ISO 8000-210:2024
(Main)Data quality — Part 210: Sensor data: Data quality characteristics
Data quality — Part 210: Sensor data: Data quality characteristics
This document specifies quality characteristics of data that are recorded by sensors as a stream of single, discrete digital values. The following are within the scope of this document: — quality characteristics of sensor data; — types of anomalies in sensor data; — relationships between quality characteristics of sensor data and anomalies in sensor data; — application of quality characteristics 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 210: Données des capteurs: Caractéristiques de qualité des données
General Information
Standards Content (Sample)
International
Standard
ISO 8000-210
First edition
Data quality —
2024-12
Part 210:
Sensor data: Data quality
characteristics
Qualité des données —
Partie 210: Données des capteurs: Caractéristiques de qualité des
données
Reference number
© ISO 2024
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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
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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
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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
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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 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 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 c
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