Decision and expected population
Clarify the business or control decision, the complete population required and material critical-data elements.
Control-led approach
Start with the expected outcome. Trace backward to the data and controls that make it defensible.
DQIntegrity combines data-journey analysis, control architecture, evidence challenge and practical remediation. The method is designed for complex environments where ownership and failure modes cross systems, functions, entities and providers.
Visual framework
From Data Integrity Diagnosis to Defensible Outcomes
The practical route from structural exposure to proof, control design, remediation assurance and sustainable ownership.
Six stages
A structured route from uncertainty to sustainable control.
End-to-end data journey
Trace source, movement, transformation, enrichment, storage, consumption and output.
Failure modes and assumptions
Test where records can disappear, duplicate, change meaning, arrive late or bypass expected controls.
Preventive and detective architecture
Design reconciliations, correctness tests, thresholds, exception routes and ownership.
Operation and accountability
Determine whether control execution, investigation, escalation and remediation can be demonstrated.
Sustainable monitoring
Integrate reporting, governance, root-cause treatment and change control into the operating model.
What DQIntegrity does not do
Clear positioning protects independence.
DQIntegrity provides
- Independent diagnosis and control challenge
- Requirements and control architecture
- Assurance of delivery and evidence
- Remediation planning and senior reporting
- Specialist or retained advisory
DQIntegrity does not claim
- To replace legal, audit or regulated advice
- To be a software product or data-quality tool
- To certify AI models or regulatory compliance
- To own client decisions or final regulatory submissions
- To provide unlimited staff augmentation
Control confidence should be evidenced.