ISO/IEC 24668:2022
(Main)Information technology — Artificial intelligence — Process management framework for big data analytics
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
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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 2022
© ISO/IEC 2022
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© ISO/IEC 2022 – All rights reserved
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 2022 – All rights reserved
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
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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).
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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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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 2022 – All rights reserved
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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© ISO/IEC 2022 – All rights reserved
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]
© ISO/IEC 2022 – All rights reserved
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
© ISO/IEC 2022 – All rights reserved
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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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
© ISO/IEC 2022 – All rights reserved
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
© ISO/IEC 2022 – All rights reserved
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.
© ISO/IEC 2022 – All rights reserved
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.
© ISO/IEC 2022 – All rights reserved
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.
© ISO/IEC 2022 – All rights reserved
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 initiatives.
The purpose of the CDP2 process is to support people throughout the organization to
Purpose
achieve their plans, objectives and targets through capability enhancement.
The outcomes of this process include:
a) understand and develop the underlying capabilities of the organization;
Outcomes
b) evaluate the set of results achieved to improve future performance and provide
sustainable benefits to all their stakeholders;
c) recognize their efforts and achievements in a timely and appropriate manner
Table 8 — CDP3 Functional knowledge
ID CDP3
Name Functional knowledge
This process covers the identification and deployment of industry domain workforce as
Context
part of the big data analytics teams at all levels.
The purpose of the CDP3 process is to encourage the BDA leaders to learn quickly and
Purpose
rapidly respond with accountability in their individual enhanced role.
The outcomes of this process include:
a) encourage stakeholders to participate in activities that contribute to the wider
acceptance to big data analytics initiative across the organization;
Outcomes
b) use approaches to understand, anticipate and respond to the different needs and
expectations from big data analytics implementation team;
c) promote a culture which supports the generation of new ideas and new ways of
thinking to encourage innovation though big data analytics.
Table 9 — CDP4 Capability renewal
ID CDP4
Name Capability renewal
This process covers identification, analysis and understanding of external indicators,
such as global and local economic, market or societal and technology trends, which can
Context
affect the organization and translate these into potential future scenarios for big data
analytics initiative.
The purpose of the CDP4 process is to use a structured approach for generating and
Purpose prioritizing creative ideas and allocating resources to execute them innovatively within
appropriate timescales with updated tools and techniques of big data analytics.
The outcomes of this process include:
a) identify, evaluate and develop new and emerging technology portfolio to improve the
agility of organization;
Outcomes
b) establish and manage learning and collaboration networks to identify opportunities
for creativity, innovation and improvement in technology and human resources.
6.4 Data management processes
Tables 10 to 13 contain the relevant processes related to data management:
— Table 10: DMP1 Data identification;
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— Table 11: DMP2 Data quality;
— Table 12: DMP3 Data governance;
— Table 13: DMP4 Big data infrastructure.
Table 10 — DMP1 Data identification
ID DMP1
Name Data identification
This process covers the key step of identifying data elements. This involves identifica-
Context tion of data elements so that the team will not lose sight of any important dimension or
factor that plays a key role in the outcome analysis.
The purpose of DMP1 process is to identify, define, classify, and collect data for all data
Purpose elements available for the information flow in the context of the project or department
or function.
The outcomes of this process include:
a) data elements relevant to the process or function or department are identified;
b) data elements can be classified into categories, such as unstructured, transactional,
hierarchical, reference;
Outcomes
c) meta data (operational definition) such as units, frequency, source or sources,
functional definition, range, possible functional correlation, producer or consumer
or ownership or steward is derived;
d) new data sources are identified;
e) data collection is performed.
Table 11 — DMP2 Data quality
ID DMP2
Name Data quality
This process covers the key aspects of a practical, comprehensive, and well-managed
data quality strategy that can eliminate scattershot efforts in different business units
Context
and help ensure that business users throughout an organization have access to consist-
ent and accurate information.
The purpose of DMP2 process is to identify that a program should address the root
Purpose causes of data inconsistencies, fix errors through data cleansing, and unite separate data
quality initiatives.
The outcomes of this process include:
a) structured data has normalized relational mapping;
b) unstructured data has valid references to structured data;
Outcomes
c) data complies on the aspects of accuracy, completeness, timeliness, validity,
consistency, integrity, etc;
d) orphaned or inconsistent data should either be sanitized from the resulting dataset
or filtered if a live dataset is used.
© ISO/IEC 2022 – All rights reserved
Table 12 — DMP3 Data governance
ID DMP3
Name Data governance
This process covers the key steps of establishing data governance for the organization.
An important aspect of data is correctly defining the ownership of the data. At times this
can be a very difficult task. Many organizations think information technology (IT) should
be in charge of the data because IT owns the system where the data are housed however,
Context IT is rarely the actual owner of the data. When establishing the owner, it is important to
understand who can answer questions about the data, provide definitions to the attrib-
utes, and determine the validity of the data. Those people are usually the true owners of
the data. These people need to be involved in defining business rules for data cleansing,
correcting the data, and matching and consolidation.
The purpose of DMP3 process is to identify tools and define broad ranges of processes to
Purpose
implement an effective data governance across the organization.
The outcomes of this process include:
a) a governing council is established;
b) data stewards are identified;
Outcomes
c) business rules associated with consolidating and updating data are defined;
d) implementation and sustenance plans are defined.
Table 13 — DMP4 Big data infrastructure
ID DMP4
Name Big data infrastructure
This process covers the key steps of implementing big data infrastructure for enabling
data analytics. The big data paradigm is a rapidly changing field with rapidly changing
Context technologies. Very few organizations operate solely on data organic to that organization
these days. This means that systems that collect and analyse big data need to be able to
securely and reliably interoperate and share data.
The purpose of DMP4 process is to implement a big data infrastructure which is a system
Purpose
that leverages big data engineering and employs a big data paradigm to process big data.
The outcomes of this process include:
a) Appropriate software and tools for distributed systems and storage and mining
(NoSQL and relational databases, distributed file systems, and other distributed
processing system) are identified;
b) system implementation executed;
Outcomes
c) big data architecture is identified and implemented;
d) compliance with security and privacy aspect of big data is ensured;
e) skill enablement is executed.
6.5 Analytics development processes
Tables 14 to 17 contain the relevant processes related to analytics development;
— Table 14: ADP1 Analytics activity definition;
— Table 15: ADP2 Analytics practices;
— Table 16: ADP3 Success criteria definition;
— Table 17: ADP4 Risk identification.
© ISO/IEC 2022 – All rights reserved
Table 14 — ADP1 Analytics activity definition
ID ADP1
Name Analytics activity definition
Context This process covers planning the analytics activities in terms of enterprise-wide scope,
targets and having a consistent perspective for analytics for the organization.
Purpose The purpose of the ADP1 process is establish an analytics implementation plan to man-
age a unified big data and analytics platform.
Outcome The outcomes of this process include:
a) identification of small-scale analytics projects that suggest cross-functional or
enterprise potential;
b) identification of business areas that can have potential benefit from analytics;
c) focus on high value and high impact targets;
d) establish a priority matrix including criteria for executing projects;
e) successful implementation of projects.
Table 15 — ADP2 Analytics practices
ID ADP2
Name Analytics practices
Context This process covers the analytics implementation facets for the organization and develop
a process to choose the combination of techniques for analytics development for or in the
organization.
Purpose The purpose of the ADP2 process is to establish consistent technology practices across
the organization in implementing big data analytics projects. The process focuses on the
analytics implementation and not on IT practices and data infrastructure needed for up-
stream and downstream integration and deployment.
Outcomes The outcomes of this process include:
a) life cycle methodology selection criteria and guidelines;
b) architecture considerations and selection guidelines;
c) guidelines on analytics methods selection;
d) tools selection criteria and guidelines.
© ISO/IEC 2022 – All rights reserved
Table 16 — ADP3 Success criteria definition
ID ADP3
Name Success criteria definition
Context This process covers defining critical success factors for implementing analytics projects,
including performance baselines, target validation with stakeholders and setting the
criteria for accuracy.
Purpose The purpose of ADP3 process is to set and validate baselines for process performance,
target and accuracy for project implementations.
Outcomes The outcomes of this process include:
a) baseline confirmation for the target process or function performance of project scope;
b) target validation or re-set of targets in collaboration with stakeholders through a
combination of diligence, proof of concept and pilot;
c) accuracy determination as an outcome of project completion.
Table 17 — ADP4 Risk identification
ID ADP4
Name Risks identification
Context This process covers the risks that should be addressed as part of the outcome of big data
analytics projects. The stakeholders (process owners) need to be aware of the constraints
and risks involved in the decision-making process after implementing big data analytics.
Purpose The purpose of ADP4 process is to identify, classify and set boundaries of the risks in-
volved in decision making process after implementing big data analytics.
Outcomes The outcomes of this process include:
a) identification of the constraints of scalability if any;
b) criteria for decision oversight being set;
c) identification of constraints of trustworthiness if any;
d) any possible issues with transparency being called out;
e) identification of any bias in training data set within the scope of the project.
6.6 Technology integration processes
Tables 18 and 19 contain the relevant processes related to technology integration:
— Table 18: TIP1 Data integration;
— Table 19: TIP2 Systems integration.
© ISO/IEC 2022 – All rights reserved
Table 18 — TIP1 Data integration
ID TIP1
Name Data integration
Context This process covers the key aspects of implementing a consolidated data mart at an enter-
prise level so that key stakeholders can get a relational view and holistic understanding of
the business customers, products or services and operations.
Purpose The purpose of TIP1 process is to identify an enterprise level data warehouse and if
needed with a strong MDM platform EDW (along with MDM) provides an enterprise-wide
infrastructure to standardize, integrate, and establish an authoritative source for data
from disparate sources (CRM or ERP or PoS or HRMS or PIM or Web etc.) of information
that have either similar or duplicate or both attributes to support business operations and
decisions analytics.
Outcome The outcomes of this process include:
a) identification of the EDW system and MDM (if needed);
b) implementation of EDW and MDM (if needed).
Table 19 — TIP2 Systems integration
ID TIP2
Name Systems integration
Context This process covers the key aspects of providing the primary interface to external compo-
nents of big data analytics engine including data providers and consumers.
Purpose The purpose of TIP2 process is to establish the mechanisms to import data from data pro-
vider for further analysis or processing and export data to consumers through APIs.
Outcome The outcomes of this process include:
a) secure data connection and access is established;
b) data import is executed;
c) access rights management is established;
d) data export (e.g. via application programming interface, protocol or query language)
is performed.
7 Overview of process assessment model
7.1 General
In ISO/IEC 33001:2015, the PAM is described as a model suitable for the purpose of assessing a specified
process quality characteristic based on one or more PRM’s. The PRM defined in Clause 5 establishes a
PAM that provides a common basis for performing assessments on big data processes, enabling the
results to be reported using a common rating scale.
A PAM combines the basic set of process descriptions from one or more PRM’s in the selected PMF.
The two-dimensional model, as depicted in Figure 2, consists of a set of processes defined in terms of
their purpose and process outcomes, and a PMF that contains a set of process attributes related to the
process capability. The process attributes apply across all processes. They can be grouped into process
capability levels that can be used to characterize the process. The assessment output includes a set of
process profiles and process capability level rating for each process assessed.
In order to maximize the repeatability of assessments, documented evidence justifying the ratings must
be recorded and retained. This evidence is in the form of assessment indicators, which typically take
the form of objectively demonstrated characteristics of Information products, practices and resources
associated with the processes assessed. A PAM contains details of the assessment indicators to be used.
Such assessment indicators can be documented through the use of some form of database, checklists
© ISO/IEC 2022 – All rights reserved
or questionnaires. Figure 2 shows the relationship with the PRM, assessment process, measurement
framework according to ISO/IEC 33001:2015 and ISO/IEC 33002:2015. See Annex A for information on
mapping of indicators to process attribute outcomes.
Figure 2 — Process assessment model relationships
7.2 Process dimension
Process dimension in Figure 2 is represented by processe
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