Data Quality and the Rising Cost of Data Debt: Why AI Is Forcing a Reckoning with Long-Standing Data Quality Problems

According to the 2026 State of Data Integrity and AI Readiness, data quality has become the most urgent — and most underestimated — challenge standing between organizations and AI value. Years of deferred data hygiene have compounded into a growing data quality debt, and AI’s intolerance for bad data is exposing the consequences faster than ever.

In this session, we’ll dig into why data quality now tops the list of data integrity priorities, how poor-quality data undermines AI training, inference, and agentic decision-making, and why “fix it later” is no longer a viable strategy.

Expect a straightforward conversation about why data quality has shifted from an IT concern to a C-suite risk — and how addressing it unlocks more reliable, scalable, and trustworthy AI outcomes.