Most data systems don't break. They slowly forget.
A definition shifts. A metric gets reused in a new context. An assumption outlives the reason for it. Nothing crashes.
Why this isn't a data quality problem
Data quality tooling checks whether values are within expected bounds. Drift is different — the values are fine. The meaning moved.
| Event | Data quality check | Reality |
|---|---|---|
revenue now excludes intercompany |
Passes | Every trend line has a discontinuity |
| Churn window changed 30→60 days | Passes | Two teams' numbers stop agreeing |
| Table repurposed after migration | Passes | Old dashboards read it as if unchanged |
| New segment definition | Passes | Historical comparisons are meaningless |
Every one of those passes every test you have. Dashboards still load. Confidence quietly erodes until someone finally says "I don't trust this number" in a meeting, and nobody can trace when it stopped being true.
Drift is technical debt with no ticket
That's what makes it insidious. Code debt is visible — you can see the TODO, the failing test, the module nobody will touch. Semantic debt has no artefact. It accumulates in the gap between what a definition says and how it's actually used.
What catches it
- A behaviour layer — statistical profiles of how concepts are actually queried
- Comparison of declared definition against observed usage
- Alerting on divergence, not on value ranges
- Versioning, so the moment of change is recoverable
- Autonomous rebuild, so the fix isn't a quarterly review
The mechanism matters: you detect drift by noticing that usage stopped matching declaration. That signal exists in your query logs already. Almost nobody reads it.
The full breakdown — how drift accumulates, the detection mechanism, and why autonomous maintenance is the only sustainable answer — is here:
👉 Stop Semantic Decay: Why AI Needs an Autonomous Compiler
Originally published at colrows.com/blogs/knowledge-drift-and-semantic-decay
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