ISO/IEC TR 25005-2:2025
(Main)Information technology — Data use in smart cities — Part 2: Use case analysis and common considerations
Information technology — Data use in smart cities — Part 2: Use case analysis and common considerations
This document provides use cases and common considerations for use cases analysis for data use in smart cities. In particular, this document includes: a) methods for collecting use cases; b) methods of analysing the collected use cases about data use in smart cities; c) common considerations about data use in smart cities based on the analysis of collected use cases.
Technologies de l'information — Utilisation de données dans les villes intelligentes — Partie 2: Analyse des cas d'utilisation et considérations générales
General Information
Standards Content (Sample)
Technical
Report
ISO/IEC TR 25005-2
First edition
Information technology — Data use
2025-10
in smart cities —
Part 2:
Use case analysis and common
considerations
Technologies de l'information — Utilisation de données dans les
villes intelligentes —
Partie 2: Analyse des cas d'utilisation et considérations générales
Reference number
© ISO/IEC 2025
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ii
Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Abbreviated terms . 2
5 Methods for collecting use cases of data use in smart cities . 3
5.1 Use case template structure and description .3
5.1.1 Structure of use case template .3
5.1.2 Description of use case template .3
5.2 Use cases selecting criteria .3
6 Methods of use case analysis of data use in smart cities . 4
6.1 Key variables in considerations for analysis of data use .4
6.2 Framework for analysing use cases of data use in smart cities .4
6.3 Process of use case analysis .6
7 Common considerations about data use in smart cities . 8
7.1 Overview .8
7.2 General .8
7.3 Considerations for data availability . 12
7.3.1 Application scenarios . 12
7.3.2 Stakeholders . 13
7.3.3 Types of data used . .14
7.3.4 How those data are used . 15
7.3.5 Challenges, difficulties, and problems .16
7.3.6 Strategies and solutions .16
7.4 Considerations for data quality assurance .17
7.4.1 Application scenarios .17
7.4.2 Stakeholders .18
7.4.3 Types of data used . .19
7.4.4 How those data are used .19
7.4.5 Challenges, difficulties, and problems . 20
7.4.6 Strategies and solutions . 20
7.5 Considerations for ease of data use .21
7.5.1 Application scenarios .21
7.5.2 Stakeholders . 22
7.5.3 Types of data used . . 22
7.5.4 How those data are used . 23
7.5.5 Challenges, difficulties, and problems . 23
7.5.6 Strategies and solutions .24
7.6 Considerations for data use security .24
7.6.1 Application scenarios .24
7.6.2 Stakeholders . 25
7.6.3 Types of data used . . . 26
7.6.4 How those data are used . 26
7.6.5 Challenges, difficulties, and problems .27
7.6.6 Strategies and solutions .27
7.7 Considerations for data-enabled innovation . 28
7.7.1 Application scenarios . 28
7.7.2 Stakeholders . 29
7.7.3 Types of data used . . 30
7.7.4 How those data are used . 30
7.7.5 Challenges, difficulties, and problems .31
© ISO/IEC 2025 – All rights reserved
iii
7.7.6 Strategies and solutions .31
Annex A (informative) Unified use case template for data use in smart cities .33
Annex B (informative) List of collected use cases of data use in smart cities .35
Annex C (informative) Variable code information .43
Annex D (informative) Example of use case analysis from UC 18 to UC 23 .44
Bibliography .56
© ISO/IEC 2025 – All rights reserved
iv
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
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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 Joint Technical Committee ISO/IEC JTC 1, Information technology.
A list of all parts in the ISO/IEC 25005 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.
© ISO/IEC 2025 – All rights reserved
v
Introduction
This document aims to provide common considerations about data use in smart cities based on the analysis
of collected use cases.
The objectives and implications of this document are:
— to support effective, sustainable, comprehensive and innovative use of data as city-wide strategic
resources for better performance, operation, service and sustainability of city;
— to support human-centred standardization collaboration on data use for better harmonization,
connectivity, interoperability and reusability of data as strategic resources and assets in digital
transformation of city;
— to synthesize common considerations that enable data availability, data usefulness, data connectivity,
data security, and data enabled intelligent predictions and actions from city wide multi-stakeholder’s
interests from collected use cases;
— to collect good practices that enable mapping, building, operating, assessing and continuous improvement
of data use in ICT development and application, investment, procurement, monitoring, auditing and
performance assessment;
— to improve digital enhancement of total capabilities of data use and evidence-based decision making in
smart cities such as data use for public health emergency and control across multi-dimensions, multi-
domains, multi-layers and multi-regions;
— to support data-based and data-driven and data-enabled ICT development and application in smart cities
including but limited to digital governance, legal governance, data quality governance, ICT governance,
data security governance, smart governance;
— to support the appropriate use of rights related to data distributed within and between cities, including
intellectual property rights and data privacy derived from human rights.
© ISO/IEC 2025 – All rights reserved
vi
Technical Report ISO/IEC TR 25005-2:2025(en)
Information technology — Data use in smart cities —
Part 2:
Use case analysis and common considerations
1 Scope
This document provides use cases and common considerations for use cases analysis for data use in smart cities.
In particular, this document includes:
a) methods for collecting use cases;
b) methods of analysing the collected use cases about data use in smart cities;
c) common considerations about data use in smart cities based on the analysis of collected use cases.
2 Normative references
There is no normative reference in this document.
3 Terms and definitions
For the purposes of this document, the terms and definitions given in the following apply.
ISO and IEC maintain terminological 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/
3.1
data use
handling or dealing with data for a specific purpose
Note 1 to entry: In smart cities, data use refers to activities enabling data value realization along with data value-chain,
including data availability, data quality assurance, ease of data use, data use security, and data-enabled innovation
from city wide multi-stakeholder’s interests.
[SOURCE: ISO/IEC 5207:2024, 3.30, modified — Note 1 to entry has been changed.]
3.2
data value chain
intelligent use, management and reuse of data to deliver insight
Note 1 to entry: Data value chain in this document includes data availability, data quality assurance, ease of data
use, data use security, and data enabled innovation for intelligent predictions and actions from city wide multi-
stakeholder’s interests.
[SOURCE: ISO/IEC 17917:2024,2.5, modified — Note 1 to entry has been added.]
© ISO/IEC 2025 – All rights reserved
3.3
data protection
process and practice in place to ensure that data is safeguarded from unauthorized access or alterations
including destruction available to those who need it, and handled in a way that is consistent with the
expectations of privacy set forth by laws and policies
3.4
raw data
data in its originally acquired, direct form from its source before subsequent processing
[SOURCE: ISO 5127:2017,3.1.10.04]
3.5
stakeholder
person or organization that can affect, be affected by, or perceive themselves to be affected by a decision or
activity
[SOURCE: ISO Guide 73:2009, 3.2.1.1, modified — Note 1 to entry has been deleted.]
3.6
use case
description of a sequence of interactions used to help identify, clarify, and organize considerations to support
a specific business goal
[SOURCE: ISO/IEC 27561:2024, 3.46, modified — “considerations" has taken the place of “requirements".]
3.7
use case scenario
set of circumstances under which the sequence of interaction describing a use case (3.6) takes place
[SOURCE: ISO 20077-1:2017, 3.19, modified — EXAMPLE, Note 1 and Note 2 have been deleted.]
4 Abbreviated terms
AI artificial intelligence
API existing data access interfaces
DIKW data-information-knowledge-wisdom
DIKWPA data–information–knowledge–wisdom–purpose-action
HCI human–computer interaction
ICT Information and communication technology
IoT internet of things
IT information technology
IPR intellectual property rights
MoU memorandum of understanding
UAS uncrewed aircraft system
© ISO/IEC 2025 – All rights reserved
5 Methods for collecting use cases of data use in smart cities
5.1 Use case template structure and description
5.1.1 Structure of use case template
In order to collect related use cases to generate the common considerations for data use in smart cities, a
unified use case template has been designed and provided, which consists of nine parts (See Annex A for
details).
5.1.2 Description of use case template
The use case template provides a guide to describe following key components to be considered for data use
in smart cities scenarios:
a) Use case title, use of specific type of data in the specific business scenario in the development of smart
cities needs to be clarified in the use case title.
b) Use case submission date and version, including submission time and version of use case;
c) Brief introduction to use case, including use case background, purposes and business for quickly
understanding the use case;
d) Highlights, good practices of data use in smart cities;
e) Use case scenarios, application purpose and stakeholders, describe the business processes, work flows
and the scenario where measures have been taken to enable data availability and data usefulness,
and to enable data are used effectively and securely city wide, as well as the purposes of data use are
achieved; Identify the types of stakeholders and their roles and responsibilities in use case scenarios
and the functions and activities they undertake.
f) Types of data used and how those data are used. Describe what types of data that are used and
provenance of those data as well as how they are used in the use case.
g) Challenges, difficulties and problems of city-wide data use, identify problems, challenges and difficulties
of city-wide data use in this specific use case and their reasons;
h) Strategies and solutions of city-wide data use, the problem-solving strategies and solutions to city wide
data use which take multiple stakeholders’ demands taking into consideration, in this specific use case; and
i) Additional information, source of use case for further information.
5.2 Use cases selecting criteria
Smart city initiative refers to a strategic program or set of actions implemented by governments,
organizations, or partnerships aimed at using advanced technology, urban data, and innovative practices to
improve urban living. The goal is to enhance the quality of life and service for residents, as well as support
sustainability and resilience of city. To ensure the effective implementation of smart city initiatives, it is
essential to establish clear criteria for selecting use cases. These criteria help prioritize and guide decisions
on the most beneficial and impactful applications of data. When setting the criteria for selecting the
appropriate use cases, the characteristics of data use in smart cities like integrating various data, creating
economic values, adopting advanced technologies, emphasizing data protection, etc. have been taken into
consideration. Correspondingly, the critical experiences and lessons learned from the schemes, measures,
or solutions adopted by use cases of data use in smart cities are expected to be reproduced or expanded in
different areas or domains of smart cities. Based on these, the key factors that are considered when choosing
use cases of data use in smart cities are provided as below.
a) Relevance: Related to data use strategies, policies, regulations, plans, standards, rules and norms;
related to the standardized collaboration practice of data use in smart cities and the transformative
use of data and technology; related to data-based, data-driven, data-enabled innovation for smart
© ISO/IEC 2025 – All rights reserved
predications and actions in the development of smart cities; related to integration of physical, digital
and social systems for improving quality of life of citizen and sustainability and resilience of city, etc.
b) Economy: Effect of the selected use cases is significant, sustainable in economic and social aspects,
which has strong self-hematopoietic ability, can generate economic benefits through operation, and save
or reduce government operating costs.
c) Essential: Application scenarios and solutions described in the use cases can meet the fundamental
needs of data use in smart cities with a focus on data protection based on the rights of data owners.
d) Exemplary: The selected use cases are operable, demonstrative and instructive, have certain reference
significance and application value, and have conditions for replication and promotion.
e) Innovation: The selected use cases feature innovative and forward-looking in ideas, methods and
working mechanisms which can play a leading role in the field and industry and form an innovative
leading effect.
f) Scalability: The selected use cases are characterized by generic and inclusive which can be extended
to various stakeholders with different interests of data use in smart cities and to different application
scenarios.
g) Data protection: The selected use cases take concerns of security, safety, privacy and rights protection
into consideration for responsible data use in smart cities.
In addition, the richness and analyticity of the use case text and the update time of the use case are
considered.
6 Methods of use case analysis of data use in smart cities
6.1 Key variables in considerations for analysis of data use
This document analyses six key variables including use case scenarios, stakeholders, types of data and
how those data are used, challenges, difficulties and problems, and solutions through use case scenario
orientation, stakeholder orientation, problem and demand orientation, and commonality best practice
orientation. The six variables are analysed as follows:
a) Use case scenario analysis refers to the analysis of business processes, workflows and business scenarios
that enable data are available, data are useful, data are easy to use, data are used securely, data are used
for enabling intelligent applications and services, data use by design for ICT in all use cases;
b) Stakeholder analysis refers to the analysis of the types, roles, responsibilities and activities of
stakeholders in all use cases in a certain use case scenario;
c) Analysis of what types of data are used and the provenance of those data;
d) How data are used in the use case, a figure to describe the processes and data controls could be provided;
e) Challenge, difficulty and problem analysis refer to the analysis of problems, challenges, difficulties and
universality influencing factors in the use of smart city data in all use cases;
f) Solution analysis refers to the analysis of solutions, paths and technical implementation considerations
about problems in the use of smart city data in all use cases.
6.2 Framework for analysing use cases of data use in smart cities
Based on the theory of DIKWPA Model, the combination of five dimensions and the use case template are
designed to support data use and reuse, which has been adopted as the framework for use case analysis of
data use in smart cities. And this is shown in Figure 1.
© ISO/IEC 2025 – All rights reserved
Figure 1 — Framework of use case analysis
The DIKWPA Model, which stands for Data, Information, Knowledge, Wisdom, Purpose and Action, is an
expended framework derived from the classic DIKW theory of information use and knowledge management.
This model explores the transformation of sensory inputs (Data) into meaningful patterns (Information), the
© ISO/IEC 2025 – All rights reserved
consolidation of these patterns into actionable knowledge (Knowledge), the application of such knowledge in
making prudent decisions (Wisdom), and the guidance of this wisdom towards achieving specific objectives
(Purpose). Finally, it encompasses the translation of these objectives into decision plans and tangible actions
(Action). The core structure of the DIKWP model integrates considerations of semantics, human cognition,
and ethical issues. DIKWPA Model made a difference to revolutionize industries, enhance human well-
being, and address complex global challenges. The main applications of this model include AI and Artificial
consciousness, health and medicine, education and cognitive development, business and innovation
management, governance and public policy, blockchain and decentralized systems, environmental
sustainability, cultural preservation and social science, security and defence, interdisciplinary research and
education, etc. Utilizing the use case template (for further details, see 4.1.2 and Annex A), DIKWPA model
assists in analysing use cases for data value realization in smart cities. The dimension of data availability
and secure data usage are integrated throughout the entire process of data utilization. The dimension of
data quality is considered an input that provides fundamental support for data-based value realization.
The connectivity is viewed as the as the process that facilitates data-driven value realization, while data
enabling dimension is seen as an output that enables data-enabled value realization.
In DIKWPA data value realization chain, the use and reuse of data drive the realization of data value by
transforming data into information, which representation real time operation data, and knowledge,
which symbolize virtual-time design data. Wisdom is supported by information and knowledge, aiding in
thoughtful decision-making. Purpose is derived from wisdom basis, and in turn, drives the process of action.
At each stage of DIKWPA, data is meticulously processed to extract information and knowledge. Through
a data-driven approach, empowers the use of this information and knowledge to make informed decisions
and initiate actions. Additionally, in a feedback loop, data from execution results of executed actions can be
reintegrated into the DIKWPA process to generate new information and knowledge. The outcomes of actions
and practices implemented are gathered as new data, allowing the DIKWPA cycle to commence anew. This
cyclical process allows for sustainable creation of value from data.
6.3 Process of use case analysis
The process of use case analysis is displayed in Figure 2, mainly including coding, clustering, mapping, and
generating considerations. See Annex C for variable code information.
© ISO/IEC 2025 – All rights reserved
Key
UC use case
AS application scenario
SH stakeholder
TD types of data used
TH how those data are used
© ISO/IEC 2025 – All rights reserved
P challenges, difficulties, and problems
S strategies and solutions
CL cluster
C consideration
Figure 2 — Flow chart of use case analysis process
The main steps of use case analysis are as follows:
a) Conduct open coding to analyse variables including application scenarios, stakeholders, types of data
used, how those data are used, challenges, difficulties, and problems, as well as strategies and solutions
of every use case;
b) Induce all the coding results into different clusters and name them for every variable;
c) Map all the clusters of every variable with the five-dimension analysis framework of data are available,
data are useful, data are easy to use, data are used securely, as well as data are used for enabling
intelligent applications and services;
d) Generating common considerations for five dimensions from application scenarios, stakeholders, types
of data used, how those data are used, challenges, difficulties, and problems, as well as strategies and
solutions of every use case.
In order to explain the use case analysis process clearly, 5 use cases coded as UC 18-23 from 5 countries have
been selected to display the results at each stage. See Annex D for the example of use case analysis process.
7 Common considerations about data use in smart cities
7.1 Overview
In order to realize data value in smart cities, DIKWPA model has been chosen as the theoretical foundation
to construct data value chain. Based on the model of input-processing-output, there exists the evolution from
data to information to knowledge, wisdom, purpose and action which is displayed in Figure 1. Accordingly,
for data input, data are useful (data quality assurance) plays the fundamental role in providing valuable data
resources; for data processing, data are easy to use (ease of data use) is crucial for data integration; and
for data output, data are used for enabling intelligent applications and services (data-enabled innovation)
acts as the ultimate objective of data use. At the same time, data are available (data availability) and data
are used securely (data use security) are regarded as the supporting factors covering the whole process of
data value realization. From the aforementioned five dimensions, the common considerations for data use
in smart cities have been proposed based on twenty-three selected use cases analysis (See Annex B). Under
these five dimensions, starting from stakeholders, ICT considerations, challenges, difficulties and problems,
solutions, combined with the data value realization process and in accordance with the problem-oriented
analysis ideas, the common considerations for data use in smart city are obtained by coding, analysing and
summarizing use case text.
Among the five dimensions, the dimension of data availability mainly focuses on the activities such as data
use planning and design, data creation and collection, data aggregation and fusion, data processing and
storage, data sharing and opening; the dimension of data quality assurance focuses on almost all stages of
the data value realization process; the dimension of ease of data use mainly focuses on almost all stages of
the data value realization process; the dimension of data use security also focuses on almost all stages of the
data value realization process; the dimension of data-enabled innovation mainly focuses on the activities
such as data use planning and design, data analysis and application.
7.2 General
According to the process of use case analysis, the common considerations for 5 dimensions are specifically
described based on the classification of every variable, and all the results of clustering are described in
Table 1.
© ISO/IEC 2025 – All rights reserved
Table 1 — Overview of clarifications for 6 variables
Use case vari- Type
Name of Type
able No.
Application Type 1 Comprehensive, horizontal or data-driven application scenarios
scenarios (AS)
Type 2 Specialized, vertical or scenario-driven application scenarios
Stakeholders Type 1 Data planners
(SH)
Type 2 Data collectors
Type 3 Data providers
Type 4 Data administrators
Type 5 Data operators
Type 6 Digital infrastructure maintainers
Type 7 Data users
Type 8 Data regulators
Types of data Type 1 Raw input data
used (TD)
Type 2 Processed data
Type 3 Output data
How those Type 1 Data access
data are used
Type 2 Data observation
(TH)
Type 3 Data interpretation
Type 4 Data analysis
Type 5 Event detection and response
Type 6 Data consolidation or collation
Type 7 Data recording
Type 8 Data products or services creation
© ISO/IEC 2025 – All rights reserved
TTabablele 1 1 ((ccoonnttiinnueuedd))
Use case vari- Type
Name of Type
able No.
Difficulties Type 1 Difficulties, Type 1.1 Challenges from insufficient software
and problems and problems and hardware infrastructures
(P) of data availa-
Type 1.2 Challenges from inefficient data collec-
bility
tion and integration
Type 1.3 Challenges from limited data acquisi-
tion channels
Type 1.4 Challenges from incomplete institu-
tions
Type 2 Difficulties, Type 2.1 Challenges from low data quality
and problems
Type 2.2 Challenges from inadequate data quali-
of data quality
ty assessment criteria
assurance
Type 3 Difficulties, Type 3.1 Challenges from poor interoperability
and problems
Type 3.2 Challenges from insufficient software
of ease of data
and hardware infrastructures
use
Type 3.3 Challenges from inadequate sharing
mechanisms
Type 4 Difficulties, Type 4.1 Challenges from data or network inse-
and problems curity
of data use
Type 4.2 Challenges from personal privacy
security
insecurity
Type 4.3 Challenges from inadequate data secu-
rity protection mechanisms
Type 5 Difficulties, Type 5.1 Challenges from challenges from un-
and problems qualified conditions
of data-ena-
Type 5.2 Challenges from multiple scenarios
bled innova-
Type 5.3 Challenges from inadequate manage-
tion
ment
© ISO/IEC 2025 – All rights reserved
TTabablele 1 1 ((ccoonnttiinnueuedd))
Use case vari- Type
Name of Type
able No.
Strategies and Type 1 Strategies and Type 1.1 Enhancement of software and hard-
solutions (S) solutions of ware infrastructure
data availa-
Type 1.2 Improvement of collecting and inte-
bility
grating data
Type 1.3 Enrichment of acquiring data compre-
hensively from multiple channels
Type 1.4 Improvement of relevant institutions
and management system
Type 2 Strategies and Type 2.1 Improvement of data quality
solutions of
Type 2.2 Establishment of data quality assess-
data quality
ment criteria and rules
assurance
Type 3 Strategies and Type 3.1 Enhancement of interoperability
solutions of
Type 3.2 Improvement of software and hard-
ease of data
ware infrastructure for data opening
use
and sharing
Type 3.3 Establishment of data sharing mecha-
nisms
Type 4 Strategies Type 4.1 Improvement of data / network secu-
and solutions rity
of data use
Type 4.2 Improvement of personal privacy
security
security
Type 4.3 Establishment of data security protec-
tion mechanisms
Type 5 Strategies and Type 5.1 Optimization of data empowerment
solutions of conditions
data-enabled
Type 5.2 Consolidation of data management
innovation
Application scenarios (AS) can be categorized into two types, including Type 1 of Comprehensive, horizontal
or data-driven application scenarios and Type 2 of Specialized, vertical or scenario-driven application
scenarios.
Stakeholders (SH) can be categorized into eight types, including Type 1 of Data Planners defined as in the
data ecosystem, data planners are the institutions or individuals responsible for formulating data policies,
coordinating digital construction, and managing the data governance framework. Type 2 of Data Collectors
defined as data collectors are the institutions directly responsible for collecting, storing, and managing
data. Type 3 of Data Providers defined as individuals, organizations, and institutions that generate, own,
and publish raw data. Type 4 of Data Administrators defined as responsible for coordinating and managing
data resources, ensuring effective access to and use of data. Type 5 of Data Operators defined as Data
operators are the entities that process raw data into valuable information products and provide relevant
data services. Type 6 of Digital Infrastructure Maintainers defined as they are the builders and maintainers
of the infrastructure that supports the flow of data. Type 7 of Data Users defined as users who utilize data
resources for decision making, analysis, and service delivery, and are the final beneficiaries of data value.
And Type 8 of Data Regulators defined as roles responsible for overseeing and ensuring compliance and
security of data utilization.
Types of data used (TD) can be categorized into three types, including Type 1 of Raw Input Data referring to
data in its originally acquired, direct form from its source before subsequent processing. Type 2 of Processed
Data referring to data which have been transformed from raw data or from an earlier data stage into a more
refined stage by data cleaning, sorting, linking, verifying and similar operations. And Type 3 of Output Data
referring to data that has been further integrated, analysed, and refined after the data processing stage,
which is transferred outside the system to form data products or services that can be used directly for
decision support, report generation, predictive analytics, etc.
© ISO/IEC 2025 – All rights reserved
How those data are used (TH) can be categorized into eight types, including Type 1 of Data Access referring to
process that enables users to retrieve or read published data. Type 2 of Data Observation referring to actions
of examining, studying, and monitoring data points, collections, or sets with the purpose of gaining insights,
understanding trends, identifying patterns, or making informed decisions. Type 3 of Data Interpretation
referring to process data to discover its meaning to the extent required by an application. Type 4 of Data
Analysis referring to systematic investigation of the data and their flow in a real or planned system. Type
5 of Event Detection and Response referring to recognition of event indicator and/or information about a
new situation, and action taken to protect and restore the normal operational conditions. Type 6 of Data
Consolidation or Collation referring to process of sharing or combining data from two or more applications
to create a single data application with more functionality. Type 7 of Data Recording referring to set of
reference service life data compiled into a prescribed format. And Type 8 of Data Products or Services
Creation referring to developing and deploying data-based tools, applications, or services to solve a specific
problem or meet a specific need.
As for challenges, difficulties, and problems (P), the five dimensions have been directly adopted as the
baseline to generate the specific classifications. Challenges, difficulties, and problems of data availability
can be categorized into four types, including Type 1 of challenges from insufficient software and hardware
infrastructures, Type 2 of challenges from inefficient data collection and integration, Type 3 of challenges
from limited data acquisition channels, and Type 4 of challenges from incomplete institutions. Challenges,
difficulties, and problems of data quality assurance can be categorized into two types, including Type 1 of
challenges from low data quality, and Type 2 of challenges from inadequate data quality assessment criteria.
Challenges, difficulties, and problems of ease of data use can be categorized into three types, including Type
1 of challenges from poor interoperability, Type 2 of challenges from insufficient software and hardware
infrastructures, and Type 3 of challenges from inadequate sharing mechanisms. Challenges, difficulties, and
problems of data use security can be categorized into three types, including Type 1 of challenges from data
or network insecurity, Type 2 of challenges from personal privacy insecurity, and Type 3 of challenges from
inadequate data security protection mechanisms. Challenges, difficulties, and problems of data-enabled
innovation can be categorized into three types, including Type 1 of challenges from unqualified conditions,
Type 2 of challenges from multiple scenarios, and Type 3 of challenges from inadequate management.
Similarly, for strategies and solutions (S), the five dimensions have been directly adopted as the baseline
to generate the specific classifications. Strategies and solutions of data availability can be categorized
into four types, including Type 1 of enhancement of software and hardware infrastructure, Type 2 of
improvement of collecting and integrating data, Type 3 of enrichment of acquiring data comprehensively
from multiple channels, and Type 4 of improvement of relevant institutions and management system.
Strategies and solutions of data quality assurance can be categorized into two types for, including Type
1 of improvement of data quali
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