Give important fields a definition
A field named “date” may describe when a request was received, when it was updated, or when work is due. Those meanings are not interchangeable. A study begins with the records a feature needs, the meaning of each important field, and the decisions that depend on it.
Identify the source of a change
If two tools hold the same detail, the team needs to know which one is responsible for updating it. A customer record and a work item may share an identifier without sharing every field. An early data map can show the permitted direction of updates and the information that should remain separate.
Examine what happens to imperfect records
A sample can include a missing value, a repeated identifier, an outdated entry, or a message that arrives twice. These examples help test the interpretation rules before a complete integration is built. An invented or appropriately redacted dataset is often sufficient for the first discussion.
Make assumptions visible
A useful output might be a field dictionary, an example message set, or an annotated information map. It should say which source documents were available and which relationships remain assumptions. A clean sample does not prove the quality of an entire database. Access rights, personal information, and any real transfer of records require separate arrangements. The examples here are explanatory and contain no customer data.