No production system stays perfectly aligned with its original design.
Over time, systems drift.
New features are added. Integrations evolve. Data models expand to support new use cases.
Small changes accumulate until the system behaves differently than it did when it was first built.
Data drift is common
One of the most frequent forms of drift appears in data.
Fields are introduced with slightly different meanings. External systems send unexpected formats. Legacy records remain long after the system evolves.
Without careful validation and migration strategies, datasets can become inconsistent.
Integration drift creates subtle bugs
Integrations are especially vulnerable to drift.
External APIs evolve. New fields appear. Pagination behavior changes.
If integrations assume those systems remain stable, small changes can introduce subtle bugs that are difficult to detect.
Reconciliation keeps systems healthy
Long-running systems benefit from reconciliation processes.
These jobs periodically compare data between systems and correct inconsistencies.
Without reconciliation, small discrepancies can accumulate over time.
Systems that operate for years inevitably evolve.
The goal is not to prevent drift entirely.
The goal is to detect it early and correct it before it becomes operational risk.