Artificial intelligence: Data quality for analytics and machine learning (ML) - Part 4: Data quality process framework

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.