Clean master data before automating a workflow

A workflow can follow its rules exactly and still route work to the wrong team. The input may contain duplicate suppliers, inactive products, or missing location codes. Master data means the shared reference records that operational transactions use. Clean these records according to the decisions they control, not an abstract goal of perfect data.

Start at the next automated decision

List the fields the proposed automation reads. For each field, describe what happens if it is missing, stale, or ambiguous. An old marketing description may be tolerable. An unclear routing location should stop dispatch until someone resolves it. This ranking keeps cleanup tied to operational risk.

Choose a working owner for each catalog. Give that owner a way to approve corrections without editing unrelated transaction history. Keep the submitted value alongside its normalized form when staff need to understand why a change occurred.

Separate mechanical checks from judgment

PostgreSQL provides not-null, unique, check, and foreign-key constraints for different data restrictions.[1] These can reject missing required values, invalid relationships, or prohibited duplicates according to the configured rules. They do not establish whether a supplier is the intended legal entity or whether a business description remains useful.

Use automatic checks for clear rules, then route uncertain cases to review. Normalize spacing and agreed code formats before matching records. Do not merge two entries merely because punctuation differs. Shared addresses and shared contact numbers can represent legitimate relationships.

Hypothetical example: maintenance request routing

A hypothetical facilities team wants requests to select a contractor automatically from an asset category. The category catalog includes retired labels and several spellings of the same equipment class. The team first maps approved synonyms to stable category codes.

An owner reviews ambiguous assets instead of guessing their category. New requests with unresolved categories enter a visible manual queue. The automation starts with approved categories only. This limits initial coverage, but the team can explain why each request took its route.

Release a controlled subset

Establish acceptance rules before a cleanup sprint. Test the cleaned subset through the actual workflow, including rejected inputs. A catalog that looks tidy in a spreadsheet can still fail if the workflow expects a different code or interprets blank values differently.

Cleanup is continuing work, not a gate that stays closed until every record is ideal. Define how new records enter, how owners retire old values, and how operators report mistakes. Reserve time for exceptions so the automated route does not become a hidden backlog. Keep a sample of rejected records so the catalog owner can improve the entry rules without hiding unresolved work.

• Identify required fields for each automated decision.
• Approve canonical codes and a synonym mapping.
• Assign reviewers for uncertain matches and missing values.
• Test inactive records and unavailable references.
• Measure unresolved exceptions separately from completed cleanup.