Intelligent Data Quality Architectures for AI Powered Enterprise Transformation and Compliance
DOI:
https://doi.org/10.15662/IJEETR.2025.0704016Keywords:
Intelligent Data Quality, AI Governance, Enterprise Transformation, Data Observability, Metadata Management, Machine Learning, Data Governance, Compliance, GDPR, Data Engineering, Anomaly Detection, Data ArchitectureAbstract
In the era of AI-driven enterprises, data quality has emerged as a foundational pillar for achieving reliable automation, intelligent decision-making, and regulatory compliance. Intelligent Data Quality Architectures (IDQA) integrate advanced analytics, machine learning, metadata management, and governance frameworks to ensure that enterprise data remains accurate, consistent, complete, and trustworthy across heterogeneous systems. This paper explores the design and implementation of AI-powered data quality architectures that enable enterprises to transform legacy systems into intelligent ecosystems capable of real-time validation, anomaly detection, and adaptive data remediation.
The study emphasizes the convergence of data engineering, AI governance, and compliance frameworks such as GDPR, HIPAA, and ISO standards. It highlights how intelligent architectures leverage data observability, automated lineage tracking, and predictive quality scoring to reduce operational risk and enhance business agility. Furthermore, it investigates how enterprises can operationalize data quality as a continuous, self-learning process embedded within data pipelines.
By synthesizing literature and proposing a structured research methodology, the paper demonstrates how intelligent data quality systems can support enterprise digital transformation initiatives while ensuring regulatory compliance and ethical AI deployment. The findings suggest that organizations adopting AI-enabled data quality frameworks achieve improved decision accuracy, reduced compliance violations, and enhanced scalability in data-driven environments
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