Utarbeidet med KI.
A test conversion uncovers problems while they are still inexpensive to fix. Skipping it is the most common reason production deployment goes wrong.
The point is to uncover problems that only arise at scale: timeouts, unsupported characters, fields that are too short. A small sample will not reveal these.
Note how long each step takes. This forms the basis for planning the actual production deployment and determining whether you need a weekend or an evening.
Errors must be corrected in the scripts or source data, not manually in the sandbox. If you correct them manually, the error will reappear the next time the script is run.
Count the rows in each table in the source and in Dataverse. Discrepancies must be explained, not accepted—a lower count usually means that a filter has removed something you needed.
Select twenty customers you know and verify that contacts, sales opportunities, and addresses are linked correctly. The right number with incorrect links is worse than an obviously incorrect number.
Ask some experienced users to perform common tasks in the sandbox. They will spot things no checklist can catch because they know what the data should look like.
Sequence, timing, who does what, and what triggers you to abort. The trial conversion provides the figures that make the plan realistic.
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