Session Outline
Your organization’s data is only as valuable as your ability to make it easily discoverable, understandable, and readily available to the data consumers across your enterprise who can put it to best use. And in an AI era, it’s even more critical to ensure the quality of data you share is trustworthy, reliable, and AI-ready.
Key Takeaways
- Automate data profiling and data quality scoring using existing data catalog metadata
- Increase data quality visibility, beyond IT, for all data consumers to more quickly identify data fitness and establish data trust
- Certify AI models and supporting data is AI-ready for production use
- Leverage data observability to ensure your most-critical data sources, such as those supporting AI use, remains reliable
- Easily assess, drill into and collaborate around data quality issues to get to resolution faster
