Make hygiene a maintained workflow
Course overview · 4 min reading + 12 min practice, estimated
Principles and method
Assign owners to quality rules and create a queue for exceptions. Measure critical defects and correction time with clear denominators. Prevent recurring issues at collection or integration points rather than repeatedly cleaning exports. Schedule freshness review based on how quickly information changes and the purpose it serves. Keep a change log for bulk updates and validate a representative sample afterward. Data quality should be part of ordinary operation, with an explicit pause route when a defective import or integration spreads errors.
Worked example
An import repeatedly converts unknown availability into immediately available. The team stops the import, fixes the mapping and reviews affected records instead of asking recruiters to correct them indefinitely by hand.
Put it into practice
Design a weekly hygiene routine and an import failure response.
Use fictional information and keep your work in your own notes.
Compare your approach: self-review guidance
Include prevention, exception ownership and validation after repair. A useful routine focuses on consequential defects and does not reward staff for making uncertain fields look complete.
Sources and further reading
Original Academy teaching and fictional examples. These references provide context, not endorsement. Edition 2026.09; updated 2026-09-24.
- GOV.UK: Responsible AI in recruitment
UK guidance on procuring and deploying recruitment AI.
- NIST: AI Risk Management Framework
Voluntary framework for organising AI risks and controls.