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AS ISO/IEC 5259.2:2025
[Current]Artificial intelligence - Data quality for analytics and machine learning (ML), Part 2: Data quality measures
AS ISO/IEC 5259-2:2025 identically adopts ISO/IEC 5259-2:2024, which specifies a data quality model, data quality measures and guidance on reporting data quality in the context of analytics and machine learning (ML). Applicable to all types of organizations who want to achieve their data quality objectives.
Published: 09/05/2025
Pages: 39
Table of contents
Cited references
Content history
Table of contents
Header
About this publication
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Symbols and abbreviated terms
5 Data quality components and data quality models for analytics and machine learning
5.1 Data quality components in data life cycle
5.2 Data quality model
6 Data quality characteristics and quality measures
6.1 General
6.2 Inherent data quality characteristics
6.2.1 Accuracy
6.2.1.1 General
6.2.1.2 QMs for accuracy
6.2.2 Completeness
6.2.2.1 General
6.2.2.2 QMs for completeness
6.2.3 Consistency
6.2.3.1 General
6.2.3.2 QMs for consistency
6.2.4 Credibility
6.2.4.1 General
6.2.4.2 QMs for credibility
6.2.5 Currentness
6.2.5.1 General
6.2.5.2 QMs for currentness
6.3 Inherent and system-dependent data quality characteristics
6.3.1 Accessibility
6.3.1.1 General
6.3.1.2 QMs for accessibility
6.3.2 Compliance
6.3.2.1 General
6.3.2.2 QMs for compliance
6.3.3 Efficiency
6.3.3.1 General
6.3.3.2 QMs for efficiency
6.3.4 Precision
6.3.4.1 General
6.3.4.2 QMs for precision
6.3.5 Traceability
6.3.5.1 General
6.3.5.2 QMs for traceability
6.3.6 Understandability
6.3.6.1 General
6.3.6.2 QMs for understandability
6.4 System-dependent data quality characteristics
6.4.1 Availability
6.4.1.1 General
6.4.1.2 QMs for availability
6.4.2 Portability
6.4.2.1 General
6.4.2.2 QMs for portability
6.4.3 Recoverability
6.4.3.1 General
6.4.3.2 QMs for recoverability
6.5 Additional data quality characteristics
6.5.1 Auditability
6.5.1.1 General
6.5.1.2 QMs for auditability
6.5.2 Balance
6.5.2.1 General
6.5.2.2 QMs for balance
6.5.3 Diversity
6.5.3.1 General
6.5.3.2 QMs for diversity
6.5.4 Effectiveness
6.5.4.1 General
6.5.4.2 QMs for effectiveness
6.5.5 Identifiability
6.5.5.1 General
6.5.5.2 QMs for identifiability
6.5.6 Relevance
6.5.6.1 General
6.5.6.2 QMs for relevance
6.5.7 Representativeness
6.5.7.1 General
6.5.7.2 QMs for representativeness
6.5.8 Similarity
6.5.8.1 General
6.5.8.2 QMs for similarity
6.5.9 Timeliness
6.5.9.1 General
6.5.9.2 QMs for Timeliness
7 Implementing a data quality model and data quality measures for an analytics or ML task
8 Data quality reporting
8.1 Data quality reporting framework
8.2 Data quality measure information
8.3 Guidance to organizations
Annex A
Annex B
Annex C
Annex D
Annex E
Bibliography
Cited references in this standard