The agreement layer for a 23-campus system.
CSU's student-data problem is not a technology gap. It is the cost of getting 23 campuses to agree on what data means — one decision at a time. This document scopes a three-week engagement that makes each of those agreements cheaper.
This page is our current understanding of the problem and the shape of a fix. It asks nothing of any campus. It exists to be corrected.
the problem isn't the data — it's the agreeing
Where it actually breaks.
Semantics
The same fact means different things on different campuses. Nine encodings of one term — and until governance conforms to one standard, the same question returns a different answer depending on which platform it's asked through (QuickSight Q, Databricks, Snowflake).
A team of seven and roughly a year to reconcile ONE field, manually.
Decision
No mechanism exists for 23 campuses to agree. Every standardization is a political negotiation with no neutral evidence base and no named sign-off.
Somebody has to sign off and say this is what we have all agreed to — and nobody can.
Decay
Finished projects don't stay finished. The crosswalk is a completed project with no maintenance owner. Drift resumed the day it shipped; the next field starts from zero.
The year of work depreciates like a car.
The status quo has a price: every new field is another team-of-seven project, and the last one started depreciating the day it finished.
so don't ask anyone to change — change where meaning lives
No campus is ever asked to change anything.
FA26, F226, and F26 all remain valid at the source, forever. Campus Solutions instances remain the systems of record.
The canonical definition exists only in the overlay — and each campus ratifies only its own mapping into it. Adoption costs a campus nothing and takes nothing away from anyone.
here is what that looks like in your environment
The system.
This is what we understand today. Correct anything — every correction makes the report better.
Each campus's mapping row turns cyan as its representative ratifies it — agreement you can watch happen. Illustrative.
Reads schemas, the existing crosswalk, glossary exports.
Watches for new variants against ratified definitions; drift becomes a new recommendation, not a surprise.
Drafts each canonical definition with its evidence and the alternatives it rejected.
Each ratifies only their own mapping row. Nobody signs for the system.
Convenes the process; never signs for the campuses.
This band is what the Decision Records section shows one entry of.
Fig. 1 — your estate flows as it always has. Agents read metadata and draft. Humans ratify. The overlay remembers.
and here is the loop that runs inside it
How the agreement layer works.
Observe
Read metadata and the existing crosswalk — ground truth CSU already owns.
you see: every variant we found, listed
Recommend
AI drafts the canonical definition with its evidence and the alternatives it rejected.
you see: the draft definition, its evidence, and what we rejected
Ratify
The step that leaves the agents and enters the humans.
you see: your campus's row, and only yours, awaiting your name
Record
Who agreed, to what, why, and under what conditions — one lookup, forever.
you see: who agreed, to what, and why — one lookup, forever
Maintain
Drift detected against the ratified definition; changes re-enter at Recommend.
you see: drift flagged the day it appears, not in a dashboard later
AI arbitrates facts. Humans arbitrate values. The record is the product.
every pass through the loop leaves one of these behind
What one settled decision looks like.
records accumulate into waves
Where this goes.
Readiness
This document.
a plan your team has corrected, in decision-record form
Term pilot
Canonical Term in the overlay, graded against the team-of-7 crosswalk, distributed ratification with real campus reps. Runs in CSU's AWS.
one field the whole system agrees on — graded against your own crosswalk
Governed expansion
Student/Person slice, FERPA-tagged, PII masking — the stepping stone to de-identified cross-campus IR datasets.
the same agreement machinery on governed, FERPA-tagged data, in your AWS
The 23-campus agreement layer
Every new definition enters as a decision record; drift is detected, not discovered in a dashboard.
a standing process: definitions enter as drafts, exit as agreements
The mechanics above are not a proposal — they run in production elsewhere:
A 350+ facility logistics operator
Unified data dictionary with full lineage, ontology-first. Facilities were never homogenized.
A global storage manufacturer
3,000+ tables from 10+ enterprise systems into a governed medallion architecture on AWS. Each table reviewed and approved by the client's own data team; seven quality dimensions and per-row provenance; run inside the client's own AWS account.
No campus changes anything. The system finally agrees.
Where we'd start: one small field — academic term — graded against work your team has already done.