A storage platform can enforce any retention clock you hand it. What it can’t do is know which clock a record is on, and the only moment that answer can enter the system is the write. So the real policy decision is a schema token: required, or defaulted?
Naming a system of record settles which copy to believe after two numbers already disagree. The fork itself gets created at write time, by every screen that leaves a copied field editable.
Storage is cheap, so the instinct is to keep everything. In a regulated shop that instinct is a second way to be out of compliance, because some records you’re required to destroy on a clock.
Your audit trail proves the raw record wasn’t tampered with. It says nothing about which definition turned that record into the number you put in a deck, and definitions move.
When an agent generates a query, it binds to the column name, not the steward’s definition. A misleading name used to be a nuisance a new hire tripped over once. Now it’s a hazard that ships the wrong number every run.
Every governance program is built to add definitions and none of them can retire one. In a regulated shop that gets worse, because the instinct to keep every record gets misread as a ban on ever deprecating a definition. They’re different objects.
The whole agent-reliability project is trying to engineer the one thing a tool can’t have: accountability. Data governance already named this. The agent is a custodian, and we keep trying to promote it to owner.
The samples post ended on seating a data steward. This is what you build around that seat in a regulated life sciences org: the roles you actually need, the order to do it in, and why the regulation is the only budget argument that ever works.
Someone asked how many samples we have and three systems gave three different numbers. The dependency order behind vocabulary, ontology, semantic layers, catalogs, and contracts, and why the piece everything stands on is a role most orgs never seat: the data steward.
Enterprise data strategy has moved its destination from ‘break the silos’ to ‘vectorize everything.’ The step in between is the layer that makes vectors actually work, and it’s the one most roadmaps quietly skip.