015:i:1:p:35-47
    DOI: 10.1007/s10842-014-0190-4
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    References listed on IDEAS

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    1. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    2. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
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    Cited by:

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    2. Rajesh Chidananda Reddy & Biplab Bhattacharjee & Debasisha Mishra & Anandadeep Mandal, 2022. "A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy," Information Systems and e-Business Management, Springer, vol. 20(1), pages 223-255, March.
    3. Jang, Kyeong Kook & Bae, Joonheui & Kim, Kyung Hoon, 2021. "Servitization experience measurement and the effect of servitization experience on brand resonance and customer retention," Journal of Business Research, Elsevier, vol. 130(C), pages 384-397.
    4. Ingram, Julie & Maye, Damian & Bailye, Clive & Barnes, Andrew & Bear, Christopher & Bell, Matthew & Cutress, David & Davies, Lynfa & de Boon, Auvikki & Dinnie, Liz & Gairdner, Julian & Hafferty, Caitl, 2022. "What are the priority research questions for digital agriculture?," Land Use Policy, Elsevier, vol. 114(C).
    5. Grimaldi, Didier & Fernandez, Vicenc, 2017. "The alignment of University curricula with the building of a Smart City: A case study from Barcelona," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 298-306.
    6. Mattila, Juri & Seppälä, Timo, 2015. "Blockchains as a Path to a Network of Systems - An Emerging New Trend of the Digital Platforms in Industry and Society," ETLA Reports 45, The Research Institute of the Finnish Economy.
    7. Fleming, Aysha & Jakku, Emma & Fielke, Simon & Taylor, Bruce M. & Lacey, Justine & Terhorst, Andrew & Stitzlein, Cara, 2021. "Foresighting Australian digital agricultural futures: Applying responsible innovation thinking to anticipate research and development impact under different scenarios," Agricultural Systems, Elsevier, vol. 190(C).
    8. Marco Bettiol & Mauro Capestro & Eleonora Di Maria & Stefano Micelli, 2020. "At The Roots Of The Fourth Industrial Revolution: How ICT Investments Affect Industry 4.0 Adoption," "Marco Fanno" Working Papers 0253, Dipartimento di Scienze Economiche "Marco Fanno".
    9. Bell, David & Lycett, Mark & Marshan, Alaa & Monaghan, Asmat, 2021. "Exploring future challenges for big data in the humanitarian domain," Journal of Business Research, Elsevier, vol. 131(C), pages 453-468.
    10. Nadežda Jankelová & Zuzana Joniaková, 2021. "The role of innovative work behaviour and knowledge-based dynamic capabilities in increasing the innovative performance of agricultural enterprises," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(9), pages 363-372.

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    More about this item

    Keywords

    Big data; Digitalization; Value creation; Business models; Technological change; C80; L86; O33; M15;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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