Data Governance Models for Managing Big Data in Cloud Computing Platforms

Abstract

This paper explores data governance models for managing large-scale datasets in cloud computing environments, focusing on the interplay between regulatory compliance, scalability, and security in contexts that demand robust yet flexible governance strategies. The discussion emphasizes how organizations can optimize data handling processes, determine control parameters, and ensure data quality while maintaining high operational efficiency. Approaches to data classification, metadata management, and access control are investigated through a formal lens, where mathematical formulations help clarify decision-making rules and control assignments for various data categories. The models presented account for dynamic workload changes, shifting data migration patterns, and evolving regulatory frameworks that affect storage, retrieval, and processing of data in the cloud. Furthermore, this paper proposes ways to integrate governance policies seamlessly across different cloud infrastructures, ensuring secure data flows throughout distributed systems. The potential pitfalls of adopting overly complex frameworks are addressed, highlighting situations where certain governance methods may produce suboptimal outcomes under specific constraints. Limitations and application considerations are also detailed, including resource overhead, scalability boundaries, and practical implementation challenges in real-world cloud systems. Through in-depth analysis and a range of mathematical formulations, the paper offers an advanced perspective on designing and sustaining comprehensive data governance solutions in cloud computing platforms.