Integrating Artificial Intelligence in Big Data Analytics: A Framework for Automated Data Processing and Insight Generation

Abstract

The exponential growth of data in the digital era has necessitated advanced analytical approaches to extract meaningful insights efficiently. Integrating Artificial Intelligence (AI) in Big Data Analytics presents a transformative paradigm by automating data processing and enhancing decision-making capabilities. This paper explores a structured framework that leverages AI-driven techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), to streamline data ingestion, cleaning, transformation, and analysis. The proposed framework consists of an intelligent data pipeline that automates feature extraction, anomaly detection, and predictive modeling, significantly reducing human intervention and computational overhead. Key benefits of AI-integrated big data analytics include improved accuracy, scalability, and real-time processing capabilities. However, challenges such as data privacy, bias in AI models, and the need for high-performance computing infrastructure must be addressed for optimal implementation. This paper provides a comprehensive discussion on AI methodologies employed in big data analytics, examines case studies where AI has significantly improved analytical outcomes, and proposes future directions to enhance AI-driven automated data processing. By adopting AI-enhanced big data analytics, organizations can unlock valuable insights, optimize decision-making, and gain a competitive advantage in the data-driven economy.