The Role of Big Data in Advancing Artificial Intelligence: Methods and Case Studies

Abstract

Big Data is a key driver that pushes Artificial Intelligence advancement by
creating complex systems that promote automated operations while predicting
outcomes and making wise decisions. Current data growth on structured and
unstructured platforms requires the implementation of distributed processing
systems, including cloud storage solutions and real-time data streaming. This
paper examines the interconnected relationship between Big Data features and
AI while analyzing fundamental approaches that handle extensive datasets
during AI model creation, fine-tuning, and installation. This paper investigates
how deep learning combines federated learning with reinforcement learning
to manage big data environments. Our research includes three practical examples
of how Big Data benefits healthcare through predictive analytics for disease
detection and customized care, how automated vehicles use Big Data processing
to achieve safety and efficiency, and how financial systems use AI processing of
transactional data to detect fraud. The study indicates that Big Data enhances the
functional capability of the AI model alongside performance, yet data privacy,
computational efficiency, and ethical factors remain essential problems. The
research reveals modern trends and upcoming investigation paths that will
define the upcoming generation of AI programs based on Big Data.
Keywords: Artificial intelligence, Big data, Case studies, Data-driven AI, Data