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Enabling the digital transformation of industry: The roles of AI, big data, analytics, and related data ecosystem

Posted 01 June 2021 | By Wael William Diab, MS, MBA  | PDF Link PDF

Enabling the digital transformation of industry: The roles of AI, big data, analytics, and related data ecosystem

This article outlines the work of SC 42, the technical subcommittee for artificial intelligence (AI) of the ISO/IEC’s joint technical committee 1, which aims to accelerate adoption of the standards while simultaneously addressing emerging issues to enable successful digitalization of sectors.
Introduction and overview
The digital transformation of industry promises to revolutionize how we live, work, and play, unlocking the vast potential of new use cases and applications. Key in this inflection is the role of information technologies, such as AI, big data, analytics, and related data ecosystems.1-3 The International Electrotechnical Commission (IEC) and International Organization for Standardization (ISO) have together developed international standards for information and communication technologies in more than 22 areas, and the organizations’ joint technical committee, ISO/IEC JTC 1, has focused on developing and maintaining standards for those technologies. Within that joint structure, the work of the technical subcommittee for artificial intelligence, SC 42, aims to accelerate adoption of the standards while simultaneously addressing emerging issues to enable successful digitalization of sectors.
Enabling intelligent insights
Many of the emerging services rely on the ability to provide insights based on the large amount of data being generated, such as operational real-time data and batch data. The technologies on which SC 42 is working will facilitate such services, for example:
  • AI technologies will allow insights and analytics that go far beyond what legacy analytic systems could provide in terms of efficiency, speed, and applications that have yet to be envisioned. This is a radical departure from the way in which analytic systems have traditionally been designed akin to the “plug-and-play” approach in enterprise and consumer applications more than a decade ago.
  • Big data technologies will streamline and, in many cases, enable analytics to be performed on massive data sets. This will be achieved by designing computer system architecture around how the data sets will be generated and used in a particular application, rather than applying the same computer system to an application regardless of what the data looks like in terms of its variety, volume, variability, and so on.
In addition to enabling these services, which are the cornerstone of digital transformation, SC 42 also considers some of the concerns about them and their usage and application, for instance:
  • Data quality standards for machine learning and analytics are crucial for ensuring the applied technologies produce useful insights and eliminate faulty features.
  • Governance standards in the areas of AI and business process framework for big data analytics address how the technologies can be governed and overseen from a management perspective.
  • Standards looking at trustworthiness, ethics, and societal concerns from the outset will ensure rapid deployment.
SC 42 is also working on a novel approach to help instil confidence when technologies such as AI are used through a management system standard.
SC 42 ecosystem approach
The subcommittee is taking an ecosystem approach by looking at emerging requirements from a comprehensive range of perspectives, such as regulatory, business, domain specific, societal, and ethical. The subcommittee assimilates these requirements for the context of use of the technologies it works on, translating them to technical requirements and developing horizontal deliverables that are applicable across industry sectors. This platform approach allows the subcommittee to collaborate with other organizations and committees. It provides a framework for application domains, such as smart manufacturing,4 to build upon and use the work of SC 42 (Figure 1).
SC 42 international standards in enabling smart manufacturing
As already noted, the subcommittee develops standards with a view of the entire ecosystem and platform approach so that its standards can be used with other efforts, such as application domain standards. The list below is exemplary rather than comprehensive.
Application guidance and use cases
SC 42 collects use cases to ensure its horizontal standards are broadly applicable. Moreover, it is also developing a standard that will provide guidance for AI applications that can be used as a starting point for application domain developers and/or application domain standards groups looking to use AI in their roadmaps.
Foundational standards
Technologies such as AI are expected to be ubiquitous in the near future so it is not surprising that the diversity and number of stakeholders will continue to increase. Foundational standards provide for a common language and frameworks that can be used by this increasingly diverse set of stakeholders. SC 42 has taken on this important task for AI and also recently completed the foundational standards for big data.
Aspects such as the trustworthiness of the technology are critical because the technologies are widely applicable. SC 42 is developing a suite of standards that provides an overview of emerging issues such as trustworthiness, robustness, and bias in AI, along with technical standards to address them, for instance, the application of the ISO generic 31000 risk management framework to AI.
Ethical aspects and societal considerations
New requirements around ethics and societal concerns have emerged with recent information technologies, and AI is no exception. SC 42 is addressing these concerns across the board in its deliverables – for example, ethical and societal concerns around use cases – as well as specifically, by having deliverables that tie these requirements to the technical standards being developed.
Data ecosystem
Within its areas of focus, SC 42 is looking at the data ecosystem and has launched a four-part series on data quality for machine learning and analytics. This complements the work in progress on big data analytics, as well as the published work on big data.
Governance implications
SC 42 is collaborating with its sister subcommittee, SC 40, which covers IT service management and governance, to develop a standard targeted at governance implications that can arise. The standard is aimed at executives or boards looking to deploy the technology and it addresses the issues with that perspective in mind.
Computational aspects
SC 42 is also examining the computational aspects associated with knowledge management, which is focused on the front end of the process, through to the existing computational techniques used in an AI system and emerging computational needs. Moreover, the committee has a project that looks at the assessment of classification performance for machine learning models. This type of work will ensure that AI systems will factor in the practical needs of application domains, such as smart manufacturing.
Management systems standard
The unique aspect of AI technology has created the need for a methodology that covers process developers and deployers of AI systems use and increases user confidence by providing a platform that can be used for third-party certification. SC 42 is leveraging the management systems standard (MMS) approach and has started work on ISO/IEC 42001 for an AI management system. MSSs have been successful in other areas, such as ISO 9001, which specifies requirements for a quality management system, and the idea is to apply a similar approach for AI.
Concluding remarks
The ability to provide insights is at the core of the digital transformation and the emerging applications and services powering it. IT systems and technologies, such as AI and big data, are key enablers. The international standards that SC 42 is developing in this area are crucial to removing barriers to adoption while addressing concerns. Nonetheless, these standards and technologies will have to be used in concert with operational technology standards, such as those being developed by ISO. SC 42’s approach to consider the entire ecosystem and develop horizontal standards allows for a platform that makes such collaboration easier. From an IT perspective, SC 42 is uniquely positioned to work with standards experts, who have in-depth domain knowledge and decades of experience in sister ISO and IEC committees, as well as outside liaison partners.
About the author
Wael William Diab, MS, MBA, currently chairs the ISO/IEC JTC 1/SC 42, the international standardization committee on AI. He is a business and technology strategist and industry-recognized expert on digital transformation with more than 20 years’ executive experience at Fortune 500 companies in Silicon Valley, during which time he has been active in standardization and related activities. Diab is listed as one of the most prolific inventors of all time, with more than 895 patents to his name in the field of information and communications technology. He is also secretary of the Industrial Internet Consortium’s (IIC’s) steering committee and chair of its liaison and technology working groups and industrial AI and global event series task groups and a recipient of the IIC Individual Contributor Award. He is also a member of the IoT Solutions World Congress program committee and chairs its AI forum. He chaired the AI track of the 22nd Global Standards Collaboration meeting in 2019. In 2011, Diab was recognized by the David Packard Medal of Achievement and Innovator Award for his leadership in green technology. He is author of the book, Ethernet in the First Mile: Access for Everyone, and coauthor of the Industrial Analytics: The Engine Driving the IIoT Revolution whitepaper and the Industrial IoT Analytics Framework. Diab holds masters’ degrees in electrical engineering (Stanford University) and business administration (The Wharton School, Philadelphia). He can be contacted at  
Citation Diab WW. Enabling the digital transformation of industry: The roles of AI, big data, analytics, and related data ecosystem. RF Quarterly. 2021;1(2): 43-6. Regulatory Affairs Professionals Society.
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