Citation:

Vatsal Kishorbhai Mavani, 2025. "Intelligent Data Governance: A Declarative, AI-Powered Framework for Monitoring Quality across Heterogeneous Data Ecosystems", ESP Journal of Engineering & Technology Advancements  5(4): 29-39.

Abstract:

The integrity of large-scale, heterogeneous data ecosystems is fundamental to the reliability of downstream AI systems. Existing data quality solutions, however, rely on brittle, imperative scripting and fail to adapt to the complex data distribution shifts inherent in modern enterprise environments. This paper introduces a novel, AI-powered framework that recasts data quality monitoring as an intelligent, adaptive process. The framework features two primary AI-driven innovations: 1) A Natural Language Intent Engine, powered by a Large Language Model (LLM), that translates conversational user requests directly into formal declarative monitoring specifications, democratizing access to data quality tooling. 2) An AI-driven Model Selection Agent that analyzes the metadata of each data source to autonomously select and deploy the most suitable deep learning model (e.g., LSTMs, VAEs) for adaptive anomaly detection. This automated, context-aware approach moves beyond static, one-size-fits-all methods. A comprehensive case study in the demanding healthcare domain validates the framework's scalability, high anomaly detection accuracy, and operational efficiency, establishing a new paradigm for intelligent, automated, and truly declarative enterprise data governance. This research provides a foundational architecture for the next generation of intelligent data governance platforms and opens new research avenues in automated data remediation and the application of Explainable AI (XAI) to engender trust and transparency in data quality assurance.

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Keywords:

Adaptive Monitoring, AI-Powered Anomaly Detection, Cloud-Native, Data Profiling, Data Quality, Declarative Framework, Enterprise Data Governance, Healthcare Data, Intelligent Data Governance, OpenTelemetry, Terraform.