DATA GOVERNANCE AND COMPLIANCE IN CLOUD-BASED BIG DATA ANALYTICS: A DATABASE-CENTRIC REVIEW

Abstract

This study examines the evolving landscape of data governance in cloud-based big data analytics, emphasizing the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), and blockchain. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 120 articles were systematically reviewed to explore how organizations are addressing the challenges of managing large-scale, decentralized datasets while ensuring regulatory compliance and data security. The findings reveal that AI and ML are increasingly being used to automate governance tasks, predict compliance risks, and provide real-time auditing, while blockchain plays a critical role in ensuring data integrity and transparency across distributed cloud environments. Moreover, the research underscores the need for flexible and scalable governance models that can adapt to evolving regulations like GDPR and CCPA. Additionally, best practices such as multi-layered security approaches and strong collaboration with cloud service providers were identified as key strategies for enhancing governance frameworks. These insights contribute to the ongoing discourse on the modernization of data governance, highlighting the importance of dynamic, automated, and proactive approaches to managing data in cloud-based environments. This study provides a comprehensive understanding of current practices and technological innovations, offering actionable recommendations for organizations navigating the complexities of cloud-based data governance.