Integrated Data Analytics, Business Intelligence, and Machine Learning Architecture for SMEs

Small and medium enterprises (SMEs) are pivotal drivers of employment, Gross Domestic Product (GDP) growth, and innovation globally, yet they continue to face persistent barriers to adopting data-driven technologies, and literature lacks actionable, resource-sensitive guidance for practitioners. The systematic review maps algorithm-specific applications across industries including XGBoost for credit risk, LightGBM for real-time BI automation, and Support Vector Machines for high-dimensional small datasets while identifying persistent gaps in methodological transparency, geographic diversity, and prescriptive guidance. Addressing these gaps, the paper makes three original contributions: (1) a critical synthesis distinguishing access gaps from outcome gaps in SME BI adoption; (2) an analysis of why cost-efficiency remains under-weighted relative to strategic growth as an adoption driver, despite resource constraints; and (3) a phased three-stage implementation roadmap guiding SMEs from frugal descriptive dashboards through predictive modeling to prescriptive decision automation.