A conceptual framework for enterprise data analytics governance and decision intelligence systems

Abstract

The rapid expansion of enterprise data ecosystems has intensified the need for robust governance mechanisms that ensure data quality, security, compliance, and strategic alignment with organizational objectives. At the same time, decision intelligence systems are emerging as critical enablers of data-driven decision-making by integrating advanced analytics, artificial intelligence, and contextual business rules. This review paper develops a conceptual framework that unifies enterprise data analytics governance with decision intelligence systems to enhance organizational performance, transparency, and agility. The study systematically examines existing governance models, data management architectures, and decision intelligence paradigms, identifying key gaps in their integration, particularly in areas such as real-time analytics, cross-functional data stewardship, and explainability of algorithmic decisions. The proposed framework introduces a layered architecture comprising data governance foundations, analytics orchestration, and decision intelligence execution, supported by feedback loops and continuous learning mechanisms. It emphasizes critical components including metadata management, data lineage tracking, policy enforcement, model governance, and human-in-the-loop decision validation. Furthermore, the framework incorporates emerging technologies such as cloud-native data platforms, distributed data fabrics, and explainable artificial intelligence to ensure scalability and trustworthiness. By aligning governance structures with decision intelligence workflows, the framework enables organizations to transition from descriptive and predictive analytics toward prescriptive and autonomous decision-making capabilities. The paper also highlights practical implications for enterprise implementation, including improved regulatory compliance, enhanced risk management, and optimized business outcomes across domains such as finance, healthcare, and supply chain operations. Ultimately, this study contributes to the literature by offering a comprehensive, integrative perspective that bridges the gap between data governance and intelligent decision systems, providing a foundation for future research and practical deployment in complex, data-intensive environments.