Prerequisite: None
In today’s fast-moving AI landscape, effective data governance is key to getting the most out of AI systems. Many organizations are just starting to set up AI governance teams, often seeing it as an extension of their data governance efforts. This presentation will dive into how data governance plays a crucial role in AI development, ensuring quality outcomes and facilitating compliance with emerging regulations. We’ll explore the evolution of data governance models and ways to collaborate for smoother AI development.
AI is a priority for organizations looking to fully leverage their data, but it is essential to understand data quality and any restrictions that apply to use of data in AI. Data governance teams are uniquely positioned to support data science teams with critical metadata and risk-based access policies. Taking advantage of this opportunity can lead to establishing processes that support compliant data use while also improving model operations and developer experience.
What you will learn:
- Common themes in emerging AI regulations and what they mean for data governance
- How data governance has evolved along with suggestions to move into the AI future
- Intersections between data governance, AI governance, and data science
- How data cards and model cards can be used to facilitate transparency
- How understanding AI data needs can drive resources to help reduce data debt