Earlier this year, I gave a keynote at 2017 IEEE Joint Conferences on BigData Service, and 11th IEEE International Symposium on Service-Oriented System Engineering (SOSE 2017) with a focus on
Earlier this year, I gave a keynote at 2017 IEEE Joint Conferences on BigData Service, and 11th IEEE International Symposium on Service-Oriented System Engineering (SOSE 2017) with a focus on Cognitive Assistant in the Enterprise, and Cognitive Services. There was requests to share the slides, which is released below with a bit of delay, hopefully it’s still relevant.
The talk went into two key areas:
- Services, and cognitive: how the software architecture for services is impacted by cognitive technology, and the software architecture and methods for cognitive services, and in particular enterprise cognitive assistants, usually with a chatbot interface, as well
- Cognitive business process management: how cognitive technologies is fundamentally impacting and enabling the automation of a large school of manual processes in the enterprise, and in personal space, for that matter, through cognitive understanding of processes that are described and interacted among people, as opposed to prescribed in models.
In each of technologies, I also provided concrete examples and, some from my own work, with related references.
The Journey to Cognitive Enterprise Services: The Framework for Enterprise Services, and Business Processes
While I was visiting CSE, UNSW, Sydney, Australia, I gave a talk on the journey to cognitive enterprise services, including a framework for cognitive services sales and delivery, showing how the lifecycle of Enterprise Services from offering definition, marketing, sales, client account on boarding, and IT services operation is transformed in the cognitive era. I presented the result of our research work in realizing the framework for cognitive enterprise services. I also talked about how enterprise services operation, specifically on IT services processes, and in general enterprise processes are transformed with the notion of cognitive business process and interactive bots.
Earlier this year, I gave a keynote speech at ASSRI (Fifth Australasian Symposium on Service Research and Innovation) 2016. The deck first reviews recent advances in the technology, and how the future of services computing, and business process management field is shaped in a cognitive world, where artificial intelligence, natural language processing and machine learning techniques are applied to the dark data in the enterprise.
A complementary perspective to this discussion, from the business process management side, is presented in the following recent vision/position paper, which I co-authored with Rick Hull, a colleague from IBM Research:
Recently with a number of colleagues, Seyed-Mehdi-Reza Beheshti, Boualem Benatallah, Sherif Sakr, Daniela Grigori, Moshe Chai Barukh, Ahmed Gater, and Seung Hwan Ryu, we have published a book on the topic of data analytics for business processes.
The book offers a technical introduction to the field of process analytics, and presents the state of the art in research and practical techniques of process analytics. It covers a large body of knowledge including process data querying, analysis, matching and correlating process data and models to assist practitioners as well as researchers in understanding underlying concepts, problems, methods, tools and techniques for modern process analytics. The book also provides a review of commercial process analytics tools, and their practical applications.
The book has been forworded by Prof. Fabio Casati, from University of Trento in Italy, who is an expert and thought leader in this space.
More Info: Process Analytics Book Page.
Jim Spohrer, as the founder of Service Science Research at IBM Research, authored the below note, and I as the current Almaden Chair of Service Science PIC at IBM Research, co-edited it with him about the state of Service Science Research at IBM. The original post is published at Service SIG Community website.
This short article should be viewed as a down payment on a longer, more complete history of the evolution of service research at IBM. At best this current article is one person’s perspective, edited and sanity checked by my co-author, and including the names of many others who would need to be interviewed and consulted to provide a truly multi-perspective and comprehensive view. Of course, to learn more you can also attend Frontiers in Service July 9-12, in San Jose, CA, and ask an IBMer at that event!
Paul Maglio, returning from the HICSS conference (Hawaiian International Conference for Systems Sciences) in 2002, suggested the need for a new Human Sciences Research area in IBM Research. He consulted the first author as well as others, and the idea for the Almaden Service Research (ASR) group was born. In December 2002, a group of seven was established, and doubled in size each over several years, spreading to other research labs, until over 10% of IBM Research’s 3000 researchers identified with the area of service science and research, and began contributing to the establishment of SSME (Service Science Management and Engineering) as well as related conferences, journals, courses, and even degree programs at universities around the world. In 2011, SSME was one of 100 Icons of Progress used in the celebration of IBM’s Centennial.
The need for a service research group in IBM Research was driven by the tremendous growth and success of IBM’s Global Technology and Global Business Services Groups (GTS and GBS), including the 2002 acquisition of PWC Consulting Group by IBM. IT and business process outsourcing and help desks led to the creation of global service delivery centers around the world, and improving the productivity, quality, compliance, and innovativeness of these global service centers was the clear focus of IBM service research. By 2007, the Almaden Service Research (ASR) group, just one now relatively small arm of the overall IBM Research service research effort, had achieved significant impact,: the Component Business Modeling (Jorge Sanz) tool was being used by thousands of IBM GBS consultants, GTS was using Solution Design Manager (Ruoyi Zhou) to improve productivity on costing and pricing large deals with growing analytics capabilities across deals, Intelligent Document Gateway (Vikas Krishna) was transforming business processes of IBM internal services to employees, Business Insights Workbench (Jeff Kreulen, Scott Spangler, Ying Chen) was being used to help desk productivity as well as intellectual property related process for IBM and IBM customers. ASR was recognized with 1 exceptional, 4 outstanding, and 11 accomplishment awards by IBM Research, realizing a 10x ROI, and externally stimulating the growth of over 500 SSME-related university courses and degree programs as well as contributing to an estimated $1B in government funding for service research and innovation. In academic community, SSME programs and research were adopted in the academic curriculum of a number of universities including UC Berkeley, Arizona State University and San Jose State.
The guiding service innovation framework used by ASR is summarized in Figure 1 below. During the formative years, the group was managed to have impact on six major areas of service research, three firm-level and three ecosystem-level: (1) improve existing service offering, both internal and external (2) innovate new service offerings, both internal and external (3) inform firm-level service offering portfolio transformation and optimization, such as outsourcing and insourcing decision-making methods (4) assist customers and partners on their own service transformation journey with lessons learned from activity areas 1,2,3, (5) increase service research intellectual property, scientific publications, professional association and university interactions, (6) influence ecosystem-level dynamics and evolution, including mergers and acquisitions decision-making methods. The firm, as a service system entity which is part of and constrained by a larger evolving ecology of nested, networked service system entities, is compelled to make decisions that impact the capabilities, constraints, rights, and responsibilities of itself and other entities.
|1. Improve existing offerings
|3. Optimize, transform, innovate portfolio of offerings, including outsourcing and insourcing decision-making(firm level)|
|2. Create new offerings
|4. Co-create best practices with business partners
(suppliers and customer value chains)
|6. Optimize, transform, innovate environment, including divestitures, mergers and acquisitions decision-making(ecosystem level)|
|5. Co-create societal best practices
(patents, publications, professional associations, universities courses, etc.)
Figure 1: Service research to improve investment decision-making in 6 areas
The service-oriented architecture used to describe an evolving enterprise entity must balance optimization, transformation, and innovation forces, often referred to as run-transform-innovate investment decision-making by IBM’s CIO and business transformation group. All of this impacts the identity and career paths of people as well. For example, when an IBM customer outsources data centers or business processes to IBM, hundreds of former customer employees may be rebadged as IBM employees. When IBM acquires a firm, not only does that create a new group of IBM employees, but hundreds of IBMers may be shifted in job roles to align with and help grow the acquired firm as part of IBM. Also, when IBM divests of a business unit or outsources a portion of IBM to another firm, many IBMers are re-badged into different organizations. These flows of people across organizational boundaries, the changing skills of people, the changing laws and regulatory context, are all as important as the constantly evolving technological capabilities in reshaping IBM – and all of these issues are within the domain of study for T-shaped service scientists, who may have multidisciplinary communication breadth as well as depth in a specific areas such as technology, business, social organizational change, economics and public policy, or other domains of knowledge relevant to decision-making in and about service systems.
The growth of service in the GDP of nations and the revenues of IBM has been a long-term process over many decades, and so a vast number of internal and external influences shaped the formalization and growth of service research at IBM. Nevertheless, the story of ASR has become a noteworthy internal and external inflection point in the evolution of broader story of the evolution of service science and research. Specifically, because ASR formed in 2002, IBM was one of the first large firms to embrace Vargo and Lusch’s Service-Dominant Logic (S-D Logic) as a foundational worldview and mindset for a science of service in 2004. The notion of S-D Logic resource-integrators and service system entities is tightly coupled, as well as the importance of value propositions in shaping entity interactions and outcomes. Also, IBM was one of the first large firms to invest heavily in engaging universities and governments in a dialogue around the need to invest more in service research and innovation, and embracing the work of Neely, Ng, and other European service researchers leading efforts to understand product-service systems, servitization processes of manufacturing firms, outcome-based service contracting, and more (see IBM-Cambridge SSME report ). The Handbook of Service Science , Research Priorities for a Science of Service Systems , and other publications too numerous to mention have had a major impact on the evolution of service science inside and outside IBM.
Nevertheless, the service journey is far from easy and far from over. Service is still defined in different ways by different disciplines from economics to computer science to marketing and operations. Rather than use the term service as an adjective meaning value co-creation or the term service system to describe all entities capable of establishing win-win value co-creation interactions with other entities, IBM (wisely) preferred the term smarter systems, and launched a Smarter Planet initiative in 2008. Smarter government and nations, states, cities, smarter health and hospitals, smarter education and universities, as well as smarter water and utilities, smarter transportation, smarter manufacturing, smarter agriculture – and more became the communications framework for talking about the world as a system of systems, including business and societal systems. By dropping the word “service” the conversation about systems could more easily include manufacturing and agriculture as system of systems being reconfigured and transformed without confusing a world still largely entrenched in Goods-Dominant Logic (G-D Logic). After all, except for those embracing S-D Logic, referring to factories (manufacturing) or farms (agriculture) as types of service systems on a servitization journey seems like misuse of the term service. However, the notions of factories and farms, along with all other business and societal systems, on a smarter systems journey largely driven by technological advancements does not raise eyebrows at all. Still the concept of shifting from mere transactional exchange with customers to a value co-creation relationship with customers and citizens on platforms (e.g., Smarter Cities Intelligent Operation Center) was a clear aspect of a Smarter Planet, described as an increasingly instrumented, interconnected, and intelligent system of systems.
Beginning in 2014, IBM Research was reorganized, followed by a related reorganization of all of IBM in early 2015. The prior discipline structure of IBM Research (system, electrical, software engineering, computer science, service science) has been replaced with an organizational structure that reinforces the view that all smarter systems innovations require an integration of hardware, software, and service. So ASR and its service researchers, as well as groups of software researchers and hardware systems research, were reorganized into new research organizations aimed at IT as a Service, Integrated Industry Solutions, and Cognitive Computing (aka Cognition as a Service, or even Integrated IBM as a Service), and a number of focused labs with a system focus including THINKLab. The work of the IBM Research Professional Interest Community (PIC) for Service Science and Service Computing PIC areas continues, as does the broader IBM Service Science and Innovation Technical community, even though ASR as a separate group no longer exists. The Service Science researchers, integrated into new organizations of IBM Research, are investigating novel directions including systematic approaches for creating new value-added services, the study of service systems and ecosystem of services offered due to the proliferation of cloud services, mobile apps and their interconnection with social systems, people, economy and organizations.
A number of Service Science books are published as the result of service science research, and collaboration with academic and industrial partners under the Service Science Innovation short-book series.
Also, externally the service science journey continues with a notable participation from academia and industry including the following three noteworthy threads as representatives:: (1) the International Society of Service Innovation Professionals (ISSIP.org), (2) the Karlsruhe Service Research Institute (KSRI), and (3) other Smarter Service System initiatives. ISSIP was established as a non-profit umbrella professional association by Cisco, HP, IBM and other organizations to promote service innovation for our interconnect world, and to assist in the talent, technology, business, and societal development needs of institutions and individuals as T-shaped service innovators with breadth and depth. Service research leaders at the Karslruhe Service Research Institute in Germany have recently completed a textbook on the fundamentals of service systems that provides entry points for multiple disciplines into an integrated service science view of business and societal system of systems getting smarter.
Smart service system initiatives are diverse and include experimental funding programs by the USA National Science Foundations to boost innovation capacity of universities and industry to collaborate in translational research leading to smarter service systems.  Also, the INFORMS Journal of Service Science provides a channel for peer-reviewed articles related to smarter service systems . More broadly, internationally, Service Science is also an active area of research and investigation with communities of interest in Asia (in countries such as China, Taiwan, Thailand, etc.) and in Australia with Australian Services Science Society.
This short article has provided a glimpse at the evolution of service research at IBM. The story should be expanded to include outcomes and perspectives from other IBM Research labs around the world, IBM GTS and GBS business units, as well as the perspective of business and societal partners. In the research context, there are a number of recent work on defining a research agenda for services science [4,5,6]. The journey continues, and while it is always hard to predict what the future holds, a few items are worth brief mention: (1) in the era of cognitive systems, smart service systems will increasingly include cognitive or digital assistants (e.g., Watson and SIRI-like systems) for all occupations and societal roles. It is forseeable that smart Service Science research focus on leveraging advances in AI, big data-enabled intelligence and cognitive computing, and innovating to enable the creation of intelligent technologies and societies that are integrating well with human societies, (2) in the era of “make a job, not just take a job, “universities will be creating more T-shaped service innovators who can work well on teams to sense customer-needs and rapidly created integrated solutions to address customer opportunities, and (3) all of these changes will have public policy implications, and service science will increasingly be the study of both business and societal systems at the ecosystem level down to the customer-to-customer interaction level on diverse provider platforms.
- IfM and IBM. (2008). Succeeding through service innovation: A service perspective for education, research, business and government. Cambridge, United Kingdom: University of Cambridge Institute for Manufacturing. ISBN: 978-1-902546-65-0.
- Maglio, P. P., Kieliszewski, C. A., & Spohrer, J. C. (2010). Handbook of service science (p. 143). New York: Springer.
- Ostrom, AL, Bitner, MJ, Brown, SW, Burkhard, KA, Goul, M, Smith-Daniels, V, Demirkan, H, and Rabinovich, E (2010). Moving forward and making a difference: research priorities for the science of service. Journal of Service Research.
- Medina-Borja, A, (2015) Editorial Column—Smart Things as Service Providers: A Call for Convergence of Disciplines to Build a Research Agenda for the Service Systems of the Future, Service Science. Volume: 7, Issue: 1, pp. ii-v
- Maglio, PP, Kwan, SK, and Spohrer, J (2015) Commentary—Toward a Research Agenda for Human-Centered Service System Innovation, Service Science. Volume: 7, Issue: 1, pp. 1-10.
- Ostrom, AL, Parasuraman, A, Bowen, DE, Patricio, L, Voss, CA (2015) Service Research Priorities in a Rapidly Changing Context. Journal of Service Research. 18(2), 127-159.
Dr. James (“Jim”) C. Spohrer is Director IBM Global University Programs and leads IBM’s Cognitive Systems Institute. The Cognitive Systems Institute works to align cognitive systems researchers in academics, government, and industry globally to improve productivity and creativity of problem-solving professionals, transforming learning, discovery, and sustainable development. IBM University Programs works to align IBM and universities globally for innovation amplification and T-shaped skills. Jim co-founded IBM’s first Service Research group, ISSIP Service Science community, and was founding CTO of IBM’s Venture Capital Relations Group in Silicon Valley. He was awarded Apple Computers’ Distinguished Engineer Scientist and Technology title for his work on next generation learning platforms. Jim has a Yale PhD in Computer Science/Artificial Intelligence and MIT BS in Physics. His research priorities include service science, cognitive systems for smart holistic service systems, especially universities and cities. With over ninety publications and nine patents, he is also a PICMET Fellow and a winner of the S-D Logic award.
Hamid R. Motahari-Nezhad, PhD, is a Research Staff Member, data analytics research lead for services in Computing-as-a-Service Department at IBM Almaden Research Center, and Co-Chair of Service Science Professional Interest Community at IBM Research. His research interest include services science, data analytics, cognitive computing and its applications in the area of business process and services computing. Hamid is a Senior Member of IEEE, a member of ACM and ISSIP (International Society of Service Innovation Professionals).
Recent advances in AI, and specifically cognitive computing had received a lot of attention and interest from researchers and practitioners. One notable area, which particularly has generated a lot of interest, is cognitive assistants, specifically with the recent proliferation of intelligent apps in personal assistant space including Siri, Google Now, Cortana, and others.
While there is a significant progress made in the development of personal assistants, there are still many open challenges and the need for innovation to enable the development of cognitive assistants, specifically in enterprise and government context. The following slides were presented in a discussion group, in which I tried to review the opportunities, gaps/challenges and share lesson learned from Watson Jeopardy challenge experience on how to build a coalition and partnership between industry, academia and government to create an Open Collaboration Platform to tackle a such big challenge, see here:
In reviewing these, and comparing human intelligence in terms of cognitive abilities with machine intelligence, one fundamental question is whether the same level of human cognitive abilities (discussed in slide 10 above) is needed by a cognitive agent? And, if not, how do we characterize the cognitive skills needed by a cogs, and in particular personal cogs, work-focused cogs and specialized/expert cogs? And, in general, how the division of the cognitive skills of human and machine would look like in order to realize the partnership (augmenting human intelligence)?
And, a related discussion point, is building on Jeopardy! DeepQA challenge experience (mentioned in slide 22), in forming and supporting an open collaboration model between academia, industry and government on cognitive assistance. How to enable and grow such an open collaboration platform where visions, data, knowledge/expertise, and interoperable artifacts (algorithms and APIs) can be shared to support advancing cognitive assistance vision?
I presented a vision on Cognitive BPM, which is an updated perspective on this topic first presented in a paper in BPCAS 2014 (http://www.bpcas.org/program/), including a number of research work towards this vision, and some research directions. The presentation was in a call in ISSIP society (The International Society of Service Innovation Professionals). A summary the notes is available in ISSIP LinkedIn page, and ISSIP Speaker Series. Below is the presentation slides:
Reports are coming on studies that suggest organizations started to see the opportunity and benefits of big data technology adoption for driving business decisions (Forbes on IDG survey). While IDG survey suggests that the investment related to big data analytics in the enterprise will increase steadily in 2014, other surveys still do not show signs of rapid growth in investments by organizations (CNN iReport on Bain & Company survey).
The real issue that may be underlying this observation is the nature of big data problem in the enterprise that have to be understood and addressed to support greater adoption. The enterprise big data is characterized as enabling analytical tools and technique to process large volume of data efficiently (mainly on top of the stack of HDFS, Hadoop, noSQL, etc.), however, arguments emerging that the enterprise big data problems is not about size, or even small data analysis is the next big thing, and the fact the loosely coupled small data could be more interesting aspect of big data in the enterprise.
While I strongly assert the importance of small data in the enterprise, I would go a step beyond by saying the big data problem in the enterprise today is how to make sense of massive number of data islands, a lot of small and some large, some centered around employees and generated by them, some shared in group settings using sharing and social media inside the enterprise, some stored in large enterprise application databases and document repositories and other information outside of the enterprise wall that the enterprise may care about to serve their customer better. The overarching problem in this context is how to link this data, interpret and understand it and make it available for data and business analytics purposes.
One trend to watch for in this space is development in the graph databases and graph knowledge representation, and how they are evolved to intelligently discover entities, and their relationships and make the graph available for analysis. The graph database providers are focused and advanced a great deal in improving the performance of data analysis on top of knowledge graphs, but more innovation needed on forming knowledge graphs over data islands.