scope:

This document provides 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. It includes guidance on the data quality process for:

- supervised machine learning with regard to the labelling of data used for training machine learning systems, including common organizational approaches for training data labelling;

- unsupervised machine learning;

- semi-supervised machine learning;

- reinforcement learning;

- analytics.

This document is applicable to training and evaluation data that comes 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.