Bringing Real, Accurate Data into Your Tree

And then we can bring data into the canvas. So here, I have some raw tables. So I have some data relating to companies, and I have data relating to deals, one through this company. And I start using SQL styles just to build out this analysis. So I've done a simple join on this information, and this will actually give me a lot of a a lot of the figures I need. I will be able to aggregate from this. But I also need to build in some snapshots in time of ARR. And for that, I've, had to consider the fields I have and the flags and the time frames and create some logic. So this is a really important place to be able to come in and define your metrics because ARR might be quite straightforward, but you might be dealing with something else, like cost of customer acquisition, for example, or customer lifetime value or something that just isn't as well defined. People might be using it differently in your organization, and here's a really good place to get people in, tag them, get them to work with the data, and agree on a definition that you can use across the board. Yeah. This is this is great. This is kind of just the one thing I think is really powerful about this is you as you pointed out here, here's a metric you may have built, a kind of an early version of the a metric a metric for the tree, but you've now of the whole thing. You've got the raw data, the the the analysis, the metric, and the structure, so you can really start to iterate through all these different elements together, and let them all cook as you build out.