ISO/FDIS 8000-210
(Main)Data quality — Part 210: Sensor data: Data quality characteristics
Data quality — Part 210: Sensor data: Data quality characteristics
This deliverable specifies quality characteristics of data that is recorded by sensors as a stream of single, discrete digital values by sensors. The following are within the scope of this deliverable: — application of quality characteristics of sensor data that is a stream of single, discrete digital values; — types of anomalies in sensor data; — quality characteristics of sensor data; — relationship with other ISO standards. The following are outside the scope of this deliverable: — analogue, image, video and sound data produced by sensors; — methods to measure data quality characteristics.
Qualité des données — Partie 210: Données des capteurs: Caractéristiques de qualité des données
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International
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ISO/TC 184/SC 4
Data quality —
Secretariat: ANSI
Part 210:
Voting begins on:
2024-09-18
Sensor data: Data quality
characteristics
Voting terminates on:
2024-11-13
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TO BE CONSIDERED IN THE LIGHT OF THEIR POTENTIAL
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MADE IN NATIONAL REGULATIONS.
Reference number
FINAL DRAFT
International
Standard
ISO/TC 184/SC 4
Data quality —
Secretariat: ANSI
Part 210:
Voting begins on:
Sensor data: Data quality
characteristics
Voting terminates on:
RECIPIENTS OF THIS DRAFT ARE INVITED TO SUBMIT,
WITH THEIR COMMENTS, NOTIFICATION OF ANY
RELEVANT PATENT RIGHTS OF WHICH THEY ARE AWARE
AND TO PROVIDE SUPPOR TING DOCUMENTATION.
© ISO 2024
IN ADDITION TO THEIR EVALUATION AS
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
BEING ACCEPTABLE FOR INDUSTRIAL, TECHNO-
LOGICAL, COMMERCIAL AND USER PURPOSES, DRAFT
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
INTERNATIONAL STANDARDS MAY ON OCCASION HAVE
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
TO BE CONSIDERED IN THE LIGHT OF THEIR POTENTIAL
or ISO’s member body in the country of the requester.
TO BECOME STAN DARDS TO WHICH REFERENCE MAY BE
MADE IN NATIONAL REGULATIONS.
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 Reference number
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Quality characteristics of sensor data . 1
4.1 General .1
4.2 Accuracy .3
4.3 Completeness .3
4.4 Consistency .3
4.5 Precision . . .3
4.6 Timeliness .4
5 Types of anomaly in sensor data . 4
5.1 General .4
5.2 Data anomalies for individual sensors .5
5.2.1 General .5
5.2.2 Offset .5
5.2.3 Drift .6
5.2.4 Trim .7
5.2.5 Spike .8
5.2.6 Noise .9
5.2.7 Data loss .10
5.2.8 Lack of amount .11
5.2.9 Shift . 12
5.2.10 Drop or rise . 13
5.2.11 Stuck .14
5.2.12 Bound oscillation . 15
5.2.13 Inconsistent frequency .16
5.2.14 Different resolution.17
5.2.15 Incorrect timestamp .18
5.2.16 Latency .19
5.3 Data anomalies for collections of multiple sensors . 20
5.3.1 General . 20
5.3.2 Dissimilarity . . 20
5.3.3 Rule violation .21
5.3.4 Inconsistent timestamp . 22
6 Relationship between quality characteristics and data anomalies .23
7 Application of quality characteristics of sensor data .24
Annex A (informative) Document identification .28
Annex B (informative) Comparison with other documents .29
Bibliography .32
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 .
2)
The influence on performance originates from data being the formalized representation of information .
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.
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 appli
...
ISO/TC 184/SC 4
Secretariat: ANSI
Date: 2024-06-1609-04
Data quality —
Part 210:
Sensor data: Data quality characteristics
FDIS stage
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
EmailE-mail: copyright@iso.org
Website: www.iso.org
Published in Switzerland
ii © ISO 2024 – All rights reserved
ii
Contents
Foreword . iv
Introduction . v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Quality characteristics of sensor data . 2
4.1 General. 2
4.2 Accuracy . 3
4.3 Completeness . 4
4.4 Consistency . 4
4.5 Precision . 4
4.6 Timeliness . 5
5 Types of anomaly in sensor data . 5
5.1 General. 5
5.2 Data anomalies for individual sensors . 6
5.3 Data anomalies for collections of multiple sensors . 20
6 Relationship between quality characteristics and data anomalies . 23
7 Application of quality characteristics of sensor data . 24
Annex A (informative) Document identification . 28
Annex B (informative) Comparison with other documents . 29
Bibliography . 33
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 © ISO 2024 – All rights reserved
iv
Introduction
0.1 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 1)
Sustainable Development Goals . .
2 2)
The influence on performance originates from data being the formalized representation of information . . 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.
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
https://sdgs.un.org/goals
1)
https://sdgs.un.org/goals
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”.
2)
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”.
v
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.
The nature of digital data is 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.
— — creating and applying requirements for data and information;
EXAMPLE 6 ISO 8000--110 specifies how to exchange characteristic data that are master data.
— — monitoring and measuring information and data quality;
EXAMPLE 7 ISO 8000--8 specifies approaches to measuring information and data quality.
— — improving 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 cannotdoes not address
any process issues and people issues, including training, that have caused the inconsistency.
vi © ISO 2024 – All rights reserved
vi
0.2 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).
4 3)
ISO has identified ISO 8000--1, ISO 8000--2 and ISO 8000--8 as horizontal deliverables . .
0.3 0.3 Role of this document
As a contribution to the overall capability of the ISO 8000 series, this document describes quality
characteristics and related data anomalies of data produced by sensors. This document focuses, in particular,
on data that are a stream of individual, discrete digital values. The quality characteristics and data anomalies
can serve as the basis for quality criteria to measure and improve the quality of sensor data. Such criteria are
suitable when preparing data prior to data analysis or exploitation of the data.
Sensors are a fundamental enabler of digital transformation, which has resulted in the proliferation of sensor
networks and sensing devices connected to the Internet-‑of-‑Things (see ISO/IEC 30141). Such sensors
capture data about a wide range of aspects of the physical world. These data have significant volume, velocity
and variety, making them an essential asset serving as the basis for insight and foresight that improves
decision making across organizations of all types.
While offering this potential, sensor data are also vulnerable to disruption from a wide range of sources,
including limited capacity of hardware (such as processors, memory and batteries), software, congested
wireless communications and impact from harsh operating environments. The data are, therefore, likely to
include data anomalies, which require detection and handling in order to improve
...
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