Machine Learning Driven Decision Making in the Modern Data Era
DOI:
https://doi.org/10.62671/perfect.v3i1.224Keywords:
Machine Learning, Data-Driven Decision Making, Big Data, Workflow Automation, Explainable AI, Human–Machine CollaborationAbstract
The era of modern data has seen an unprecedented increase in the number of data generated, which generates an opportunity as well as a challenge to the decision making. Machine learning (ML) has become the significant solution to work with big and multifaceted data, recognize trends, and provide foresight and change the usual decision-making processes. This review examines the principles, methods and uses of ML-based decision systems in various industries, such as healthcare, finance, retail, transportation and education. It also analyses problems of data quality, bias, transparency, ethical considerations and developments related to explainable and trustworthy AI. Lastly, future trends, human-machine cooperation, and research perspectives are addressed, with a focus on the possibility of the ML to accelerate, more precise and answerable decisions in the world that runs on data.
References
AL-Inizi, M. S. (2025). Enhancing governmental decision-making through predictive analytics with machine learning-based data-driven framework. Babylonian Journal of Machine Learning, 2025, 86–96. DOI: https://doi.org/10.58496/BJML/2025/007
Bai, Z., Shangguan, W., Cai, B., & Chai, L. (2019). Deep reinforcement learning based high-level driving behavior decision-making model in heterogeneous traffic. In 2019 Chinese Control Conference (CCC) (pp. 8600–8605). IEEE. DOI: https://doi.org/10.23919/ChiCC.2019.8866005
Bari, M. D., & Ara, A. (2024). The impact of machine learning on prescriptive analytics for optimized business decision-making. Anjuman. DOI: https://doi.org/10.2139/ssrn.5050060
Begum, T. (2023). Predictive analytics for machine learning and deep learning. In Handbook of Big Data Research Methods (pp. 148–164). Edward Elgar Publishing. DOI: https://doi.org/10.4337/9781800888555.00014
Bhattacherjee, A., & Badhan, A. K. (2024). Convergence of data analytics, big data, and machine learning: Applications, challenges, and future direction. In Data Analytics and Machine Learning: Navigating the Big Data Landscape (pp. 317–334). Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-97-0448-4_15
Boppiniti, S. T. (2019). Machine learning for predictive analytics: Enhancing data-driven decision-making across industries. International Journal of Sustainable Development in Computing Science, 1(3), 13.
Bousdekis, A., Lepenioti, K., Apostolou, D., & Mentzas, G. (2021). A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics, 10(7), 828. DOI: https://doi.org/10.3390/electronics10070828
Carillo, K. D. (2017). Let’s stop trying to be “sexy”: Preparing managers for the (big) data-driven business era. Business Process Management Journal, 23(3), 598–622. DOI: https://doi.org/10.1108/BPMJ-09-2016-0188
Chaudhary, P. S., Khurana, M. R., & Ayalasomayajula, M. (2024). Real-world applications of data analytics, big data, and machine learning. In Data Analytics and Machine Learning: Navigating the Big Data Landscape (pp. 237–263). Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-97-0448-4_12
Chen, Y., & Zhou, Y. (2020). Machine learning based decision making for time varying systems: Parameter estimation and performance optimization. Knowledge-Based Systems, 190, 105479. DOI: https://doi.org/10.1016/j.knosys.2020.105479
Chowdhury, R. H. (2024). Harnessing machine learning in business analytics for enhanced decision-making. World Journal of Advanced Engineering Technology and Sciences, 12(2), 674–683. DOI: https://doi.org/10.30574/wjaets.2024.12.2.0341
Chowdhury, R. H. (2024). The evolution of business operations: Unleashing the potential of artificial intelligence, machine learning, and blockchain. World Journal of Advanced Research and Reviews, 22(3), 2135–2147. DOI: https://doi.org/10.30574/wjarr.2024.22.3.1992
Coglianese, C., & Lehr, D. (2016). Regulating by robot: Administrative decision making in the machine-learning era. Georgetown Law Journal, 105, 1147.
Cravero, A., Pardo, S., Sepúlveda, S., & Muñoz, L. (2022). Challenges to use machine learning in agricultural big data: A systematic literature review. Agronomy, 12(3), 748. DOI: https://doi.org/10.3390/agronomy12030748
Cui, T., Du, N., Yang, X., & Ding, S. (2024). Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach. Technological Forecasting and Social Change, 198, 122944. DOI: https://doi.org/10.1016/j.techfore.2023.122944
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of big data: Evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.01.021
El Naqa, I., & Murphy, M. J. (2015). What is machine learning? In Machine learning in radiation oncology: Theory and applications (pp. 3–11). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-18305-3_1
Elgendy, N., Elragal, A., & Päivärinta, T. (2022). DECAS: A modern data-driven decision theory for big data and analytics. Journal of Decision Systems, 31(4), 337–373. DOI: https://doi.org/10.1080/12460125.2021.1894674
Gade, K. R. (2021). Data-driven decision making in a complex world. Journal of Computational Innovation, 1(1).
Ge, Z. (2022). Artificial intelligence and machine learning in data management. In The Future and FinTech: ABCDI and Beyond (pp. 281–310). DOI: https://doi.org/10.1142/9789811250903_0008
Gubbi, S., Hamet, P., Tremblay, J., Koch, C. A., & Hannah-Shmouni, F. (2019). Artificial intelligence and machine learning in endocrinology and metabolism: The dawn of a new era. Frontiers in Endocrinology, 10, 185. DOI: https://doi.org/10.3389/fendo.2019.00185
Hoang, T. M., Vahid, A., Tuan, H. D., & Hanzo, L. (2024). Physical layer authentication and security design in the machine learning era. IEEE Communications Surveys & Tutorials, 26(3), 1830–1860. DOI: https://doi.org/10.1109/COMST.2024.3363639
Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 147–169. DOI: https://doi.org/10.1080/10919392.2015.1125187
James, L., & Rhoads, J. (2024). Machine learning for transformative marketing strategies: Applications, challenges, and future directions in the era of data-driven decision making. Nuvern Machine Learning Reviews, 1(1), 1–0.
Jane, J. B., & Ganesh, E. N. (2019). A review on big data with machine learning and fuzzy logic for better decision making. International Journal of Scientific & Technology Research, 8(10), 1221–1225.
Jawad, Z. N., & Balázs, V. (2024). Machine learning-driven optimization of enterprise resource planning (ERP) systems: A comprehensive review. Beni-Suef University Journal of Basic and Applied Sciences, 13(1), 4. DOI: https://doi.org/10.1186/s43088-023-00460-y
Kadkhodazadeh, M., Valikhan Anaraki, M., Morshed-Bozorgdel, A., & Farzin, S. (2022). A new methodology for reference evapotranspiration prediction and uncertainty analysis under climate change conditions based on machine learning, multi criteria decision making and Monte Carlo methods. Sustainability, 14(5), 2601. DOI: https://doi.org/10.3390/su14052601
Kapadiya, D., Shekhawat, C., & Sharma, P. (2023, November 23). A study on largescale applications of big data in modern era. In Proceedings of the 5th International Conference on Information Management & Machine Intelligence (pp. 1–6). DOI: https://doi.org/10.1145/3647444.3647880
Kazbekova, G., Ismagulova, Z., Zhussipbek, B., Abdrazakh, Y., Iskendirova, G., & Toilybayeva, N. (2024). Machine learning enhanced framework for big data modeling with application in industry 4.0. International Journal of Advanced Computer Science & Applications, 15(3). DOI: https://doi.org/10.14569/IJACSA.2024.0150332
Koteluk, O., Wartecki, A., Mazurek, S., Kołodziejczak, I., & Mackiewicz, A. (2021). How do machines learn? Artificial intelligence as a new era in medicine. Journal of Personalized Medicine, 11(1), 32. DOI: https://doi.org/10.3390/jpm11010032
Kovacs-Györi, A., Ristea, A., Havas, C., Mehaffy, M., Hochmair, H. H., Resch, B., Juhasz, L., Lehner, A., Ramasubramanian, L., & Blaschke, T. (2020). Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning. ISPRS International Journal of Geo-Information, 9(12), 752. DOI: https://doi.org/10.3390/ijgi9120752
Kumar, A., Dhanka, S., Bansal, R., Sharma, A., Singh, J., Khan, A. A., & Maini, S. (2024). Smart crop selection: Harnessing machine learning for sustainable agriculture in the era of industry 5.0. In Industry 5.0 and Emerging Technologies: Transformation Through Technology and Innovations (pp. 111–134). Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-70996-8_6
Kumar, S. A., Ananda Kumar, T. D., Beeraka, N. M., Pujar, G. V., Singh, M., Narayana Akshatha, H. S., & Bhagyalalitha, M. (2022). Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Medicinal Chemistry, 14(4), 245–270. DOI: https://doi.org/10.4155/fmc-2021-0243
Kumar, S., Machireddy, J. R., Sankaran, T., & Sholapurapu, P. K. (2025). Integration of machine learning and data science for optimized decision-making in computer applications and engineering. Journal of Information Systems Engineering and Management, 10. DOI: https://doi.org/10.52783/jisem.v10i45s.8990
Liu, J., & Zhang, C. (2023). Effective analysis and intelligent decision making of consumer electronics data based on machine learning under smart city. IEEE Transactions on Consumer Electronics, 70(1), 4205–4212. DOI: https://doi.org/10.1109/TCE.2023.3339760
Lu, J., Yan, Z., Han, J., & Zhang, G. (2019). Data-driven decision-making (D3M): Framework, methodology, and directions. IEEE Transactions on Emerging Topics in Computational Intelligence, 3(4), 286–296. DOI: https://doi.org/10.1109/TETCI.2019.2915813
Mahmoud, H. H., & Ismail, T. (2020, December 29). A review of machine learning use-cases in telecommunication industry in the 5G era. In 2020 16th International Computer Engineering Conference (ICENCO) (pp. 159–163). IEEE. DOI: https://doi.org/10.1109/ICENCO49778.2020.9357376
Mansouri, S. S., Sivaram, A., Savoie, C. J., & Gani, R. (2025). Models, modeling and model-based systems in the era of computers, machine learning and AI. Computers & Chemical Engineering, 194, 108957. DOI: https://doi.org/10.1016/j.compchemeng.2024.108957
Muccini, H., & Vaidhyanathan, K. (2019). A machine learning-driven approach for proactive decision making in adaptive architectures. In ICSA Companion (pp. 242–245). DOI: https://doi.org/10.1109/ICSA-C.2019.00050
Myakala, P. K. (2019). How machine learning simplifies business decision-making. Complexity International Journal (CIJ), 23(3), 407–410.
Ning, C., & You, F. (2019). Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming. Computers & Chemical Engineering, 125, 434–448. DOI: https://doi.org/10.1016/j.compchemeng.2019.03.034
Olayinka, O. H. (2019). Leveraging predictive analytics and machine learning for strategic business decision-making and competitive advantage. International Journal of Computer Applications Technology and Research, 8(12), 473–486.
Oliver, N. (2019). Governance in the era of data-driven decision-making algorithms. In Women Shaping Global Economic Governance (p. 171). DOI: https://doi.org/10.18356/7f891b82-en
Oluoha, O. M., Odeshina, A., Reis, O., Okpeke, F., Attipoe, V., & Orieno, O. (2022). Optimizing business decision-making with advanced data analytics techniques. Iconic Research and Engineering Journals, 6(5), 184–203.
Perumal, P., Senthilkumar, K., Thirunavukkarasu, T., & Mishra, B. R. (2024). Data-driven strategies on growth through AI and machine learning. In Advancing Intelligent Networks Through Distributed Optimization (pp. 127–142). IGI Global. DOI: https://doi.org/10.4018/979-8-3693-3739-4.ch007
Priya, K., James, S. K., Babu, A. A., Sharanyaa, S., & Prasad, S. V. (2024, October 4). Artificial intelligence and machine learning: Revolutionizing data-driven decisions. In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT) (pp. 1–6). IEEE. DOI: https://doi.org/10.1109/GCAT62922.2024.10924120
Ramya, J., Yerraguravagari, S. S., Gaikwad, S., & Gupta, R. K. (2024). AI and machine learning in predictive analytics: Revolutionizing business strategies through big data insights. Library of Progress–Library Science, Information Technology & Computer, 44(3).
Rani, S., Kumar, R., Panda, B. S., Kumar, R., Muften, N. F., Abass, M. A., & Lozanović, J. (2025). Machine learning-powered smart healthcare systems in the era of big data: Applications, diagnostic insights, challenges, and ethical implications. Diagnostics, 15(15), 1914. DOI: https://doi.org/10.3390/diagnostics15151914
Saggi, M. K., & Jain, S. (2022). A survey towards decision support system on smart irrigation scheduling using machine learning approaches. Archives of Computational Methods in Engineering, 29(6), 4455–4478. DOI: https://doi.org/10.1007/s11831-022-09746-3
Salem, A. M., Eyupoglu, S. Z., & Ma’aitah, M. K. (2024). The influence of machine learning on enhancing rational decision-making and trust levels in e-government. Systems, 12(9), 373. DOI: https://doi.org/10.3390/systems12090373
Sarioguz, O., & Miser, E. (2024). Data-driven decision-making: Transforming management in the information age. International Research Journal of Modernization in Engineering Technology and Science, 6(2), 1642–1652.
Sarker, I. H. (2021). Data science and analytics: An overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science, 2(5), 377. DOI: https://doi.org/10.1007/s42979-021-00765-8
Sarker, I., Colman, A., Han, J., & Watters, P. (2022). Context-aware machine learning and mobile data analytics: Automated rule-based services with intelligent decision-making. Springer Nature. DOI: https://doi.org/10.1007/978-3-030-88530-4
Schmitt, M. (2023). Automated machine learning: AI-driven decision making in business analytics. Intelligent Systems with Applications, 18, 200188. DOI: https://doi.org/10.1016/j.iswa.2023.200188
Shafik, W. (2024). Machine learning techniques for multicriteria decision-making. In Multi-Criteria Decision-Making and Optimum Design with Machine Learning (pp. 165–194). CRC Press. DOI: https://doi.org/10.1201/9781032635170-13
Shang, C., & You, F. (2019). Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era. Engineering, 5(6), 1010–1016. DOI: https://doi.org/10.1016/j.eng.2019.01.019
Shin, G. H., & Jung, M. (2025). Preparing VTS for the MASS era: A machine learning-based VTSO recruitment model. Journal of Marine Science and Engineering, 13(11), 2127. DOI: https://doi.org/10.3390/jmse13112127
Singh, V., Cheng, S., Kwan, A. C., & Ebinger, J. (2025). United States Food and Drug Administration regulation of clinical software in the era of artificial intelligence and machine learning. Mayo Clinic Proceedings: Digital Health, 3(3), 100231. DOI: https://doi.org/10.1016/j.mcpdig.2025.100231
Sun, A. Y., & Scanlon, B. R. (2019). How can big data and machine learning benefit environment and water management: A survey of methods, applications, and future directions. Environmental Research Letters, 14(7), 073001. DOI: https://doi.org/10.1088/1748-9326/ab1b7d
Syeda-Mahmood, T. (2018). Role of big data and machine learning in diagnostic decision support in radiology. Journal of the American College of Radiology, 15(3), 569–576. DOI: https://doi.org/10.1016/j.jacr.2018.01.028
Taylor, R. A., Pare, J. R., Venkatesh, A. K., Mowafi, H., Melnick, E. R., Fleischman, W., & Hall, M. K. (2016). Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach. Academic Emergency Medicine, 23(3), 269–278. DOI: https://doi.org/10.1111/acem.12876
Thomassey, S., & Zeng, X. (2018). Introduction: Artificial intelligence for fashion industry in the big data era. In Artificial Intelligence for Fashion Industry in the Big Data Era (pp. 1–6). Springer Singapore. DOI: https://doi.org/10.1007/978-981-13-0080-6_1
Tileubay, S., Doszhanov, B., Mailykhanova, B., Kulmurzayev, N., Sarsenbayeva, A., Akanova, Z., & Toxanova, S. (2023). Applying big data analysis and machine learning approaches for optimal production management. International Journal of Advanced Computer Science & Applications, 14(12). DOI: https://doi.org/10.14569/IJACSA.2023.0141266
Tsoukalas, A., Albertson, T., & Tagkopoulos, I. (2015). From data to optimal decision making: A data-driven, probabilistic machine learning approach to decision support for patients with sepsis. JMIR Medical Informatics, 3(1), e3445. DOI: https://doi.org/10.2196/medinform.3445
Usuga Cadavid, J. P., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in production planning and control: A state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing, 31(6), 1531–1558.
Usuga Cadavid, J. P., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in production planning and control: A state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing, 31(6), 1531–1558. DOI: https://doi.org/10.1007/s10845-019-01531-7
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. DOI: https://doi.org/10.1016/j.jmsy.2018.01.003
Weng, Y. (2024). Big data and machine learning in defence. International Journal of Computer Science and Information Technology, 16(2), 25–35. DOI: https://doi.org/10.5121/ijcsit.2024.16203
Zareba, M., Cogiel, S., Danek, T., & Weglinska, E. (2024). Machine learning techniques for spatio-temporal air pollution prediction to drive sustainable urban development in the era of energy and data transformation. Energies, 17(11), 2738. DOI: https://doi.org/10.3390/en17112738
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