Data Quality in the Age of Artificial Intelligence: A Comprehensive Review of Frameworks, Challenges, and Governance Strategies

Abstract

Data quality has emerged as a fundamental pillar of scientific integrity, organizational performance, and evidence-based decision-making in the contemporary digital landscape. As artificial intelligence systems become increasingly integrated into data-driven processes across healthcare, business, and public administration, the importance of robust data quality frameworks has intensified significantly. This comprehensive narrative review synthesizes scientific and technical literature published between 1996 and 2022, complemented by international standards including ISO/IEC 25012 and ISO 8000, to provide an integrated overview of data quality frameworks, governance structures, and ethical considerations in the era of AI. Across diverse sectors, accuracy, completeness, consistency, timeliness, and accessibility consistently emerge as universal quality dimensions that underpin reliable data utilization. Evidence from healthcare, business, and public administration demonstrates that poor data quality leads to substantial financial losses, operational inefficiencies, and erosion of stakeholder trust. Emerging frameworks increasingly integrate FAIR principles—Findability, Accessibility, Interoperability, and Reusability—while incorporating ethical safeguards, including bias mitigation in AI systems. This review reveals that data quality is not solely a technical concern but fundamentally a socio-organizational challenge that requires robust governance structures and continuous assurance throughout the data lifecycle. Embedding quality and ethical governance into data management practices is crucial for producing trustworthy, reusable, and reproducible data that supports sound science and informed decision-making across all sectors.