March 21, 2024
In today’s rapidly changing digital environment, data governance is essential for organizations seeking to manage their data more effectively.
There are numerous reasons why data governance has become so critical. Organizations are now grappling with numerous data types, ranging from structured data to newer data types such as text data, machine data, image data, and audio data. AI/ML models also need to be governed to ensure that the input to and the output from these models is trustworthy.
Traditional data governance objectives (e.g., ensuring data accuracy, consistency, and compliance) remain foundational. However, they now must be integrated with new concerns, including the management of diverse and voluminous data, ethical data use, and the governance of AI/ML assets—all of which involve technology considerations.
This TDWI Checklist Report examines five important pillars and best practices for the comprehensive governance of modern data- and AI-related assets.