ISO/IEC FDIS 8183

Information technology — Artificial intelligence — Data life cycle framework

This document provides an overarching data life cycle framework that is instantiable for any AI system from data ideation to decommission. This document is applicable to the data processing throughout the AI system life cycle including the 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, sizes and nature, that use data in the development and use of AI systems.

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General Information

• Status: Not Published
• ICS: 35.020 - Information technology (IT) in general
• Technical Committee: ISO/IEC JTC 1/SC 42 - Artificial intelligence
• Drafting Committee: ISO/IEC JTC 1/SC 42 - Artificial intelligence
• Current Stage: 5020 - FDIS ballot initiated: 2 months. Proof sent to secretariat
• Start Date: 26-Apr-2023
• Completion Date: 26-Apr-2023

Standards Content (Sample)

DRAFT INTERNATIONAL STANDARD
ISO/IEC DIS 8183
ISO/IEC JTC 1/SC 42 Secretariat: ANSI
Voting begins on: Voting terminates on:
2022-07-26 2022-10-18
Information technology — Artificial intelligence — Data
life cycle framework
ICS: 35.020
THIS DOCUMENT IS A DRAFT CIRCULATED
FOR COMMENT AND APPROVAL. IT IS
THEREFORE SUBJECT TO CHANGE AND MAY
This document is circulated as received from the committee secretariat.
NOT BE REFERRED TO AS AN INTERNATIONAL
STANDARD UNTIL PUBLISHED AS SUCH.
IN ADDITION TO THEIR EVALUATION AS
BEING ACCEPTABLE FOR INDUSTRIAL,
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USER PURPOSES, DRAFT INTERNATIONAL
STANDARDS MAY ON OCCASION HAVE TO
BE CONSIDERED IN THE LIGHT OF THEIR
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WHICH REFERENCE MAY BE MADE IN
Reference number
NATIONAL REGULATIONS.
ISO/IEC DIS 8183:2022(E)
RECIPIENTS OF THIS DRAFT ARE INVITED
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NOTIFICATION OF ANY RELEVANT PATENT
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PROVIDE SUPPORTING DOCUMENTATION. © ISO/IEC 2022
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ISO/IEC DIS 8183:2022(E)
DRAFT INTERNATIONAL STANDARD
ISO/IEC DIS 8183
ISO/IEC JTC 1/SC 42 Secretariat: ANSI
Voting begins on: Voting terminates on:
Information technology — Artificial intelligence — Data
life cycle framework
ICS: 35.020
COPYRIGHT PROTECTED DOCUMENT
THIS DOCUMENT IS A DRAFT CIRCULATED
FOR COMMENT AND APPROVAL. IT IS
© ISO/IEC 2022
THEREFORE SUBJECT TO CHANGE AND MAY
This document is circulated as received from the committee secretariat.

All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may

NOT BE REFERRED TO AS AN INTERNATIONAL

be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on STANDARD UNTIL PUBLISHED AS SUCH.

the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below

IN ADDITION TO THEIR EVALUATION AS

or ISO’s member body in the country of the requester. BEING ACCEPTABLE FOR INDUSTRIAL,

TECHNOLOGICAL, COMMERCIAL AND
ISO copyright office
USER PURPOSES, DRAFT INTERNATIONAL
CP 401 • Ch. de Blandonnet 8
STANDARDS MAY ON OCCASION HAVE TO
BE CONSIDERED IN THE LIGHT OF THEIR
CH-1214 Vernier, Geneva
POTENTIAL TO BECOME STANDARDS TO
Phone: [phone]
WHICH REFERENCE MAY BE MADE IN
Reference number
Email: [e-mail]
NATIONAL REGULATIONS.
Website: www.iso.org ISO/IEC DIS 8183:2022(E)
RECIPIENTS OF THIS DRAFT ARE INVITED
Published in Switzerland
TO SUBMIT, WITH THEIR COMMENTS,
NOTIFICATION OF ANY RELEVANT PATENT
RIGHTS OF WHICH THEY ARE AWARE AND TO
© ISO/IEC 2022 – All rights reserved
PROVIDE SUPPORTING DOCUMENTATION. © ISO/IEC 2022
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ISO/IEC DIS 8183:2022(E)
30 Contents

31 Introduction ........................................................................................................................................................... v

32 1 Scope ................................................................................................................................................................. 1

33 2 Normative references ................................................................................................................................. 1

34 3 Terms and definitions ................................................................................................................................ 1

35 4 Symbols and abbreviated terms ............................................................................................................. 1

36 5 Data life cycle overview ............................................................................................................................. 2

37 6 Data life cycle framework ......................................................................................................................... 2

38 6.1 General ............................................................................................................................................................. 2

39 6.2 Stage 1: Idea conception ............................................................................................................................ 3

40 6.3 Stage 2: Business requirements ............................................................................................................. 4

41 6.4 Stage 3: Data planning ................................................................................................................................ 4

42 6.5 Stage 4: Data acquisition ........................................................................................................................... 5

43 6.6 Stage 5: Data preparation ......................................................................................................................... 5

44 6.7 Stage 6: Building a model .......................................................................................................................... 6

45 6.8 Stage 7: System deployment .................................................................................................................... 7

46 6.9 Stage 8: System operation ......................................................................................................................... 7

47 6.10 Stage 9: Data decommissioning ........................................................................................................ 8

48 6.11 Stage 10: System decommissioning ................................................................................................ 8

49 7 Stages and processes within the data life cycle ................................................................................. 8

50 7.1 General ............................................................................................................................................................. 8

51 Bibliography ....................................................................................................................................................... 11

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ISO/IEC DIS 8183:2022(E)
53 Foreword

54 ISO (the International Organization for Standardization) and IEC (the International Electrotechnical

55 Commission) form the specialized system for worldwide standardization. National bodies that are

56 members of ISO or IEC participate in the development of International Standards through technical

57 committees established by the respective organization to deal with particular fields of technical activity.

58 ISO and IEC technical committees collaborate in fields of mutual interest. Other international

59 organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the

60 work. In the field of information technology, ISO and IEC have established a joint technical committee,

61 ISO/IEC JTC 1.

62 The procedures used to develop this document and those intended for its further maintenance are

63 described in the ISO/IEC Directives, Part 1. In particular the different approval criteria needed for the

64 different types of document should be noted. This document was drafted in accordance with the editorial

65 rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives).

66 Attention is drawn to the possibility that some of the elements of this document may be the subject of

67 patent rights. ISO and IEC will not be held responsible for identifying any or all such patent rights. Details

68 of any patent rights identified during the development of the document will be in the Introduction and/or

69 on the ISO list of patent declarations received (see www.iso.org/patents).

70 Any trade name used in this document is information given for the convenience of users and does not

71 constitute an endorsement.

72 For an explanation on the voluntary nature of standards, the meaning of ISO specific terms and

73 expressions related to conformity assessment, as well as information about ISO's adherence to the World

74 Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see the following

75 URL: www.iso.org/iso/foreword.html.

76 This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,

77 Subcommittee SC 42, Artificial intelligence.

78 Any feedback or questions on this document should be directed to the user’s national standards body. A

79 complete listing of these bodies can be found at www.iso.org/members.html.
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ISO/IEC DIS 8183:2022(E)
80 Introduction

81 AI systems are being adopted by organizations of all types, sizes and purposes. Data is essential to the

82 development and operation of AI systems.

83 In the field of AI systems, there are any number of data life cycles in use and under consideration for

84 different purposes (e.g. data quality, bias in data, data governance, development and use of AI systems).

85 Without an overarching framework, these different data life cycles can be challenging to correctly

86 interpret by those without previous knowledge, context and expertise. There is a risk that these multiple

87 data life cycles will not be applied as intended.

88 The purpose of this document is to provide a data life cycle framework, including terms and concepts,

89 that can be referenced by specialized data life cycles. The aim is to make it easier for users in different

90 roles to understand and correlate specialized data life cycles and how they apply to their organization’s

91 needs by describing a set of high-level data life cycle stages.
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ISO/IEC DIS 8183:2022(E)
92 Information technology — Artificial intelligence — Data life cycle
93 framework
94 1 Scope

95 This document is applicable to the data processing throughout the AI system life cycle including the

96 acquisition, creation, development, deployment, maintenance and decommissioning. This document is

97 applicable to the acquisition, creation, development, deployment, maintenance and decommissioning of

98 data in AI systems. This document does not define specific services, platforms or tools. This document is

99 applicable to all organizations, regardless of type, sizes and nature, that use data in the development and

100 use of AI systems.
101 2 Normative references

102 The following documents are referred to in the text in such a way that some or all of their content

103 constitutes requirements of this document. For dated references, only the edition cited applies. For

104 undated references, the latest edition of the referenced document (including any amendments) applies.

105 ISO/IEC 22989:-1), Information technology — Artificial intelligence — Artificial intelligence concepts and

106 terminology
107 3 Terms and definitions

108 For the purposes of this document, the terms and definitions given in ISO/IEC DIS 22989:— apply.

109 ISO and IEC maintain terminological databases for use in standardization at the following addresses:

110 — ISO Online browsing platform: available at https://www.iso.org/obp
111 — IEC Electropedia: available at http://www.electropedia.org/
112 4 Symbols and abbreviated terms
113 AI artificial intelligence
114 DPIA data protection impact assessment
115 JSON JavaScript object notation
116 ML machine learning
1) Under preparation. Stage at the time of publication: ISO/IEC FDIS 22989:2022
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ISO/IEC DIS 8183:2022(E)
117 OWL web ontology language
118 PII personally identifiable information
119 XML extensible markup language
120 5 Data life cycle overview

121 The data life cycle for AI systems encompasses the processing of data from the earliest conception of a

122 new AI system to the eventual decommissioning of the system and is separated into a number of distinct

123 stages. Each stage will often, but not always, be part of a data life cycle for an AI system.

124 A data life cycle represents all the stages through which data can pass within any system that uses data of

125 any kind. It is designed to support the achievement of objectives related to system governance, system

126 utility, data quality and data security, by ensuring that data processing is given due consideration during

127 the planning, development, use and decommissioning of the system.

128 The detailed purpose and timing of use of these stages throughout the life cycle are influenced by multiple

129 factors, including societal, commercial, organizational and technical considerations, each of which can

130 vary during the life of a system. This document describes the following 10 stages:

131  Stage 1 - Idea conception ;
132  Stage 2 - Business requirements ;
133  Stage 3 - Data planning;
134  Stage 4 - Data acquisition;
135  Stage 5 - Data preparation;
136  Stage 6 - Building model;
137  Stage 7 - System deployment;
138  Stage 8 - System operation;
139  Stage 9 - Data decommissioning;
140  Stage 10 - System decommissioning.
141 6 Data life cycle framework
142 6.1 General

143 The data life cycle framework, shown in Figure 1, identifies a set of distinct stages that data used in an AI

144 system go through from data planning to data decommissioning. Figure 1 also includes idea conception,

145 business requirements and system decommissioning which are system level life cycle stages.

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ISO/IEC DIS 8183:2022(E)

146 For information regarding datasets refer to FDIS ISO/IEC 23053:-2) [3]. Life cycle processes, appropriate

147 to a defined task, can be assigned to each stage. Life cycle processes describe the actions taken on the

148 data within the life cycle stage.

149 Stage 9 (data decommissioning) and stage 10 (system decommissioning) both pertain to

150 decommissioning but stage 9 specifically covers what happens to the data (e.g. deletion, destruction,

151 return) while stage 10 covers what happens to the system irrespective of what happens to the data.

152
153 Figure 1 — Data life cycle framework

154 NOTE 1 The single-headed arrows in Figure 1 depict a linear path through the life cycle stages while the double-

155 headed arrows show feedback paths between life cycle stages.

156 NOTE 2 The verification and validation of the model refers to the internal development process whose output is

157 a model. The validation and verification of the system refers to the system as a whole extending through its entire

158 period of operation.
159 6.2 Stage 1: Idea conception

160 Idea conception is when a need or requirement for a new or revised AI system is recognized. The AI

161 system can be used as a partial or a complete solution to an existing or potential problem or opportunity

162 faced by the organization.
2) Under preparation. Stage at the time of publication: ISO/IEC FDIS 23053:—
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163 Idea conception can also be driven by broader organizational context needs (e.g. economic, technical,

164 strategic, market or legal requirements). Ultimately, this idea should be expressed as one or more

165 questions that the AI system can answer.
166 6.3 Stage 2: Business requirements

167 The business requirements stage involves one or more stakeholders, with appropriate authority or

168 influence deciding 1) to investigate whether the idea can be turned into a functioning system and 2)

169 deciding whether to invest further in the idea. This stage involves:
170  determining the ambition of the project (e.g. vision, goals and strategy);

171  determining assets including those available and those that need to be acquired;

172  specifying the data requirements, a key element for AI syste
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