ISO 8000-220:2025
(Main)Data quality — Part 220: Sensor data: Quality measurement
Data quality — Part 220: Sensor data: Quality measurement
This document specifies quality measures for quantitatively measuring quality characteristics that are specified by ISO 8000-210 for use with sensor data. The following are within the scope of this document: — fundamental principles and assumptions for measuring the quality of sensor data; — quality measures for sensor data, in respect of corresponding quality characteristics and data anomalies; — requirements for using data quality characteristics and data quality measures for measuring the quality of sensor data. The following are outside the scope of this document: — analogue, image, video and audio data that are captured by sensors; — signal processing that converts or modifies analogue data to create digital data; — methods to measure and improve data quality.
Qualité des données — Partie 220: Titre manque
General Information
Buy Standard
Standards Content (Sample)
International
Standard
ISO 8000-220
First edition
Data quality —
2025-09
Part 220:
Sensor data: Quality measurement
Reference number
© ISO 2025
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Fundamental principles and assumptions . 1
5 Quality measures for sensor data . . 2
5.1 General .2
5.2 Quality measures for accuracy .3
5.3 Quality measures for completeness.4
5.4 Quality measures for consistency .4
5.5 Quality measures for precision .6
5.5.1 Representational precision .6
5.5.2 Measurement precision .7
5.6 Quality measures for timeliness .9
6 Implementation requirements .11
Annex A (informative) Document identification .12
Bibliography .13
iii
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out through
ISO technical committees. Each member body interested in a subject for which a technical committee
has been established has the right to be represented on that committee. International organizations,
governmental and non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely
with the International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of ISO document should be noted. This document was drafted in accordance with the editorial rules of the
ISO/IEC Directives, Part 2 (see www.iso.org/directives).
ISO draws attention to the possibility that the implementation of this document may involve the use of (a)
patent(s). ISO takes no position concerning the evidence, validity or applicability of any claimed patent
rights in respect thereof. As of the date of publication of this document, ISO had not received notice of (a)
patent(s) which may be required to implement this document. However, implementers are cautioned that
this may not represent the latest information, which may be obtained from the patent database available at
www.iso.org/patents. ISO shall not be held responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www.iso.org/iso/foreword.html.
This document was prepared by Technical Committee ISO/TC 184, Automation systems and integration,
Subcommittee SC 4, Industrial data.
A list of all parts in the ISO 8000 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.
iv
Introduction
0.1 Foundations of the ISO 8000 series
Digital data deliver value by enhancing all aspects of organizational performance including:
— operational effectiveness and efficiency;
— safety and security;
— reputation with customers and the wider public;
— compliance with statutory regulations;
— innovation;
— consumer costs, revenues and stock prices.
In addition, many organizations are now addressing these considerations with reference to the United
1)
Nations Sustainable Development Goals .
The influence on performance originates from data being the formalized representation of information
(see NOTE 1 in this subclause). This information enables organizations to make reliable decisions. This
decision making can be performed by human beings and also automated data processing, including artificial
intelligence systems.
NOTE 1 ISO 8000-2 defines information as “knowledge concerning objects, such as facts, events, things, processes,
or ideas, including concepts, that within a certain context has a particular meaning”.
Organizations become dependent on digital data through widespread adoption of digital computing and
associated communication technologies. This dependency amplifies the negative consequences of the lack of
quality in these data, leading to the decrease of organizational performance.
The biggest impact of digital data comes from two key factors:
— the data having a structure that reflects the nature of the subject matter;
EXAMPLE 1 A research scientist writes a report using a software application for word processing. This report
includes a table that uses a clear, logical layout to show results from an experiment. These results indicate how
material properties vary with temperature. The report is read by a designer, who uses the results to create a product
that works in a range of different operating temperatures.
— the data being computer processable (machine readable) rather than just being for a person to read and
understand.
EXAMPLE 2 A research scientist uses a database system to store the results of experiments on a material. This
system controls the format of different values in the data set. The system generates an output file of digital data.
This file is processed by a software application for engineering analysis. The application determines the optimum
geometry when using the material to make a product.
ISO 9000 explains that quality is not an abstract concept of absolute perfection. Rather, quality is the
conformance of characteristics to requirements. This actuality means that any item of data can be of high
quality for one purpose but not for a different purpose. The quality is different because the requirements are
different between the two purposes.
EXAMPLE 3 Time data are processed by calendar applications and also by control systems for propulsion units
on spacecraft. These data include start times for meetings in a calendar application and activation times in a control
system. These start times require less precision than the activation times.
1) https://sdgs.un.org/goals
v
The nature of digital data are fundamental to establishing requirements that are relevant to the specific
decisions made by each organization.
EXAMPLE 4 ISO 8000-1 identifies that data have syntactic (format), semantic (meaning) and pragmatic (usefulness)
characteristics.
To support the delivery of high-quality data, the ISO 8000 series addresses:
— data governance, data quality management and maturity assessment;
EXAMPLE 5 ISO 8000-61 specifies a process reference model for data quality management.
— creation and application of requirements for data and information;
EXAMPLE 6 ISO 8000-110 specifies how to exchange characteristic data that are master data.
— monitoring and measurement of information and data quality;
EXAMPLE 7 ISO 8000-8 specifies approaches to measuring information and data quality.
— improvement of data and, consequently, information quality;
EXAMPLE 8 ISO/TS 8000-81 specifies an approach to data profiling, which identifies opportunities to improve
data quality.
— issues that are specific to the type of content in a data set.
EXAMPLE 9 ISO/TS 8000-311 specifies how to address quality considerations for product shape data.
Data quality management covers all aspects of data processing, including creating, collecting, storing,
maintaining, transferring, exploiting and presenting data to deliver information.
Effective data quality management is systemic and systematic, requiring an understanding of the root
causes of data quality issues. This understanding is the basis for both correcting existing inconsistencies
and implementing solutions that prevent future reoccurrence of those nonconformities.
EXAMPLE 10 If a data set includes dates in multiple formats including “yyyy-mm-dd”, “mm-dd-yy” and “dd-mm-yy”,
then data cleansing can correct the consistency of the values. Such cleansing requires additional information, however,
to resolve ambiguous entries (such as, “04-05-20”). The cleansing also cannot address any process issues and people
issues, including training, that have caused the inconsistency.
0.2 Understanding more about the ISO 8000 series
ISO 8000-1 provides a detailed explanation of the structure and scope of the whole ISO 8000 series.
ISO 8000-2 specifies the single, common vocabulary for the ISO 8000 series. This vocabulary supports
understanding the overall subject matter of data quality. ISO 8000-2 presents the vocabulary structured by
a series of topic areas (for example, terms relating to quality and terms relating to data and information).
2)
ISO has identified ISO 8000-1, ISO 8000-2 and ISO 8000-8 as horizontal deliverables .
0.3 Role of this document
As a contribution to the overall capability of the ISO 8000 series, this document addresses how to quantify
the quality of data recorded as a stream of single, discrete digital values by sensors, typically in sensor
networks and sensing devices connected to the Internet of Things (see ISO/IEC 30141). This quantification
is through a set of quality measures corresponding to the quality characteristics and related data anomalies
specified by ISO 8000-210. These quality measures are suitable for use when improving the quality of sensor
data in the data processing stage prior to data analysis or exploitation.
2) Deliverable dealing with a subject relevant to a number of committees or sectors or of crucial importance to ensure
coherence across standardization deliverables.
vi
This document is suitable for use in industry fields that include smart manufacturing, social infrastructure
and healthcare, in circumstances where sensor data are collected by sensor networks and sensing devices
connected to the Internet of Things.
This document supports activities that affect:
— one or more information systems;
— data flows within the organization and with external organizations;
— any phase of the data life cycle.
Organizations can use this document individually or in conjunction with other parts in the ISO 8000 series.
Annex A contains an identifier that conforms to ISO/IEC 8824-1. The identifier unambiguously identifies this
document in an open information system.
0.4 B
...
International
Standard
ISO 8000-220
First edition
Data quality —
Part 220:
Sensor data: Quality measurement
PROOF/ÉPREUVE
Reference number
© ISO 2025
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland
PROOF/ÉPREUVE
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Fundamental principles and assumptions . 1
5 Quality measures for sensor data . . 2
5.1 General .2
5.2 Quality measures for accuracy .3
5.3 Quality measures for completeness.4
5.4 Quality measures for consistency .4
5.5 Quality measures for precision .6
5.5.1 Representational precision .6
5.5.2 Measurement precision .7
5.6 Quality measures for timeliness .9
6 Implementation requirements .11
Annex A (informative) Document identification .12
Bibliography .13
PROOF/ÉPREUVE
iii
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out through
ISO technical committees. Each member body interested in a subject for which a technical committee
has been established has the right to be represented on that committee. International organizations,
governmental and non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely
with the International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of ISO document should be noted. This document was drafted in accordance with the editorial rules of the
ISO/IEC Directives, Part 2 (see www.iso.org/directives).
ISO draws attention to the possibility that the implementation of this document may involve the use of (a)
patent(s). ISO takes no position concerning the evidence, validity or applicability of any claimed patent
rights in respect thereof. As of the date of publication of this document, ISO had not received notice of (a)
patent(s) which may be required to implement this document. However, implementers are cautioned that
this may not represent the latest information, which may be obtained from the patent database available at
www.iso.org/patents. ISO shall not be held responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www.iso.org/iso/foreword.html.
This document was prepared by Technical Committee ISO/TC 184, Automation systems and integration,
Subcommittee SC 4, Industrial data.
A list of all parts in the ISO 8000 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.
PROOF/ÉPREUVE
iv
Introduction
0.1 Foundations of the ISO 8000 series
Digital data deliver value by enhancing all aspects of organizational performance including:
— operational effectiveness and efficiency;
— safety and security;
— reputation with customers and the wider public;
— compliance with statutory regulations;
— innovation;
— consumer costs, revenues and stock prices.
In addition, many organizations are now addressing these considerations with reference to the United
1)
Nations Sustainable Development Goals .
The influence on performance originates from data being the formalized representation of information
(see NOTE 1 in this subclause). This information enables organizations to make reliable decisions. This
decision making can be performed by human beings and also automated data processing, including artificial
intelligence systems.
NOTE 1 ISO 8000-2 defines information as “knowledge concerning objects, such as facts, events, things, processes,
or ideas, including concepts, that within a certain context has a particular meaning”.
Organizations become dependent on digital data through widespread adoption of digital computing and
associated communication technologies. This dependency amplifies the negative consequences of the lack of
quality in these data, leading to the decrease of organizational performance.
The biggest impact of digital data comes from two key factors:
— the data having a structure that reflects the nature of the subject matter;
EXAMPLE 1 A research scientist writes a report using a software application for word processing. This report
includes a table that uses a clear, logical layout to show results from an experiment. These results indicate how
material properties vary with temperature. The report is read by a designer, who uses the results to create a product
that works in a range of different operating temperatures.
— the data being computer processable (machine readable) rather than just being for a person to read and
understand.
EXAMPLE 2 A research scientist uses a database system to store the results of experiments on a material. This
system controls the format of different values in the data set. The system generates an output file of digital data.
This file is processed by a software application for engineering analysis. The application determines the optimum
geometry when using the material to make a product.
ISO 9000 explains that quality is not an abstract concept of absolute perfection. Rather, quality is the
conformance of characteristics to requirements. This actuality means that any item of data can be of high
quality for one purpose but not for a different purpose. The quality is different because the requirements are
different between the two purposes.
EXAMPLE 3 Time data are processed by calendar applications and also by control systems for propulsion units
on spacecraft. These data include start times for meetings in a calendar application and activation times in a control
system. These start times require less precision than the activation times.
1) https://sdgs.un.org/goals
PROOF/ÉPREUVE
v
The nature of digital data are fundamental to establishing requirements that are relevant to the specific
decisions made by each organization.
EXAMPLE 4 ISO 8000-1 identifies that data have syntactic (format), semantic (meaning) and pragmatic (usefulness)
characteristics.
To support the delivery of high-quality data, the ISO 8000 series addresses:
— data governance, data quality management and maturity assessment;
EXAMPLE 5 ISO 8000-61 specifies a process reference model for data quality management.
— creation and application of requirements for data and information;
EXAMPLE 6 ISO 8000-110 specifies how to exchange characteristic data that are master data.
— monitoring and measurement of information and data quality;
EXAMPLE 7 ISO 8000-8 specifies approaches to measuring information and data quality.
— improvement of data and, consequently, information quality;
EXAMPLE 8 ISO/TS 8000-81 specifies an approach to data profiling, which identifies opportunities to improve
data quality.
— issues that are specific to the type of content in a data set.
EXAMPLE 9 ISO/TS 8000-311 specifies how to address quality considerations for product shape data.
Data quality management covers all aspects of data processing, including creating, collecting, storing,
maintaining, transferring, exploiting and presenting data to deliver information.
Effective data quality management is systemic and systematic, requiring an understanding of the root
causes of data quality issues. This understanding is the basis for both correcting existing inconsistencies
and implementing solutions that prevent future reoccurrence of those nonconformities.
EXAMPLE 10 If a data set includes dates in multiple formats including “yyyy-mm-dd”, “mm-dd-yy” and “dd-mm-yy”,
then data cleansing can correct the consistency of the values. Such cleansing requires additional information, however,
to resolve ambiguous entries (such as, “04-05-20”). The cleansing also cannot address any process issues and people
issues, including training, that have caused the inconsistency.
0.2 Understanding more about the ISO 8000 series
ISO 8000-1 provides a detailed explanation of the structure and scope of the whole ISO 8000 series.
ISO 8000-2 specifies the single, common vocabulary for the ISO 8000 series. This vocabulary supports
understanding the overall subject matter of data quality. ISO 8000-2 presents the vocabulary structured by
a series of topic areas (for example, terms relating to quality and terms relating to data and information).
2)
ISO has identified ISO 8000-1, ISO 8000-2 and ISO 8000-8 as horizontal deliverables .
0.3 Role of this document
As a contribution to the overall capability of the ISO 8000 series, this document addresses how to quantify
the quality of data recorded as a stream of single, discrete digital values by sensors, typically in sensor
networks and sensing devices connected to the Internet of Things (see ISO/IEC 30141). This quantification
is through a set of quality measures corresponding to the quality characteristics and related data anomalies
specified by ISO 8000-210. These quality measures are suitable for use when improving the quality of sensor
data in the data processing stage prior to data analysis or exploitation.
2) Deliverable dealing with a subject relevant to a number of committees or sectors or of crucial importance to ensure
coherence across standardization deliverables.
PROOF/ÉPREUVE
vi
This document is suitable for use in industry fields that include smart manufacturing, social infrastructure
and healthcare, in circumstances where sensor data are collected by sensor networks and sensing devices
connected to the Internet of Things.
This document supports activities that affect:
— one or more information systems;
— data flows within the organization and with external organizations;
— any phase of the data life cycle.
Organizations can use this document individually or in conjunction with other parts in the ISO 8000 series.
Annex A contains an identifier that conforms to ISO/IEC 8824-1. The identifier unambiguousl
...
ISO/PRF 8000-220:2025(en)
First edition
2025-05
ISO/TC 184/SC 4
Secretariat: ANSI
Date: 2025-05-0807-04
Data quality —
Part 220:
Sensor data: Quality measurement
Qualité des données —
Partie 220: Données des capteurs: Mesure de la qualité
PROOF
ISO/PRF 8000-220:2025(en)
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication
may be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying,
or posting on the internet or an intranet, without prior written permission. Permission can be requested from either ISO
at the address below or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: + 41 22 749 01 11
E-mail: copyright@iso.org
Website: www.iso.org
Published in Switzerland
ii
ISO/PRF 8000-220:2025(en)
Contents
Foreword . iv
Introduction . v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Fundamental principles and assumptions . 1
5 Quality measures for sensor data . 2
5.1 General. 2
5.2 Quality measures for accuracy . 3
5.3 Quality measures for completeness . 4
5.4 Quality measures for consistency . 4
5.5 Quality measures for precision . 6
5.6 Quality measures for timeliness . 9
6 Implementation requirements . 10
Annex A (informative) Document identification . 11
Bibliography . 12
iii
ISO/PRF 8000-220:2025(en)
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out through
ISO technical committees. Each member body interested in a subject for which a technical committee has been
established has the right to be represented on that committee. International organizations, governmental and
non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely with the
International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types of
ISO document should be noted. This document was drafted in accordance with the editorial rules of the
ISO/IEC Directives, Part 2 (see www.iso.org/directives).
ISO draws attention to the possibility that the implementation of this document may involve the use of (a)
patent(s). ISO takes no position concerning the evidence, validity or applicability of any claimed patent rights
in respect thereof. As of the date of publication of this document, ISO had not received notice of (a) patent(s)
which may be required to implement this document. However, implementers are cautioned that this may not
represent the latest information, which may be obtained from the patent database available at
www.iso.org/patents. ISO shall not be held responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www.iso.org/iso/foreword.html.
This document was prepared by Technical Committee ISO/TC 184, Automation systems and integration,
Subcommittee SC 4, Industrial data.
A list of all parts in the ISO 8000 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.
iv
ISO/PRF 8000-220:2025(en)
Introduction
0.1 Foundations of the ISO 8000 series
Digital data deliver value by enhancing all aspects of organizational performance including:
— operational effectiveness and efficiency;
— safety and security;
— reputation with customers and the wider public;
— compliance with statutory regulations;
— innovation;
— consumer costs, revenues and stock prices.
In addition, many organizations are now addressing these considerations with reference to the United Nations
1)
Sustainable Development Goals .
The influence on performance originates from data being the formalized representation of information (see
NOTE 1 in this subclause). This information enables organizations to make reliable decisions. This decision
making can be performed by human beings and also automated data processing, including artificial
intelligence systems.
NOTE 1 ISO 8000-2 defines information as “knowledge concerning objects, such as facts, events, things, processes, or
ideas, including concepts, that within a certain context has a particular meaning”.
Organizations become dependent on digital data through widespread adoption of digital computing and
associated communication technologies. This dependency amplifies the negative consequences of the lack of
quality in these data, leading to the decrease of organizational performance.
The biggest impact of digital data comes from two key factors:
— the data having a structure that reflects the nature of the subject matter;
EXAMPLE 1 A research scientist writes a report using a software application for word processing. This report includes
a table that uses a clear, logical layout to show results from an experiment. These results indicate how material properties
vary with temperature. The report is read by a designer, who uses the results to create a product that works in a range
of different operating temperatures.
— the data being computer processable (machine readable) rather than just being for a person to read and
understand.
EXAMPLE 2 A research scientist uses a database system to store the results of experiments on a material. This system
controls the format of different values in the data set. The system generates an output file of digital data. This file is
processed by a software application for engineering analysis. The application determines the optimum geometry when
using the material to make a product.
ISO 9000 explains that quality is not an abstract concept of absolute perfection. Rather, quality is the
conformance of characteristics to requirements. This actuality means that any item of data can be of high
1)
1) https://sdgs.un.org/goals
v
ISO/PRF 8000-220:2025(en)
quality for one purpose but not for a different purpose. The quality is different because the requirements are
different between the two purposes.
EXAMPLE 3 Time data are processed by calendar applications and also by control systems for propulsion units on
spacecraft. These data include start times for meetings in a calendar application and activation times in a control system.
These start times require less precision than the activation times.
The nature of digital data isare fundamental to establishing requirements that are relevant to the specific
decisions made by each organization.
EXAMPLE 4 ISO 8000-1 identifies that data have syntactic (format), semantic (meaning) and pragmatic (usefulness)
characteristics.
To support the delivery of high-quality data, the ISO 8000 series addresses:
— data governance, data quality management and maturity assessment;
EXAMPLE 5 ISO 8000-61 specifies a process reference model for data quality management.
— creation and application of requirements for data and information;
EXAMPLE 6 ISO 8000-110 specifies how to exchange characteristic data that are master data.
— monitoring and measurement of information and data quality;
EXAMPLE 7 ISO 8000-8 specifies approaches to measuring information and data quality.
— improvement of data and, consequently, information quality;
EXAMPLE 8 ISO/TS 8000-81 specifies an approach to data profiling, which identifies opportunities to improve data
quality.
— issues that are specific to the type of content in a data set.
EXAMPLE 9 ISO/TS 8000-311 specifies how to address quality considerations for product shape data.
Data quality management covers all aspects of data processing, including creating, collecting, storing,
maintaining, transferring, exploiting and presenting data to deliver information.
Effective data quality management is systemic and systematic, requiring an understanding of the root causes
of data quality issues. This understanding is the basis for both correcting existing inconsistencies and
implementing solutions that prevent future reoccurrence of those nonconformities.
EXAMPLE 10 If a data set includes dates in multiple formats including “yyyy-mm-dd”, “mm-dd-yy” and “dd-mm-yy”,
then data cleansing can correct the consistency of the values. Such cleansing requires additional information, however,
to resolve ambiguous entries (such as, “04-05-20”). The cleansing also cannot address any process issues and people
issues, including training, that have caused the inconsistency.
0.2 Understanding more about the ISO 8000 series
ISO 8000-1 provides a detailed explanation of the structure and scope of the whole ISO 8000 series.
ISO 8000-2 specifies the single, common vocabulary for the ISO 8000 series. This vocabulary supports
understanding the overall subject matter of data quality. ISO 8000-2 presents the vocabulary structured by a
series of topic areas (for example, terms relating to quality and terms relating to data and information).
vi
ISO/PRF 8000-220:2025(en)
2)
ISO has identified ISO 8000-1, ISO 8000-2 and ISO 8000-8 as horizontal deliverables .
0.3 Role of this document
As a contribution to the overall capability of the ISO 8000 series, this document addresses how to quantify the
quality of data recorded as a stream of single, discrete digital values by sensors, typically in sensor networks
and sensing devices connected to the Internet of Things (see ISO/IEC 30141). This quantification is through a
set of quality measures corresponding to the quality characteristics and related data anomalies specified by
ISO 8000-210. These quality measures are suitable for use when improving the quality of sensor data in the
data processing stage prior to data analysis or exploitation.
This document is suitable for use in industry fields that include smart manufacturing, social infrastructure and
healthcare, in circumstances where sensor data are collected by sensor networks and sensing devices
connected to the Internet of Things.
This document supports activities that affect:
— one or more information systems;
— data flows within the organization and with external organizations;
— any phase of the data life cycle.
Organizations can use this document individually or in conjunction with other parts in the ISO 8000 series.
Annex AAnnex A contains an identifier that conforms to ISO/IEC 8824-1. The identifier unambiguously
identifies this document in an open information system.
0.4 Benefits of the ISO 8000 series
By implementing parts of the ISO 8000 series to improve organizational performance, an organization can
achieve the following benefits:
— objective validation of the foundations for digital transformation of the organization;
— a sustainable basis for data in digital form becoming a fundamental asset class that the organization relies
on to deliver value;
— securing evidence-based trust from other parties (including supply chain partners and regulators) about
the repeatability and reliability of data and information processing in the organization;
— portability of data with resulting protection against loss of intellectual property and reusability across the
organization and applications;
— effective and efficient interoperability between all parties in a supply chain to achieve traceability of data
back to original sources;
— readiness to acquire or supply services where the other party expects to work with common
understanding of explicit data requirements.
2)
Deliverable dealing with a subject relevant to a number of committees or sectors or of crucial importance to ensure
coherence across standardization deliverables.
vii
...
Questions, Comments and Discussion
Ask us and Technical Secretary will try to provide an answer. You can facilitate discussion about the standard in here.