ISO/IEC 8183:2023

INTERNATIONAL ISO/IEC
STANDARD 8183
First edition
2023-07
Information technology — Artificial
intelligence — Data life cycle
framework
Reference number
© ISO/IEC 2023
© ISO/IEC 2023
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© ISO/IEC 2023 – All rights reserved

Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Symbols and abbreviated terms.1
5 Data life cycle overview . 1
6 Data life cycle framework .2
6.1 General . 2
6.2 Stage 1: Idea conception . 3
6.3 Stage 2: Business requirements . 4
6.4 Stage 3: Data planning . 4
6.5 Stage 4: Data acquisition. 5
6.6 Stage 5: Data preparation . 5
6.7 Stage 6: Building a model . 6
6.8 Stage 7: System deployment . 6
6.9 Stage 8: System operation . 7
6.10 Stage 9: Data decommissioning . 7
6.11 Stage 10: System decommissioning . 7
7 Stages and processes within the data life cycle . 7
Bibliography .10
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© ISO/IEC 2023 – All rights reserved

Foreword
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Commission) form the specialized system for worldwide standardization. National bodies that are
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The procedures used to develop this document and those intended for its further maintenance
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accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives or
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This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 42, Artificial intelligence.
Any feedback or questions on this document should be directed to the user’s national standards
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Introduction
Artificial intelligence (AI) systems are being adopted by organizations of all types, sizes and purposes.
Data are essential to the development and operation of AI systems.
In the field of AI systems, there are many 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.
This document provides a data life cycle overview in Clause 5, describes a data life cycle framework in
Clause 6 and provides more information on the stages or processes of the data life cycle in Clause 7.
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© ISO/IEC 2023 – All rights reserved

INTERNATIONAL STANDARD ISO/IEC 8183:2023(E)
Information technology — Artificial intelligence — Data
life cycle framework
1 Scope
This document defines the stages and identifies associated actions for data processing throughout the
artificial intelligence (AI) system life cycle, including acquisition, creation, development, deployment,
maintenance and decommissioning. This document does not define specific 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.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies. For
undated references, the latest edition of the referenced document (including any amendments) applies.
ISO/IEC 22989, Information technology — Artificial intelligence — Artificial intelligence concepts and
terminology
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 22989 apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
4 Symbols and abbreviated terms
AI artificial intelligence
DPIA data protection impact assessment
JSON JavaScript object notation
ML machine learning
OWL web ontology language
PII personally identifiable information
XML extensible markup language
5 Data life cycle overview
The data life cycle for AI systems encompasses the processing of data from the earliest conception of
a new AI system to the eventual decommissioning of the system and is separated into a number of
distinct stages. Each stage will often, but not always, be part of a data life cycle for an AI system.
© ISO/IEC 2023 – All rights reserved

A data life cycle represents all the stages through which data can pass within any system that uses data
of any kind. It is designed to support the achievement of objectives related to system governance, system
utility, data quality and data security, by ensuring that data processing is given due consideration
during the planning, development, use and decommissioning of the system.
The detailed purpose and timing of use of these stages throughout the life cycle are influenced by
multiple factors, including societal, commercial, organizational and technical considerations, each of
which can vary or at times be combined with other stages during the life of a system. This document
describes the following 10 stages:
— stage 1 – idea conception;
— stage 2 – business requirements;
— stage 3 – data planning;
— stage 4 – data acquisition;
— stage 5 – data preparation;
— stage 6 – building model;
— stage 7 – system deployment;
— stage 8 – system operation;
— stage 9 – data decommissioning;
— stage 10 – system decommissioning.
1)
For information about a data life cycle for data usage, see ISO/IEC 5212: — .
6 Data life cycle framework
6.1 General
The data life cycle framework, shown in Figure 1, identifies a set of conceptually distinct stages that
data used in an AI system go through from data planning to data decommissioning. Figure 1 also
includes idea conception, business requirements and system decommissioning, which are system-
level life cycle stages. For information regarding data sets, refer to ISO/IEC 23053:2022, 6.5. Life cycle
processes appropriate to a defined task can be assigned to each stage. Life cycle processes describe the
actions taken on the data within the life cycle stage.
Stage 9 (data decommissioning) and stage 10 (system decommissioning) both pertain to
decommissioning but stage 9 specifically covers what happens to the data (e.g. secure deletion,
archiving, repurposing) while stage 10 covers what happens to the system irrespective of what happens
to the data that is being processed.
1) Under preparation. Stage at the time of publication: ISO/IEC DIS 5212:2023.
© ISO/IEC 2023 – All rights reserved

Key
primary development pathway
feedback pathway
stage with data processing
stage outside the data processing boundary
NOTE 1 The single-headed arrows depict a linear path through the life cycle stages, while the double-headed
arrows show feedback paths between life cycle stages.
NOTE 2 The verification and validation of the model refers to the internal development process, whose output
is a model. The validation and verification of the system refers to the system as a whole, extending through its
entire period of operation.
Figure 1 — Data life cycle framework
6.2 Stage 1: Idea conception
Idea conception is when a need or requirement for a new or revised AI system is recognized. The AI
system can be used as a partial or complete solution to an existing or potential problem or opportunity
faced by the organization.
Idea conception can also be driven by broader organizational context needs (e.g. economic, technical,
strategic, market or legal requirements). Ultimately, this idea can be expressed as one or more questions
that the AI system can answer. The questions to which the AI system provides answers should be
mapped to and aligned with business objectives and metrics.
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6.3 Stage 2: Business requirements
The business requirements stage can include one or more stakeholders with appropriate authority or
influence deciding to investigate whether the idea can be turned into a functioning system and deciding
whether to invest further in the idea. This stage inv
...