AI-Driven Data Lakes: A Smarter Approach to Big Data Analytics

Abstract

AI-driven data lakes represent a significant evolution in big data infrastructure, transforming traditional passive data repositories into intelligent systems capable of self-organization and proactive insights generation. This article examines how artificial intelligence is revolutionizing data lake architectures to address challenges like poor searchability, inconsistent data quality, and difficulties establishing data lineage. Key innovations discussed include automated metadata management and tagging, self-organizing data catalogs, intelligent data quality management, and real-time analytics enablement. The article presents evidence of these technologies' transformative impact on business outcomes through enhanced data discovery, improved analyst productivity, and significant return on investment. A detailed case study of a global manufacturing company demonstrates the practical implementation and benefits of AI-driven data lakes. While acknowledging implementation challenges such as training data requirements, explainability concerns, governance integration, and skills gaps, the article concludes with a forward-looking perspective on the future of autonomous data management systems that can self-optimize, seamlessly integrate cross-organizational data, proactively identify insights, and continuously learn from user interactions.