Prioritise quality by decision impact
Course overview · 4 min reading + 12 min practice, estimated
Principles and method
Data quality includes accuracy, completeness, consistency, timeliness and appropriate handling. A field can be complete but wrong, or current but irrelevant. Identify which fields affect contact, eligibility, reporting or commitments and prioritise them. Define validation rules at the right stage. Requiring every field at entry encourages invented values, while never validating critical fields creates downstream failures. Keep unknown explicit. Quality work should reduce a real error or uncertainty, not chase a perfect database without a purpose.
Worked example
A vacancy has a complete salary field but the value is monthly while reports assume annual. The quality rule checks units and meaning, not just whether a cell is filled.
Put it into practice
Rank ten fictional fields by consequence and define checks for the top four.
Use fictional information and keep your work in your own notes.
Compare your approach: self-review guidance
Include semantic checks such as currency and time basis. Explain when each value can reasonably be known and how missing information is routed for follow-up.
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.