Architecting Intelligence Into Master Data Platforms: An Evidence Mapping Approach To AI-Enabled Dashboards For Compliance And Quality Monitoring

This study examines the growing need to embed intelligence directly into master data platforms to improve regulatory compliance, operational transparency, and data quality monitoring in complex enterprise environments. Organizations increasingly struggle to maintain consistent governance and audit readiness as data volumes expand across distributed systems, creating a research problem centered on how artificial intelligence and structured evidence mapping can enhance visibility and decision support. The purpose of this research is to develop and evaluate a framework that integrates AI enabled dashboards with master data governance workflows to support continuous compliance and quality assurance. A mixed methodological approach was applied, combining evidence mapping of prior governance and analytics models with architectural synthesis and scenario based evaluation in enterprise data management contexts. The findings demonstrate that intelligent dashboards, when supported by automated metadata analysis, anomaly detection, and lineage driven monitoring, significantly improve detection of data quality risks, accelerate audit preparation, and enhance stewardship effectiveness. The study introduces an architectural model that aligns monitoring, policy enforcement, and predictive analytics within a unified governance layer. These contributions extend existing research in data governance and enterprise analytics by providing a practical and scalable design framework. The results highlight the strategic importance of integrating intelligence into master data platforms, offering guidance for industry practitioners and establishing a foundation for further academic research on autonomous governance and continuous data assurance.