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This document provides the means for understanding and associating the individual documents of the ISO/IEC “Artificial intelligence — Data quality for analytics and ML” 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

This document specifies a data quality model, data quality measures and guidance on reporting data quality in the context of analytics and machine learning (ML). This document is applicable to all types of organizations who want to achieve their data quality objectives.

This document specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving the quality of data used in the areas of analytics and machine learning. This document does not define a detailed process, methods or metrics. Rather it defines the requirements and guidance for a quality management process along with a reference process and methods that can be tailored to meet the requirements in this document. The requirements and recommendations set out in this document are generic and are intended to be applicable to all organizations, regardless of type, size or nature.

This document establishes general common organizational approaches, regardless of the type, size or nature of the applying organization, to ensure data quality for training and evaluation in analytics and machine learning (ML). It includes guidance on the data quality process for: — supervised ML with regard to the labelling of data used for training ML systems, including common organizational approaches for training data labelling; — unsupervised ML; — semi-supervised ML; — reinforcement learning; — analytics. This document is applicable to training and evaluation data that come from different sources, including data acquisition and data composition, data preparation, data labelling, evaluation and data use. This document does not define specific services, platforms or tools.