Why most organizations are solving the wrong data problem for AI

Justin Heller spent nearly 11 years building an enterprise data management program from the ground up, most recently as former SVP and Chief Data Officer at Synchrony Financial. He joins Saket to unpack the discipline shift that most organizations miss: structured and unstructured data have to be managed differently.

The rise of generative AI has exposed a relevance gap, the piece most data quality programs were never built to measure, tied to the absence of a records management discipline capable of categorizing, labeling, and disposing of files at scale.

Topics discussed:

• Building a data management operating model from the ground up
• Applying industry frameworks to establish a common data management language
• Detecting versus preventing bad data through quality controls
• Managing metadata across structured and unstructured repositories
• Redefining ROI as relevance of information in the age of generative AI
• Why records management is having a resurgence because of AI
• Avoiding a police-state approach to data management
• MVP-first approach to scaling AI foundations enterprise-wide