Leveraging the Power of Data: How can Businesses Enhance their Understanding of the Workforce
Open accessAbstract
Data analytics is being used in businesses to increase efficiency and improve the accuracy of decision-making.The ability of an organization to adapt to the ever-changing landscape of Human Resource (HR) analytics is critical and significant to the long-term success of the organization.HR analytics provides practitioners in the HR field with evidence-based research that assists them in making decisions, with the end goal of expanding the major influence that HR has on the performance of the organization.As a direct consequence of this, HR analytics is no longer considered only an auxiliary role but rather a fundamental component of the organization's operations.However, data quality and integration, privacy and ethical concerns, limited skills and resources, and change management present obstacles.To address these issues, this article provides a variety of analytical perspectives.Improving data integrity and integration requires the implementation of data governance frameworks and the use of innovative technologies.Improving the skills of HR professionals and fostering collaboration between the HR and IT departments are required to develop HR data analytics capabilities.In addition, nurturing a data-driven culture requires promoting data literacy and establishing explicit channels of communication.As to address the concerns regarding privacy and ethics, robust data protection measures and transparent policies are required.Thus, by critically evaluating various approaches and provide analytical perspectives, organisations could navigate the challenges and realise the benefits of HR data analytics.
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The monetary value of HR analytics depends on how analytics feeds into the wider strategic process, not just access to data and tools.
“Some authors argue that the monetary value of HR analytics is more than just about access to data and tools for professionals but how analytics feeds into this wider strategic process (Saleh et al., 2023).”
From HR Analytics and Financial Performance: A Quantitative Study of Capability, Integration, and Data-Driven Decision-Making (2026) · cites this paper as Saleh et al., 2023
The introduction says HR analytics can help align HR strategies with business objectives, and 'The importance of HR Data Analysis' links that alignment to improved business outcomes. The conclusion also says its benefits require more than analytical tools, including skills, a data-driven culture, and integration into HR practices. The paper does not specifically discuss monetary value or establish that monetary value depends on this strategic integration. (LLM, conf=0.84)
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- Bosua, R., Venkitachalam, K., & Tejay, G. P. (2016). What's up with big data? Examining the drivers and consequences of big data adoption in organizations. In International Conference on HCI in Business (pp. 25-39). Springer.
- Chen, H., Chiang, R. H., & Storey, V. C. (2019). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 43(4), 1187-1213.
- Chuang, C. C., Yang, H. L., Lin, Y. H., & Lai, H. Y. (2021). Skill requirements of HR analytics: An exploratory study from the perspective of HR professionals. International Journal of Information Management, 56, 102268.
- Davenport, T. H. (2013). Analytics 3.0. Harvard Business Review, 91(12), 64-72.
- Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266.
- Kaplan, A. M., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
- Kavanagh, M. J., Gueutal, H. G., Tannenbaum, S. I., & Sargent, L. D. (2019). Talent assessment in the big data era: New tools, old issues. Annual Review of Organizational Psychology and Organizational Behaviour, 6, 157-183.
- Kiron, D., Prentice, P. K., & Ferguson, R. B. (2017). The analytics mandate. MIT Sloan Management Review, 58(4), 1-15.
- Petersen, K., & Feldt, R. (2021). Applying natural language processing techniques to analyze qualitative data in software engineering research. Empirical Software Engineering, 26(2), 1-34.
- Ragin, D. F., Fleenor, J. W., & Kudisch, J. D. (2019). Text mining in organizational research: Guidelines and new opportunities. Organizational Research Methods, 22(1), 3-22.
Cited by
Sorted by citations
Hossain, M. Z., Urme, U. N., & Akash, M. A. U. H. (2026) · European Journal of Management, Economics and Business · doi:10.59324/ejmeb.2026.3(2).06
- Authors
- M Saleh, E., Mahadi, N., & Maya Salleh, N.
- Affiliations
- University of Technology Malaysia (MY)
- ORCID
- Mahadi, Nomahaza
- Venue
- International Journal of Academic Research in Business and Social Sciences
- Published
- 2023-10-12
- Volume
- 13
- Issue
- 10
- ISSN
- 2222-6990 (ISSN-L)
- OpenAlex
- W4388835927
- Cited by
- 1
- References
- 0
- Citations by year
- 2025: 1
- FWCI
- 0.29
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Academic Research in Business and Social Sciences (publishedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
- Keywords
- HR analytics, workforce analytics, data governance, data quality, data integration, data literacy, data-driven culture, data privacy, data ethics, change management, evidence-based decision making
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