One of the recurrent things I hear from data leaders is they they lack the confidence to use the data that they have across the organization because it doesn't have all the context needed to understand it. And so the CEO gets a hold of Google Analytics, and instantly they ask questions like, why is this conversion rate higher than this one? What are we not doing here that we're not doing the other place? And not understanding maybe that there's different levels of intent behind that action, that the way we're sequencing people through those moves just means they're going to be different. This is a really simple example of taking the BigQuery data behind Google Analytics and just modeling it out as a metric tree. So we can go from, like, the contribution of different sources to to raw website sessions, so really low intent actions, to to people that then go on to view the pricing page, to people that sign up for a newsletter, maybe something that's quite medium intent, but not very high intent. And then very high intent signals, like signing up for a demo or activating a premium trial. And we can see the total contribution that these make to our North Star, which in this case is the number of generated leads. So because of this, we keep all that context. We can lay it out in a way that's really easy for anyone in the business to understand. But we've still got the fidelity to be able to go, well, I want to explore this number further. And like I would in Google Analytics, I want to see, you know, the classic one, what countries are contributing to these events. And then I can go back to my original canvas without having changed anything. So this becomes a really helpful artifact to share around the company for them to be able to see our data and understand it in a whole new way.