And so- What do, uh, data practitioners need to worry about in terms of, uh, preparing for this agentic world?

Well, I think this is a thrilling time to be a data person, um, in this agentic world personally, but I do think it is changing the game a little bit.

Um, and well, one of the fundamentals of great data work and great data science work is building trust in your objectivity and building trust in the underlying data quality and data assets that you're using, right? So people need to know when you're doing a, you know, deep analysis of something that you're using the correct data, that you understand the problem space, and that you are speaking objectively as you tell the story of what you found.

And in some ways, that is the same issue we're having right now with AI, where AI is making it easier to do a lot of the analysis faster, right? So I can now throw a model at a problem of like, "Hey," like, "help me understand the drivers of change in this complicated space," or, you know, "Help me improve the forecasting model I have for X." That's, you know, it, it gives me a faster and maybe a, a deeper ability to drive at the problem space, but it still relies on having really high quality data that is well documented in a way that I am, I am confident that the agent is correctly utilizing the data set, and that as I get the sort of findings back from it, that I've been able to validate them and build confidence in a way that I can maintain the objectivity and trust that I have as the person speaking for.

And so in that sense, the old adage in data of garbage in, garbage out is the same.

Like you need to make sure you, you are avoiding that situation, you have high quality data going in, that your underlying data engineering is top-notch, that you have rich metadata and documentation around your data sets, that when you use a, a, you know, an agent to help with your analysis, that it is correctly interpreting your data set, right? And you're giving it the right product and business context to be able to do so.

And then on the flip side, that you're really, you are stress testing it, and like you, you are taking accountability as a data person for the work that is happening by that agent in that data, and that you can then interpret and speak for those findings in depth.

So do data teams have to spend their whole days like writing documentation to, say, describe data sets?