This article presents an AI-based data observability framework for modern data platforms. It focuses on anomaly detection, predictive analytics, and automated root cause analysis across the five pillars of data observability: freshness, distribution, volume, schema, and lineage. The paper reports significant improvements over traditional monitoring approaches, including 65% reduction in mean time to resolution and 42% improvement in data trust scores. Published in the International Journal of Novel Research and Development (IJNRD), Volume 11, Issue 4, April 2026.
Nitin Goswami (2026) studied this question.