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Standards Development
This document provides a data life cycle framework that is useable for any AI system, from idea conception to system decommissioning. This document does not define specific systems, services, platforms or tools. This document is applicable to all organizations, regardless of type, size or nature, that use data in the development and use of AI systems.
AI systems are being adopted by organizations of all types, sizes and purposes. Data is a key component of all AI systems.
In the field of AI systems, there are any number of data life cycles in use and under consideration for different purposes (e.g. data quality, bias in data, data governance, development and use of AI systems). Without an overarching framework, these different data life cycles can be challenging to correctly interpret by those without previous knowledge, context and expertise. There is a risk that these multiple data life cycles will not be applied as intended.
The purpose of this document is to provide a data life cycle framework, including terms and concepts, that can be referenced by specialized data life cycles. The aim is to make it easier for users in different roles to understand and correlate specialized data life cycles and how they apply to their organization’s needs by describing a set of high-level data life cycle stages.
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