AI-Driven Unified Data Governance Framework for Enterprise Platforms
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I2P109Keywords:
Data Governance, Artificial Intelligence, Machine Learning, Enterprise Platforms, Regulatory Compliance, Data Quality, Data Lineage, Automation, Data Mesh, Data FabriAbstract
As enterprises undergo rapid digital transformation, the volume, velocity, and variety of data have grown exponentially, leading to unprecedented challenges in data management, security, and compliance. Traditional, manual data governance frameworks are no longer sufficient to handle the scale and complexity of modern data ecosystems, often resulting in data silos, poor data quality, and regulatory non-compliance. This paper proposes a comprehensive AI-Driven Unified Data Governance Framework designed for modern enterprise platforms. By integrating artificial intelligence (AI) and machine learning (ML) at the core of the data governance lifecycle, the proposed framework automates critical functions such as data discovery, classification, quality assurance, lineage tracking, and policy enforcement. We present a layered architectural model that seamlessly operates across hybrid and multi-cloud environments, supporting paradigms like data mesh and data fabric. Through four detailed enterprise case studies spanning financial services, healthcare, global retail, and smart manufacturing, we demonstrate the empirical benefits of AI-driven governance, including up to 72% reduction in compliance reporting time and significant improvements in data quality and operational efficiency. Furthermore, we provide a comparative analysis of leading enterprise governance platforms and introduce a six-level AI Governance Maturity Model. Finally, the paper explores future trends, including the integration of Large Language Models (LLMs), autonomous self-healing data pipelines, and federated learning, providing a strategic technology roadmap for the next decade of enterprise data governance.
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