I've found that the "Purpose" tag rule works best when it's paired with a short, standardized vocabulary. If you let people free-text it, you just get a different kind of inconsistency. We enforce a dropdown with maybe eight options like "Core Metric," "Exploratory Analysis," "Operational Report." It turns that moment of thought into a clear signal.
On the external lineage, that's a tougher one. Relying on manual notes in the description is brittle. We tried to solve it by having our dbt CI job automatically update the description of the corresponding Hailuo source asset with a link to the dbt docs and the model name. It's a hack, but it makes the external dependency visible without relying on human memory.
Garbage in, garbage out.
That's a really clever hack! Automating the description update from the CI job feels like the only scalable way to handle it. But doesn't that require engineers to set up the whole CI/CD workflow in the first place? Or is there a lighter way for an ops team to get that link in place?
The initial setup might be straightforward, but that's the easy part. The real test is when you're six months in and the inevitable schema change happens. Your neat UI pipeline shatters because there's no concept of contracts or tests. An ops person can restart a container, but debugging why all the dashboards broke requires understanding the data, not the platform.
SQL is enough
Exactly right. That's when the governance and the system you built around the tool becomes the real product. An ops person can learn to write a data contract in plain English, like "this field must always be populated with a valid email format." The problem isn't writing the rule, it's creating the mechanism that enforces it and alerts *before* a change merges.
So the question shifts from "can ops handle it" to "has the team invested in building those guardrails?" Without them, you're just waiting for the breakage.
Raise the signal, lower the noise.
You're so right about the data modeling part. That's exactly where we got stuck after the initial excitement wore off. Our ops person built a few really useful dashboards, but when we started trying to combine customer support ticket data with our Shopify orders, everything just fell apart. The joins were possible, but the logic was spread across like five different UI transforms and nobody but the original builder could untangle it.
Your point about maintaining it being a nightmare is spot on. We ended up with twelve slightly different versions of "customer lifetime value" because each department built their own without a shared model. The lesson for us was that the ops person needs to learn a bit of that engineering mindset *before* they start building, even if they don't write the production code. Like, agreeing on a single source of truth for core business entities upfront. It's a governance and design problem more than a pure tool skill.
If it's not measurable, it's not marketing.