Completeness
Did the full expected population arrive and remain intact?
Explore completeness controls →Data Quality & Integrity knowledge resource
Understanding data integrity in decision-critical systems.
DQIntegrity is a non-commercial professional knowledge resource exploring how organisations can understand, evidence and strengthen the completeness, correctness, traceability and control of important data.
No services are offered through this site. The material is published for professional learning, analysis and reference. There is no contact form, enquiry route or commercial engagement pathway.
A simplified view of the decision-critical data journey: Source → Move → Transform → Control → Decide. Integrity must be evidenced across the route, not inferred from the final output.
Core idea
Stable outputs do not prove sound inputs.
Decision-critical systems can continue to operate while records are missing, mappings are wrong, meanings drift or controls fail between organisational boundaries. Data integrity therefore requires more than a quality score or catalogue entry.
Correctness
Did values, mappings and meaning remain right through the journey?
Explore correctness controls →Traceability & control
Can the route, transformation, exception and accountable decision be reconstructed?
Explore the framework →Original framework
End-to-End Data & Control Integrity
A seven-layer way to reason from business event and expected population through movement, transformation, control, use and accountable outcome.
Knowledge map
Explore by question, framework or application.
Data quality vs data integrityWhere dimensions end and proof begins.Continuous control monitoringHow persistent evidence differs from periodic assurance.A data catalogue is not a controlWhy visibility and lineage are necessary but insufficient.AI, automation & synthetic dataData provenance, monitoring and reconstructable evidence around automated decisions.Financial crime & AML dataIntegrity of the data used by monitoring and control processes.Banking & paymentsIntegrity across regulated, high-volume and decision-critical flows.
Evidence
Frameworks should be tested against real failure patterns.
The Global Data Integrity Evidence Library separates official findings from DQIntegrity interpretation and links back to primary sources wherever possible.
Explore the evidence library