AI 101 For Data Quality And Governance Professionals

Everyone at your company is suddenly "using AI." You're the one who gets asked whether the answers are right.

You've spent years learning how data goes wrong: duplicates, confusing data, columns that quietly changed meaning. Now that same data feeds AI prompts, agents, and chat assistants, and nobody has explained how those work or where they break. This session does that, in plain language, with no code, for people who already understand data and don't need to be talked down to.

Chip Bloche will start with the vocabulary. What an AI prompt actually is. Why the wording of an AI prompt changes the answer. What "context" means, and why an AI with the wrong context gives you a confident, well-written, wrong result.

Then we'll get into the failures, in your terms. Why does the same question give a different answer on Wednesday than it did on Monday, and how do you audit that? What happens when a table underneath the AI changes and no one is warned? Who owns the mistake when a hallucinated fact ends up in a board deck? These are questions about data quality, repeatability, and accountability. You've been asking them for years. AI just makes them happen faster and more expensive to ignore.

The through line is this: AI doesn't fix data quality. It amplifies whatever quality you already have. That makes your job more important, not less.

To make it concrete, Chip will show what checking AI's work looks like in practice, using DataOps TestGen to profile the data an AI is drawing on, run tests against it, and score it. You'll see what "grounded" actually means when you can point at the evidence. He'll show how using AI can help fix AI.

You'll leave with the vocabulary, the failure modes to watch for, the questions to ask your technical colleagues, and a clear picture of how AI extends the data quality skills you already have.

Bring your lunch and your questions.

Your Presenter

Chip Bloche

VP, Data Engineering and Quality Innovation, DataKitchen

VP, Data Engineering and Quality Innovation at DataKitchen and principal architect of TestGen. Over 30 years designing OLTP databases, systems integration, and data warehouse solutions for BI and ML applications.