From Broken Data to Trusted AI

AI does not scale on hope, dashboards, or one heroic data scientist. It scales on data the business actually trusts.

This talk explores how actionable data quality metrics create the foundation for reliable AI by turning data quality into an operational partnership with the business. Instead of treating data issues as technical defects, we use metrics to expose where data breaks, how it impacts decisions, and what must change upstream to fix it.

As data quality becomes measurable and owned, organizations unlock AI use cases that were previously risky or impossible. Forecasts stabilize, automations become dependable, and AI agents can operate with confidence rather than constant human intervention.

Attendees will learn how to design data quality metrics that drive accountability, strengthen business partnerships, and move AI initiatives from experimentation to production without gambling on bad data.