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The Emergence of the Third Platform

By Andras Szakal, Vice President and Chief Technology Officer, IBM U.S. Federal

By 2015 there will be more than 5.6 billion personal devices in use around the world. Personal mobile computing, business systems, e-commerce, smart devices and social media are generating an astounding 2.5 billion gigabytes of data per day. Non-mobile network enabled intelligent devices, often referred to as the Internet of Things (IoT), is poised to explode to over 1 trillion devices by 2015.

Rapid innovation and astounding growth in smart devices is driving new business opportunities and enterprise solutions. Many of these new opportunities and solutions are based on deep insight gained through analysis of the vast amount of data being generated.

The expansive growth of personal and pervasive computing power continues to drive innovation that is giving rise to a new class of systems and a pivot to a new generation of computing platform. Over the last fifty years, two generations of computing platform have dominated the business and consumer landscape. The first generation was dominated by the monolithic mainframe, while distributed computing and the Internet characterized the second generation. Cloud computing, Big Data/Analytics, the Internet of Things (IoT), mobile computing and even social media are the core disruptive technologies that are now converging at the cross roads of the emergence of a third generation of computing platform.

This will require new approaches to enterprise and business integration and interoperability. Industry bodies like The Open Group must help guide customers through the transition by facilitating customer requirements, documenting best practices, establishing integration standards and transforming the current approach to Enterprise Architecture, to adapt to the change in which organizations will build, use and deploy the emerging third generation of computing platform.

Enterprise Computing Platforms

An enterprise computing platform provides the underlying infrastructure and operating environment necessary to support business interactions. Enterprise systems are often comprised of complex application interactions necessary to support business processes, customer interactions, and partner integration. These interactions coupled with the underlying operating environment define an enterprise systems architecture.

The hallmark of successful enterprise systems architecture is a standardized and stable systems platform. This is an underlying operating environment that is stable, supports interoperability, and is based on repeatable patterns.

Enterprise platforms have evolved from the monolithic mainframes of the 1960s and 1970s through the advent of the distributed systems in the 1980s. The mainframe-based architecture represented the first true enterprise operating platform, referred to henceforth as the First Platform. The middleware-based distributed systems that followed and ushered in the dawn of the Internet represented the second iteration of platform architecture, referred to as the Second Platform.

While the creation of the Internet and the advent of web-based e-commerce are of historical significance, the underlying platform was still predominantly based on distributed architectures and therefore is not recognized as a distinct change in platform architecture. However, Internet-based e-commerce and service-based computing considerably contributed to the evolution toward the next distinct version of the enterprise platform. This Third Platform will support the next iteration of enterprise systems, which will be born out of multiple simultaneous and less obvious disruptive technology shifts.

The Convergence of Disruptive Technologies

The emergence of the third generation of enterprise platforms is manifested at the crossroads of four distinct, almost simultaneous, disruptive technology shifts; cloud computing, mobile computing, big data-based analytics and the IoT. The use of applications based on these technologies, such as social media and business-driven insight systems, have contributed to both the convergence and rate of adoption.

These technologies are dramatically changing how enterprise systems are architected, how customers interact with business, and the rate and pace of development and deployment across the enterprise. This is forcing vendors, businesses, and governments to shift their systems architectures to accommodate integrated services that leverage cloud infrastructure, while integrating mobile solutions and supporting the analysis of the vast amount of data being generated by mobile solutions and social media. All this is happening while maintaining the integrity of the evolving businesses capabilities, processes, and transactions that require integration with business systems such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM).

Cloud computing and the continued commoditization of computer storage are key facilitating elements of this convergence. Cloud computing lowers the complexity of enterprise computing through virtualization and automated infrastructure provisioning, while solid-state and software-based Internet storage has made big data practical and affordable. Cloud computing solutions continue to evolve and offer innovative services like Platform as a Service (PaaS)-based development environments that integrate directly with big data solutions. Higher density, cloud-based and solid-state storage continue to lower the cost and complexity of storage and big data solutions.

The emergence of the smartphone and enterprise mobile computing is a key impetus for the emergence of big data solutions and an explosion of innovative storage technologies. The modern mobile platform, with all its rich applications, device sensors, and access to social networks, is almost single-handedly responsible for the explosion of data and the resulting rush to provide solutions to analyze and act on the insight contained in the vast ocean of personalized information. In turn, this phenomenon has created a big data market ecosystem based on the premise that open data is the new natural resource.

The emergence of sensor-enabled smartphones has foreshadowed the potential value of making everyday devices interconnected and intelligent by adding network-based sensors that allow devices to enhance their performance by interacting with their environment, and through collaboration with other devices and enterprise systems in the IoT. For example, equipment manufacturers are using sensors to gain insight into the condition of fielded equipment. This approach reduces both the mean time to failure and pinpoints manufacturing quality issues and potential design flaws. This system of sensors also integrates with the manufacturer’s internal supply chain systems to identify needed parts, and optimizes the distribution process. In turn, the customer benefits by avoiding equipment downtime through scheduling maintenance before a part fails.

Over time, the IoT will require an operating environment for devices that integrates with existing enterprise business systems. But this will require that smart devices effectively integrate with cloud-based enterprise business systems, the enterprise customer engagement systems, as well as the underlying big data infrastructure responsible for gleaning insight into the data this vast network of sensors will generate. While each of these disruptive technology shifts has evolved separately, they share a natural affinity for interaction, collaboration, and enterprise integration that can be used to optimize an enterprise’s business processes.

Evolving Enterprise Business Systems

Existing enterprise systems (ERP, CRM, Supply Chain, Logistics, etc.) are still essential to the foundation of a business or government and form Systems of Record (SoR) that embody core business capabilities and the authoritative processes based on master data records. The characteristics of SoR are:

  • Encompass core business functions
  • Transactional in nature
  • Based on structured databases
  • Authoritative source of information (master data records)
  • Access is regulated
  • Changes follow a rigorous governance process.

Mobile systems, social media platforms, and Enterprise Market Management (EMM) solutions form another class of systems called Systems of Engagement (SoE). Their characteristics are:

  • Interact with end-users through open collaborative interfaces (mobile, social media, etc.)
  • High percentage of unstructured information
  • Personalized to end-user preferences
  • Context-based analytical business rules and processing
  • Access is open and collaborative
  • Evolves quickly and according to the needs of the users.

The emergence of the IoT is embodied in a new class of system, Systems of Sensors (SoS), which includes pervasive computing and control. Their characteristics are:

  • Based on autonomous network-enabled devices
  • Devices that use sensors to collect information about the environment
  • Interconnected with other devices or enterprise engagement systems
  • Changing behavior based on intelligent algorithms and environmental feedback
  • Developed through formal product engineering process
  • Updates to device firmware follow a continuous lifecycle.

The Third Platform

The Third Platform is a convergence of cloud computing, big data solutions, mobile systems and the IoT integrated into the existing enterprise business systems.

The Three Classes of System

Figure 1: The Three Classes of Systems within the Third Platform

The successful implementation and deployment of enterprise SoR has been embodied in best practices, methods, frameworks, and techniques that have been distilled into enterprise architecture. The same level of rigor and pattern-based best practices will be required to ensure the success of solutions based on Third Platform technologies. Enterprise architecture methods and models need to evolve to include guidance, governance, and design patterns for implementing business solutions that span the different classes of system.

The Third Platform builds upon many of the concepts that originated with Service-Oriented Architecture (SOA) and dominated the closing stanza of the period dominated by the Second Platform technologies. The rise of the Third Platform provides the technology and environment to enable greater maturity of service integration within an enterprise.

The Open Group Service Integration Maturity Model (OSIMM) standard[1] provides a way in which an organization can assess its level of service integration maturity. Adoption of the Third Platform inherently addresses many of the attributes necessary to achieve the highest levels of service integration maturity defined by OSIMM. It will enable new types of application architecture that can support dynamically reconfigurable business and infrastructure services across a wide variety of devices (SoS), internal systems (SoR), and user engagement platforms (SoE).

Solution Development

These new architectures and the underlying technologies will require adjustments to how organizations approach enterprise IT governance, to lower the barrier of entry necessary to implement and integrate the technologies. Current adoption requires extensive expertise to implement, integrate, deploy, and maintain the systems. First market movers have shown the rest of the industry the realm of the possible, and have reaped the rewards of the early adopter.

The influence of cloud and mobile-based technologies has changed the way in which solutions will be developed, delivered, and maintained. SoE-based solutions interact directly with customers and business partners, which necessitates a continuous delivery of content and function to align with the enterprise business strategy.

Most cloud-based services employ a roll-forward test and delivery model. A roll-forward model allows an organization to address functional inadequacies and defects in almost real-time, with minimal service interruptions. The integration and automation of development and deployment tools and processes reduces the risk of human error and increases visibility into quality. In many cases, end-users are not even aware of updates and patch deployments.

This new approach to development and operations deployment and maintenance is referred to as DevOps – which combines development and operations tools, governance, and techniques into a single tool set and management practice. This allows the business to dictate, not only the requirements, but also the rate and pace of change aligned to the needs of the enterprise.

[1] The Open Group Service Integration Maturity Model (OSIMM), Open Group Standard (C117), published by The Open Group, November 2011; refer to: www.opengroup.org/bookstore/catalog/c117.htm

Andras2

Figure 2: DevOps: The Third Platform Solution Lifecycle

The characteristics of an agile DevOps approach are:

  • Harmonization of resources and practices between development and IT operations
  • Automation and integration of the development and deployment processes
  • Alignment of governance practices to holistically address development and operations with business needs
  • Optimization of the DevOps process through continuous feedback and metrics.

In contrast to SoE, SoR have a slower velocity of delivery. Such systems are typically released on fixed, pre-planned release schedules. Their inherent stability of features and capabilities necessitates a more structured and formal development approach, which traditionally equates to fewer releases over time. Furthermore, the impact changes to SoR have on core business functionality limits the magnitude and rate of change an organization is able to tolerate. But the emergence of the Third Platform will continue to put pressure on these core business systems to become more agile and flexible in order to adapt to the magnitude of events and information generated by mobile computing and the IoT.

As the technologies of the Third Platform coalesce, organizations will need to adopt hybrid development and delivery models based on agile DevOps techniques that are tuned appropriately to the class of system (SoR, SoS or SoS) and aligned with an acceptable rate of change.

DevOps is a key attribute of the Third Platform that will shift the fundamental management structure of the IT department. The Third Platform will usher in an era where one monolithic IT department is no longer necessary or even feasible. The line between business function and IT delivery will be imperceptible as this new platform evolves. The lines of business will become intertwined with the enterprise IT functions, ultimately leading to the IT department and business capability becoming synonymous. The recent emergence of the Enterprise Market Management organizations is an example where the marketing capabilities and the IT delivery systems are managed by a single executive – the Enterprise Marketing Officer.

The Challenge

The emergence of a new enterprise computing platform will usher in opportunity and challenge for businesses and governments that have invested in the previous generation of computing platforms. Organizations will be required to invest in both expertise and technologies to adopt the Third Platform. Vendors are already offering cloud-based Platform as a Service (PaaS) solutions that will provide integrated support for developing applications across the three evolving classes of systems – SoS, SoR, and SoE. These new development platforms will continue to evolve and give rise to new application architectures that were unfathomable just a few years ago. The emergence of the Third Platform is sure to spawn an entirely new class of dynamically reconfigurable intelligent applications and devices where applications reprogram their behavior based on the dynamics of their environment.

Almost certainly this shift will result in infrastructure and analytical capacity that will facilitate the emergence of cognitive computing which, in turn, will automate the very process of deep analysis and, ultimately, evolve the enterprise platform into the next generation of computing. This shift will require new approaches, standards and techniques for ensuring the integrity of an organization’s business architecture, enterprise architecture and IT systems architectures.

To effectively embrace the Third Platform, organizations will need to ensure that they have the capability to deliver boundaryless systems though integrated services that are comprised of components that span the three classes of systems. This is where communities like The Open Group can help to document architectural patterns that support agile DevOps principles and tooling as the Third Platform evolves.

Technical standardization of the Third Platform has only just begun; for example, standardization of the cloud infrastructure has only recently crystalized around OpenStack. Mobile computing platform standardization remains fragmented across many vendor offerings even with the support of rigid developer ecosystems and open sourced runtime environments. The standardization and enterprise support for SoS is still nascent but underway within groups like the Allseen Alliance and with the Open Group’s QLM workgroup.

Call to Action

The rate and pace of innovation, standardization, and adoption of Third Platform technologies is astonishing but needs the guidance and input from the practitioner community. It is incumbent upon industry communities like the Open Group to address the gaps between traditional Enterprise Architecture and an approach that scales to the Internet timescales being imposed by the adoption of the Third Platform.

The question is not whether Third Platform technologies will dominate the IT landscape, but rather how quickly this pivot will occur. Along the way, the industry must apply the open standards processes to ensure against the fragmentation into multiple incompatible technology platforms.

The Open Group has launched a new forum to address these issues. The Open Group Open Platform 3.0™ Forum is intended to provide a vendor-neutral environment where members share knowledge and collaborate to develop standards and best practices necessary to help guide the evolution of Third Platform technologies and solutions. The Open Platform 3.0 Forum will provide a place where organizations can help illuminate their challenges in adopting Third Platform technologies. The Open Platform 3.0 Forum will help coordinate standards activities that span existing Open Group Forums and ensure a coordinated approach to Third Platform standardization and development of best practices.

Innovation itself is not enough to ensure the value and viability of the emerging platform. The Open Group can play a unique role through its focus on Boundaryless Information Flow™ to facilitate the creation of best practices and integration techniques across the layers of the platform architecture.

andras-szakalAndras Szakal, VP and CTO, IBM U.S. Federal, is responsible for IBM’s industry solution technology strategy in support of the U.S. Federal customer. Andras was appointed IBM Distinguished Engineer and Director of IBM’s Federal Software Architecture team in 2005. He is an Open Group Distinguished Certified IT Architect, IBM Certified SOA Solution Designer and a Certified Secure Software Lifecycle Professional (CSSLP).  Andras holds undergraduate degrees in Biology and Computer Science and a Masters Degree in Computer Science from James Madison University. He has been a driving force behind IBM’s adoption of government IT standards as a member of the IBM Software Group Government Standards Strategy Team and the IBM Corporate Security Executive Board focused on secure development and cybersecurity. Andras represents the IBM Software Group on the Board of Directors of The Open Group and currently holds the Chair of The Open Group Certified Architect (Open CA) Work Group. More recently, he was appointed chair of The Open Group Trusted Technology Forum and leads the development of The Open Trusted Technology Provider Framework.

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Filed under big data, Cloud, Internet of Things, Open Platform 3.0

The Open Group Panel: Internet of Things – Opportunities and Obstacles

Below is the transcript of The Open Group podcast exploring the challenges and ramifications of the Internet of Things, as machines and sensors collect vast amounts of data.

Listen to the podcast.

Dana Gardner: Hello, and welcome to a special BriefingsDirect thought leadership interview series coming to you in conjunction with recent The Open Group Boston 2014 on July 21 in Boston.

Dana Gardner I’m Dana Gardner, principal analyst at Interarbor Solutions, and I’ll be your host and moderator throughout these discussions on Open Platform 3.0 and Boundaryless Information Flow.

We’re going to now specifically delve into the Internet of Things with a panel of experts. The conference has examined how Open Platform 3.0™ leverages the combined impacts of cloud, big data, mobile, and social. But to each of these now we can add a new cresting wave of complexity and scale as we consider the rapid explosion of new devices, sensors, and myriad endpoints that will be connected using internet protocols, standards and architectural frameworks.

This means more data, more cloud connectivity and management, and an additional tier of “things” that are going to be part of the mobile edge — and extending that mobile edge ever deeper into even our own bodies.

When we think about inputs to these social networks — that’s going to increase as well. Not only will people be tweeting, your device could be very well tweet, too — using social networks to communicate. Perhaps your toaster will soon be sending you a tweet about your English muffins being ready each morning.

The Internet of Things is more than the “things” – it means a higher order of software platforms. For example, if we are going to operate data centers with new dexterity thanks to software-definited networking (SDN) and storage (SDS) — indeed the entire data center being software-defined (SDDC) — then why not a software-defined automobile, or factory floor, or hospital operating room — or even a software-defined city block or neighborhood?

And so how does this all actually work? Does it easily spin out of control? Or does it remain under proper management and governance? Do we have unknown unknowns about what to expect with this new level of complexity, scale, and volume of input devices?

Will architectures arise that support the numbers involved, interoperability, and provide governance for the Internet of Things — rather than just letting each type of device do its own thing?

To help answer some of these questions, The Open Group assembled a distinguished panel to explore the practical implications and limits of the Internet of Things. So please join me in welcoming Said Tabet, Chief Technology Officer for Governance, Risk and Compliance Strategy at EMC, and a primary representative to the Industrial Internet Consortium; Penelope Gordon, Emerging Technology Strategist at 1Plug Corporation; Jean-Francois Barsoum, Senior Managing Consultant for Smarter Cities, Water and Transportation at IBM, and Dave Lounsbury, Chief Technical Officer at The Open Group.

Jean-Francois, we have heard about this notion of “cities as platforms,” and I think the public sector might offer us some opportunity to look at what is going to happen with the Internet of Things, and then extrapolate from that to understand what might happen in the private sector.

Hypothetically, the public sector has a lot to gain. It doesn’t have to go through the same confines of a commercial market development, profit motive, and that sort of thing. Tell us a little bit about what the opportunity is in the public sector for smart cities.

Barsoum_Jean-FrancoisJean-Francois Barsoum: It’s immense. The first thing I want to do is link to something that Marshall Van Alstyne (Professor at Boston University and Researcher at MIT) had talked about, because I was thinking about his way of approaching platforms and thinking about how cities represent an example of that.

You don’t have customers; you have citizens. Cities are starting to see themselves as platforms, as ways to communicate with their customers, their citizens, to get information from them and to communicate back to them. But the complexity with cities is that as a good a platform as they could be, they’re relatively rigid. They’re legislated into existence and what they’re responsible for is written into law. It’s not really a market.

Chris Harding (Forum Director of The Open Group Open Platform 3.0) earlier mentioned, for example, water and traffic management. Cities could benefit greatly by managing traffic a lot better.

Part of the issue is that you might have a state or provincial government that looks after highways. You might have the central part of the city that looks after arterial networks. You might have a borough that would look after residential streets, and these different platforms end up not talking to each other.

They gather their own data. They put in their own widgets to collect information that concerns them, but do not necessarily share with their neighbor. One of the conditions that Marshall said would favor the emergence of a platform had to do with how much overlap there would be in your constituents and your customers. In this case, there’s perfect overlap. It’s the same citizen, but they have to carry an Android and an iPhone, despite the fact it is not the best way of dealing with the situation.

The complexities are proportional to the amount of benefit you could get if you could solve them.

Gardner: So more interoperability issues?

Barsoum: Yes.

More hurdles

Gardner: More hurdles, and when you say commensurate, you’re saying that the opportunity is huge, but the hurdles are huge and we’re not quite sure how this is going to unfold.

Barsoum: That’s right.

Gardner: Let’s go to an area where the opportunity outstrips the challenge, manufacturing. Said, what is the opportunity for the software-defined factory floor for recognizing huge efficiencies and applying algorithmic benefits to how management occurs across domains of supply-chain, distribution, and logistics. It seems to me that this is a no-brainer. It’s such an opportunity that the solution must be found.

Tabet_SaidSaid Tabet: When it comes to manufacturing, the opportunities are probably much bigger. It’s where we can see a lot of progress that has already been done and still work is going on. There are two ways to look at it.

One is the internal side of it, where you have improvements of business processes. For example, similar to what Jean-Francois said, in a lot of the larger companies that have factories all around the world, you’ll see such improvements on a factory base level. You still have those silos at that level.

Now with this new technology, with this connectedness, those improvements are going to be made across factories, and there’s a learning aspect to it in terms of trying to manage that data. In fact, they do a better job. We still have to deal with interoperability, of course, and additional issues that could be jurisdictional, etc.

However, there is that learning that allows them to improve their processes across factories. Maintenance is one of them, as well as creating new products, and connecting better with their customers. We can see a lot of examples in the marketplace. I won’t mention names, but there are lots of them out there with the large manufacturers.

Gardner: We’ve had just-in-time manufacturing and lean processes for quite some time, trying to compress the supply chain and distribution networks, but these haven’t necessarily been done through public networks, the internet, or standardized approaches.

But if we’re to benefit, we’re going to need to be able to be platform companies, not just product companies. How do you go from being a proprietary set of manufacturing protocols and approaches to this wider, standardized interoperability architecture?

Tabet: That’s a very good question, because now we’re talking about that connection to the customer. With the airline and the jet engine manufacturer, for example, when the plane lands and there has been some monitoring of the activity during the whole flight, at that moment, they’ll get that data made available. There could be improvements and maybe solutions available as soon as the plane lands.

Interoperability

That requires interoperability. It requires Platform 3.0 for example. If you don’t have open platforms, then you’ll deal with the same hurdles in terms of proprietary technologies and integration in a silo-based manner.

Gardner: Penelope, you’ve been writing about the obstacles to decision-making that might become apparent as big data becomes more prolific and people try to capture all the data about all the processes and analyze it. That’s a little bit of a departure from the way we’ve made decisions in organizations, public and private, in the past.

Of course, one of the bigger tenets of Internet of Things is all this great data that will be available to us from so many different points. Is there a conundrum of some sort? Is there an unknown obstacle for how we, as organizations and individuals, can deal with that data? Is this going to be chaos, or is this going to be all the promises many organizations have led us to believe around big data in the Internet of Things?

Gordon_PenelopePenelope Gordon: It’s something that has just been accelerated. This is not a new problem in terms of the decision-making styles not matching the inputs that are being provided into the decision-making process.

Former US President Bill Clinton was known for delaying making decisions. He’s a head-type decision-maker and so he would always want more data and more data. That just gets into a never-ending loop, because as people collect data for him, there is always more data that you can collect, particularly on the quantitative side. Whereas, if it is distilled down and presented very succinctly and then balanced with the qualitative, that allows intuition to come to fore, and you can make optimal decisions in that fashion.

Conversely, if you have someone who is a heart-type or gut-type decision-maker and you present them with a lot of data, their first response is to ignore the data. It’s just too much for them to take in. Then you end up completely going with whatever you feel is correct or whatever you have that instinct that it’s the correct decision. If you’re talking about strategic decisions, where you’re making a decision that’s going to influence your direction five years down the road, that could be a very wrong decision to make, a very expensive decision, and as you said, it could be chaos.

It just brings to mind to me Dr. Suess’s The Cat in the Hat with Thing One and Thing Two. So, as we talk about the Internet of Things, we need to keep in mind that we need to have some sort of structure that we are tying this back to and understanding what are we trying to do with these things.

Gardner: Openness is important, and governance is essential. Then, we can start moving toward higher-order business platform benefits. But, so far, our panel has been a little bit cynical. We’ve heard that the opportunity and the challenges are commensurate in the public sector and that in manufacturing we’re moving into a whole new area of interoperability, when we think about reaching out to customers and having a boundary that is managed between internal processes and external communications.

And we’ve heard that an overload of data could become a very serious problem and that we might not get benefits from big data through the Internet of Things, but perhaps even stumble and have less quality of decisions.

So Dave Lounsbury of The Open Group, will the same level of standardization work? Do we need a new type of standards approach, a different type of framework, or is this a natural path and course what we have done in the past?

Different level

Lounsbury_DaveDave Lounsbury: We need to look at the problem at a different level than we institutionally think about an interoperability problem. Internet of Things is riding two very powerful waves, one of which is Moore’s Law, that these sensors, actuators, and network get smaller and smaller. Now we can put Ethernet in a light switch right, a tag, or something like that.

Also, Metcalfe’s Law that says that the value of all this connectivity goes up with the square of the number of connected points, and that applies to both the connection of the things but more importantly the connection of the data.

The trouble is, as we have said, that there’s so much data here. The question is how do you manage it and how do you keep control over it so that you actually get business value from it. That’s going to require us to have this new concept of a platform to not only to aggregate, but to just connect the data, aggregate it, correlate it as you said, and present it in ways that people can make decisions however they want.

Also, because of the raw volume, we have to start thinking about machine agency. We have to think about the system actually making the routine decisions or giving advice to the humans who are actually doing it. Those are important parts of the solution beyond just a simple “How do we connect all the stuff together?”

Gardner: We might need a higher order of intelligence, now that we have reached this border of what we can do with our conventional approaches to data, information, and process.

Thinking about where this works best first in order to then understand where it might end up later, I was intrigued again this morning by Professor Van Alstyne. He mentioned that in healthcare, we should expect major battles, that there is a turf element to this, that the organization, entity or even commercial corporation that controls and manages certain types of information and access to that information might have some very serious platform benefits.

The openness element now is something to look at, and I’ll come back to the public sector. Is there a degree of openness that we could legislate or regulate to require enough control to prevent the next generation of lock-in, which might not be to a platform to access to data information and endpoints? Where is it in the public sector that we might look to a leadership position to establish needed openness and not just interoperability.

Barsoum: I’m not even sure where to start answering that question. To take healthcare as an example, I certainly didn’t write the bible on healthcare IT systems and if someone did write that, I think they really need to publish it quickly.

We have a single-payer system in Canada, and you would think that would be relatively easy to manage. There is one entity that manages paying the doctors, and everybody gets covered the same way. Therefore, the data should be easily shared among all the players and it should be easy for you to go from your doctor, to your oncologist, to whomever, and maybe to your pharmacy, so that everybody has access to this same information.

We don’t have that and we’re nowhere near having that. If I look to other areas in the public sector, areas where we’re beginning to solve the problem are ones where we face a crisis, and so we need to address that crisis rapidly.

Possibility of improvement

In the transportation infrastructure, we’re getting to that point where the infrastructure we have just doesn’t meet the needs. There’s a constraint in terms of money, and we can’t put much more money into the structure. Then, there are new technologies that are coming in. Chris had talked about driverless cars earlier. They’re essentially throwing a wrench into the works or may be offering the possibility of improvement.

On any given piece of infrastructure, you could fit twice as many driverless cars as cars with human drivers in them. Given that set of circumstances, the governments are going to find they have no choice but to share data in order to be able to manage those. Are there cases where we could go ahead of a crisis in order to manage it? I certainly hope so.

Gardner: How about allowing some of the natural forces of marketplaces, behavior, groups, maybe even chaos theory, where if sufficient openness is maintained there will be some kind of a pattern that will emerge? We need to let this go through its paces, but if we have artificial barriers, that might be thwarted or power could go to places that we would regret later.

Barsoum: I agree. People often focus on structure. So the governance doesn’t work. We should find some way to change the governance of transportation. London has done a very good job of that. They’ve created something called Transport for London that manages everything related to transportation. It doesn’t matter if it’s taxis, bicycles, pedestrians, boats, cargo trains, or whatever, they manage it.

You could do that, but it requires a lot of political effort. The other way to go about doing it is saying, “I’m not going to mess with the structures. I’m just going to require you to open and share all your data.” So, you’re creating a new environment where the governance, the structures, don’t really matter so much anymore. Everybody shares the same data.

Gardner: Said, to the private sector example of manufacturing, you still want to have a global fabric of manufacturing capabilities. This is requiring many partners to work in concert, but with a vast new amount of data and new potential for efficiency.

How do you expect that openness will emerge in the manufacturing sector? How will interoperability play when you don’t have to wait for legislation, but you do need to have cooperation and openness nonetheless?

Tabet: It comes back to the question you asked Dave about standards. I’ll just give you some examples. For example, in the automotive industry, there have been some activities in Europe around specific standards for communication.

The Europeans came to the US and started to have discussions, and the Japanese have interest, as well as the Chinese. That shows, because there is a common interest in creating these new models from a business standpoint, that these challenges they have to be dealt with together.

Managing complexity

When we talk about the amounts of data, what we call now big data, and what we are going to see in about five years or so, you can’t even imagine. How do we manage that complexity, which is multidimensional? We talked about this sort of platform and then further, that capability and the data that will be there. From that point of view, openness is the only way to go.

There’s no way that we can stay away from it and still be able to work in silos in that new environment. There are lots of things that we take for granted today. I invite some of you to go back and read articles from 10 years ago that try to predict the future in technology in the 21st century. Look at your smart phones. Adoption is there, because the business models are there, and we can see that progress moving forward.

Collaboration is a must, because it is a multidimensional level. It’s not just manufacturing like jet engines, car manufacturers, or agriculture, where you have very specific areas. They really they have to work with their customers and the customers of their customers.

Adoption is there, because the business models are there, and we can see that progress moving forward.

Gardner: Dave, I have a question for both you and Penelope. I’ve seen some instances where there has been a cooperative endeavor for accessing data, but then making it available as a service, whether it’s an API, a data set, access to a data library, or even analytics applications set. The Ocean Observatories Initiative is one example, where it has created a sensor network across the oceans and have created data that then they make available.

Do you think we expect to see an intermediary organization level that gets between the sensors and the consumers or even controllers of the processes? Is there’s a model inherent in that that we might look to — something like that cooperative data structure that in some ways creates structure and governance, but also allows for freedom? It’s sort of an entity that we don’t have yet in many organizations or many ecosystems and that needs to evolve.

Lounsbury: We’re already seeing that in the marketplace. If you look at the commercial and social Internet of Things area, we’re starting to see intermediaries or brokers cropping up that will connect the silo of my android ecosystem to the ecosystem of package tracking or something like that. There are dozens and dozens of these cropping up.

In fact, you now see APIs even into a silo of what you might consider a proprietary system and what people are doing is to to build a layer on top of those APIs that intermediate the data.

This is happening on a point-to-point basis now, but you can easily see the path forward. That’s going to expand to large amounts of data that people will share through a third party. I can see this being a whole new emerging market much as what Google did for search. You could see that happening for the Internet of Things.

Gardner: Penelope, do you have any thoughts about how that would work? Is there a mutually assured benefit that would allow people to want to participate and cooperate with that third entity? Should they have governance and rules about good practices, best practices for that intermediary organization? Any thoughts about how data can be managed in this sort of hierarchical model?

Nothing new

Gordon: First, I’ll contradict it a little bit. To me, a lot of this is nothing new, particularly coming from a marketing strategy perspective, with business intelligence (BI). Having various types of intermediaries, who are not only collecting the data, but then doing what we call data hygiene, synthesis, and even correlation of the data has been around for a long time.

It was an interesting, when I looked at recent listing of the big-data companies, that some notable companies were excluded from that list — companies like Nielsen. Nielsen’s been collecting data for a long time. Harte-Hanks is another one that collects a tremendous amount of information and sells that to companies.

That leads into the another part of it that I think there’s going to be. We’re seeing an increasing amount of opportunity that involves taking public sources of data and then providing synthesis on it. What remains to be seen is how much of the output of that is going to be provided for “free”, as opposed to “fee”. We’re going to see a lot more companies figuring out creative ways of extracting more value out of data and then charging directly for that, rather than using that as an indirect way of generating traffic.

Gardner: We’ve seen examples of how this has been in place. Does it scale and does the governance or lack of governance that might be in the market now sustain us through the transition into Platform 3.0 and the Internet of Things.

Gordon: That aspect is the lead-on part of “you get what you pay for”. If you’re using a free source of data, you don’t have any guarantee that it is from authoritative sources of data. Often, what we’re getting now is something somebody put it in a blog post, and then that will get referenced elsewhere, but there was nothing to go back to. It’s the shaky supply chain for data.

You need to think about the data supply and that is where the governance comes in. Having standards is going to increasingly become important, unless we really address a lot of the data illiteracy that we have. A lot of people do not understand how to analyze data.

One aspect of that is a lot of people expect that we have to do full population surveys, as opposed representative sampling to get much more accurate and much more cost-effective collection of data. That’s just one example, and we do need a lot more in governance and standards.

Gardner: What would you like to see changed most in order for the benefits and rewards of the Internet of Things to develop and overcome the drawbacks, the risks, the downside? What, in your opinion, would you like to see happen to make this a positive, rapid outcome? Let’s start with you Jean-Francois.

Barsoum: There are things that I have seen cities start to do now. There are couple of examples: Philadelphia is one and Barcelona does this too. Rather than do the typical request for proposal (RFP), where they say, “This is the kind of solution we’re looking for, and here are our parameters. Can l you tell us how much it is going to cost to build,” they come to you with the problem and they say, “Here is the problem I want to fix. Here are my priorities, and you’re at liberty to decide how best to fix the problem, but tell us how much that would cost.”

If you do that and you combine it with access to the public data that is available — if public sector opens up its data — you end up with a very powerful combination that liberates a lot of creativity. You can create a lot of new business models. We need to see much more of that. That’s where I would start.

More education

Tabet: I agree with Jean-Francois on that. What I’d like to add is that I think we need to push the relation a little further. We need more education, to your point earlier, around the data and the capabilities.

We need these platforms that we can leverage a little bit further with the analytics, with machine learning, and with all of these capabilities that are out there. We have to also remember, when we talk about the Internet of Things, it is things talking to each other.

So it is not human-machine communication. Machine-to-machine automation will be further than that, and we need more innovation and more work in this area, particularly more activity from the governments. We’ve seen that, but it is a little bit frail from that point of view right now.

Gardner: Dave Lounsbury, thoughts about what need to happen in order to keep this on the tracks?

Lounsbury: We’ve touched on lot of them already. Thank you for mentioning the machine-to-machine part, because there are plenty of projections that show that it’s going to be the dominant form of Internet communication, probably within the next four years.

So we need to start thinking of that and moving beyond our traditional models of humans talking through interfaces to set of services. We need to identify the building blocks of capability that you need to manage, not only the information flow and the skilled person that is going to produce it, but also how you manage the machine-to-machine interactions.

Gordon: I’d like to see not so much focus on data management, but focus on what is the data managing and helping us to do. Focusing on the machine-to-machine and the devices is great, but it should be not on the devices or on the machines… it should be on what can they accomplish by communicating; what can you accomplish with the devices and then have a reverse engineer from that.

Gardner: Let’s go to some questions from the audience. The first one asks about a high order of intelligence which we mentioned earlier. It could be artificial intelligence, perhaps, but they ask whether that’s really the issue. Is the nature of the data substantially different, or we are just creating more of the same, so that it is a storage, plumbing, and processing problem? What, if anything, are we lacking in our current analytics capabilities that are holding us back from exploiting the Internet of Things?

Gordon: I’ve definitely seen that. That has a lot to do with not setting your decision objectives and your decision criteria ahead of time so that you end up collecting a whole bunch of data, and the important data gets lost in the mix. There is a term “data smog.”

Most important

The solution is to figure out, before you go collecting data, what data is most important to you. If you can’t collect certain kinds of data that are important to you directly, then think about how to indirectly collect that data and how to get proxies. But don’t try to go and collect all the data for that. Narrow in on what is going to be most important and most representative of what you’re trying to accomplish.

Gardner: Does anyone want to add to this idea of understanding what current analytics capabilities are lacking, if we have to adopt and absorb the Internet of Things?

Barsoum: There is one element around projection into the future. We’ve been very good at analyzing historical information to understand what’s been happening in the past. We need to become better at projecting into the future, and obviously we’ve been doing that for some time already.

But so many variables are changing. Just to take the driverless car as an example. We’ve been collecting data from loop detectors, radar detectors, and even Bluetooth antennas to understand how traffic moves in the city. But we need to think harder about what that means and how we understand the city of tomorrow is going to work. That requires more thinking about the data, a little bit like what Penelope mentioned, how we interpret that, and how we push that out into the future.

Lounsbury: I have to agree with both. It’s not about statistics. We can use historical data. It helps with lot of things, but one of the major issues we still deal with today is the question of semantics, the meaning of the data. This goes back to your point, Penelope, around the relevance and the context of that information – how you get what you need when you need it, so you can make the right decisions.

Gardner: Our last question from the audience goes back to Jean-Francois’s comments about the Canadian healthcare system. I imagine it applies to almost any healthcare system around the world. But it asks why interoperability is so difficult to achieve, when we have the power of the purse, that is the market. We also supposedly have the power of the legislation and regulation. You would think between one or the other or both that interoperability, because the stakes are so high, would happen. What’s holding it up?

Barsoum: There are a couple of reasons. One, in the particular case of healthcare, is privacy, but that is one that you could see going elsewhere. As soon as you talk about interoperability in the health sector, people start wondering where is their data going to go and how accessible is it going to be and to whom.

You need to put a certain number of controls over top of that. What is happening in parallel is that you have people who own some data, who believe they have some power from owning that data, and that they will lose that power if they share it. That can come from doctors, hospitals, anywhere.

So there’s a certain amount of change management you have to get beyond. Everybody has to focus on the welfare of the patient. They have to understand that there has to be a priority, but you also have to understand the welfare of the different stakeholders in the system and make sure that you do not forget about them, because if you forget about them they will find some way to slow you down.

Use of an ecosystem

Lounsbury: To me, that’s a perfect example of what Marshall Van Alstyne talked about this morning. It’s the change from focus on product to a focus on an ecosystem. Healthcare traditionally has been very focused on a doctor providing product to patient, or a caregiver providing a product to a patient. Now, we’re actually starting to see that the only way we’re able to do this is through use of an ecosystem.

That’s a hard transition. It’s a business-model transition. I will put in a plug here for The Open Group Healthcare vertical, which is looking at that from architecture perspective. I see that our Forum Director Jason Lee is over here. So if you want to explore that more, please see him.

Gardner: I’m afraid we will have to leave it there. We’ve been discussing the practical implications of the Internet of Things and how it is now set to add a new dimension to Open Platform 3.0 and Boundaryless Information Flow.

We’ve heard how new thinking about interoperability will be needed to extract the value and orchestrate out the chaos with such vast new scales of inputs and a whole new categories of information.

So with that, a big thank you to our guests: Said Tabet, Chief Technology Officer for Governance, Risk and Compliance Strategy at EMC; Penelope Gordon, Emerging Technology Strategist at 1Plug Corp.; Jean-Francois Barsoum, Senior Managing Consultant for Smarter Cities, Water and Transportation at IBM, and Dave Lounsbury, Chief Technology Officer at The Open Group.

This is Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator throughout these discussions on Open Platform 3.0 and Boundaryless Information Flow at The Open Group Conference, recently held in Boston. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript.

Transcript of The Open Group podcast exploring the challenges and ramifications of the Internet of Things, as machines and sensors collect vast amounts of data. Copyright The Open Group and Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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The Digital Ecosystem Paradox – Learning to Move to Better Digital Design Outcomes

By Mark Skilton, Professor of Practice, Information Systems Management, Warwick Business School

Does digital technologies raise quality and improve efficiencies but at the same time drive higher costs of service as more advanced solutions and capabilities become available demanding higher entry investment and maintenance costs?

Many new digital technologies introduce step change in performance that would have been cost prohibitive in the previous technology generations. But in some industries the technology cost per outcome have be steadily rising in some industries.

In the healthcare market the cost per treatment of health care technology was highlighted in a MIT Technology Review article (1). In areas such as new drugs for treating depression, left-ventricular assistance devices, or implantable defibrillators may be raising the overall cost of health, yet how do we value this if patient quality of life is improving and life extending. While lower cost drugs and vaccines may be enabling better overall patient outcomes

In the smart city a similar story is unfolding where governments and organizations are seeking paths to use digitization to drive improvements in jobs productivity, better lifestyles and support of environmental sustainability. While there are several opportunities to reduce energy bills, improve transport and office spaces exist with savings of 40% to 60% consumption and efficiencies complexity costs of connecting different residential, corporate offices, transport and other living spaces requires digital initiatives that are coordinated and managed. (U-city experience in South Korea (2)).

These digital paradoxes represent the digital ecosystem challenge to maximise what these new digital technologies can do to augment every objects, services, places and spaces while taking account of the size and addressable market that all these solutions can serve.

Skilton1

What we see is that technology can be both a driver of the physical and digital economy through lowering of price per function in computer storage, compute, access and application technology and creating new value; conversely the issues around driving new value is having different degrees of success in industries.

Creating value in the digital economy

The digital economy is at a tipping point, a growing 30% of business is shifting online to search and engage with consumers, markets and transactions taking account of retail , mobile and impact on supply channels (3);  80% of transport, real estate and hotelier activity is processed through websites (4); over 70% of companies and consumers are experiencing cyber-privacy challenges (5), (6) yet the digital media in social, networks, mobile devices, sensors and the explosion of big data and cloud computing networks is interconnecting potentially everything everywhere – amounting to a new digital “ecosystem.

Disruptive business models across industries and new consumer innovation are increasingly built around new digital technologies such as social media, mobility, big data, cloud computing and the emerging internet of things sensors, networks and machine intelligence. (MISQ Digital Strategy Special Issue (7)).

These trends have significantly enhanced the relevance and significance of IT in its role and impact on business and market value at local, regional and global scale.

With IT budgets increasing shifting more towards the marketing functions and business users of these digital services from traditional IT, there is a growing role for technology to be able to work together in new connected ways.

Driving better digital design outcomes

The age of new digital technologies are combining in new ways to drive new value for individuals, enterprise, communities and societies. The key is in understanding the value that each of these technologies can bring individually and in the mechanisms to creating additive value when used appropriately and cost effectively to drive brand, manage cyber risk, and build consumer engagement and economic growth.

Skilton2

Value-in-use, value in contextualization

Each digital technology has the potential to enable better contextualization of the consumer experience and the value added by providers.   Each industry market has emerging combinations of technologies that can be developed to enable focused value.

Examples of these include.

  • Social media networks

o   Creating enhanced co-presence

  • Big data

o   Providing uniqueness profiling , targeting advice and preferences in context

  • Mobility

o   Creating location context services and awareness

  • Cloud

o   Enabling access to resources and services

  • Sensors

o   Creating real time feedback responsiveness

  • Machine intelligence

o   Enabling insight and higher decision quality

Together these digital technologies can build generative effects that when in context can enable higher value outcomes in digital workspaces.

Skilton3

Value in Contextualization

The value is not in whether these technologies, objects, consumers or provider inside or outside the enterprise or market. These distinctions are out-of-context from relating them to the situation and the consumer needs and wants. The issue is how to apply and put into context the user experience and enterprise and social environment to best use and maximise the outcomes in a specific setting context rom the role perspective.

With the medical roles of patient and clinician, the aim in digitization is how mobile devices, wearable monitoring can be used most efficiently and effectively to raise patient outcome quality and manage health service costs. Especially in the developing countries and remote areas where infrastructure and investment costs, how can technologies reach and improve the quality of health and at an effective cost price point.

This phenomena is wide spread and growing across all industry sectors such as: the connected automobile with in-car entertainment, route planning services; to tele-health that offers remote patient care monitoring and personalized responses; to smart buildings and smart cities that are optimizing energy consumption and work environments; to smart retail where interactive product tags for instant customer mobile information feedback and in-store promotions and automated supply chains. The convergence of these technologies requires a response from all businesses.

These issues are not going to go away, the statistics from analysts describe a new era of a digital industrial economy (8). What is common is the prediction in the next twenty to fifty years suggest double or triple growth in demand for new digital technologies and their adoption.

Skilton4

Platforming and designing better digital outcomes

Developing efective digital workspaces will be fundamental to the value and use of these technologies. There will be not absolute winners and losers as a result of the digital paradox. What is at state is in how the cost and inovation of these technologies can be leveraged to fit specific outcomes.

Understanding the architecting practices will be essentuial in realizing the digitel enterprise. Central to this is how to develop ways to contextualize digital technologies to enable this value for consumers and customers (Value and Worth – creating new markets in the digital economy (9)).Skilton5Platforming will be a central IT strategy that we see already emerging in early generations of digital marketplaces, mobile app ecosystems and emerging cross connecting services in health, automotive, retail and others seeking to create joined up value.

Digital technologies will enable new forms of digital workspaces to support new outcomes. By driving contextualized offers that meet and stimulate consumer behaviors and demand , a richer and more effective value experience and growth potential is possible.

Skilton6The challenge ahead

The evolution of digital technologies will enable many new types of architect and platforms. How these are constructed into meaningful solutions is both the opportunity and the task ahead.

The challenge for both business and IT practitioners is how to understand the practical use and advantages as well as the pitfalls and challenges from these digital technologies

  • What can be done using digital technologies to enhance customer experience, employee productivity and sell more products and services
  • Where to position in a digital market, create generative reinforcing positive behavior and feedback for better market branding
  • Who are the beneficiaries of the digital economy and the impact on the roles and jobs of business and IT professionals
  • Why do enterprises and industry marketplaces need to understand the disruptive effects of these digital technologies and how to leverage these for competitive advantage.
  • How to architect and design robust digital solutions that support the enterprise, its supply chain and extended consumers, customers and providers

References

  1. http://www.technologyreview.com/news/518876/the-costly-paradox-of-health-care-technology/.
  2. http://www.kyoto-smartcity.com/result_pdf/ksce2014_hwang.pdf.
  3. http://www.smartinsights.com/digital-marketing-strategy/online-retail-sales-growth/
  4. http://www.statisticbrain.com/internet-travel-hotel-booking-statistics/
  5. http://www.fastcompany.com/3019097/fast-feed/63-of-americans-70-of-milennials-are-cybercrime-victims
  6. https://www.kpmg.com/Global/en/IssuesAndInsights/ArticlesPublications/Documents/cyber-crime.pdf
  7. http://www.misq.org/contents-37-2
  8. http://www.gartner.com/newsroom/id/2602817
  9. http://www2.warwick.ac.uk/fac/sci/wmg/mediacentre/wmgnews/?newsItem=094d43a23d3fbe05013d835d6d5d05c6

 

Skilton7Digital Health

As the cost of health care, the increasing aging population and the rise of medical advances enable people to live longer and improved quality of life; the health sector together with governments and private industry are increasingly using digital technologies to manage the rising costs of health care while improve patient survival and quality outcomes.

Digital Health Technologies

mHealth, TeleHealth and Translation-to-Bench Health services are just some of the innovative medical technology practices creating new Connected Health Digital Ecosystems.

These systems connect Mobile phones, wearable health monitoring devices, remote emergency alerts to clinician respond and back to big data research for new generation health care.

The case for digital change

UN Department of Economic and Social Affairs

“World population projected to reach 8.92 billion for 2050 and 9.22 Million in 2075. Life expectance is expected to range from 66 to 97 years by 2100.”

OECD Organization for Economic Cooperation and Development

The cost of Health care in developing countries is 8 to 17% of GDP in developed countries. But overall Health car e spending is falling while population growth and life expectancy and aging is increasing.

 

Skilton8Smart cities

The desire to improve buildings, reduce pollution and crime, improve transport, create employment, better education and ways to launch new business start-ups through the use of digital technologies are at the core of important outcomes to drive city growth from “Smart Cities” digital Ecosystem.

Smart city digital technologies

Embedded sensors in building energy management, smart ID badges, and mobile apps for location based advice and services supporting social media communities, enabling improved traffic planning and citizen service response are just some of the ways digital technologies are changing the physical city in the new digital metropolis hubs of tomorrow.

The case for digital change

WHO World Health Organization

“By the middle of the 21st century, the urban population will almost double globally, By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people.”

UN Inter-governmental Panel on Climate Change IPCC

“In 2010, the building sector accounted for around 32% final energy use with energy demand projected to approximately double and CO2 emissions to increase by 50–150% by mid-century”

IATA International Air Transport Association

“Airline Industry Forecast 2013-2017 show that airlines expect to see a 31% increase in passenger numbers between 2012 and 2017. By 2017 total passenger numbers are expected to rise to 3.91 billion—an increase of 930 million passengers over the 2.98 billion carried in 2012.”

Mark Skilton 2 Oct 2013Professor Mark Skilton,  Professor of Practice in Information Systems Management , Warwick Business School has over twenty years’ experience in Information Technology and Business consulting to many of the top fortune 1000 companies across many industry sectors and working in over 25 countries at C level board level to transform their operations and IT value.  Mark’s career has included CIO, CTO  Director roles for several FMCG, Telecoms Media and Engineering organizations and recently working in Global Strategic Office roles in the big 5 consulting organizations focusing on digital strategy and new multi-sourcing innovation models for public and private sectors. He is currently a part-time Professor of practice at Warwick Business School, UK where he teaches outsourcing and the intervention of new digital business models and CIO Excellence practices with leading Industry practitioners.

Mark’s current research and industry leadership engagement interests are in Digital Ecosystems and the convergence of social media networks, big data, mobility, cloud computing and M2M Internet of things to enable digital workspaces. This has focused on define new value models digitizing products, workplaces, transport and consumer and provider contextual services. He has spoken and published internationally on these subjects and is currently writing a book on the Digital Economy Series.

Since 2010 Mark has held International standards body roles in The Open Group co-chair of Cloud Computing and leading Open Platform 3.0™ initiatives and standards publications. Mark is active in the ISO JC38 distributed architecture standards and in the Hubs-of-all-things HAT a multi-disciplinary project funded by the Research Council’s UK Digital Economy Programme. Mark is also active in Cyber security forums at Warwick University, Ovum Security Summits and INFOSEC. He has spoken at the EU Commission on Digital Ecosystems Agenda and is currently an EU Commission Competition Judge on Smart Outsourcing Innovation.

 

 

 

 

 

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Are You Ready for the Convergence of New, Disruptive Technologies?

By Chris Harding, The Open Group

The convergence of technical phenomena such as cloud, mobile and social computing, big data analysis, and the Internet of things that is being addressed by The Open Group’s Open Platform 3.0 Forum™ will transform the way that you use information technology. Are you ready? Take our survey at https://www.surveymonkey.com/s/convergent_tech

What the Technology Can Do

Mobile and social computing are leading the way. Recently, the launch of new iPhone models and the announcement of the Twitter stock flotation were headline news, reflecting the importance that these technologies now have for business. For example, banks use mobile text messaging to alert customers to security issues. Retailers use social media to understand their markets and communicate with potential customers.

Other technologies are close behind. In Formula One motor racing, sensors monitor vehicle operation and feed real-time information to the support teams, leading to improved design, greater safety, and lower costs. This approach could soon become routine for cars on the public roads too.

Many exciting new applications are being discussed. Stores could use sensors to capture customer behavior while browsing the goods on display, and give them targeted information and advice via their mobile devices. Medical professionals could monitor hospital patients and receive alerts of significant changes. Researchers could use shared cloud services and big data analysis to detect patterns in this information, and develop treatments, including for complex or uncommon conditions that are hard to understand using traditional methods. The potential is massive, and we are only just beginning to see it.

What the Analysts Say

Market analysts agree on the importance of the new technologies.

Gartner uses the term “Nexus of Forces” to describe the convergence and mutual reinforcement of social, mobility, cloud and information patterns that drive new business scenarios, and says that, although these forces are innovative and disruptive on their own, together they are revolutionizing business and society, disrupting old business models and creating new leaders.

IDC predicts that a combination of social cloud, mobile, and big data technologies will drive around 90% of all the growth in the IT market through 2020, and uses the term “third platform” to describe this combination.

The Open Group will identify the standards that will make Gartner’s Nexus of Forces and IDC’s Third Platform commercial realities. This will be the definition of Open Platform 3.0.

Disrupting Enterprise Use of IT

The new technologies are bringing new opportunities, but their use raises problems. In particular, end users find that working through IT departments in the traditional way is not satisfactory. The delays are too great for rapid, innovative development. They want to use the new technologies directly – “hands on”.

Increasingly, business departments are buying technology directly, by-passing their IT departments. Traditionally, the bulk of an enterprise’s IT budget was spent by the IT department and went on maintenance. A significant proportion is now spent by the business departments, on new technology.

Business and IT are not different worlds any more. Business analysts are increasingly using technical tools, and even doing application development, using exposed APIs. For example, marketing folk do search engine optimization, use business information tools, and analyze traffic on Twitter. Such operations require less IT skill than formerly because the new systems are easy to use. Also, users are becoming more IT-savvy. This is a revolution in business use of IT, comparable to the use of spreadsheets in the 1980s.

Also, business departments are hiring traditional application developers, who would once have only been found in IT departments.

Are You Ready?

These disruptive new technologies are changing, not just the IT architecture, but also the business architecture of the enterprises that use them. This is a sea change that affects us all.

The introduction of the PC had a dramatic impact on the way enterprises used IT, taking much of the technology out of the computer room and into the office. The new revolution is taking it out of the office and into the pocket. Cell phones and tablets give you windows into the world, not just your personal collection of applications and information. Through those windows you can see your friends, your best route home, what your customers like, how well your production processes are working, or whatever else you need to conduct your life and business.

This will change the way you work. You must learn how to tailor and combine the information and services available to you, to meet your personal objectives. If your role is to provide or help to provide IT services, you must learn how to support users working in this new way.

To negotiate this change successfully, and take advantage of it, each of us must understand what is happening, and how ready we are to deal with it.

The Open Group is conducting a survey of people’s reactions to the convergence of Cloud and other new technologies. Take the survey, to input your state of readiness, and get early sight of the results, to see how you compare with everyone else.

To take the survey, visit https://www.surveymonkey.com/s/convergent_tech

Dr. Chris Harding is Director for Interoperability and SOA at The Open Group. He has been with The Open Group for more than ten years, and is currently responsible for managing and supporting its work on interoperability, including SOA and interoperability aspects of Cloud Computing, and the Platform 3.0 Forum. He is a member of the BCS, the IEEE and the AEA, and is a certified TOGAF® practitioner.

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Future Technologies

By Dave Lounsbury, The Open Group

The Open Group is looking toward the future – what will happen in the next five to ten years?

Those who know us think of The Open Group as being all about consensus, creating standards that are useful to the buy and supply side by creating a stable representation of industry experience – and they would be right. But in order to form this consensus, we must keep an eye on the horizon to see if there are areas that we should be talking about now. The Open Group needs to keep eyes on the future in order to keep pace with businesses looking to gain business advantage by incorporating emerging technologies. According to the McKinsey Global institute[1], “leaders need to plan for a range of scenarios, abandoning assumptions about where competition and risk could come from and not to be afraid to look beyond long-established models.”

To make sure we have this perspective, The Open Group has started a series of Future Technologies workshops. We initiated this at The Open Group Conference in Philadelphia with the goal of identifying emerging business and technical trends that change the shape of enterprise IT.  What are the potential disruptors? How should we be preparing?

As always at The Open Group, we look to our membership to guide us. We assembled a fantastic panel of experts on the topic who offered up insights into the future:

  • Dr. William Lafontaine, VP High Performance Computing, Analytics & Cognitive Markets at IBM Research: Global technology Outlook 2013.
  • Mike Walker, Strategy and Enterprise Architecture Advisor at HP: An Enterprise Architecture’s Journey to 2020.

If you were not able to join us in Philadelphia, you can view the Livestream session on-demand.

Dr. William Lafontaine shared aspects of the company’s Global Technology Outlook 2013, naming the top trends that the company is keeping top of mind, starting with a confluence of social, mobile analytics and cloud.

According to Lafontaine and his colleagues, businesses must prepare for not “mobile also” but “mobile first.” In fact, there will be companies that will exist in a mobile-only environment.

  • Growing scale/lower barrier of entry – More data created, but also more people able to create ways of taking advantage of this data, such as companies that excel at personal interface. Multimedia analytics will become a growing concern for businesses that will be receiving swells of information video and images.
  • Increasing complexity – the Confluence of Social, Mobile, Cloud and Big Data / Analytics will result in masses of data coming from newer, more “complex” places, such as scanners, mobile devices and other “Internet of Things”. Yet, these complex and varied streams of data are more consumable and will have an end-product which is more easily delivered to clients or user.  Smaller businesses are also moving closer toward enterprise complexity. For example, when you swipe your credit card, you will also be shown additional purchasing opportunities based on your past spending habits.  These can include alerts to nearby coffee shops that serve your favorite tea to local bookstores that sell mysteries or your favorite genre.
  •  Fast pace – According to Lafontaine, ideas will be coming to market faster than ever. He introduced the concept of the Minimum Buyable Product, which means take an idea (sometimes barely formed) to inventors to test its capabilities and to evaluate as quickly as possible. Processes that once took months or years can now take weeks. Lafontaine used the MOOC innovator Coursera as an example: Eighteen months ago, it had no clients and existed in zero countries. Now it’s serving over 4 million students around the world in over 29 countries. Deployment of open APIs will become a strategic tool for creation of value.
  • Contextual overload – Businesses have more data than they know what to do with: our likes and dislikes, how we like to engage with our mobile devices, our ages, our locations, along with traditional data of record. The next five years, businesses will be attempting to make sense of it.
  • Machine learning – Cognitive systems will form the “third era” of computing. We will see businesses using machines capable of complex reasoning and interaction to extend human cognition.  Examples are a “medical sieve” for medical imaging diagnosis, used by legal firms in suggesting defense / prosecution arguments and in next generation call centers.
  • IT shops need to be run as a business – Mike Walker spoke about how the business of IT is fundamentally changing and that end-consumers are driving corporate behaviors.  Expectations have changed and the bar has been raised.  The tolerance for failure is low and getting lower.  It is no longer acceptable to tell end-consumers that they will be receiving the latest product in a year.  Because customers want their products faster, EAs and businesses will have to react in creative ways.
  • Build a BRIC house: According to Forrester, $2.1 trillion will be spent on IT in 2013 with “apps and the US leading the charge.” Walker emphasized the importance of building information systems, products and services that support the BRIC areas of the world (Brazil, Russia, India and China) since they comprise nearly a third of the global GDP. Hewlett-Packard is banking big on “The New Style of IT”: Cloud, risk management and security and information management.  This is the future of business and IT, says Meg Whitman, CEO and president of HP. All of the company’s products and services presently pivot around these three concepts.
  • IT is the business: Gartner found that 67% of all EA organizations are either starting (39%), restarting (7%) or renewing (21%). There’s a shift from legacy EA, with 80% of organizations focused on how they can leverage EA to either align business and IT standards (25%), deliver strategic business and IT value (39%) or enable major business transformation (16%).

Good as these views are, they only represent two data points on a line that The Open Group wants to draw out toward the end of the decade. So we will be continuing these Future Technologies sessions to gather additional views, with the next session being held at The Open Group London Conference in October.  Please join us there! We’d also like to get your input on this blog.  Please post your thoughts on:

  • Perspectives on what business and technology trends will impact IT and EA in the next 5-10 years
  • Points of potential disruption – what will change the way we do business?
  • What actions should we be taking now to prepare for this future?

[1] McKinsey Global Institute, Disruptive technologies: Advances that will transform life, business, and the global economy. May 2013

Dave LounsburyDave Lounsbury is The Open Group‘s Chief Technology Officer, previously VP of Collaboration Services.  Dave holds three U.S. patents and is based in the U.S.

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Filed under Cloud, Enterprise Architecture, Future Technologies, Open Platform 3.0

Why is Cloud Adoption Taking so Long?

By Chris Harding, The Open Group

At the end of last year, Gartner predicted that cloud computing would become an integral part of IT in 2013 (http://www.gartner.com/DisplayDocument?doc_cd=230929). This looks a pretty safe bet. The real question is, why is it taking so long?

Cloud Computing

Cloud computing is a simple concept. IT resources are made available, within an environment that enables them to be used, via a communications network, as a service. It is used within enterprises to enable IT departments to meet users’ needs more effectively, and by external providers to deliver better IT services to their enterprise customers.

There are established vendors of products to fit both of these scenarios. The potential business benefits are well documented. There are examples of real businesses gaining those benefits, such as Netflix as a public cloud user (see http://www.zdnet.com/the-biggest-cloud-app-of-all-netflix-7000014298/ ), and Unilever and Lufthansa as implementers of private cloud (see http://www.computerweekly.com/news/2240114043/Unilever-and-Lufthansa-Systems-deploy-Azure-Private-cloud ).

Slow Pace of Adoption

Yet we are still talking of cloud computing becoming an integral part of IT. In the 2012 Open Group Cloud ROI survey, less than half of the respondents’ organizations were using cloud computing, although most of the rest were investigating its use. (See http://www.opengroup.org/sites/default/files/contentimages/Documents/cloud_roi_formal_report_12_19_12-1.pdf ). Clearly, cloud computing is not being used for enterprise IT as a matter of routine.

Cloud computing is now at least seven years old. Amazon’s “Elastic Compute Cloud” was launched in August 2006, and there are services that we now regard as cloud computing, though they may not have been called that, dating from before then. Other IT revolutions – personal computers, for example – have reached the point of being an integral part of IT in half the time. Why has it taken Cloud so long?

The Reasons

One reason is that using Cloud requires a high level of trust. You can lock your PC in your office, but you cannot physically secure your cloud resources. You must trust the cloud service provider. Such trust takes time to earn.

Another reason is that, although it is a simple concept, cloud computing is described in a rather complex way. The widely-accepted NIST definition (see http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf ) has three service models and four deployment models, giving a total of twelve distinct delivery combinations. Each combination has different business drivers, and the three service models are based on very different technical capabilities. Real products, of course, often do not exactly correspond to the definition, and their vendors describe them in product-specific terms. This complexity often leads to misunderstanding and confusion.

A third reason is that you cannot “mix and match” cloud services from different providers. The market is consolidating, with a few key players emerging as dominant at the infrastructure and platform levels. Each of them has its own proprietary interfaces. There are no real vendor-neutral standards. A recent Information Week article on Netflix (http://www.informationweek.co.uk/cloud-computing/platform/how-netflix-is-ruining-cloud-computing/240151650 ) describes some of the consequences. Customers are beginning to talk of “vendor lock-in” in a way that we haven’t seen since the days of mainframes.

The Portability and Interoperability Guide

The Open Group Cloud Computing Portability and Interoperability Guide addresses this last problem, by providing recommendations to customers on how best to achieve portability and interoperability when working with current cloud products and services. It also makes recommendations to suppliers and standards bodies on how standards and best practice should evolve to enable greater portability and interoperability in the future.

The Guide tackles the complexity of its subject by defining a simple Distributed Computing Reference Model. This model shows how cloud services fit into the mix of products and services used by enterprises in distributed computing solutions today. It identifies the major components of cloud-enabled solutions, and describes their portability and interoperability interfaces.

Platform 3.0

Cloud is not the only new game in town. Enterprises are looking at mobile computing, social computing, big data, sensors, and controls as new technologies that can transform their businesses. Some of these – mobile and social computing, for example – have caught on faster than Cloud.

Portability and interoperability are major concerns for these technologies too. There is a need for a standard platform to enable enterprises to use all of the new technologies, individually and in combination, and “mix and match” different products. This is the vision of the Platform 3.0 Forum, recently formed by The Open Group. The distributed computing reference model is an important input to this work.

The State of the Cloud

It is now at least becoming routine to consider cloud computing when architecting a new IT solution. The chances of it being selected however appear to be less than fifty-fifty, in spite of its benefits. The reasons include those mentioned above: lack of trust, complexity, and potential lock-in.

The Guide removes some of the confusion caused by the complexity, and helps enterprises assess their exposure to lock-in, and take what measures they can to prevent it.

The growth of cloud computing is starting to be constrained by lack of standards to enable an open market with free competition. The Guide contains recommendations to help the industry and standards bodies produce the standards that are needed.

Let’s all hope that the standards do appear soon. Cloud is, quite simply, a good idea. It is an important technology paradigm that has the potential to transform businesses, to make commerce and industry more productive, and to benefit society as a whole, just as personal computing did. Its adoption really should not be taking this long.

The Open Group Cloud Computing Portability and Interoperability Guide is available from The Open Group bookstore at https://www2.opengroup.org/ogsys/catalog/G135

Dr. Chris Harding is Director for Interoperability and SOA at The Open Group. He has been with The Open Group for more than ten years, and is currently responsible for managing and supporting its work on interoperability, including SOA and interoperability aspects of Cloud Computing, and the Platform 3.0 Forum. He is a member of the BCS, the IEEE and the AEA, and is a certified TOGAF® practitioner.

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Thinking About Big Data

By Dave Lounsbury, The Open Group

“We can not solve our problems with the same level of thinking that created them.”

- Albert Einstein

The growing consumerization of technology and convergence of technologies such as the “Internet of Things”, social networks and mobile devices are causing big changes for enterprises and the marketplace. They are also generating massive amounts of data related to behavior, environment, location, buying patterns and more.

Having massive amounts of data readily available is invaluable. More data means greater insight, which leads to more informed decision-making. So far, we are keeping ahead of this data by smarter analytics and improving the way we handle this data. The question is, how long can we keep up? The rate of data production is increasing; as an example, an IDC report[1] predicts that the production of data will increase 50X in the coming decade. To magnify this problem, there’s an accompanying explosion of data about the data – cataloging information, metadata, and the results of analytics are all data in themselves. At the same time, data scientists and engineers who can deal with such data are already a scarce commodity, and the number of such people is expected to grow only by 1.5X in the same period.

It isn’t hard to draw the curve. Turning data into actionable insight is going to be a challenge – data flow is accelerating at a faster rate than the available humans can absorb, and our databases and data analytic systems can only help so much.

Markets never leave gaps like this unfilled, and because of this we should expect to see a fundamental shift in the IT tools we use to deal with the growing tide of data. In order to solve the challenges of managing data with the volume, variety and velocities we expect, we will need to teach machines to do more of the analysis for us and help to make the best use of scarce human talents.

The Study of Machine Learning

Machine Learning, sometimes called “cognitive computing”[2] or “intelligent computing”, looks at the study of building computers with the capability to learn and perform tasks based on experience. Experience in this context includes looking at vast data sets, using multiple “senses” or types of media, recognizing patterns from past history or precedent, and extrapolating this information to reason about the problem at hand. An example of machine learning that is currently underway in the healthcare sector is medical decision aids that learn to predict therapies or to help with patient management, based on correlating a vast body of medical and drug experience data with the information about the patients under treatment

A well-known example of this is Watson, a machine learning system IBM unveiled a few years ago. While Watson is best known for winning Jeopardy, that was just the beginning. IBM has since built six Watsons to assist with their primary objective: to help health care professionals find answers to complex medical questions and help with patient management[3]. The sophistication of Watson is the reaction of all this data action that is going on. Watson of course isn’t the only example in this field, with others ranging from Apple’s Siri intelligent voice-operated assistant to DARPA’s SyNAPSE program[4].

Evolution of the Technological Landscape

As the consumerization of technology continues to grow and converge, our way of constructing business models and systems need to evolve as well. We need to let data drive the business process, and incorporate intelligent machines like Watson into our infrastructure to help us turn data into actionable results.

There is an opportunity for information technology and companies to help drive this forward. However, in order for us to properly teach computers how to learn, we first need to understand the environments in which they will be asked to learn in – Cloud, Big Data, etc. Ultimately, though, any full consideration of these problems will require a look at how machine learning can help us make decisions – machine learning systems may be the real platform in these areas.

The Open Group is already laying the foundation to help organizations take advantage of these convergent technologies with its new forum, Platform 3.0. The forum brings together a community of industry thought leaders to analyze the use of Cloud, Social, Mobile computing and Big Data, and describe the business benefits that enterprises can gain from them. We’ll also be looking at trends like these at our Philadelphia conference this summer.  Please join us in the discussion.


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Filed under Cloud, Cloud/SOA, Data management, Enterprise Architecture