Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

Data catalogs suffer from a high failure rate that is often blamed on poor technology choices but are more often the result of nontechnical issues. This session will provide insight to data and analytics leaders in evaluating metadata management requirements, assessing readiness of organizations and best practices for getting started to implement a data catalog.