AUTOMATED DATA QUALITY ENFORCEMENT USING AI-ASSISTED METADATA VALIDATION IN CLOUD-BASED ENTERPRISE PLATFORMS

Abstract

Data warehousing and enterprise analytics increasingly rely on cloud-native lakehouse architectures that unify scalable storage and distributed computation [5], [39]. Enterprise analytics workloads frequently incorporate machine learning, artificial intelligence, and regulatory reporting systems that operate over heterogeneous and high-velocity data streams [4], [28]. Most enterprise data platforms are deployed on distributed infrastructures—whether on-premise, in the cloud, or in hybrid environments—introducing significant operational complexity in system management, schema evolution, and governance enforcement [19], [35]. Ensuring data consistency and quality across such distributed environments remains a fundamental challenge [6], [7].

Distributed ETL pipelines ingest large volumes of data from dynamic and heterogeneous sources, increasing the difficulty of maintaining structural consistency and semantic integrity [15], [44]. Traditional static and manually curated validation rules are insufficient for scalable, adaptive data quality enforcement in highly dynamic platforms [7], [47]. To address these limitations, we propose an AI-assisted metadata validation framework that integrates metadata-driven rule synthesis, probabilistic anomaly detection [8], [9], schema drift monitoring [12], and governance-aware validation orchestration within a distributed lakehouse architecture.

The framework employs intelligent data profiling and adaptive constraint generation to validate structural, business, and statistical integrity during ingestion and transformation stages, enabling early anomaly detection prior to downstream analytical consumption. The proposed system was implemented and evaluated in large-scale financial and healthcare cloud environments. Experimental results demonstrate improved anomaly detection precision and recall, proactive schema drift identification, and reduced manual remediation effort. Furthermore, validation outcomes are linked to metadata lineage and policy enforcement layers, enhancing governance traceability and audit compliance.

The proposed approach offers a scalable, adaptive, and governance-aligned data quality automation framework for modern enterprise cloud data platforms.