Making decisions on the basis of data science in managing social and economic systems

Aim. To identify the prospects and possible areas of data science use in the decision-making process in the management of social and economic systems.

Objectives. To analyze existing approaches and promising directions of data science use; to analyze methods that allow to obtain real-time data for decision-making, to identify gaps and shortcomings; to briefly formulate conclusions relevant for practitioners and policy makers in the process of decision-making based on data science.

Methodology. The authors analyzed the scientific literature, applied methods of logical analysis and interpretation of data.

Results. In the course of the research, it was found that data science makes a significant contribution to the global transformation of society, allowing solving urgent problems of socioeconomic development. Methods that facilitate the acquisition of real-time data increase the effectiveness of decisions in the management of social and economic systems. These methods were analyzed, their advantages and disadvantages were identified, and examples of their use were presented. The key data requirements were defined: confidentiality, ethics, and security. A range of new questions for future research in the context of the topic under consideration was proposed.

Conclusions. The results obtained contribute to the theoretical development of new approaches to the application of data science, as well as the practical use of best practices in cases of decision-making in the management of social and economic systems. These results can form the basis for the design and implementation of solutions by policy makers and practitioners.

1. Daily time spent on social networking by Internet users worldwide from 2012 to 2024. Statista. 2024. URL: https://www.statista.com/statistics/433871/daily-social-media-usageworldwide/ (accessed on 19.08.2024).

2. Liu Y., Soroka A., Han L., Jian J., Tang M. Cloud-based Big Data analytics for customer insight-driven design innovation in SMEs. International Journal of Information Management. 2020;51:102034. DOI: 10.1016/j.ijinfomgt.2019.11.002

3. Guo H., Nativi S., Liang D., et al. Big Earth Data science: An information framework for a sustainable planet. International Journal of Digital Earth. 2020;13(7):743-767. DOI: 10.1080/17538947.2020.1743785

4. Cappa F., Franco S., Rosso F. Citizens and cities: Leveraging citizen science and Big Data for sustainable urban development. Business Strategy and the Environment. 2022;31(2):648-667. DOI: 10.1002/bse.2942

5. Medeiros M.M., Hoppen N., Maçada A.C. Data science for business: Benefits, challenges and opportunities. The Bottom Line. 2020;33(2):149-163. DOI: 10.1108/BL-12-2019-0132

6. Iqbal R., Doctor F., More B., Mahmud S., Yousuf U. Big Data analytics and computational intelligence for cyber-physical systems: Recent trends and state of the art applications. Future Generation Computer Systems. 2020;105:766-778. DOI: 10.1016/j.future.2017.10.021

7. Bhat S.A., Huang N.-F. Big data and AI revolution in precision agriculture: Survey and challenges. IEEE Access. 2021;9:110209-110222. DOI: 10.1109/ACCESS.2021.3102227

8. Donoghue T., Voytek B., Ellis S.E. Teaching creative and practical data science at scale. Journal of Statistics and Data Science Education. 2021;29(S1):S27-S39. DOI: 10.1080/10691898.2020.1860725

9. Shapiro B.R., Meng A., O’Donnell C., et al. Re-shape: A method to teach data ethics for data science education. In: Proc. 2020 CHI conf. on human factors in computing systems (CHI’20). (Honolulu, HI, April 25-30, 2020). New York, NY: Association for Computing Machinery; 2020:1-13. DOI: 10.1145/3313831.3376251

10. Rehman A., Naz S., Razzak I. Leveraging Big Data analytics in healthcare enhancement: Trends, challenges and opportunities. Multimedia Systems. 2022;28(4):1339-1371. DOI: 10.1007/s00530-020-00736-8

11. Leslie D. Tackling COVID-19 through responsible AI innovation: Five steps in the right direction. Harvard Data Science Review. 2020;(1). DOI: 10.1162/99608f92.4bb9d7a7

12. Timmermans J., Blok V., Braun R., Wesselink R., Nielsen R.Ø. Social labs as an inclusive methodology to implement and study social change: The case of responsible research and innovation. Journal of Responsible Innovation. 2020;7(3):410-426. DOI: 10.1080/23299460.2020.1787751

13. Bardhan I., Chen H., Karahanna E. Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management. MIS Quarterly. 2020;44(1):185-200. DOI: 10.25300/MISQ/2020/14644

14. Górriz J.M., Ramírez J., Ortiz A., et al. Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing. 2020;410:237-270. DOI: 10.1016/j.neucom.2020.05.078

15. Borah N., Baruah U., Ramakrishna M.T., et al. Efficient Assamese word recognition for societal empowerment: A comparative feature-based analysis. IEEE Access. 2023;11:82302-82326. DOI: 10.1109/ACCESS.2023.3301564

16. Kim S., Andersen K.N., Lee J. Platform government in the era of smart technology. Public Administration Review. 2022;82(2):362-368. DOI: 10.1111/puar.13422

17. Selwyn N., Gašević D. The datafication of higher education: Discussing the promises and problems. Teaching in Higher Education. 2020;25(4):527-540. DOI: 10.1080/13562517.2019.1689388

18. Nwosu N.T., Babatunde S.O., Ijomah T. Enhancing customer experience and market penetration through advanced data analytics in the health industry. World Journal of Advanced Research and Reviews. 2024;22(3):1157-1170. DOI: 10.30574/wjarr.2024.22.3.1810

19. Devan M., Shanmugam L., Tomar M. AI-powered data migration strategies for cloud environments: Techniques, frameworks, and real-world applications. Australian Journal of Machine Learning Research & Applications. 2021;1(2):79-111.

20. Usman F.O., Eyo-Udo N.L., Etukudoh E.A., et al. A critical review of AI-driven strategies for entrepreneurial success. International Journal of Management & Entrepreneurship Research. 2024;6(1):200-215. DOI: 10.51594/ijmer.v6i1.748

21. Eboigbe E.O., Farayola O.A., Olatoye F.O., Nnabugwu O.C., Daraojimba C. Business intelligence transformation through AI and data analytics. Engineering Science & Technology Journal. 2023;4(5):285-307. DOI: 10.51594/estj.v4i5.616

22. Ahmad T., Madonski R., Zhang D., Huang C., Mujeeb A. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews. 2022;160:112128. DOI: 10.1016/j.rser.2022.112128

23. Market share of advanced analytics and data science technologies worldwide in 2023. Statista. 2023. URL: https://www.statista.com/statistics/1258535/advanced-analytics-data-sciencemarket-share-technology-worldwide/ (accessed on 19.08.2024).