Glossary
Data quality
How accurately the recorded data reflects what actually happened.
In full
Data quality in FE is not an abstract measure. It is whether the record matches reality: the right learner, the right aim, the right dates, the right outcome, the right evidence.
It is managed through routine: checks at enrolment, monthly checks against reports, checks before each return, and a year-end routine that leaves nothing to the final week.
Where this comes up
diagnostic
A learner is flagged on a data quality or funding monitoring report
A record meets the criteria of a report that identifies data worth checking. That is a request to investigate, not a statement that the record is wrong. Some flagged records are entirely correct and simply need their evidence to be locatable.
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The data quality routine that prevents most problems
A small number of checks, run every month without exception, prevent the majority of funding, performance and audit problems in further education.
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Working data quality and funding monitoring reports
Reports that flag records for investigation are the most useful assurance tool available, and the most commonly misread. A flag is a question, not a finding.
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Qualification achievement rates: how your data becomes your results
Achievement rates are calculated from the ILR. Understanding the cohort is what turns an unexplained rate into an explainable one.
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Getting it right at the front door
Most funding-critical data is captured at enrolment. Front-door quality is the cheapest data quality there is, and the only kind that scales.
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Building a funding map for your college
A funding map sets out every stream a college receives, how each is earned, when it reconciles and what data drives it. It is the fastest way for a finance or leadership newcomer to become useful.