Big Data Analytics Applications And Opportunities With AI

Abstract

This article explores how Artificial Intelligence enhances Big Data analytics by enabling intelligent data processing, predictive insights, and real-time decision-making across diverse industries. The purpose of this research is to provide researchers, practitioners, and system architects with a comprehensive understanding of AI-driven Big Data analytics, bridging theoretical foundations with practical implementation strategies. The scope encompasses key AI techniques such as machine learning, deep learning, and natural language processing, examining their integration with large-scale data platforms to improve scalability, automation, and accuracy. This work contributes to both academic discourse and industrial practice by synthesizing contemporary research with real-world deployment scenarios, offering actionable insights for organizations embarking on AI-enabled Big Data transformation. The research highlights practical applications in healthcare, finance, smart cities, and business intelligence, while addressing challenges related to data quality, governance, security, and ethical AI adoption. For practitioners, this article provides implementation guidance and architectural considerations; for researchers, it identifies critical gaps in scalability, interpretability, and distributed learning; for architects, it offers design patterns and integration strategies for building robust AI-augmented analytics platforms. The article concludes by identifying emerging opportunities where AI-driven Big Data analytics can drive innovation, operational efficiency, and data-driven transformation.