THE CHALLENGES OF INTEGRATING BIG DATA WITH AI: A COMPREHENSIVE REVIEW

Abstract

A transformational force across sectors, the integration of big data and artificial intelligence (AI) holds the potential to yield invaluable insights and enhance decision-making capabilities.The proliferation of IoT, cloud computing, and 5G technologies has led to a rapid expansion in the volume of data generated by manufacturing operations. The utilization of extensive industrial data has facilitated significant progress in the domains of product design, manufacturing, and maintenance, surpassing initial anticipations. The advent of big data analytics (BDA) has played a pivotal role in enabling the development of intelligent industrial systems. Nevertheless, this combination is followed by a plethora of complex issues that necessitate careful thought. The main issues surrounding the combination of big data with artificial intelligence are explored in this abstract, including issues with data quality, scaling privacy, fairness, comprehension, governance, and many more. This study presents a thorough overview of related subjects like the idea of big data, model-driven, and data-driven approaches to thoroughly report BDA for intelligent manufacturing systems. The paper encompasses an analysis of the BDA architecture, its evolution, significant technological advancements, and the diverse range of applications within the domain of intelligent manufacturing systems. It draws attention to the necessity of multidisciplinary strategies, moral principles, and strategic thinking to successfully address these issues. Future research opportunities and challenges are also underlined. It is hoped that this work will inspire fresh thinking in the pursuit of realizing the goals of the BDA for advanced industrial systems.