Data Quality Profiling
Data Quality Profiling groups the core ideas a learner needs at the 3 · data understanding & eda stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
CompletenessCompleteness is a practical concept within Data Quality Profiling. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02UniquenessUniqueness is a practical concept within Data Quality Profiling. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03ValidityValidity is a practical concept within Data Quality Profiling. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04ConsistencyConsistency is a practical concept within Data Quality Profiling. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Accuracy and plausibilityAccuracy and plausibility is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
06TimelinessTimeliness is a practical concept within Data Quality Profiling. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
07Automated profiling checksAutomated profiling checks is a practical concept within Data Quality Profiling. It helps turn the broader workflow stage “3 · Data Understanding & EDA” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.