Data Quality in the Age of AI: Building Trustworthy Foundations for Intelligent Systems

As organisations accelerate their use of AI, data quality has moved from a back‑office concern to a board‑level risk. Poor‑quality data undermines model performance, introduces bias, and erodes trust in AI‑driven decisions. This webinar explores what “good data quality” really means in the age of AI, and how organisations must evolve their quality practices to support machine learning, analytics, and generative AI use cases. Attendees will learn how to assess AI data readiness, prioritise quality improvements based on risk and value, and embed continuous quality controls that scale with AI ambition.

Key Discussion Points

• Why traditional data quality approaches fall short for AI and machine learning
• Identifying and prioritising data quality risks that directly impact AI outcomes
• Designing continuous, scalable data quality controls for AI‑driven environments