1. Your output isn't a dashboard or a chart, a data model or a single insight – it's knowledge and understanding. 2. Don't reduce your work to just a "decision factory." It's not only about making decisions. 3. Many decisions don't need data.

One specific talk I'm looking for: a strong usecase of using LLMs (or other ML models) for data cleaning, data processing and just plain old analytics. We've dabbled with "text-to-sql" types of talks in the past, but would love to lean more into the data team doing data analysis work with ML/AI.

Hey data nerds! You have just a week and half left to submit a talk to Data Council '25 (in SF next year!). I'm running the analytics tracks. Any interesting ideas we should be covering on the analytics track? Any brilliant people that fly under the radar that we should elevate?

Text-to-sql is just being oversold today. Text-to-sql is being sold as a way for the CEO to replace the data analyst. When in reality it's a copolit for sql writers (which is awesome and really helpful). All about expectations.

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