Data is the lifeblood of AI productivity. By its very nature, the rapid ramp-up of AI promises to manufacture more data, move it faster through more shadow pipelines, and create extraordinary demands on data quality efforts that are already under stress. AI blurs the boundaries between data producer, engineer, and analyst, promising access to new capabilities, but all too often demanding adoption at the risk of declining quality standards. There has to be a better way.

AI redefines the purview, reach, and scalability of data quality efforts, but it doesn't change their principles. This session focuses on how to apply proven best practices in what may seem like a completely new landscape. We will:

• Demarcate the infrastructure of AI as an information environment
• Show how AI "context" functions as data, code, and documentation all at the same time, and how these functions can be disentangled, assessed, and monitored 
• Highlight real-world examples for managing context in an AI-driven data pipeline in tools like Claude Code, Databricks Genie Spaces, and Snowflake Cortex
• Teach how to leverage proactive, practical data quality strategies and techniques as an accelerator for successful AI implementation

The goal of this course is to show you how to improve AI implementation and results by capitalizing on your data quality expertise and the established methods mapped out in the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2nd Ed. (Elsevier/Academic Press, 2021) by Danette McGilvray. We'll also show you how to wield innovative AI techniques within a clear, controlled framework to empower your own data quality initiatives.

Chip Bloche and Danette McGilvray combine their expertise (his technical/AI with Danette's data quality) to show how to effectively apply data quality principles and AI techniques to ensure the effectiveness of AI outcomes. At the end of this session, you will be primed to navigate new territory with new ideas and your own trusty compass.