A Conceptual Framework for Building Robust Data Governance and Quality Assurance Models in Multi-Cloud Analytics Ecosystems

The proliferation of multi-cloud architectures has redefined enterprise data landscapes, offering flexibility, scalability, and resilience. However, these benefits come with significant challenges for ensuring consistent data governance and quality assurance across heterogeneous platforms. This paper proposes a comprehensive conceptual framework designed to address the complexities inherent in managing data assets across distributed cloud environments. Grounded in the principles of design science research and refined through expert validation, the framework introduces layered governance structures, standardized metadata management, defined stakeholder roles, and performance metrics to enable robust oversight. It also incorporates interoperability models that support seamless integration across leading cloud providers through containerization, open standards, and automation. A continuous quality assurance lifecycle is embedded to maintain data reliability through monitoring, validation, cleansing, and auditing. While conceptual, this framework lays a foundation for future empirical validation and provides actionable guidance for enterprises aiming to harmonize data governance and quality practices in an increasingly fragmented cloud ecosystem.