Session Outline
The adoption of AI has quickly gained widespread adoption, prompting Data Science teams to build and deploy ML models to increase organizational productivity. However, there significant challenges in maximizing the value of those initiatives, including: data availability, data quality, lack of trust in data, lack of cross-team collaboration, and data compliance issues. There is a growing concern about the data used to train those models, and the organization risk that that creates. Organizations need solutions to enable the governance of their AI initiatives by helping find the trusted data, trust that the model is being trained on accurate data, and that datasets and AI models are actively governed. Join this discussion on the importance of AI Governance and trusted data for executing critical data & AI initiatives.
Key Takeaways
- The significant role of data intelligence, data culture, and governance for AI
- Importance of using the right data to feed and train enterprise AI models
- Ensuring active data governance for your enterprise data & AI objects to remain safe and compliant
- Building a culture around data within an organization, maintaining trusted data, and active governance when planning or executing AI initiatives