Most AI data analysis projects don’t fail because of bad models. They fail because the data underneath isn’t trustworthy, isn’t legible, and isn’t anchored to anything a business person would recognize. Point the best LLM in the world at a 120-table sandbox of cryptic column names and undocumented joins. You’ll get a confident, wrong answer every time.
The fix is context engineering. Three layers: Data Trust (DT), Data Experience (DX), and Context (CTX). Data Trust means automated tests and quality gates so the model isn’t reasoning over silently broken data. Data Experience means curating schemas and column names so the model can find what it needs. Context means encoding the business definitions: what “active customer” really means at your company, which joins are valid, which metrics are canonical. With those three in place, the model behaves like a domain expert. Without them, it’s a clever stranger guessing.
In this on-demand webinar, we walk through the formula on real Snowflake and Databricks data, including how we took a 120-table pharma sandbox and turned it into a schema an LLM can actually work in. You’ll see before-and-after, the tests that catch context drift, and the pipelines that keep each layer current when the data or the business rules change.
If your AI data analysis is “almost working” but you can’t trust the answers in front of a stakeholder, this is the session for you.
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