ISO/IEC 24668:2022

Information technology — Artificial intelligence — Process management framework for big data analytics

This document provides a framework for developing processes to effectively leverage big data analytics across the organization irrespective of the industries or sectors. This document specifies process management for big data analytics with its various process categories taken into account along with their interconnectivities. These process categories are organization stakeholder processes, competency development processes, data management processes, analytics development processes and technology integration processes. This document describes processes to acquire, describe, store and process data at an organization level which provides big data analytics services.

Technologies de l'information — Intelligence artificielle — Cadre de gestion des processus pour les analyses des megadonnées

General Information

• Status: Published
• Publication Date: 16-Nov-2022
• ICS: 35.020 - Information technology (IT) in general
• Technical Committee: ISO/IEC JTC 1/SC 42 - Artificial intelligence
• Drafting Committee: ISO/IEC JTC 1/SC 42/WG 2 - Data
• Current Stage: 6060 - International Standard published
• Start Date: 17-Nov-2022
• Due Date: 14-Aug-2022
• Completion Date: 17-Nov-2022

Standards Content (Sample)

INTERNATIONAL ISO/IEC
STANDARD 24668
First edition
2022-11
Information technology — Artificial
intelligence — Process management
framework for big data analytics
Technologies de l'information — Intelligence artificielle — Cadre de
gestion des processus pour les analyses des megadonnées
Reference number
ISO/IEC 24668:2022(E)
© ISO/IEC 2022

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ISO/IEC 24668:2022(E)
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ISO/IEC 24668:2022(E)
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Abbreviated terms . 3
5 Overview of process reference model . 4
6 Process reference model . 6
6.1 General . 6
6.2 Organization stakeholder processes . 6
6.3 Competency development processes . 8
6.4 Data management processes . 9
6.5 Analytics development processes . 11
6.6 Technology integration processes . 13
7 Overview of process assessment model .14
7.1 General . 14
7.2 Process dimension .15
7.3 Process capability dimension . 15
7.4 Assessment indicators . 16
7.5 Process attribute rating scale . 16
8 Processes and their performance indicators .17
8.1 General . 17
8.2 Base practices (BPs) and information products (IPs) . 17
8.2.1 Organization stakeholder processes . 17
8.2.2 Competency development processes . 22
8.2.3 Data management processes . 26
8.2.4 Analytics development processes .30
8.2.5 Technology integration processes . 35
9 Process capability indicators (Levels 0 to 5) .37
9.1 General . 37
9.2 Process capability levels and process attributes . 37
Annex A (informative) Mapping of indicators to process attribute outcomes: .38
Annex B (informative) Information product characteristics .41
Bibliography .50
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ISO/IEC 24668:2022(E)
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical
activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other international
organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the
work.
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 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 or
www.iec.ch/members_experts/refdocs).
Attention is drawn to the possibility that some of the elements of this document may be the subject
of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent
rights. Details of any patent rights identified during the development of the document will be in the
Introduction and/or on the ISO list of patent declarations received (see www.iso.org/patents) or the IEC
list of patent declarations received (see https://patents.iec.ch).
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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. In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 42, Artificial intelligence.
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 and
www.iec.ch/national-committees.
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ISO/IEC 24668:2022(E)
Introduction
This document provides a process management framework for using big data analytics (BDA) across
most functions of an organization. The quantum of data, the collection, storage, utilization, technology,
the speed of data generation, structure and variety of data cannot be handled by the conventional data
handling methods and frameworks.
This document provides a BDA process reference model (BDA PRM) and then provides process
assessment model (BDA PAM). The BDA PAM are composed of two dimensions: process dimension that
includes processes based on a set of PRMs including the BDA PRM and capability dimension based on
process measurement framework (PMF).
This document defines a PRM and PAM as part of the framework for big data analytics, in accordance
with the requirements of ISO/IEC 33004:2015 and ISO/IEC 33020:2019, for use in performing a
conformity assessment in accordance with the requirements of ISO/IEC 33002:2015.
Primary audiences of this document are implementers of BDA in organizations as well as BDA
capability assessors. This document provides five process categories such as organization stakeholder,
competency development, data management, analytics development, and technology integration.
This framework can be used for:
— managing the processes that are considered to be best practices;
— enabling risk determination and process improvements of the incumbent organization.
Value delivered through automation, either prediction, or decision-making support or both using BDA
is valuable to organizations. Implementing, improving, and assessing BDA processes based on this
document expect benefits such as:
— competitive advantages;
— better decision-making;
— improve customer experiences;
— sales improvement;
— responsiveness to opportunities and threats;
— mistakes and errors reduction;
— cost reduction.
Clause 5 provides an overview of PRM and Clause 6 details out the specific processes under each
process categories for the PRM. Clause 7 provides an overview of the PAM and Clause 8 provides details
of process attributes and process performance indicators and Clause 9 provides process capability
indicators.
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INTERNATIONAL STANDARD ISO/IEC 24668:2022(E)
Information technology — Artificial intelligence — Process
management framework for big data analytics
1 Scope
This document provides a framework for developing processes to effectively leverage big data analytics
across the organization irrespective of the industries or sectors.
This document specifies process management for big data analytics with its various process categories
taken into account along with their interconnectivities. These process categories are organization
stakeholder processes, competency development processes, data management processes, analytics
development processes and technology integration processes. This document describes processes to
acquire, describe, store and process data at an organization level which provides big data analytics
services.
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/IEC 33001:2015, Information technology — Process assessment — Concepts and terminology
ISO/IEC 33003:2015, Information technology — Process assessment — Requirements for process
measurement frameworks
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 33001:2015 and the
following 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/
3.1
big data
extensive datasets, primarily in the data characteristics of volume, variety, and either velocity or
variability or both, that require a scalable technology for efficient storage, manipulation, management
and analysis
Note 1 to entry: Big data is commonly used in many different ways, for example as the name of the scalable
technology used to handle big data extensive datasets.
[SOURCE: ISO/IEC 20546:2019, 3.1.2, modified]
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ISO/IEC 24668:2022(E)
3.2
data analytics
composite concept consisting of data acquisition, data collection, data validation, data processing,
including data quantification, data visualization, and data interpretation
Note 1 to entry: Data analytics is used to understand objects represented by data, to make predictions for a given
situation, and to recommend on steps to achieve objectives. The insights obtained from analytics are used for
various purposes such as decision-making, research, sustainable development, design, planning, etc.
[SOURCE: ISO/IEC 20546:2019, 3.1.6]
3.3
data governance
development and enforcement of policies related to the management of data
Note 1 to entry: ISO/IEC 38500 specifies six principles of information technology governance: responsibility;
strategy; acquisition; performance; conformance; human behaviour. These principles also apply to data.
[SOURCE: ISO 8000-2:2022, 3.16.1]
3.4
benefit
advantage to the organization of the actionable knowledge derived from an analytic system
Note 1 to entry: It is often ascribed to big data due to the understanding that data has potential benefit that was
typically not considered previously.
[SOURCE: ISO/IEC 20546:2019, 3.1.1]
3.5
strategic plan
document specifying how data management is to be aligned to the organizational strategy
Note 1 to entry: This term has the same meaning as strategic asset management plan (SAMP) defined in
ISO 55000:2014 with data point of view.
3.6
base practices
BP
activities that contribute to achieving a specific process purpose and fulfil the process outcomes when
consistently performed
3.7
process attributes
PA
process features that can be evaluated on a scale of achievement to provide a process capability measure
3.8
capability level
set of process assessment indicators that together describe an ability to operate and perform a process
at a given capability level
3.9
process attribute rating
judgement of the degree of achievement of the process attribute for the assessed process
3.10
process
process attribute rating
means of assessing the capabilities addressed by the defined process attributes
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ISO/IEC 24668:2022(E)
3.11
outcome
observable result of the successful achievement of the process purpose
4 Abbreviated terms
BDA big data analytics
PRM process reference model
PAM process assessment model
PMF process measurement framework
BDAP big data application provider
BDFP big data framework provider
BDSP big data service partner
PaaS platform as a service
SaaS software as a service
DevOps development and operations
IP information product
PoC proof of concept
MDM master data management
EDW enterprise data warehouse
API application programming interface
FMEA failure modes and effects analysis
ER entity relationship
SIPOC supplier input process output customer
CTQ critical to quality
KRA key result area
KPI key process indicator
BSC balance score card
RACI responsible accountable consulted informed
CRM customer relationship management
ERP enterprise resource planning
PoS point of sale
HRMS human resource management software
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ISO/IEC 24668:2022(E)
PIM product information management
MSE mean squared error
MAPE mean absolute percentage error
MoM minutes of meeting
BFSI banking financial services and insurance
AMC annual maintenance contract
CSM customer service management
OSP organization stakeholder processes
CDP competency development processes
DMP data management processes
ADP analytics development processes
TIP technology integration processes
GP generic practices
5 Overview of process reference model
ISO/IEC 33001:2015 defines process reference model (PRM) as a model comprising definitions
of processes described in terms of process purpose and outcomes, together with an architecture
describing the relationships between the processes. To define a process reference model, the
requirements specified in ISO/IEC 33004:2015 should be met. ISO/IEC 33004:2015, Annex A provides
detailed requirements. ISO/IEC 33004:2015 requires that processes included in a PRM satisfy specific
requirements.
A process description shall meet the following requirements:
a) a process shall be described in terms of its purpose and outcomes;
b) the set of process outcomes shall be necessary and sufficient to achieve the purpose of the process;
c) process descriptions shall not contain or imply aspects of the process quality characteristic beyond
the basic level of any relevant PMF in accordance with ISO/IEC 33003:2015.
Figure 1 details the key process categories for advancement of data analytics in an organization.
These key process categories interplay with each other in terms of the readiness of an organization to
implement and deploy big data analytics.
There are 5 process categories such as organization stakeholder processes, competency development
processes, data management processes, analytics development processes and technology integration
processes which stand on the foundation of technology infrastructure and are guided by the
organizational leadership strategy and culture. The big data analytics processes and their categories
are not based on any particular organization and it is not mandated to implement them.
Organization stakeholder processes - organization top management is a key facilitative force in various
ways – starting from creating shared understanding for the requirements of data analytics to mapping
the benefit of embarking on such projects to strategic goals of the organization. Assignment of decision
rights and accountabilities to stakeholders is key to overall success of long-term data analytics projects.
The top management should also enable identification of the key data providers, data consumers,
application requirements and data quality and management rules to kick start the data analytics
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ISO/IEC 24668:2022(E)
journey. Defining a data centric culture and mitigation of resistance to change in such a situation should
also be addressed.
Competency development processes - data analytics projects need capabilities related to big data
application provider (BDAP), big data framework provider (BDFP), big data service partner (BDSP)
defined in ISO/IEC 20547-3. These capabilities can either be outsourced or developed from within. If
outsourcing is preferred, then one needs additional competency to manage the outsourcing. Hence,
relevant capability build-up and continuous maintenance and enhancement is critical for data analytics
projects success.
Data management processes - data requires strong management and governance, preferably integrated
with IT, information and information security management and governance which includes monitoring
of emerging data sources, measurement of data quality metrics and data ownership roles. Privacy,
security, policy compliance should be ensured. Specific legal requirement can apply.
Analytics development processes - data analytics development processes include data exploration, data
diligence (outlier and missing value), algorithm adjustment and customization, algorithm development,
algorithm validation, algorithm fine-tuning, evaluation of population stability index, etc. Analytics
development processes, throughout their life cycle, rely on close cooperation with IT function of
organization.
Technology integration processes - relevant technology infrastructure is required to implement
data analytics. Ensure the results are formatted and optimally presented to target consumers or
stakeholders. Capability should be integrated with the functional architecture. Processes to select these
functional components and integrating them into the overall data analytics architecture are crucial.
These processes include technology maturity evaluation, implementation approach (e.g. leveraging
PaaS or SaaS) definition, and configuration or version management (e.g. DevOps).
Figure 1 shows process categories and processes of big data analytics.
Figure 1 — Big data process categories and processes
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ISO/IEC 24668:2022(E)
6 Process reference model
6.1 General
Tables 1 to 19 contain the following descriptive elements according to ISO/IEC/IEEE 24774 for each
process in the PRM. The individual processes are stated in terms of process name, process purpose and
process outcomes:
a) name: the name of a process is a short noun phrase that summarizes the scope of the process,
identifying the principal concern of the process, and distinguishes it from other processes within
the scope of the PRM;
b) context: for each process, a brief overview describes the intended context of the application of the
process;
c) purpose: the purpose of the process is a high level and overall goal for performing the process;
d) outcomes: Outcomes are measurable, tangible technical or business results that are achieved by a
process. They are observable and assessable.
6.2 Organization stakeholder processes
Tables 1 to 5 contain the relevant processes related to organization stakeholders:
— Table 1: OSP1 Business analytics policy;
— Table 2: OSP2 Stakeholders decision rights and accountabilities;
— Table 3: OSP3 Alignment with organizational objectives;
— Table 4: OSP4 Change management;
— Table 5: OSP5 Data driven culture.
Table 1 — OSP1 Business analytics policy
ID OSP1
Name Business analytics policy
This process covers establishing business objectives and strategies for the organization
Context specific to big data analytics. This involves analysing the external environment and final-
izing the strategic goals and business objectives of the organization.
The purpose of the OSP1 process is to define a policy for big data analytics initiatives,
Purpose
roadmap and a guideline to implement these initiatives.
The outcomes of this process include:
a) business objectives, direction and strategies are defined and shared to the
organization and relevant stakeholders;
Outcomes
b) strategic roadmaps are developed within the constraints of the service provider
resources.
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ISO/IEC 24668:2022(E)
Table 2 — OSP2 Stakeholders decision rights and accountabilities
ID OSP2
Name Stakeholders decision rights and accountabilities
This process covers the assignment of stakeholders who are responsible, accountable,
Context consulted and informed for successful implementation of big data analytics projects and
initiatives.
The purpose of the OSP2 process is to identify and assign specific responsibilities to key
Purpose
stakeholders.
The outcomes of this process include:
a) key stakeholders are identified with expertise in big data technology and process or
domain knowledge;
Outcomes
b) assignment of roles and responsibilities are done;
c) accountability of the stakeholders is defined;
d) succession plan of the roles based on responsibilities are defined.
Table 3 — OSP3 Alignment with organizational objectives
ID OSP3
Name Alignment with organizational objectives
This process covers the alignment of the big data analytics with the overall organization
Context objectives. This is to ensure proper mobilization of resources, planning and arrive at
actionable from the recommendations out of analytics outcomes or inferences.
The purpose of the OSP3 process is to align an organization’s big data analytics initia-
Purpose
tives with its business strategy.
The outcomes of this process include:
a) big data analytics initiatives, specific to relevant departments or processes are
arrived;
Outcomes
b) each of the initiatives is aligned with the stated objectives of departments or
processes;
c) the high-level initiatives are published across the organization with relevant
stakeholders.
Table 4 — OSP4 Change management
ID OSP4
Name Change management
This process covers the change management amongst the internal stakeholders of the
Context
organization.
The purpose of the OSP4 process is to identify and manage people who are impacted
Purpose by business analytics initiatives and manage the changes, including resistance and
workarounds.
The outcomes of this process include:
a) big data analytics initiatives or projects are subjected to progress monitoring and
reviewed against expected outcomes;
b) progress is communicated to stakeholders;
Outcomes
c) the impact of the changes, issues and improvement is analysed and reported;
d) awareness sessions and trainings are organized across the organization for different
roles of stakeholders regarding big data analytics.
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ISO/IEC 24668:2022(E)
Table 5 — OSP5 Data driven culture
ID OSP5
Name Data driven culture
This process covers the organizations shared values and mission statements depicting
Context
the data driven decision making.
The purpose of the OSP5 process is to create decision making processes based on data,
Purpose analytics and related set of fact-based systems to attain better strategic intelligence
capability.
The outcomes of this process include:
a) process or business performance always measured in metrics;
b) the metrics comprise of leading and lagging indicators;
c) quantitative analysis is encouraged with possible statistical correlations;
d) possible big data analytics initiatives or projects are discussed and explored on
Outcomes
problem or opportunity areas during reviews;
e) quick wins or successful initiatives on big data analytics should be rewarded and
communicated;
f) a framework for piloting ideas exploring big data analytics for current processes or
new areas of business should be rolled out (similar to a Kaizen framework in many
organizations).
6.3 Competency development processes
Tables 6 to 9 contain the relevant processes related to competency development:
— Table 6: CDP1 Workforce planning;
— Table 7: CDP2 Capability development;
— Table 8: CDP3 Functional knowledge;
— Table 9: CDP4 Capability renewal.
Table 6 — CDP1 Workforce planning
ID CDP1
Name Workforce planning
This process covers the talent forecasting and resource estimation of the organization
Context
for executing big data analytics projects and initiatives.
The purpose of the CDP1 process is to arrive at plans to ensure availability of workforce
Purpose
and other resources for executing big data analytics projects and initiatives.
The outcomes of this process include:
a) identify future leaders in big data analytics;
Outcomes
b) align relevant responsibilities and craft succession plans in critical roles;
c) recruit the right talent.
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ISO/IEC 24668:2022(E)
Table 7 — CDP2 Capability development
ID CDP2
Name Capability development
This process covers the empowerment and training of employees of the organization for
Context
executing big data analytics projects and i
...