The Convergence of Big Data and Generative AI: A Paradigm Shift in Data Analytics

Abstract

The convergence of big data and generative artificial intelligence represents a transformative paradigm shift in data analytics and processing capabilities. This comprehensive article review examines the evolution, current state, and future implications of this technological integration across multiple sectors. The article explores advanced learning paradigms, including few-shot and zero-shot learning, which have revolutionized traditional machine learning approaches. The article delves into multimodal integration capabilities that enable simultaneous processing of diverse data types, enhancing analytical accuracy and decision-making processes. The article addresses critical technical implementation challenges, focusing on computational infrastructure demands, environmental impact, and sustainability considerations. Furthermore, the article examines crucial aspects of data privacy and ethical considerations, including security protocols, bias mitigation, and regulatory compliance. The article analysis extends to practical industry applications across healthcare, financial services, and manufacturing sectors, demonstrating substantial improvements in operational efficiency and decision-making capabilities. This article synthesizes current developments while providing insights into future directions and opportunities, emphasizing the increasingly critical role of responsible AI development and implementation strategies.