Integrating Artificial Intelligence and Big Data Analytics for Predictive Data Science Systems

Abstract

This paper examines how big data analytics is combined with artificial intelligence (AI) and machine learning (ML) to complement predictive analytics and decision-making in complex systems. The main goal is to explore th ability of the combined data-driven methods to enhance the accuracy of the analytical perspective, scalability, and real-time insights. The methodology is a synthesis and review of the available frameworks in a systematic manner and then creating a conceptual model, which will involve the combination of structured and unstructured data processing and AI/ML algorithms. Such techniques as neural networks, data mining, and cloud-based analytics are also supposed to be evaluated in terms of their qualities in processing large-scale datasets. The findings show that AI and big data integration remarkably enhance predictive performance, shorten the processing time, and allow making more informed decisions in areas like healthcare, business intelligence, and smart systems. Moreover, the paper mentions such issues as the quality of data, the complexity of the system, and the cost of integration in addition to the prospects to enhance intelligent analytics in the future. The results conclude that an integrated approach to employing big data and AI technologies should be utilized to obtain an efficient, scalable, and accurate predictive analytics within the current data-driven setting.