Authors: Professor Richard Davis, Professor Thomas Taylor, Associate Professor Anthony Martin, Chaitanya Srinivas, Yashwanth kumar
Abstract: The rapid adoption of cloud computing, artificial intelligence, big data analytics, and interconnected digital platforms has trans-formed enterprise operations while creating significant challenges related to data trust, quality, security, privacy, and govern-ance. Traditional data governance approaches often rely on static policies, manual validation, and reactive monitoring, which may be insufficient for dynamic enterprise environments where data continuously changes across distributed systems. This research proposes a Predictive Data Trust Framework for Intelligent Enterprise Digital Transformation that integrates data quality assessment, metadata management, data lineage, security controls, anomaly detection, and predictive analytics into a unified trust-oriented architecture. The proposed framework continuously evaluates enterprise data using multidimensional trust indicators, including accuracy, completeness, consistency, timeliness, reliability, provenance, security, and regulatory compli-ance. Machine learning and predictive analytics mechanisms are incorporated to identify emerging data-quality risks, detect anomalous patterns, estimate trust scores, and proactively recommend corrective actions. The framework further establishes a feedback-driven governance mechanism that enables continuous monitoring and adaptive decision-making across data pipe-lines, data platforms, analytical systems, and business applications. By combining predictive intelligence with automated gov-ernance, the proposed approach aims to reduce manual intervention, improve data reliability, strengthen regulatory compliance, and support trustworthy digital transformation initiatives. The framework provides enterprises with a scalable foundation for establishing measurable and continuously improving data trust while enabling more reliable analytics, artificial intelligence applications, and strategic decision-making. The proposed architecture can be evaluated using metrics such as data-quality im-provement, trust-score accuracy, anomaly-detection performance, governance automation rate, compliance effectiveness, and reduction in data-related operational incidents.
