Literature review: the use of data analytics to improve employee work
Keywords:
Data Analitycs, Employee Performance, Human Resource Analytics, Information Systems, IT IndustryAbstract
The rapid development of digital transformation has significantly influenced how organizations manage and utilize workforce data in modern industries. In particular, organizations in the Information Technology (IT) sector increasingly rely on data-driven strategies to enhance decision-making processes and improve employee performance. The growing availability of digital data generated through organizational information systems provides new opportunities for companies to analyze workforce behavior, productivity patterns, and operational efficiency. As a result, many organizations are beginning to adopt analytical approaches to support more objective and evidence-based human resource management practices. This study provides a focused literature review on the use of Data Analytics to improves employee performance through Information Systems practices in the IT industry. The methodology adopts a systematic literature review across major databases (IEEE Xplore, ACM DL, Google Scholar) with inclusion criteria 2015-2024. Findings indicate that descriptive to prescriptive analytics capabilities in integrated HR data (HRIS/HR analitycs) contribute to productivity, retention, and organizational learning. The contribution is a concise conceptual framework for data driven HR management and identification of research gaps in IT. The results highlight the growing importance of analytics-driven HR management in improving organizational effectiveness and workforce development.
References
[1] C. Nastase, A. Adomnitei, and A. Apetri, “Strategic Human Resource Management in the Digital Era : Technology , Transformation , and Sustainable Advantage,” Merits, vol. 5, no. 23, pp. 1–19, 2025. doi: https://doi.org/10.3390/merits5040023.
[2] N. D. Sharma, A. Verma, and S. Suresh, “Data-Driven Insights : HR Analytics for Tracking,” Int. J. Res., vol. 13, no. IV, pp. 5776–5782, 2025, doi: https://doi.org/10.22214/ijraset.2025.69743. doi: https://doi.org/10.22214/ijraset.2025.69743.
[3] S. Donthu, B. Acharya, Keerthiraj, M. Hassan, S. Prasad, and S. K. Mahapatro, “HR Analytics : Leveraging Big Data to Drive Strategic Decision-Making in Human Resource Management,” J. Informatics Educ. Res., vol. 4, no. 1, pp. 1541–1549, 2024. doi: https://doi.org/10.52783/jier.v4i1.1101.
[4] A. M. Alhamad, I. Mohamad Hilan, I. S. Mohamed Alghowl, M. I. Eljaiebi, and K. K. Mohammed Buragan, “Predicting Employee Turnover Through Advanced Hr Analytics : Implications For Engagement Strategies,” Educ. Adm. Theory Pract., vol. 30, no. 5, pp. 964–972, 2024, doi: https://doi.org/10.53555/kuey.v30i5.2995.
[5] M. Sobri, A. B. Dharma, and Nurhadi, “Penerapan Tata Kelola Teknologi Informasi Dan Komunikasi Nasional Dalam Pembuatan Simpad Terminal Barang Pada Dinas Perhubungan Kota Dumai,” SEBATIK, vol. 23, no. 1, pp. 140–147, 2019. doi: https://doi.org/10.46984/sebatik.v23i1.459.
[6] B. Saha, A. Chhapola, and A. Jain, “AI-Driven Workforce Analytics : Transforming HR Practices Using Machine Learning Models .,” Int. J. Res. Anal. Rev., vol. 7, no. 2, pp. 982–997, 2020.
[7] F. O. Usman, L. Ndubuisi, C. V. Ibeh, E. R. Daraojimba, C. A. Udeh, and A. E. Elufioye, “Data-Driven Decision Making in HR: A Review of Analytics and its Strategic Importance,” Malaysian J. Hum. Resour. Manag., vol. 1, no. 2, pp. 98–104, 2024, doi: https://doi.org/10.26480/mjhrm.02.2024.98.104.
[8] F. Wirges and A. Neyer, “Towards a process‑oriented understanding of HR analytics: implementation and application,” Rev. Manag. Sci., vol. 17, no. 6, pp. 2077–2108, 2023, doi: https://doi.org/10.1007/s11846-022-00574-0.
[9] K. Groshek, “Activating People Analytics to Drive Strategic Business Growth and Workforce Performance,” J. Hum. Resour. Manag., 2025.
[10] M. Gerber, A. Krause, J. Probst, and M. Heimann, “HR analytics between ambition and reality psychology,” in Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO), vol. 55, 2024, pp. 225–236. doi: https://doi.org/10.1007/s11612-024-00743-7.
[11] M. M. A. Al et al., “Bias in AI-driven HRM systems : Investigating discrimination risks embedded in AI recruitment tools and HR analytics,” Soc. Sci. Humanit. Open, vol. 12, no. October, p. 102082, 2025, doi: https://doi.org/10.1016/j.ssaho.2025.102082.
[12] A. Rawat, V. Rawat, and G. Joshi, “AI-Driven Performance Evaluation and Employee Perceptions of Fairness,” Int. J. Innov. Res. Creat. Technol., vol. 10, no. 6, pp. 1–9, 2024.
[13] S. Berlian and S. Hartono, “Strategies for Implementing HR Predictive Analytics to Reduce Voluntary Turnover in Technology-Based Companies,” Manag. J., vol. 2, no. 6, pp. 33–41, 2025. doi: https://doi.org/10.62872/zj5w6744.
[14] A. Sharma and T. Sharma, “Article information : improvement Article Classification : Conceptual Paper Running Heads : HR analytics and performance appraisal system,” Manag. Res. Rev., vol. 40, no. 6, pp. 1–32, 2017. doi: https://doi.org/10.1108/MRR-04-2016-0084.
[15] W. Cho, S. Choi, and H. Choi, “Human Resources Analytics for Public Personnel Management : Concepts , Cases , and Caveats,” Adm. Sci., vol. 31, no. 41, 2023. doi: https://doi.org/10.3390/admsci13020041.
[16] L. G. Tanasescu, A. Vines, and A. R. Bologa, “applied sciences Data Analytics for Optimizing and Predicting Employee Performance,” Appl. Sci., vol. 14, pp. 1–21, 2024, doi: https:// doi.org/10.3390/app14083254 Academic.
[17] L. Yu, Y. Li, and F. Fan, “behavioral sciences Employees ’ Appraisals and Trust of Artificial Intelligences ’ Transparency and Opacity,” Behav. Sci. (Basel)., vol. 13, pp. 1–14, 2023, doi: https://doi.org/10.3390/bs13040344.
[18] S. Dadaboyev, S. Hojimurodova, and G. Gulomova, “AI in HR Analytics : A Synthesis of Current Research and Future Directions,” in Proceedings of the AAAI Symposium Series, 2025. doi: https://doi.org/10.1609/aaaiss.v6i1.36026
[19] eprints, “Laporan Laba Rugi Komprehensif dan Laporan Perubahan Ekuitas,” Universitas Bina Darma. Accessed: Oct. 08, 2024. [Online]. Available: http://eprints.binadarma.ac.id/4945/
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