Automation feels like the answer when a catalog is too large to edit manually. But scale is exactly why the assumptions need to be inspected first.
A script that is right for 90% of records and silently wrong for 10% is not nearly correct. In a large catalog, it is a fast way to create a second cleanup project.
Do not automate the action until you can classify the exceptions.
Establish the real source state
Start by measuring the source formats, identifier quality, missing values, duplicates, and vendor-specific differences. A sample that contains only the cleanest vendor does not describe the system.
Separate rules from guesses
Some transformations are deterministic: trimming whitespace, normalizing a known field name, or validating an identifier pattern. Others are interpretations. Keep them separate so uncertain enrichment can be reviewed or withheld.
Design the refusal path
The most important output is often the exception queue. A useful system explains why a record was not changed and what evidence would make it safe.
Run in bounded stages
- Parse without writing.
- Compare intended output against current store state.
- Review exceptions and representative records.
- Apply a limited live batch.
- Verify platform state and logs.
- Expand only when the evidence supports it.
Automation earns speed after it earns trust. The audit is what makes that possible.