Data quality
Veri kalitesi
D1
Data quality is the degree to which data are fit for a defined purpose, considering attributes such as accuracy, completeness, relevance, consistency, reliability, and currency.
Review status: 2027-02-23
Technical explanation
It affects a dataset’s usability and reusability; in AI workflows, poor-quality or unrepresentative data can affect system trustworthiness.
Conceptual boundaries
Data quality is assessed in relation to purpose and context, rather than as a single permanent score.
Provider-neutral example
Before training a routing model, a team can check whether labels are complete, current, and relevant to the queueing decisions it will support.
Limitations
Quality assessment is a series of lifecycle actions, and poor-quality or unrepresentative data can affect AI-system trustworthiness.
Related concepts
Atomic claims and evidence
1.1NIST SP 1500-18r2, NIST Research Data Framework Version 2.0
- Source
- NIST SP 1500-18r2, NIST Research Data Framework Version 2.0
- Source role
- Authoritative source
- Exact locator
- Data Quality, p. 49
- Supported claim
- Data quality directly affects a dataset’s fitness for purpose, usability, and reusability, and includes attributes such as accuracy, completeness, update status, relevance, consistency, reliability, presentation, and accessibility.
- Last verification
- Review due
- Scope limitation
- Attribute importance depends on the dataset’s purpose and context.
2.1NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0)
- Source
- NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0)
- Source role
- Authoritative source
- Exact locator
- Appendix B, p. 38, data representation and dataset context bullets
- Supported claim
- Harmful bias and other data-quality issues can affect AI-system trustworthiness, and training datasets can become detached from their intended context or stale relative to deployment.
- Last verification
- Review due
- Scope limitation
- These are risk considerations, not proof that every dataset or system has those problems.