Missing values become fabricated facts. The useful response is a targeted correction with an observable acceptance check. That keeps the repair tied to the problem instead of turning it into an open-ended redesign.
Find the likely cause
The workflow treats blanks as an invitation to infer.
Treat this as an explanation to verify against the actual work. Look at the input, the relevant decision, and the final result together. If the evidence does not support this diagnosis, investigate the mismatch before applying a convenient but unrelated fix.
Make the targeted correction
Preserve unknown values and define safe fallback behavior.
Clean inputs make analysis and personalization more dependable by reducing ambiguity before generation begins.
Check that the repair worked
Inspect outputs generated from intentionally incomplete records.
Repeat the check on the final version that the reader or customer will encounter. An approved draft, a preview, and a published result can differ; the acceptance decision should concern the version people actually use.
Prevent the next related failure
A separate issue to watch for is this: campaign categories mean different things across files. Create a shared field dictionary and map legacy values explicitly.
Monitor the useful outcome
Track invalid records, unresolved fields, and downstream errors attributable to input quality.
Keep a short record of the original symptom, the evidence behind the diagnosis, and the result of the acceptance check. That record makes the solution reusable when the same condition appears again, without assuming that every superficially similar problem has the same cause.