Repeatability
High
Data cleaning pipelines are structurally identical across runs — normalize SKUs, resolve duplicates, impute or flag missing fields. The same logic applies to every row, making this highly automatable.
Ambiguity Tolerance
Medium
Most success criteria are crisp (no duplicate transaction IDs, consistent SKU format, valid location values), but the 15% missing store_location rows require a defined resolution rule — impute from nearby transactions, flag for review, or drop — which the user hasn't fully specified.
Data & Tool Availability
High
The CSV export is a self-contained file the agent can ingest directly. Standard tools like Python with pandas are sufficient; no external APIs or live system access are needed.
Error Cost
Medium
Errors in deduplication or location mapping could silently distort revenue reports, which is a real business risk. However, the original CSV is preserved, so mistakes are reversible — a human spot-check before using the output in reporting is prudent.
Human Judgment Required
Low
The cleaning rules are largely mechanical once defined. The only judgment call is how to handle unresolvable store_location gaps, which can be handled by a simple flagging rule rather than case-by-case human review.