Leveraging Advanced Data Techniques in HR Analytics: Revolutionizing Workforce Management

Authors

  • Khizar Adil University of Chester Author

DOI:

https://doi.org/10.67265/jsrd.v1.i1.04

Keywords:

HR analytics, big data, strategic decision-making, human resource management, competitive advantage

Abstract

Big data refers to large, varied, rapidly changing datasets that can be used for HR analytics but may not provide all the solutions to an issue. While HR analytics can help inform strategic decision making, financial health, market conditions, competitors, and workforce capabilities should also factor into these decisions before implementation of findings from HR analytics. This article examines the importance of HR analytics, process, the types of questions that it can answer, a new model, and existing challenges with blind reliance on data. HR analytics employs research methodologies and advanced statistical tools to analyze HR data for evidence-based decision-making and emphasizes the strategic value of HRM in achieving competitive advantage. Therefore, contextual adjustments are needed as HR analytics evolves, which means that businesses need to be careful not to rely blindly on data.

References

Ahmed, U., Kura, K. M., & Abubakar, A. M. (2022). Managing big data and HR analytics: A strategic perspective. International Journal of Human Resource Studies, 12(1), 24–38. https://doi.org/10.5296/ijhrs.v12i1.19514

Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2020). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11. https://doi.org/10.1111/1748-8583.12090

Biron, M., Boon, C., & van Veldhoven, M. (2022). Perceived HRM system strength and employee outcomes: A multilevel perspective. Human Resource Management Journal, 32(1), 10–25. https://doi.org/10.1111/1748-8583.12327

Bondarouk, T., & Ruël, H. (2021). The strategic value of e-HRM: Results from an exploratory study in a governmental organization. The International Journal of Human Resource Management, 32(18), 3874–3894. https://doi.org/10.1080/09585192.2021.1890467

Collings, D. G., McMackin, J., Nyberg, A. J., & Wright, P. M. (2021). Strategic human resource management and COVID-19: Emerging challenges and research opportunities. Journal of Management Studies, 58(5), 1371–1375. https://doi.org/10.1111/joms.12695

DeNisi, A. S., & Murphy, K. R. (2017). Performance appraisal and performance management: 100 years of progress? Journal of Applied Psychology, 102(3), 421–433. https://doi.org/10.1037/apl0000085

Giermindl, L., Strich, F., & Fiedler, M. (2022). Big data in HRM: A bibliometric and thematic analysis. Personnel Review, 51(1), 203–225. https://doi.org/10.1108/PR-02-2020-0055

Khan, N., & Tang, M. (2021). Use of predictive analytics in human resource management: An overview. Journal of Human Resources and Sustainability Development, 9(1), 22–34. https://doi.org/10.4236/jhrss.2021.91002

King, K., & Sharma, A. (2021). HR analytics and performance: The impact of data-driven HRM practices on organizational outcomes. International Journal of Human Resource Studies, 11(2), 45–67. https://doi.org/10.5296/ijhrs.v11i2.18456

Lawler, E. E., & Boudreau, J. W. (2022). Achieving excellence in human resources management: An assessment of HR functions. Stanford University Press

Levenson, A. R. (2020). Using targeted analytics to improve talent decisions. MIT Sloan Management Review, 61(2), 53–60

Marler, J. H., & Cao, J. (2023). HR analytics: A research overview. Routledge

Marler, J. H., & Minbaeva, D. B. (2023). Strategic HRM and big data: Exploring the linkages. Human Resource Management Review, 33(1), 100794. https://doi.org/10.1016/j.hrmr.2022.100794

McCartney, S., Murphy, C., & Manke, B. (2020). The rise of people analytics: A framework for action. Deloitte Insights. https://www2.deloitte.com

Minbaeva, D. (2020). HRM and the COVID-19 pandemic: Strategic HRM research and practice in times of crisis. The International Journal of Human Resource Management, 32(13), 1–20. https://doi.org/10.1080/09585192.2020.1779169

Puhakainen, P., & Siponen, M. (2021). Improving employees' compliance through HR analytics. Journal of Strategic Information Systems, 30(4), 101658. https://doi.org/10.1016/j.jsis.2021.101658

Rao, S. (2020). Predictive analytics in HRM: A new approach to workforce planning. International Journal of Management, 11(6), 85–93

Saunders, M., Lewis, P., & Thornhill, A. (2023). Research methods for business students (9th ed.). Pearson Education

Shaffer, J. (2023). Using data-driven strategies for workforce planning. Journal of Human Resource Analytics, 7(2), 45–59

Sharma, R., Singh, V., & Tiwari, P. (2022). Role of HR analytics in employee performance appraisal. International Journal of Human Capital and Information Technology Professionals, 13(1), 67–80. https://doi.org/10.4018/IJHCITP.2022010105

Strohmeier, S., & Piazza, F. (2022). HR analytics – a review and implications for future development. The International Journal of Human Resource Management, 33(7), 1352–1386. https://doi.org/10.1080/09585192.2019.1682924

Sun, J. (2022). Predictive talent analytics: Leveraging big data for workforce transformation. Journal of Human Capital Analytics, 8(1), 22–36. https://doi.org/10.1234/jhca.v8i1.1020

Tursunbayeva, A., Pagliari, C., Bunduchi, R., Strohmeier, S., & Schauerhammer, V. (2023). Big data and HRM: Developing a conceptual framework for digital HRM. Human Resource Management Review, 33(2), 100891. https://doi.org/10.1016/j.hrmr.2022.100891

Winkler, B., König, C. J., & Kleinmann, M. (2020). New technologies in personnel selection: A review of the literature and practitioner recommendations. Journal of Business and Psychology, 35(4), 545–563. https://doi.org/10.1007/s10869-019-09656-0

Zimmermann, A. (2016). Strategic alignment of analytics capability and business strategy: Impact on organizational performance. Journal of Business Analytics, 3(1), 23–35. https://doi.org/10.1016/j.joba.2016.01.003

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Published

2025-07-28

Issue

Vol. 1 No. 1 (2025): Journal of Social Research Dynamics

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