Artificial Intelligence Powered Real-Time Data Governance Architecture for Enterprise Lakehouse and Analytics Platforms
DOI:
https://doi.org/10.15662/IJEETR.2025.0706039Keywords:
Artificial Intelligence, Data Governance, Enterprise Lakehouse, Analytics Platforms, Real-Time Monitoring, Machine Learning, Data Quality, Metadata Management, Compliance, Data SecurityAbstract
Enterprises today generate massive volumes of structured and unstructured data across multiple operational systems, cloud platforms, and IoT networks. Efficient management, governance, and real-time accessibility of this data are critical for business intelligence, compliance, and analytics-driven decision-making. Traditional data governance approaches often struggle to ensure data quality, lineage, security, and compliance in dynamic, high-velocity environments such as lakehouse architectures that combine data lakes and data warehouses.
This research proposes an Artificial Intelligence (AI) powered real-time data governance architecture for enterprise lakehouse and analytics platforms. The framework integrates AI and machine learning to automate data classification, metadata management, anomaly detection, quality monitoring, and access control. By continuously analyzing data streams and monitoring governance policies, the system ensures consistent enforcement of data quality, regulatory compliance, and secure access across the enterprise.
The methodology involves designing the architecture, implementing AI-based governance modules, and simulating enterprise analytics workloads to evaluate performance, accuracy, and policy adherence. Results demonstrate that AI-powered governance significantly improves data quality, accelerates data-driven decision-making, enhances compliance monitoring, and reduces manual oversight. The proposed architecture supports scalable, real-time, and intelligent management of enterprise lakehouse platforms, enabling organizations to maximize the value of their analytics initiatives while maintaining robust governance standards.
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