The Integration of Artificial Intelligence and Big Data: A Review of Technologies, Applications, and Challenges
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Keywords
artificial intelligence, big data, AI–big data integration
Abstract
Artificial intelligence (AI) and big data are now symbiotic pillars underpinning next-generation intelligent infrastructures. Big data serves as a rich, dynamic, and heterogeneous information reservoir—characterized by scale, variety, and velocity—while AI acts as the cognitive engine that extracts meaning from this deluge through self-directed learning, contextual pattern detection, and iterative decision refinement. Yet current scholarly work on their convergence tends to prioritize narrow technical enhancements or domain-specific deployments, falling short of offering a cohesive theoretical lens to articulate *how* these two paradigms co-evolve and synergize in practice. To bridge this conceptual and practical void, this study proposes an integrative analytical architecture grounded in three interlocking layers: (1) enabling technological foundations, (2) intelligent sense-making processes, and (3) context-sensitive deployment pathways. By unifying conceptual rigor with empirical grounding, this work advances a holistic, implementation-ready framework for building intelligent systems that are not only high-performing and scalable, but also ethically grounded, transparent, and attuned to real-world socio-technical contexts.
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