The Integration of Artificial Intelligence and Big Data: A Review of Technologies, Applications, and Challenges
TL;DR
This study proposes an integrative analytical architecture grounded in three interlocking layers: enabling technological foundations, intelligent sense-making processes, and context-sensitive deployment pathways that provide 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.
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 enhan cements 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 advan ces 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.