Big Data (BD) has emerged as a pivotal tool for small and medium-sized enterprises (SMEs), offering substantial benefits in enhancing business performance and growth. This review investigates the impact of BD on SMEs, specifically focusing on business improvement, economic performance, and revenue growth. The objective of this systematic review is to evaluate the drivers and barriers of BD adoption in SMEs and assess its overall impact on operational efficiency and business outcomes. A comprehensive systematic review of 93 research papers published between 2014 and 2024 was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The methodology included detailed analysis of research approaches, addressing biases and gaps in the literature. BD adoption in SMEs led to significant improvements in operational efficiency, revenue generation, and competitiveness. However, the studies reveal persistent challenges, such as limited financial resources and technical expertise. The review identified a reporting bias, with 47% of studies using quantitative methods, 28% employing case studies, and mixed-method and qualitative studies underrepresented (22% and 17%, respectively). This imbalance highlights a potential overreliance on quantitative approaches, which may limit the depth of insights gained. While BD offers considerable potential for driving innovation and enhancing competitiveness in SMEs, addressing the current methodological biases and resource-related barriers is crucial to fully harness its benefits. Future research should focus on diverse approaches to provide a holistic understanding of BD’s impact on SMEs.

In recent years, Big Data (BD) has become a crucial tool for Small and Medium-sized Enterprises (SMEs), impacting their performance and growth in significant ways. This systematic literature review aims to analyze the effects of BD on SMEs by examining 93 research papers published from 2014 to 2024. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review focuses on key drivers and barriers to BD adoption, including business improvement, economic performance, and revenue growth. The methodology involved a comprehensive analysis of research methodologies used in the studies, addressing biases, gaps, and the need for diverse approaches. The findings reveal that while BD has led to notable enhancements in operational efficiency and revenue for many SMEs, challenges such as limited resources and technical expertise remain. A significant reporting bias was observed, with 47% of the literature comprising quantitative studies, followed by 28% case studies, while mixed-methods and qualitative studies were underrepresented (22% and 17%, respectively). This imbalance suggests a potential overemphasis on quantitative approaches, limiting the diversity of insights available. Addressing these biases is essential to fully harness the potential of BD for SMEs to drive innovation, enhance competitiveness, and achieve better performance in the increasingly data-driven business environment.