Data Quality SLA Specification

Data artifactGovernance & ComplianceSafety, Security & Governancearc:DataQualitySLASpec

A specification of measurable data-quality targets (completeness, accuracy, duplicate rate, timeliness, format conformance) with accountability for a production knowledge base.

Responsibility. States the committed data-quality targets that drive enforcement and monitoring.

Also known as: Data quality SLA, Quality targets

Direct neighbourhood (hover for relationship types)

Relationships

configures structural

Design guidance

SHOULD set timeliness targets per use case, since news, support, regulatory and trading agents need different freshness.
SHOULD translate abstract quality goals into concrete metrics that drive specific technical implementations.

Quantitative guidance

As stated by the sources; verify before use.

Typical enterprise SLAs: completeness >=98%, accuracy 99% on a representative test set, duplicate rate <0.5%, 95% of changes reflected within 24 h, 99.9% schema conformance (Ch6.4).
Timeliness examples: news <5 min end-to-end lag, product support ~24 h, trading sub-second (Ch6.4).

Classification

Patterns: Five-dimension quality model (completeness, accuracy, consistency, timeliness, validity)
Quality attributes: Transparency and accountability (NIST AI RMF: accountable and transparent)Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
Risks mitigated: 'Good enough' 97-99% quality proving insufficient for RAG

Sources

Ch6.4: T. Nguyen, "Data Quality Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.4. ISBN: 9798244538229.