In the rapidly advancing fields of Data, Artificial Intelligence and Machine Learning, Data governance is essential for ensuring the ethical, effective and secure management of data. Many large data organizations face challenges in establishing effective data governance infrastructures to support advanced data uses, interoperability, robust data security, and high-quality data. This is especially acute when dealing with heterogeneous systems and the need to share data with numerous external organizations. To address these challenges, data organizations must adopt data governance frameworks that prioritize interoperability, data privacy, security, and data quality. This requires establishing standards not only for data structure, storage, and usage within organizations but also for how data is governed and circulated throughout the data ecosystem. This paper argues that all these cannot be achieved without a unified approach to information governance across organizations. Lack of coherence can lead to data inaccessibility, decreased efficiency, poor data quality, increased security risks, and compromised data integrity. To enhance adaptability, flexibility, and efficiency in data usage we propose a unified framework that integrates the principles of information, interoperability, security, and data quality governance. This paper explores the key elements of unified data governance, the challenges organizations face in implementing it, and best practices for success. Furthermore, we explore a proposed architecture for the unified governance framework and the role of different components in supporting this framework, including data cataloging, metadata management, access control management and data security monitoring and alerting. There is also a section which aims to further the discourse on what data governance looks like across the AI/ML data lifecycle.

AI Data-catalogs Data-governance Data-security Unified Machine Learning