A Comprehensive Review of AI and ML in Data Governance and Data Quality

As industries rely more on data-driven decisionmaking methods, it becomes clear that both high-quality data and strong governance frameworks are essential. Modern organizations manage substantial and varied datasets but face significant difficulties maintaining data accuracy and compliance. Traditional data governance and quality systems that depend on manual human intervention along with rulebased validation and scheduled audits fail to address the complexities and vast scale of present data environments. The field of data quality governance has been revolutionized by Artificial Intelligence (AI) and Machine Learning (ML) technologies which automate decision-making through adaptive and scalable predictive solutions. The technologies enhance data profiling capabilities alongside anomaly detection functions while managing metadata more effectively and improving lineage tracking and regulatory compliance through reduced manual intervention. Organizations can anticipate identifying data quality issues, impose governance policies, and streamline data management processes by leveraging AI/ML methods like supervised learning, unsupervised learning, deep learning, and reinforcement learning. This review paper amalgamates 10 research papers from 2021 to 2024 on AI-based data quality and governance to present a synthesis of the methodologies, frameworks, and case studies that demonstrate the efficacy of AI in enhancing data integrity. The article also discusses the limitations of AI-based solutions, such as model interpretability, computation limits, and regulation issues, and maps the future research direction. Although AI-based solutions exhibit encouraging progress, their successful adoption calls for meticulous integration with prevailing governance models, harmonization with regulation needs, and organizational culture towards data-driven automation.