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Intelligent Big Data Governance Using Policy-Driven Automation in Cloud-Based Enterprise Platforms

Abstract

The rapid expansion of cloud-based enterprise platforms has intensified the need for robust big data governance frameworks that ensure data quality, security, compliance, and usability. Traditional governance approaches often struggle to scale with the volume, velocity, and variety of modern data ecosystems. This paper explores an intelligent big data governance model that leverages policy-driven automation to streamline governance processes in cloud environments. By integrating artificial intelligence and machine learning techniques, the proposed approach enables dynamic policy enforcement, real-time monitoring, and adaptive decision-making. Policy-driven automation facilitates consistent data handling practices, reduces manual intervention, and enhances regulatory compliance across distributed systems. Furthermore, the model addresses critical challenges such as data lineage tracking, metadata management, and access control. Through a combination of rule-based engines and intelligent analytics, organizations can achieve improved data reliability, transparency, and operational efficiency. This study highlights the significance of automated governance frameworks in supporting scalable and resilient enterprise platforms while minimizing risks associated with data misuse and non-compliance. The findings suggest that intelligent governance systems are essential for organizations aiming to harness the full potential of big data in cloud-driven digital transformation.

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