Repeatability
High
The transformation rules are consistent: normalize priority strings, extract room/unit and issue category from free text, and flag duplicates. The same logic applies to every row, making this highly automatable at scale.
Ambiguity Tolerance
Medium
Priority normalization and category extraction have clear rules, but blank priority fields require inference from the description text, and deduplication across channels requires fuzzy matching — both introduce judgment calls where success criteria are not fully crisp.
Data & Tool Availability
High
The user has already compiled the data into a single CSV with all necessary fields. No external APIs, live systems, or permissions are required — just the file and a processing script or agent.
Error Cost
Low
The output is a transformed CSV, not a triggered action. Miscategorized rows or missed duplicates are easy to spot in a human review pass and trivially correctable before the data is used downstream.
Human Judgment Required
Low
Most decisions here are rule-based or pattern-matching. The edge cases — ambiguous priority on blank fields, near-duplicate requests with different wording — benefit from a light human review but don't require deep domain expertise or relationship context.