And

this is something that I advocate for as well with stream users and data engineers and data teams in general. Yeah, think about that product that you're shipping to the end user. And as much as you can, get your requirements, actually tie it to whatever OKRs that your business has that are related to the upstream data that you're serving because that will actually, and well, it should definitely influence a lot of the decisions you make in your data architecture because what you're able to actually ship in terms of data freshness SLAs, data uptime SLAs, this is the schema, this is the contract. It's not going to break. And if it does break, then it's a seven one issue that the whole company has to band around to fix. And this is one of those things that I see it does get overlooked because a lot of data projects are just innately very technical. We got to build this pipeline or we got to ship this new model or things along those lines. But a lot of the times the technical requirements, and this is all products where the technical requirements will miss on some key business requirement. Data teams have to spend a little more effort because they're bridging the gap between multiple groups, right? Because they'll hook up with engineering systems that have a prod operational database. They'll hook up with marketing and CRM tools. They're really trying to build that single source of truth. So they're juggling all these stakeholders and ultimately delivering reports and operational analytical systems that are either externally visible to customers or internally visible to high leverage stakeholders like executives who want to say, how is the business performance forming this month? So, you know, and I think this is just something that we have to keep advocating in terms of, hey, think about your data as a product. Think about the goals of your end user, the OKRs of your end users and the ABCs and going all the way to DEF, all the things that you mentioned as well. You know, I think that all teams should start with that as a blueprint.

Yeah, I agree with that. And I think that's why people are so excited about data products. And you actually, in kind of what you said there, it kind of like DNC of the ABCs, which is like downstream consumers, like various people, applications, use cases, they depend on these data products. And so you need to understand like, how is it going to be used and actually document that, right? And say, hey, these are the supported use cases. These are the ones that aren't.