Next-Gen Data Governance: Leveraging Machine Learning for Scalable Enterprise Decision-Making in Cloud (International Journal of Engineering & Extended Technologies Research (IJEETR), 2024)
The exponential growth of enterprise data, driven by cloud adoption and digital transformation, has intensified the need for robust and scalable data governance frameworks. Traditional governance approaches, which rely heavily on manual processes and static rules, are increasingly inadequate in addressing the complexity, velocity, and variety of modern data ecosystems. This paper explores next-generation data governance strategies that integrate machine learning (ML) techniques to enhance scalability, automation, and decision-making in cloud environments. Machine learning enables intelligent data classification, anomaly detection, policy enforcement, and predictive analytics, thereby reducing human intervention and improving governance efficiency. The study examines how organizations can leverage ML-driven governance models to ensure data quality, compliance, security, and accessibility while supporting real-time decision-making. Furthermore, it discusses the architectural considerations, implementation challenges, and ethical implications associated with deploying ML in governance frameworks. By analyzing current trends and methodologies, this research highlights the transformative potential of combining cloud computing with machine learning to create adaptive, resilient, and intelligent governance systems. The findings suggest that ML-enabled governance not only enhances operational efficiency but also empowers enterprises to derive strategic value from data in a rapidly evolving digital landscape.
Keywords: Data governance; machine learning; cloud computing; data quality; data security; automation; enterprise decision-making; data compliance; predictive analytics; scalable systems