CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps

Table 1. Core Data Quality Table

Dimension	Definition in article	How it is monitored	Typical gate/action
Completeness	Degree to which required values or events are present.	Null-rate checks, count checks, missing-field ratios, expected arrival patterns.	Warn, quarantine partial data, or block downstream publication.
Consistency	Degree to which data satisfies integrity constraints and cross-field logic.	Constraint validation, duplicate detection, referential checks, cross-stream reconciliation.	Route to remediation stage or reject records that violate integrity logic.
Validity	Degree to which data values conform to domain/type/rule definitions.	Schema checks, type/range checks, regex/business-rule validation.	Transform, discard, or divert invalid records to error handling.
Timeliness	Degree to which data arrives while still relevant for the intended use case.	Latency, freshness, recency, event-time vs processing-time checks.	Fast-track, alert, or stop-check depending on SLA breach severity.