Guide
What is schema drift, and how do you manage it?
Schema drift is change in the structure or meaning of source data over time: new fields, renamed fields, changed types or a field quietly reused for something else. It matters because downstream mappings, reports and AI tools assume the old shape.
Why does schema drift cause problems?
Problems usually surface where data is consumed, such as a dashboard that stops matching finance's numbers, long after the change was made upstream. Fixes then get applied in the report instead of at the source, and definitions drift further apart.
Example: a field changes meaning
Illustrative example. A billing system adds a new status value, "P" for paused, to status_cd. The common data model maps status_cd to record_status, where only active and closed were expected.
The change is detected and proposed as an update: add paused to record_status and decide whether paused customers count as active in reporting. A reviewer approves the change, and the updated definition is published to the systems that rely on it.
Are changes applied automatically?
No. In Neuroplastiq, proposed updates go to your team for review. Changes are not applied to the model without someone approving them.
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Content owner: Gareth Sutton. Last reviewed .