ISO 8000-220:2025

International
Standard
ISO 8000-220
First edition
Data quality —
2025-09
Part 220:
Sensor data: Quality measurement
Reference number
© ISO 2025
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Published in Switzerland
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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

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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.

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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
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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.

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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 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.

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International Standard ISO 8000-220:2025(en)
Data quality —
Part 220:
Sensor data: Quality measurement
1 Scope
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.
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 8000-2, Data quality — Part 2: Vocabulary
ISO 8000-210, Data quality — Part 210: Sensor data: Data quality characteristics
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 8000-2 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 Fundamental principles and assumptions
Data quality measures are the foundation for being able to measure and improve quality of the data. Such
measures are useful for all kinds of data, including sensor data recorded as a stream of single, discrete
digital values by sensor networks and sensing devices connected to the Internet of Things. These measures
provide a quantification of either the data quality characteristics or data anomalies applicable to the data

(see Table 1 for an example of a data quality measure for the data quality characteristic accuracy). Each
measure is the result of a measurement function that generates the result from an appropriate combination
of quality measure elements. Each data quality characteristic or data anomaly can be quantified by more
than one data quality measure.
Table 1 — Example data quality measure for the data quality characteristic accuracy
Measurement
Data quality measure Quality measure elements within the function
function
A = count of those data values evaluated as being accurate
Accuracy of a data set A / B
B = count of all data values evaluated
5 Quality measures for sensor data
5.1 General
When sensor data fail to be of sufficient quality, the data typically contain one or more data anomalies.
EXAMPLE 1 If the accuracy of a set of sensor data is low, then the set typically contains anomalies including offset,
drift, trim, spike and noise.
Removing or reducing anomalies from the data can, therefore, improve the quality of the data.
To support this purpose, this clause specifies quality measures for each data quality characteristic and
corresponding data anomaly specified by ISO 8000-210.
Quality measures are of one of two kinds:
— those specific to a quality characteristic and indicating the degree or value to which data values captured
over a specific period of time conform to criteria or requirements for that quality characteristic;
— those specific to an anomaly and indicating the degree or value to which that data anomaly found during
a specific period of time deviates from a reference (expected) data pattern of a property being observed
by a sensor.
When the quality measure of a specific quality characteristic is low, the corresponding quality measures of
data anomalies will typically be relatively high. These corresponding anomalies are the ones that affect the
quality characteristic. Therefore, it is possible to enhance the quality measure of the quality characteristic
by identifying the data anomalies and lowering their quality measures through data handling such as data
removal and compensation.
NOTE 1 Quality measures indicate various types of value such as the range [0,1] as a binary value, percentage,
score, bound, frequency, and probability.
NOTE 2 The quality measures can be modified according to the intended use or for the convenience of users.
EXAMPLE 2 A percentage is converted to the count of data values that meet requirements.
EXAMPLE 3 A quality measure is converted to a normalized value in the range [0,1].
NOTE 3 Some data anomalies can affect more than one data quality characteristic. For each combination of anomaly
and quality characteristic, the corresponding quality measure can differ from the measure for other combinations.
This documents specifies quality measures for the following data quality characteristics:
— accuracy (see 5.2);
— completeness (see 5.3);
— consistency (see 5.4);
— precision (see 5.5);
— timeliness (see 5.6).
5.2 Quality measures for accuracy
Accuracy quality measures indicate the degree to which sensor data, in a specific context of use, correctly
represent the true value of the intended property of a concept or event (see Table 2 for example quality
measures). These quality measures also include those for data anomalies such as offset, drift, trim, spike
and noise, which can affect accuracy.
Table 2 — Qua
...