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Measuring benefits from big data analytics projects: an action research study

Author

Listed:
  • Maria Hoffmann Jensen

    (Aarhus University, BTECH)

  • John Stouby Persson

    (Aalborg University)

  • Peter Axel Nielsen

    (Aalborg University)

Abstract

Big data analytics (BDA) projects are expected to provide organizations with several benefits once the project closes. Nevertheless, many BDA projects are unsuccessful as benefits did not materialize as expected. Organization can manage the expected benefits by measuring these, yet very few organizations actually measure on benefits post project development, and little has been written about BDA benefits measurements that extends beyond those typically identified in the project business case. This study examines how we should establish measures for BDA benefits in the context of a large wind turbine manufacturer investing in BDA to improve their practices when defining BDA benefits measures. We present lessons learned from our action research, that were found useful in establishing BDA benefit measurements. There are three lessons on (1) change, (2) specification of who, and (3) explicitness in establishing a useful BDA benefit measure. We contribute to BDA benefits realization in proposing the lessons to establish BDA benefits measurements. Finally, we discuss the lessons and contributions related to research on BDA value creation and benefits management.

Suggested Citation

  • Maria Hoffmann Jensen & John Stouby Persson & Peter Axel Nielsen, 2023. "Measuring benefits from big data analytics projects: an action research study," Information Systems and e-Business Management, Springer, vol. 21(2), pages 323-352, June.
    • Handle: RePEc:spr:infsem:v:21:y:2023:i:2:d:10.1007_s10257-022-00620-0
    DOI: 10.1007/s10257-022-00620-0
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    References listed on IDEAS

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    1. Jonathan J.J.M. Seddon & Wendy L. Currie, 2017. "A model for unpacking big data analytics in high-frequency trading," Post-Print hal-01404316, HAL.
    2. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    3. Kwon, Ohbyung & Lee, Namyeon & Shin, Bongsik, 2014. "Data quality management, data usage experience and acquisition intention of big data analytics," International Journal of Information Management, Elsevier, vol. 34(3), pages 387-394.
    4. Guido Schryen, 2013. "Revisiting IS business value research: what we already know, what we still need to know, and how we can get there," European Journal of Information Systems, Taylor & Francis Journals, vol. 22(2), pages 139-169, March.
    5. Larson, Deanne & Chang, Victor, 2016. "A review and future direction of agile, business intelligence, analytics and data science," International Journal of Information Management, Elsevier, vol. 36(5), pages 700-710.
    6. Côrte-Real, Nadine & Oliveira, Tiago & Ruivo, Pedro, 2017. "Assessing business value of Big Data Analytics in European firms," Journal of Business Research, Elsevier, vol. 70(C), pages 379-390.
    7. Seddon, Jonathan J.J.M. & Currie, Wendy L., 2017. "A model for unpacking big data analytics in high-frequency trading," Journal of Business Research, Elsevier, vol. 70(C), pages 300-307.
    8. Ranjan, Jayanthi & Foropon, Cyril, 2021. "Big Data Analytics in Building the Competitive Intelligence of Organizations," International Journal of Information Management, Elsevier, vol. 56(C).
    9. R Baskerville & A T Wood-Harper, 1998. "Diversity in information systems action research methods," European Journal of Information Systems, Taylor & Francis Journals, vol. 7(2), pages 90-107, June.
    10. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    11. Steven Ji-fan Ren & Samuel Fosso Wamba & Shahriar Akter & Rameshwar Dubey & Stephen J. Childe, 2017. "Modelling quality dynamics, business value and firm performance in a big data analytics environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5011-5026, September.
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