Join us for this free 1-hour webinar from GigaOm Research and Dataiku. The webinar features GigaOm analyst Andrew Brust and special guest, Patrick Masi-Phelps, from Dataiku, a leader across the entire AI data lifecycle.

In this 1-hour webinar, you will discover:

• How to assess data comprehensiveness and inclusion
• What data explainability is and how it ties into data quality
• The ramifications of data quality in AI as they relate to fairness

Why Attend

Assuring data quality inspires trust, increases confidence, incorporates data, and with it, promotes adoption of data-driven practices. That’s why data quality has long been important in the world of BI and data analysis. But in the context of machine learning and AI, data quality is held to even higher standards.

With training data for machine learning models, new criteria, like statistical soundness, “explainability,” data ownership, and governance, come into play. And the more classic data quality requirements are still well in-place, too. Can your team learn and appreciate the new criteria for data quality and enforce their application? The answer is yes, but to get it done, you’ll need to instill new thinking, new practices, and new platforms.