DATA GOVERNANCE CHALLENGES IN CLOUD-NATIVE AND HYBRID DATA ARCHITECTURES

Abstract

The growing use of cloud-native and hybrid data architectures has changed the enterprise data ecosystems providing agility and scalability, as well as cost optimization. These environments however raise complicated data governance issues that jeopardize data security and compliance, quality, ownership and financial control. This paper discusses cloud-native and hybrid-specific issues in governance and breaks them into seven dimensions, namely security and privacy, compliance, data quality, ownership and accountability, monitoring, scalability, and cost governance.

The paper presents a comparison of the governance issues in both cloud-native and hybrid settings using an exploratory conceptual approach grounded in secondary data and identify the differences. Results indicate that cloud-native systems have challenges with decentralization, short-lived workloads, and scaling quickly, and systems based on hybrid systems are challenged by their heterogeneity, conflicts between regulations, and cross-border data sovereignty. In its paper, the author suggests a single governance framework with the help of metadata-based solutions, automated compliance, well-defined stewardship patterns, end-to-end observability, FinOps, and adaptive governance facilitated by AI and blockchain. The recommendations are designed to change governance into a compliance-related necessity to the strategic facilitator of trust, accountability, and innovation. These frameworks should be empirically proved by future studies by means of case studies and industry applications.