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Applications and Competitive Advantages of Data Mining and Business Intelligence in SMEs Performance: A Systematic Review

Author

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  • Shao V. Tsiu

    (Department of Electrical & Electronic Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa)

  • Mfanelo Ngobeni

    (Department of Electrical & Electronic Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa)

  • Lesley Mathabela

    (Department of Electrical & Electronic Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa)

  • Bonginkosi Thango

    (Department of Electrical & Electronic Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa)

Abstract

Small and medium-sized enterprises (SMEs) face unique challenges that can be effectively addressed through the adoption of data mining and business intelligence (BI) tools. This systematic literature review scrutinizes the deployment and efficacy of BI and data mining technologies across SME sectors, assessing their impact on operational efficiency, strategic decision-making, and market competitiveness. Therefore, drawing from a methodologically rigorous analysis of 93 scholarly articles published between 2014 and 2024, the review elucidates the evolving landscape of BI tools and techniques that have shaped SME practices. It reveals that advanced analytics such as predictive modeling and machine learning are increasingly being adopted, though significant gaps remain, particularly shaped by economic factors. The utilization of BI and data mining enhances decision-making processes and enables SMEs to adapt effectively to market dynamics. Despite these advancements, SMEs encounter barriers such as technological complexity, high implementation costs, and substantial skills gaps, impeding effective utilization. Our review, grounded in the analysis of business intelligence tools used indicates that dashboards (31.18%) and clustering techniques (10.75%) are predominantly utilized, highlighting their strategic importance in operational settings. However, a considerable number of studies (66.67%) do not specify the BI tools or data mining techniques employed, pointing to a need for more detailed methodological transparency in future research. The predominant focus on the ICT and manufacturing sectors underscores the industrial context sector specific applicability of these technologies, with ICT accounting for 45.16% and manufacturing 22.58% of the studies. We advocate for targeted educational programs, development of user-friendly and cost-effective BI solutions, and strategic partnerships to facilitate knowledge transfer and technological empowerment in SMEs. Empirical research validating the impacts of BI and data mining on SME performance is crucial, providing a directional pathway for future academic inquiries and policy formulation.

Suggested Citation

  • Shao V. Tsiu & Mfanelo Ngobeni & Lesley Mathabela & Bonginkosi Thango, 2025. "Applications and Competitive Advantages of Data Mining and Business Intelligence in SMEs Performance: A Systematic Review," Businesses, MDPI, vol. 5(2), pages 1-49, May.
    • Handle: RePEc:gam:jbusin:v:5:y:2025:i:2:p:22-:d:1650689
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    References listed on IDEAS

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    1. NoorUl Ain & Giovanni Vaia & William Delone & Mehwish Waheed, 2019. "Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review," Post-Print hal-03882087, HAL.
    2. Hassani, Abdeslam & Mosconi, Elaine, 2022. "Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Gao, Yang & Liu, Siqiang & Yang, Lu, 2025. "Artificial intelligence and innovation capability: A dynamic capabilities perspective," International Review of Economics & Finance, Elsevier, vol. 98(C).
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