Authors: Frederick Mitchell, Patrick Edwards, Stephen Bailey, Chaitanya Srinivas, Niharika
Abstract: The rapid adoption of artificial intelligence (AI), cloud computing, and advanced analytics has increased the need for scalable, reliable, secure, and intelligent enterprise data architectures. Traditional data management environments often operate through fragmented systems, heterogeneous data sources, isolated processing platforms, and manually driven governance practices, limiting the ability of organizations to deliver timely and trusted data for AI-driven decision-making. This research proposes a Smart Data Architecture Framework for AI-Driven Enterprise Data Management that integrates enterprise data sources, cloud and distributed data platforms, intelligent data integration, metadata management, data governance, data quality, security, and AI-enabled analytics within a unified architectural model. The proposed framework emphasizes automated metadata discovery, intelligent data classification, real-time data quality monitoring, predictive anomaly detection, semantic data integration, lineage tracking, and policy-driven governance. AI and machine learning capabilities are incorporated to support adaptive data management, identify potential data-quality issues, optimize data-processing workflows, and improve the availability of trusted enterprise data for analytical and AI applications. The framework also incorporates security and privacy mechanisms to support controlled access, compliance, and responsible use of enterprise data. By establishing an integrated architecture across data ingestion, storage, processing, governance, intelligence, and consumption layers, the proposed approach aims to reduce data fragmentation, improve data reliability and accessibility, and strengthen organizational readiness for AI adoption. The framework provides a structured foundation for organizations seeking to modernize enterprise data management and develop scalable, intelligent, and governance-aware data ecosystems.
DOI: http://doi.org/10.5281/zenodo.23075696
