Episode 38 — Validate Data Quality Early: Completeness, Accuracy, Labeling, and Lineage (Domain 3)
Data quality is the most significant determinant of AI model performance and reliability, a key principle of Domain 3. This episode covers the technical aspects of data validation, including checking for completeness, accuracy, and the integrity of data labeling. For the AAIR exam, candidates must understand how poor data quality can lead to "garbage in, garbage out" scenarios where even the most advanced models produce erroneous or biased results. We discuss the importance of data lineage—knowing where data came from and how it has been transformed—as a prerequisite for both quality control and regulatory compliance. Examples of common data quality failures include inconsistent time-stamps in time-series data or noisy labels in supervised learning sets. By implementing automated data quality checks early in the pipeline, risk professionals can prevent flawed data from poisoning the training process, thereby ensuring the resulting model is as robust and trustworthy as possible. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
