Exploring the Role of Machine Learning and Big Data Analytics in Enhancing Decision-Making Processes: A Systematic Literature Review

Wella, Wella (2025) Exploring the Role of Machine Learning and Big Data Analytics in Enhancing Decision-Making Processes: A Systematic Literature Review. JOIV : International Journal on Informatics Visualization, 9 (4). pp. 1783-1791. ISSN 25499904

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Abstract

This Systematic Literature Review (SLR) analyzes the influence of Machine Learning (ML) and Big Data Analytics (BDA) on decision-making processes in several industries. The study aims to explore the potential of machine learning and big data analytics in enhancing decision-making, examining the tools and platforms used, and identifying the challenges encountered during deployment. Employing the PRISMA technique, 31 publications published from 2019 to 2024 were meticulously selected through a stringent screening process, using Scopus as the principal database. The results indicate that machine learning and big data analytics substantially enhance predictive accuracy, operational efficiency, and data privacy measures, while facilitating seamless integration with current systems. Furthermore, these technologies are becoming progressively accessible to Small and Medium Enterprises (SMEs). In the healthcare sector, machine learning models have exhibited a diagnosis accuracy of 99% in detecting breast cancer. Nonetheless, the report underscores other research deficiencies, particularly the necessity for more cost-effective solutions designed for SMEs. These limitations signify opportunities for future study to investigate ML and BDA applications in underexamined areas, such as logistics and manufacturing. This research highlights the necessity of creating economical, scalable, and industry-specific machine learning and big data analytics solutions to address existing difficulties. This systematic literature review (SLR) seeks to elucidate the function of machine learning (ML) and big data analytics (BDA) in decision-making, thereby assisting researchers and practitioners in enhancing the utilization of these technologies across many industrial applications.

Item Type:	Article
Creators:	Wella, Wella
Contributors:
Keywords:	Machine learning; big data analytics; decision-making; data privacy; PRISMA; SMEs.
Subjects:	000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods > Artificial Intelligence, Machine Learning, Pattern Recognition, Data Mining
Sustainable Development Goals:	Goal 16. Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels Goal 17. Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development Goal 09. Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Divisions:	Faculty of Engineering & Informatics > Information System
Date Deposited:	02 Dec 2025 07:10
URI:	https://kc.umn.ac.id/id/eprint/42534

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