Decision & expected population
What decision or control depends on the data, and what complete population should exist?
Analytical method
Start with the expected outcome. Trace backward to the data and controls that make it defensible.
The DQIntegrity method is a way to reason about complex data journeys. It is published here as a professional framework for general reference.
Six questions
From uncertainty to reconstructable evidence.
End-to-end data journey
Where does the data originate, move, transform and become decision-useful?
Failure modes & assumptions
Where can records be lost, meaning drift, mappings fail or assumptions become invisible?
Preventive & detective controls
What prevents failure, what detects it and what evidence does each control retain?
Operation & accountability
Who owns exceptions, remediation and the decision to accept or resolve residual risk?
Sustainable monitoring
How is integrity evidenced over time rather than inferred from periodic stability?
Visual framework
From Data Integrity Diagnosis to Defensible Outcomes
