Enhancing data platform observability with AI-driven metadata analytics

Mathew, Thomas Aerathu (2025) Enhancing data platform observability with AI-driven metadata analytics. World Journal of Advanced Engineering Technology and Sciences, 15 (2). 039-047. ISSN 2582-8266

This article explores the transformative potential of AI-driven metadata analytics for enhancing data platform observability across modern enterprise ecosystems. As organizations navigate increasingly complex data landscapes comprising cloud warehouses, orchestration tools, and visualization platforms, traditional monitoring approaches fall short of providing comprehensive visibility. The integration of artificial intelligence with metadata management emerges as a solution that enables proactive issue detection, automated root cause analysis, and predictive insights. Through examining metadata types, sources, and analytical approaches, the article demonstrates how organizations can achieve operational excellence, strengthen governance capabilities, and realize substantial business returns. From machine learning anomaly detection to causal inference techniques, these advanced approaches convert raw metadata into actionable intelligence, creating more resilient, efficient, and compliant data operations that serve as competitive differentiators in data-driven markets.