Authors: Benjamin R. Sullivan Professor, Scott D. Bennett, Lucy K. Turner Associate Professor, Chaitanya Srinivas, Yashwanth kumar
Abstract: Data stewardship has become a fundamental component of enterprise data governance, enabling organizations to establish trusted, accurate, and well-managed data assets that support operational efficiency, regulatory compliance, and strategic decision-making. As enterprises increasingly adopt cloud computing, artificial intelligence, big data analytics, and distributed information systems, the volume, variety, and complexity of organizational data have grown substantially, making effective stewardship essential for maintaining enterprise data trust. While technological advancements have improved data management capabilities, sustainable data trust depends on well-defined stewardship models that clearly assign accountability, promote cross-functional collaboration, enforce governance policies, and ensure continuous improvement in data quality. This study presents a systematic evidence mapping of data stewardship models for enterprise data trust by synthesizing existing research on stewardship frameworks, governance structures, organizational roles, leadership practices, metadata management, data quality management, compliance strategies, and technology-enabled stewardship approaches. The evidence mapping systematically classifies published literature according to stewardship models, governance adoption, organizational responsibilities, implementation methodologies, enabling technologies, industry applications, evaluation metrics, and research methodologies. The analysis reveals that successful enterprise data stewardship programs are characterized by strong executive sponsorship, clearly defined stewardship roles, standardized governance policies, effective metadata management, continuous data quality monitoring, stakeholder engagement, and collaborative decision-making across business and technology functions. Furthermore, the findings demonstrate that data stewardship significantly improves enterprise data consistency, transparency, regulatory compliance, operational efficiency, and organizational trust by ensuring that critical business information remains accurate, complete, secure, and accessible throughout its lifecycle. The study also highlights the increasing integration of artificial intelligence, machine learning, cloud-native data platforms, metadata automation, knowledge graphs, intelligent data catalogs, and analytics-driven governance into modern stewardship programs. Despite these advancements, organizations continue to encounter challenges including fragmented data ownership, organizational resistance to governance adoption, legacy system integration, inconsistent stewardship practices, evolving regulatory requirements, scalability limitations, and shortages of skilled data governance professionals. The evidence mapping identifies several promising directions for future research, including autonomous data stewardship, AI-assisted governance, explainable artificial intelligence for stewardship decision-making, semantic knowledge graph–based governance, real-time data quality monitoring, intelligent compliance automation, and adaptive governance frameworks. Overall, this study provides researchers, enterprise architects, governance professionals, and organizational leaders with a comprehensive understanding of data stewardship models that enhance enterprise data trust and offers practical guidance for designing scalable, governance-driven, technology-enabled stewardship programs that support sustainable digital transformation, trusted enterprise information, and long-term organizational value.
