AS ISO/IEC 5259.1:2024 identically adopts ISO/IEC 5259 1:2024, which provides the means for understanding and associating the individual documents of the AS ISO/IEC 5259 series and is the foundation for conceptual understanding of data quality for analytics and machine learning. It also discusses associated technologies and examples (e.g. use cases and usage scenarios)
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 concepts for analytics and machine learning
5.1 Data quality considerations for analytics and machine learning
5.1.1 General
5.1.2 Machine learning and data quality
5.1.3 Data characteristics that pose quality challenges for analytics and machine learning
5.1.4 Data sharing, data re-use and data quality for analytics and machine learning
5.2 Data quality concept framework for analytics and machine learning
5.2.1 Overview
5.2.2 Data quality management
5.2.2.1 Data quality model
5.2.2.2 Data quality measures
5.2.2.3 Data quality assessment
5.2.2.4 Data quality improvement
5.2.2.5 Data quality reporting
5.2.3 Data quality governance
5.2.4 Data provenance
5.3 Data life cycle for analytics and ML
5.3.1 Overview
5.3.2 Data life cycle model
5.3.2.1 Overall model
5.3.2.2 Stage 1: data requirements
5.3.2.3 Stage 2: data planning
5.3.2.4 Stage 3: data acquisition
5.3.2.5 Stage 4: data preparation
5.3.2.6 Stage 5: data provisioning
5.3.2.7 Stage 6: data decommissioning
5.3.3 Processes across the multiple stages
5.3.3.1 General
5.3.3.2 Data security
5.3.3.3 Data privacy
Annex A
Bibliography
Cited references in this standard
Content history
ISO/IEC 5259.1:2024
DR AS ISO/IEC 5259.1:2024
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