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Big Data and Human Resources Management: The Rise of Talent Analytics

Author

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  • Manuela Nocker

    (Essex Business School, University of Essex, Essex SS1 1LW, UK)

  • Vania Sena

    (Essex Business School, University of Essex, Essex SS1 1LW, UK)

Abstract

The purpose of this paper is to discuss the opportunities talent analytics offers HR practitioners. As the availability of methodologies for the analysis of large volumes of data has substantially improved over the last ten years, talent analytics has started to be used by organizations to manage their workforce. This paper discusses the benefits and costs associated with the use of talent analytics within an organization as well as to highlight the differences between talent analytics and other sub-fields of business analytics. It will discuss a number of case studies on how talent analytics can improve organizational decision-making. From the case studies, we will identify key channels through which the adoption of talent analytics can improve the performance of the HR function and eventually of the whole organization. While discussing the opportunities that talent analytics offer organizations, this paper highlights the costs (in terms of data governance and ethics) that the widespread use of talent analytics can generate. Finally, it highlights the importance of trust in supporting the successful implementation of talent analytics projects.

Suggested Citation

  • Manuela Nocker & Vania Sena, 2019. "Big Data and Human Resources Management: The Rise of Talent Analytics," Social Sciences, MDPI, vol. 8(10), pages 1-19, September.
    • Handle: RePEc:gam:jscscx:v:8:y:2019:i:10:p:273-:d:271925
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    References listed on IDEAS

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    1. Alain Verbeke & Wenlong Yuan, 2013. "The Drivers of Multinational Enterprise Subsidiary Entrepreneurship in C hina: A New Resource-Based View Perspective," Journal of Management Studies, Wiley Blackwell, vol. 50(2), pages 236-258, March.
    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. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    4. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    5. Daron Acemoglu & Pascual Restrepo, 2017. "Robots and Jobs: Evidence from US Labor Markets," Boston University - Department of Economics - Working Papers Series dp-297, Boston University - Department of Economics.
    6. Sinan Aral & Erik Brynjolfsson & Lynn Wu, 2012. "Three-Way Complementarities: Performance Pay, Human Resource Analytics, and Information Technology," Management Science, INFORMS, vol. 58(5), pages 913-931, May.
    7. Wendelin Kuepers & Stephan Sonnenburg & Martin Zierlod, 2017. "ReThinking Management: Perspectives and Impacts of Cultural Turns and Beyond," Post-Print hal-02518976, HAL.
    8. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    9. Erik Brynjolfsson & Lorin M. Hitt, 2000. "Beyond Computation: Information Technology, Organizational Transformation and Business Performance," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 23-48, Fall.
    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. Braganza, Ashley & Brooks, Laurence & Nepelski, Daniel & Ali, Maged & Moro, Russ, 2017. "Resource management in big data initiatives: Processes and dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 328-337.
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    Cited by:

    1. Dr. Zahra Ishtiaq Paul & Hafiz Muhammad Sohail Khan, 2024. "Reshaping the future of HR: Human Resource Analytics and Talent Management," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(2), pages 332-340.
    2. Wonhyuk Cho & Seeyoung Choi & Hemin Choi, 2023. "Human Resources Analytics for Public Personnel Management: Concepts, Cases, and Caveats," Administrative Sciences, MDPI, vol. 13(2), pages 1-22, January.
    3. Md. Nazmus Sakib & Shah Ridwan Chowdhury & Mohammad Younus & Nehad Laila Sanju & Farhana Foysal Satata & Mahafuza Islam, 2024. "How HR analytics evolved over time: a bibliometric analysis on Scopus database," Future Business Journal, Springer, vol. 10(1), pages 1-22, December.
    4. Carolyn Axtell & Mark Taylor & Bridgette Wessels, 2019. "Big Data and Employee Wellbeing: Walking the Tightrope between Utopia and Dystopia," Social Sciences, MDPI, vol. 8(12), pages 1-5, November.

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