DATA-DRIVEN HR: LEVERAGING ANALYTICS TO IMPROVE EMPLOYEE EXPERIENCE AND PERFORMANCE

Authors

Yossy Oktalina , Ramon Zamora , Dhenny Asmarazisa

DOI:

10.54443/morfai.v6i1.4752

Published:

2025-12-19

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Abstract

This article examines the transformative role of data analytics in modern human resource management, known as Data-Driven HR. The background outlines the shift from traditional, intuition-based HR practices to a strategic, evidence-based approach necessitated by the digital economy. The primary objective is to demonstrate how organizations can leverage HR analytics to systematically enhance both employee experience and organizational performance. The methodology involves a comprehensive review of existing literature and analysis of case studies from organizations that have implemented HR analytics initiatives. The results indicate that systematic data collection and analysis in areas such as recruitment, engagement, and talent development lead to more informed decision-making, predictive insights, and personalized employee interventions. The discussion highlights that successful implementation requires robust data infrastructure, analytical competency, and a strong ethical framework to address privacy concerns. In conclusion, Data-Driven HR represents a fundamental evolution in the function, enabling a more proactive, employee-centric, and performance-oriented management paradigm. For optimal impact, HR leaders must cultivate data literacy and foster a culture of evidence-based practice while safeguarding employee trust.

Keywords:

Data-Driven HR, HR Analytics, Employee Experience, Performance Management

References

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Author Biographies

Yossy Oktalina, Universitas Riau Kepulauan, Indonesia

Ramon Zamora, Universitas Riau Kepulauan, Indonesia

Dhenny Asmarazisa, Universitas Riau Kepulauan, Indonesia

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How to Cite

Yossy Oktalina, Ramon Zamora, & Dhenny Asmarazisa. (2025). DATA-DRIVEN HR: LEVERAGING ANALYTICS TO IMPROVE EMPLOYEE EXPERIENCE AND PERFORMANCE. Multidiciplinary Output Research For Actual and International Issue (MORFAI), 6(1), 552–560. https://doi.org/10.54443/morfai.v6i1.4752

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