The use of big data analytics for human resource management in an organization
https://doi.org/10.35854/1998-1627-2024-5-575-583
Abstract
Aim. To examine the application of big data in the field of human resource management and in organizational network analysis as a methodology to study the patterns of employee interaction within a formal organizational structure to improve the effectiveness of the human resource management system.
Objectives. Theoretical analysis of existing methodologies for evaluating data on human resources of an organization to provide new opportunities for enterprise transformation; identification of hidden information on human resources and its use for internal staff development, retention and training; study of the possibilities of using organizational network analysis (ONA) to improve the effectiveness of human resource management.
Methods. The authors used system and logical approaches, general scientific methods (analysis, synthesis), methods of comparative and economic analysis, analytical processing of information, graphical presentation of information.
Results. The sources of big data are analyzed, the main indicators characterizing the state of personnel and allowing to forecast its development, dynamics of preservation of human resources in the organization, its training are allocated. The directions of using organizational network analysis in the personnel management system are proposed.
Conclusions. The identified potential advantages of using organizational network analysis in the analytics of data on human resources in the organization will contribute to the reduction of staff turnover, optimization of staff structure, improvement of experience and knowledge sharing within the organization. This, according to the author’s position, will lead the enterprise to a positive economic effect and open new opportunities for its transformation
About the Authors
K. L. AverinRussian Federation
Kirill L. Averin - postgraduate student
44A Lermontovskiy Ave., St. Petersburg 190020
T. N. Kosheleva
Russian Federation
Tatiana N. Kosheleva - D.Sc. in Economics, Associate Professor, Correspondent Member of the IHEAS, Professor at the Department of Management of Socio-Economic Systems; Head of the Department of Socio-Economic Disciplines and Service
44A Lermontovskiy Ave., St. Petersburg 190020
38 Pilotov st., St. Petersburg 196210
O. S. Elkina
Russian Federation
Olga S. Elkina - D.Sc. in Economics, Associate Professor, Professor at the Department of Management
of Socio-Economic Systems
44A Lermontovskiy Ave., St. Petersburg 190020
References
1. Averin K.L., Kosheleva T.N. Some issues of intensifying the activities of personnel services of enterprises. In: Modern problems of management. Proc. 17th All-Russ. sci.-pract. conf. of students, graduates and young scientists (St. Petersburg, April 20, 2023). St. Petersburg: St. Petersburg Electrotechnical University “LETI”; 2023:129-130. (In Russ.).
2. Ostaeva N. What problems are solved using HR analytics and how to implement it in a company. Skillbox Media. Aug. 10, 2023. URL: https://skillbox.ru/media/management/kakie-zadachi-reshayut-s-pomoshchyu-hranalitiki-i-kak-vnedrit-eye-v-kompanii/ (accessed on 11.03.2024). (In Russ.).
3. Reducing employee attrition with ONA: A case study from a European IT company. Blog on HR analytics. Nov. 06, 2023. URL: https://edwvb.blogspot.com/2023/11/reducing-employee-attrition-with-ona.html (accessed on 11.03.2024). (In Russ.).
4. Truss C., Soane E., Edwards C., et al. Working life: Employee attitudes and engagement. London: Chartered Institute of Personnel and Development; 2006. 54 p.
5. Robinson D., Perryman S., Hayday S. The drivers of employee engagement. Brighton: Institute for Employment Studies; 2004. 87 p. (IES Report No. 408).
6. 28 important HR metrics to measure. SpriggHR. Sep. 04. 2020. URL: https://sprigghr.com/blog/hr-professionals/28-important-hr-metrics-to-measure/ (accessed on 11.03.2024).
7. Cherkinskaya N. BIG Data in service in the MTS retail network. HRD. Mar. 11, 2019. URL: https://hr-tv.ru/articles/big-data-na-sluzhbe-u-roznichnoj-seti-mts.html (accessed on 10.04.2024). (In Russ.).
8. Mitrofanova A. Managing employee turnover. Upravlenie personalom i intellektual’nymi resursami v Rossii = Human Resources and Intellectual Resources Management in Russia. 2015;4(4):47-51. (In Russ.). DOI: 10.12737/13240
9. Sorokin A.V., Prokop’ev A.V. Personnel management. 2nd ed. Rubtsovsk: Rubtsovsk Industrial Institute; 2021. 68 p. (In Russ.).
10. Lin J. Organizational network analytics help improve workplace inclusivity. G2.com. Apr. 13, 2023. URL: https://www.g2.com/articles/organizational-network-analytics-help-improve-workplace-inclusivity (accessed on 11.03.2024).
11. Pulse polls: Advantages and disadvantages. HR-Portal. Mar. 05, 2023. URL: https://hr-portal.ru/blog/puls-oprosy-preimushchestva-i-nedostatki (accessed on 11.03.2024). (In Russ.)
12. Cross R., Kaše R., Kilduff M., King Z. Bridging the gap between research and practice in organizational network analysis: A conversation between Rob Cross and Martin Kilduff. Human Resource Management. 2013;52(4):627-644. DOI: 10.1002/hrm.21545
13. Borgatti S.P., Everett M.G., Johnson J.C. Analyzing social networks. London: Sage Publications Ltd; 2018. 384 p.
14. What is organizational network analysis? And how does it benefit companies? i4cp. Institute for Corporate Productivity. May 10, 2021. URL: https://www.i4cp.com/productivity-blog/what-organizational-network-analysis-is-and-how-it-benefits-companies (accessed on 11.03.2024).
15. Organizational network analysis, intervening in organizational network. The intact one. Mar. 07, 2023. URL: https://theintactone.com/2023/03/07/organizational-network-analysis-intervening-in-organizational-networks/ (accessed on 11.03.2024).
16. Rasmussen T., Ulrich D. Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics. 2015;44(3):236-242. DOI: 10.1016/j.orgdyn.2015.05.008
Review
For citations:
Averin K.L., Kosheleva T.N., Elkina O.S. The use of big data analytics for human resource management in an organization. Economics and Management. 2024;30(5):575-583. (In Russ.) https://doi.org/10.35854/1998-1627-2024-5-575-583