Integrating Heterogeneous Data: A Systematic Review of Challenges and Evolution Solution

Abstract

Data integration has become a cornerstone of modern data-driven systems, enabling organizations to combine heterogeneous, distributed data sources into unified, actionable forms. Despite substantial advancements, challenges such as semantic heterogeneity, scalability, data quality, and automation continue to limit the efficiency and reliability of integration techniques. This paper presents a comprehensive systematic literature review that investigates the major challenges, existing techniques, and emerging trends in data integration research. Following a rigorous four-stage selection process, high-quality studies published were analyzed to synthesize both theoretical frameworks and practical solutions. The reviewed literature reveals an evolution from traditional rule-based and ontology-driven approaches toward AI-assisted, machine learning-based, and cloud-enabled integration architectures. The study identifies ongoing research gaps and highlights the need for scalable, intelligent data integration frameworks, supported by reported improvements such as a 13.2% increase in precision and a 30% reduction in performance costs achieved by modern methods.