Scoping
Clarify the decision, data journey, concern and boundaries.
Specialist Data Quality & Integrity advisory
Data fails quietly.
Until it does not.
Most organisations assume their monitoring, screening, reporting and AI are working. Incomplete or incorrect data can silently weaken them long before the problem becomes visible.
DQIntegrity - Data Quality & Integrity - helps regulated organisations prove that decision-critical data is complete, correct, traceable and controlled - from source to outcome.
CompletenessDid the expected population arrive, intact and on time?
CorrectnessDid values, mappings and meaning remain valid?
TraceabilityCan the journey and decisions be reconstructed?
Control evidenceDid controls detect, escalate and resolve exceptions?
The structural problem
Stable outputs do not prove sound inputs.
Most failures begin in one of two ways: expected data never arrives, or data arrives but changes meaning. The system can continue to run, dashboards can remain stable and alert volumes can look normal - while coverage and assurance erode underneath.
What never arrivedDropped, delayed, filtered or partially transferred records create invisible gaps in decision coverage.
What changed along the wayMapping errors, truncation, defaults, type conversion and semantic drift can preserve presence while corrupting meaning.
What cannot be provenFragmented ownership and weak evidence leave leadership unable to defend the integrity of outcomes.
Control architecture
Seven linked layers - from business event to accountable outcome.
DQIntegrity treats completeness, correctness, traceability, control operation and remediation evidence as one connected assurance problem, not as isolated data-quality metrics.
Commercial services
Defined interventions - not generic data-quality programmes.
Engagements begin where the organisation feels the symptoms but cannot yet prove the structural cause.
1
Independent Data Quality & Integrity Diagnostic
Locate hidden breaks across data journeys, expected populations, controls, evidence and ownership.
Explore services →2
Continuous Control Monitoring
Design detective controls at each material transfer, transformation, filtering and publication point.
Explore monitoring →3
Financial Crime Data Integrity Review
Assess whether monitoring and screening operate on the right population with credible control evidence.
Explore financial crime →4
AI & Automation Assurance
Prove provenance, input integrity, control boundaries and defensibility before scaling decision automation.
Explore AI assurance →How an engagement begins
A clear route from first discussion to defensible next action.
The opening sequence is deliberately simple. It establishes the decision and boundaries, examines the available evidence, diagnoses where integrity breaks down, and agrees the proportionate next step.
Evidence
Identify and review the available artefacts, controls, mappings and records.
Diagnosis
Determine where completeness, correctness, traceability or control evidence breaks down.
Decision
Agree findings, priorities and the most appropriate next action.
Principal-led, confidential and focused
The first discussion determines fit and proportionate scope - not a larger programme before the evidence supports one.
Two client routes
Support for regulated buyers - and for providers entering regulated environments.
The proposition remains independent and evidence-led. For regulated organisations, the focus is diagnosis, control and assurance. For providers, it is qualification, translation, bank-readiness and implementation assurance.
Regulated organisations
Banks, payment firms, insurers, fintechs and public bodies that need defensible data and control integrity.
See regulated contexts →Technology and delivery providers
Products and services that need a more credible, governed and implementable proposition for regulated buyers.
See provider support →
Evidence and insight
A framework grounded in recurring institutional failure patterns.
Public enforcement, inquiry and audit findings show a repeated pattern: material harm emerges when data populations, transformations, controls or evidence are assumed rather than continuously proven.
Explore the documented cases and primary sources
See the public mapping, then request the extended evidence brief with fuller case summaries and source references.
If the data fails, the decision fails.
Start with the point where confidence is assumed but not yet proven.
A confidential first discussion can establish whether the issue needs a focused diagnostic, control design, remediation assurance or retained senior Data Quality & Integrity advisory.