How can we inspect the quality of generated set-up data?

Quality in set-up data shows up in consistency between records, not in how fluent any one record reads. A buyer, or a reseller reviewing a sample before listing it, can inspect several layers.

Start with referential integrity. Every account should point to an existing approval and party, every task to an account, and every event to a real entity. Orphans suggest weak generation.

Next, check policy coherence. Take a sample of cases, read the stated rules, and test whether the generated end state follows them. Conditions on the approval should appear as tasks or constraints on the account. Waivers should come from a role allowed to grant them.

Then look at the exception mix. A set that contains only clean cases will train an agent that fails on the first duplicate party. Ask what share of cases are exceptional, and whether the exceptions are varied or repeated templates.

Finally, inspect grounding and disclosure. The data is generated, so check for near-copies of any source pattern, for rare combinations that could point to a specific organisation, and for identifiers that look like real ones.

For training, these checks decide whether the data teaches the right behaviour. For evaluation, they decide whether a pass or fail is meaningful.