External-Proof ReadyPublic

Data Quality Implementation

Trust source readiness before clinical, research, public-health, or analytics workflows depend on it.

DQM turns source inspection into operational evidence: completeness, conformance, freshness, drift, remediation state, and source-level trust signals.

Review DQM Schedule Walkthrough

Owner

DQM / Data Quality Monitor

Audience

Data stewards, integration teams, quality leaders, and technical reviewers

Source authority

DQM owns quality issue, source readiness, and drift truth.

Implemented workflows

Source profiling
Conformance checks
Completeness scoring
Drift review
Remediation evidence
Trust-gate publication

System responsibilities

Own data-quality issue truth
Classify findings by source and workflow
Publish operator-safe summaries to downstream proof surfaces

Data inputs and outputs

FHIR, HL7 v2, bulk-data, and lakehouse extracts enter customer-controlled checks
DQM scores readiness and issue state
Safe summaries feed Data Motion and Atlas Nexus

Boundary

Public outside, protected detail inside.

Raw source records, patient identifiers, and remediation work queues remain authenticated and private.

Evidence

Proof paths and implementation artifacts.

DQM solution

Public product page and access model.

FHIR evidence

Standards-facing validation material.

Evidence room

Gated packets for diligence review.

Architecture visuals

Diagrams explain the operating logic.

Mermaid diagram

Data Quality Lifecycle

Explains DQM source readiness and remediation evidence flow.

stateDiagram-v2
  [*] --> Profiled
  Profiled --> Scored
  Scored --> Categorized
  Categorized --> Remediation
  Remediation --> TrustGate
  TrustGate --> OperatorSafeSummary
  OperatorSafeSummary --> [*]

DQM and trust architecture

Existing architecture view for trust gates.

Known limits

What remains gated, deferred, or status-bound.

Public pages summarize quality posture; raw DQM findings require authenticated access.

Live-proof language requires named packet approval.