Data Quality and AI Strategy: Garbage In, Gen AI Out

Danette McGilvray, a leading voice in data quality, delivers a clear message: AI strategy without data quality is a house built on sand. The conversation covers practical frameworks for assessing and improving data quality, why generative AI amplifies data problems rather than solving them, and what healthcare organizations must get right before investing in advanced analytics.

Generative AI does not fix bad data; it amplifies it at scale and with confidence. When garbage data enters generative models, the output appears sophisticated and authoritative while remaining fundamentally unreliable. This creates a false sense of trust in outputs that mask underlying quality problems.

Data quality must be treated as a strategic investment, not a cleanup project that happens after AI deployment fails. Healthcare organizations that succeed invest in data governance long before they invest in machine learning models, treating data quality as foundational rather than preparatory.

Measuring data quality requires frameworks that connect data integrity to business and clinical outcomes, not just technical accuracy metrics. A field might be technically complete while containing clinically dangerous values that no statistical audit would catch.