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Q&A with Marshall Van Alstyne, Professor, Boston University School of Management and Research Scientist MIT Center for Digital Business

By The Open Group

The word “platform” has become a nearly ubiquitous term in the tech and business worlds these days. From “Platform as a Service” (PaaS) to IDC’s Third Platform to The Open Group Open Platform 3.0™ Forum, the concept of platforms and building technology frames and applications on top of them has become the next “big thing.”

Although the technology industry tends to conceive of “platforms” as the vehicle that is driving trends such as mobile, social networking, the Cloud and Big Data, Marshall Van Alstyne, Professor at Boston University’s School of Management and a Research Scientist at the MIT Center for Digital Business, believes that the radical shifts that platforms bring are not just technological.

We spoke with Van Alstyne prior to The Open Group Boston 2014, where he presented a keynote, about platforms, how they have shifted traditional business models and how they are impacting industries everywhere.

The title of your session at the Boston conference was “Platform Shift – How New Open Business Models are Changing the Shape of Industry.” How would you define both “platform” and “open business model”?

I think of “platform” as a combination of two things. One, a set of standards or components that folks can take up and use for production of goods and services. The second thing is the rules of play, or the governance model – who has the ability to participate, how do you resolve conflict, and how do you divide up the royalty streams, or who gets what? You can think of it as the two components of the platform—the open standard together with the governance model. The technologists usually get the technology portion of it, and the economists usually get the governance and legal portions of it, but you really need both of them to understand what a ‘platform’ is.

What is the platform allowing then and how is that different from a regular business model?

The platform allows third parties to conduct business using system resources so they can actually meet and exchange goods across the platform. Wonderful examples of that include AirBnB where you can rent rooms or you can post rooms, or eBay, where you can sell goods or exchange goods, or iTunes where you can go find music, videos, apps and games provided by others, or Amazon where third parties are even allowed to set up shop on top of Amazon. They have moved to a business model where they can take control of the books in addition to allowing third parties to sell their own books and music and products and services through the Amazon platform. So by opening it up to allow third parties to participate, you facilitate exchange and grow a market by helping that exchange.

How does this relate to the concept of the technology industry is defining the “third platform”?

I think of it slightly differently. The tech industry uses mobile and social and cloud and data to characterize it. In some sense this view offers those as the attributes that characterize platforms or the knowledge base that enable platforms. But we would add to that the economic forces that actually shape platforms. What we want to do is give you some of the strategic tools, the incentives, the rules that will actually help you control their trajectory by helping you improve who participates and then measure and improve the value they contribute to the platform. So a full ecosystem view is not just the technology and the data, it also measures the value and how you divide that value. The rules of play really become important.

I think the “third platform” offers marvelous concepts and attributes but you also need to add the economics to it: Why do you participate, who gets what portions of the value, and who ultimately owns control.

Who does control the platform then?

A platform has multiple parts. Determining who controls what part is the art and design of the governance model. You have to set up control in the right way to motivate people to participate. But before we get to that, let’s go back and complete the idea of what’s an ‘open platform.’

To define an open platform, consider both the right of access and the right to manipulate platform resources, then consider granting those rights to four different parties. One is the user—can they access one another, can they access data, can they access system resources? Another group is developers—can they manipulate system resources, can they add new features to it, can they sell through the platform? The third group is the platform providers. You often think of them as those folks that facilitate access across the platform. To give you an example, iTunes is a single monolithic store, so the provider is simply Apple, but Android, in contrast, allows multiple providers, so there’s a Samsung Android store, an LTC Android store, a Google Android store, there’s even an Amazon version that uses a different version of Android. So that platform has multiple providers each with rights to access users. The fourth group is the party that controls the underlying property rights, who owns the IP. The ability modify the underlying standard and also the rights of access for other parties is the bottom-most layer.

So to answer the question of what is ‘open,’ you have to consider the rights of access of all four groups—the users, developers, the providers and IP rights holders, or sponsors, underneath.

Popping back up a level, we’re trying to motivate different parties to participate in the ecosystem. So what do you give the users? Usually it’s some kind of value. What do you give developers? Usually it’s some set of SDKs and APIs, but also some level of royalties. It’s fascinating. If you look back historically, Amazon initially tried a publishing royalty where they took 70% and gave a minority 30% back to developers. They found that didn’t fly very well and they had to fall back to the app store or software-style royalty, where they’re taking a lower percentage. I think Apple, for example, takes 30 percent, and Amazon is now close to that. You see ranges of royalties going anywhere from a few percent—an example is credit cards—all the way up to iStock photo where they take roughly 70 percent. That’s an extremely high rate, and one that I don’t recommend. We were just contracting for designs at 99Designs and they take a 20 percent cut. That’s probably more realistic, but lower might perhaps even be better—you can create stronger network effect if that’s the case.

Again, the real question of control is how you motivate third parties to participate and add value? If you are allowing them to use resources to create value and keep a lot of that value, then they’re more motivated to participate, to invest, to bring their resources to your platform. If you take most of the value they create, they won’t participate. They won’t add value. One of the biggest challenges for open platforms—what you might call the ‘Field of Dreams’ approach—is that most folks open their platform and assume ‘if you build it, they will come,’ but you really need to reward them to do so. Why would they want to come build with you? There are numerous instances of platforms that opened but no developer chooses to add value—the ecosystem is too small. You have to solve the chicken and egg problem where if you don’t have users, developers don’t want to build for you, but if you don’t have developer apps, then why do users participate? So you’ve got a huge feedback problem. And those are where the economics become critical, you must solve the chicken and egg problem to build and roll out platforms.

It’s not just a technology question; it’s also an economics and rewards question.

Then who is controlling the platform?

The answer depends on the type of platform. Giving different groups a different set of rights creates different types of platform. Consider the four different parties: users, developers, providers, and sponsors. At one extreme, the Apple Mac platform of the 1980s reserved most rights for development, for producing hardware (the provider layer), and for modifying the IP (the sponsor layer) all to Apple. Apple controlled the platform and it remained closed. In contrast, Microsoft relaxed platform control in specific ways. It licensed to multiple providers, enabling Dell, HP, Compaq and others to sell the platform. It gave developers rights of access to SDKs and APIs, enabling them to extend the platform. These control choices gave Microsoft more than six times the number of developers and more than twenty times the market share of Apple at the high point of Microsoft’s dominance of desktop operating systems. Microsoft gave up some control in order to create a more inclusive platform and a much bigger market.

Control is not a single concept. There are many different control rights you can grant to different parties. For example, you often want to give users an ability to control their own data. You often want to give developers intellectual property rights for the apps that they create and often over the data that their users create. You may want to give them some protections against platform misappropriation. Developers resent it if you take their ideas. So if the platform sees a really clever app that’s been built on top of its platform, what’s the guarantee that the platform simply doesn’t take it or build a competing app? You need to protect your developers in that case. Same thing’s true of the platform provider—what guarantees do they provide users for the quality of content provided on their ecosystem? For example, the Android ecosystem is much more open than the iPhone ecosystem, which means you have more folks offering stores. Simultaneously, that means that there are more viruses and more malware in Android, so what rights and guarantees do you require of the platform providers to protect the users in order that they want to participate? And then at the bottom, what rights do other participants have to control the direction of the platform growth? In the Visa model, for example, there are multiple member banks that help to influence the general direction of that credit card standard. Usually the most successful platforms have a single IP rights holder, but there are several examples of that have multiple IP rights holders.

So, in the end control defines the platform as much as the platform defines control.

What is the “secret” of the Internet-driven marketplace? Is that indeed the platform?

The secret is that, in effect, the goal of the platform is to increase transaction volume and value. If you can do that—and we can give you techniques for doing it—then you can create massive scale. Increasing the transaction value and transactions volume across your platform means that the owner of the platform doesn’t have to be the sole source of content and new ideas provided on the platform. If the platform owner is the only source of value then the owner is also the bottleneck. The goal is to consummate matches between producers and consumers of value. You want to help users find the content, find the resources, find the other people that they want to meet across your platform. In Apple’s case, you’re helping them find the music, the video, the games, and the apps that they want. In AirBnB’s case, you’re helping them find the rooms that they want, or Uber, you’re helping them find a driver. On Amazon, the book recommendations help you find the content that you want. In all the truly successful platforms, the owner of the platform is not providing all of that value. They’re enabling third parties to add that value, and that’s one reasy why The Open Group’s ideas are so important—you need open systems for this to happen.

What’s wrong with current linear business models? Why is a network-driven approach superior?

The fundamental reason why the linear business model no longer works is that it does not manage network effects. Network effects allow you to build platforms where users attract other users and you get feedback that grows your system. As more users join your platform, more developers join your platform, which attracts more users, which attracts more developers. You can see it on any of the major platforms. This is also true of Google. As advertisers use Google Search, the algorithms get better, people find the content that they want, so more advertisers use it. As more drivers join Uber, more people are happier passengers, which attracts more drivers. The more merchants accept Visa, the more consumers are willing to carry it, which attracts more merchants, which attracts more consumers. You get positive feedback.

The consequence of that is that you tend to get market concentration—you get winner take all markets. That’s where platforms dominate. So you have a few large firms within a given category, whether this is rides or books or hotels or auctions. Further, once you get network effects changing your business model, the linear insights into pricing, into inventory management, into innovation, into strategy breakdown.

When you have these multi-sided markets, pricing breaks down because you often price differently to one side than another because one side attracts the other. Inventory management practices breakdown because you’re selling inventory that you don’t even own. Your R&D strategies breakdown because now you’re motivating innovation and research outside the boundaries of the firm, as opposed to inside the internal R&D group. And your strategies breakdown because you’re not just looking for cost leadership or product differentiation, now you’re looking to shape the network effects as you create barriers to entry.

One of the things that I really want to argue strenuously is that in markets where platforms will emerge, platforms beat product every time. So the platform business model will inevitably beat the linear, product-based business model. Because you’re harnessing new forces in order to develop a different kind of business model.

Think of it the following way–imagine that value is growing as users consume your product. Think of any of the major platforms, as more folks use Google, search gets better, the more recommendations improve on Amazon, and the easier it is to find a ride on Uber, so more folks want to be on there. It is easier to scale network effects outside your business than inside your business. There’s simply more people outside than inside. The moment that happens, the locus control, the locus of innovation, moves from inside the firm to outside the firm. So the rules change. Pricing changes, your innovation strategies change, your inventory policies change, your R&D changes. You’re now managing resources outside the firm, rather than inside, in order to capture scale. This is different than the traditional industrial supply economies of scale.

Old systems are giving away to new systems. It’s not that the whole system breaks down, it’s simply that you’re looking to manage network effects and manage new business models. Another way to see this is that previously you were managing capital. In the industrial era, you were managing steel, you were managing large amounts of finance in banking, you were managing auto parts—huge supply economies of scale. In telecommunications, you were managing infrastructure. Now, you’re managing communities and these are managed outside the firm. The value that’s been created at Facebook or WhatsApp or Instagram or any of the new acquisitions, it’s not the capital that’s critical, it’s the communities that are critical, and these are built outside the firm.

There is a lot of talk in the industry about the Nexus of Forces as Gartner calls it, or Third Platform (IDC). The Open Group calls it Open Platform 3.0. Your concept goes well beyond technology—how does Open Platform 3.0 enable new business models?

Those are the enablers—they’re shall we say necessary, but they’re not sufficient. You really must harness the economic forces in addition to those enablers—mobile, social, Cloud, data. You must manage communities outside the firm, that’s the mobile and the social element of it. But this also involves designing governance and setting incentives. How are you capturing users outside the organization, how are they contributing, how are they being motivated to participate, why are they spreading your products to their peers? The Cloud allows it to scale—so Instagram and What’s App and others scale. Data allows you to “consummate the match.” You use that data to help people find what they need, to add value, so all of those things are the enablers. Then you have to harness the economics of the enablers to encourage people to do the right thing. You can see the correct intuition if you simply ask what happens if all you offer is a Cloud service and nothing more. Why will anyone use it? What’s the value to that system? If you open APIs to it, again, if you don’t have a user base, why are developers going to contribute? Developers want to reach users. Users want valuable functionality.

You must manage the motives and the value-add on the platform. New business models come from orchestrating not just the technology but also the third party sources of value. One of the biggest challenges is to grow these businesses from scratch—you’ve got the cold start chicken and egg problem. You don’t have network effects if you don’t have a user base, if you don’t have users, you don’t have network effects.

Do companies need to transform themselves into a “business platform” to succeed in this new marketplace? Are there industries immune to this shift?

There is a continuum of companies that are going to be affected. It starts at one end with companies that are highly information intense—anything that’s an information intensive business will be dramatically affected, anything that’s community or fashion-based business will be dramatically affected. Those include companies involved in media and news, songs, music, video; all of those are going to be the canaries in the coalmine that see this first. Moving farther along will be those industries that require some sort of certification—those include law and medicine and education—those, too, will also be platformized, so the services industries will become platforms. Farther down that are the ones that are heavily, heavily capital intensive where control of physical capital is paramount, those include trains and oil rigs and telecommunications infrastructure—eventually those will be affected by platform business models to the extent that data helps them gain efficiencies or add value, but they will in some sense be the last to be affected by platform business models. Look for the businesses where the cost side is shrinking in proportion to the delivery of value and where the network effects are rising as a proportional increase in value. Those forces will help you predict which industries will be transformed.

How can Enterprise Architecture be a part of this and how do open standards play a role?

The second part of that question is actually much easier. How do open standards play a role? The open standards make it much easier for third parties to attach and incorporate technology and features such that they can in turn add value. Open standards are essential to that happening. You do need to ask the question as to who controls those standards—is it completely open or is it a proprietary standard, a published standard but it’s not manipulable by a third party.

There will be at least two or three different things that Enterprise Architects need to do. One of these is to design modular components that are swappable, so as better systems become available, the better systems can be swapped in. The second element will be to watch for components of value that should be absorbed into the platform itself. As an example, in operating systems, web browsing has effectively been absorbed into the platform, streaming has been absorbed into the platform so that they become aware of how that actually works. A third thing they need to do is talk to the legal team to see where it is that the third parties property rights can be protected so that they invest. One of the biggest mistakes that firms make is to simply assume that because they own the platform, because they have the rights of control, that they can do what they please. If they do that, they risk alienating their ecosystems. So they should talk to their potential developers to incorporate developer concerns. One of my favorite examples is the Intel Architecture Lab which has done a beautiful job of articulating the voices of developers in their own architectural plans. A fourth thing that can be done is an idea borrowed from SAP, that builds Enterprise Architecture—they articulate an 18-24 month roadmap where they say these are the features that are coming, so you can anticipate and build on those. Also it gives you an idea of what features will be safe to build on so you won’t lose the value you’ve created.

What can companies do to begin opening their business models and more easily architect that?

What they should do is to consider four groups articulated earlier— those are the users, the providers, the developers and the sponsors—each serve a different role. Firms need to understand what their own role will be in order that they can open and architect the other roles within their ecosystem. They’ll also need to choose what levels of exclusivity they need to give their ecosystem partners in a different slice of the business. They should also figure out which of those components they prefer to offer themselves as unique competencies and where they need to seek third party assistance, either in new ideas or new resources or even new marketplaces. Those factors will help guide businesses toward different kinds of partnerships, and they’ll have to be open to those kinds of partners. In particular, they should think about where are they most likely to be missing ideas or missing opportunities. Those technical and business areas should open in order that third parties can take advantage of those opportunities and add value.

 

vanalstynemarshallProfessor Van Alstyne is one of the leading experts in network business models. He conducts research on information economics, covering such topics as communications markets, the economics of networks, intellectual property, social effects of technology, and productivity effects of information. As co-developer of the concept of “two sided networks” he has been a major contributor to the theory of network effects, a set of ideas now taught in more than 50 business schools worldwide.

Awards include two patents, National Science Foundation IOC, SGER, SBIR, iCorp and Career Awards, and six best paper awards. Articles or commentary have appeared in Science, Nature, Management Science, Harvard Business Review, Strategic Management Journal, The New York Times, and The Wall Street Journal.

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Discussing Enterprise Decision-Making with Penelope Everall Gordon

By The Open Group

Most enterprises today are in the process of jumping onto the Big Data bandwagon. The promise of Big Data, as we’re told, is that if every company collects as much data as they can—about everything from their customers to sales transactions to their social media feeds—executives will have “the data they need” to make important decisions that can make or break the company. Not collecting and using your data, as the conventional wisdom has it, can have deadly consequences for any business.

As is often the case with industry trends, the hype around Big Data contains both a fair amount of truth and a fair amount of fuzz. The problem is that within most organizations, that conventional wisdom about the power of data for decision-making is usually just the tip of the iceberg when it comes to how and why organizational decisions are made.

According to Penelope Gordon, a consultant for 1Plug Corporation who was recently a Cloud Strategist at Verizon and was formerly a Service Product Strategist at IBM, that’s why big “D” (Data) needs to be put back into the context of enterprise decision-making. Gordon, who spoke at The Open Group Boston 2014, in the session titled “Putting the D Back in Decision” with Jean-Francois Barsoum of IBM, argues that a focus on collecting a lot of data has the potential to get in the way of making quality decisions. This is, in part, due to the overabundance of data that’s being collected under the assumption that you never know where there’s gold to be mined in your data, so if you don’t have all of it at hand, you may just miss something.

Gordon says that assuming the data will make decisions obvious also ignores the fact that ultimately decisions are made by people—and people usually make decisions based on their own biases. According to Gordon, there are three types of natural decision making styles—heart, head and gut styles—corresponding to different personality types, she said; the greater the amount of data the more likely that it will not balance the natural decision-making style.

Head types, Gordon says, naturally make decisions based on quantitative evidence. But what often happens is that head types often put off making a decision until more data can be collected, wanting more and more data so that they can make the best decision based on the facts. She cites former President Bill Clinton as a classic example of this type. During his presidency, he was famous for putting off decision-making in favor of gathering more and more data before making the decision, she says. Relying solely on quantitative data also can mean you may miss out on other important factors in making optimal decisions based on either heart (qualitative) or instinct. Conversely, a gut-type presented with too much data will likely just end up ignoring data and acting on instinct, much like former President George W. Bush, Gordon says.

Gordon believes part of the reason that data and decisions are more disconnected than one might think is because IT and Marketing departments have become overly enamored with what technology can offer. These data providers need to step back and first examine the decision objectives as well as the governance behind those decisions. Without understanding the organization’s decision-making processes and the dynamics of the decision-makers, it can be difficult to make optimal and effective strategic recommendations, she says, because you don’t have the full picture of what the stakeholders will or will not accept in terms of a recommendation, data or no data.

Ideally, Gordon says, you want to get to a point where you can get to the best decision outcome possible by first figuring out the personal and organizational dynamics driving decisions within the organization, shifting the focus from the data to the decision for which the data is an input.

“…what you’re trying to do is get the optimal outcome, so your focus needs to be on the outcome, so when you’re collecting the data and assessing the data, you also need to be thinking about ‘how am I going to present this data in a way that it is going to be impactful in improving the decision outcomes?’ And that’s where the governance comes into play,” she said.

Governance is of particular importance now, Gordon says, because decisions are increasingly being made by individual departments, such as when departments buy their own cloud-enabled services, such as sales force automation. In that case, an organization needs to have a roadmap in place with compensation to incent decision-makers to adhere to that roadmap and decision criteria for buying decisions, she said.

Gordon recommends that companies put in place 3-5 top criteria for each decision that needs to be made so that you can ensure that the decision objectives are met. This distillation of the metrics gives decision-makers a more comprehensible picture of their data so that their decisions don’t become either too subjective or disconnected from the data. Lower levels of metrics can be used underneath each of those top-level criteria to facilitate a more nuanced valuation. For example, if an organization needing to find good partner candidates scored and ranked (preferably in tiers) potential partners using decision criteria based on the characteristics of the most attractive partner, rather than just assuming that companies with the best reputation or biggest brands will be the best, then they will expeditiously identify the optimal partner candidates.

One of the reasons that companies have gotten so concerned with collecting and storing data rather than just making better decisions, Gordon believes, is that business decisions have become inherently more risky. The required size of investment is increasing in tandem with an increase in the time to return; time to return is a key determinant of risk. Data helps people feel like they are making competent decisions but in reality does little to reduce risk.

“If you’ve got lots of data, then the thinking is, ‘well, I did the best that I could because I got all of this data.’ People are worried that they might miss something,“ she said. “But that’s where I’m trying to come around and say, ‘yeah, but going and collecting more data, if you’ve got somebody like President Clinton, you’re just feeding into their tendency to put off making decisions. If you’ve got somebody like President Bush and you’re feeding into their tendency to ignore it, then there may be some really good information, good recommendations they’re ignoring.”

Gordon also says that having all the data possible to work with isn’t usually necessary—generally a representative sample will do. For example, she says the U.S Census Bureau takes the approach where it tries to count every citizen; consequently certain populations are chronically undercounted and inaccuracies pass undetected. The Canadian census, on the other hand, uses representative samples and thus tends to be much more accurate—and much less expensive to conduct. Organizations should also think about how they can find representative or “proxy” data in cases where collecting data that directly addresses a top-level decision criteria isn’t really practical.

To begin shifting the focus from collecting data inputs to improving decision outcomes, Gordon recommends clearly stating the decision objectives for each major decision and then identifying and defining the 3-5 criteria that are most important for achieving the decision objectives. She also recommends ensuring that there is sufficient governance and a process in place for making decisions including mechanisms for measuring the performance of the decision-making process and the outcomes resulting from the execution of that process. In addition, companies need to consider whether their decisions are made in a centralized or decentralized manner and then adapt decision governance accordingly.

One way that Enterprise Architects can help to encourage better decision-making within the organizations in which they work is to help in developing that governance rather than just providing data or data architectures, Gordon says. They should help stakeholders identify and define the important decision criteria, determine when full population rather than representative sampling is justified, recognize better methods for data analysis, and form decision recommendations based on that analysis. By gauging the appropriate blend of quantitative and qualitative data for a particular decision maker, an Architect can moderate gut types’ reliance on instinct and stimulate head and heart types’ intuition – thereby producing an optimally balanced decision. Architects should help lead and facilitate execution of the decision process, as well as help determine how data is presented within organizations in order to support the recommendations with the highest potential for meeting the decision objectives.

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penelopegordonPenelope Gordon recently led the expansion of the channel and service packaging strategies for Verizon’s cloud network products. Previously she was an IBM Strategist and Product Manager bringing emerging technologies such as predictive analytics to market. She helped to develop one of the world’s first public clouds.

 

 

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The Open Group Boston 2014 Preview: Talking People Architecture with David Foote

By The Open Group

Among all the issues that CIOs, CTOs and IT departments are facing today, staffing is likely near the top of the list of what’s keeping them up at night. Sure, there’s dealing with constant (and disruptive) technological changes and keeping up with the latest tech and business trends, such as having a Big Data, Internet of Things (IoT) or a mobile strategy, but without the right people with the right skills at the right time it’s impossible to execute on these initiatives.

Technology jobs are notoriously difficult to fill–far more difficult than positions in other industries where roles and skillsets may be much more static. And because technology is rapidly evolving, the roles for tech workers are also always in flux. Last year you may have needed an Agile developer, but today you may need a mobile developer with secure coding ability and in six months you might need an IoT developer with strong operations or logistics domain experience—with each position requiring different combinations of tech, functional area, solution and “soft” skillsets.

According to David Foote, IT Industry Analyst and co-founder of IT workforce research and advisory firm Foote Partners, the mash-up of HR systems and ad hoc people management practices most companies have been using for years to manage IT workers have become frighteningly ineffective. He says that to cope in today’s environment, companies need to architect their people infrastructure similar to how they have been architecting their technical infrastructure.

“People Architecture” is the term Foote has coined to describe the application of traditional architectural principles and practices that may already be in place elsewhere within an organization and applying them to managing the IT workforce. This includes applying such things as strategy and capability roadmaps, phase gate blueprints, benchmarks, performance metrics, governance practices and stakeholder management to human capital management (HCM).

HCM components for People Architecture typically include job definition and design, compensation, incentives and recognition, skills demand and acquisition, job and career paths, professional development and work/life balance.

Part of the dilemma for employers right now, Foote says, is that there is very little job title standardization in the marketplace and too many job titles floating around IT departments today. “There are too many dimensions and variability in jobs now that companies have gotten lost from an HR perspective. They’re unable to cope with the complexity of defining, determining pay and laying out career paths for all these jobs, for example. For many, serious retention and hiring problems are showing up for the first time. Work-around solutions used for years to cope with systemic weaknesses in their people management systems have stopped working,” says Foote. “Recruiters start picking off their best people and candidates are suddenly rejecting offers and a panic sets in. Tensions are palpable in their IT workforce. These IT realities are pervasive.”

Twenty-five years ago, Foote says, defining roles in IT departments was easier. But then the Internet exploded and technology became far more customer-facing, shifting basic IT responsibilities from highly technical people deep within companies to roles requiring more visibility and transparency within and outside the enterprise. Large chunks of IT budgets moved into the business lines while traditional IT became more of a business itself.

According to Foote, IT roles became siloed not just by technology but by functional areas such as finance and accounting, operations and logistics, sales, marketing and HR systems, and by industry knowledge and customer familiarity. Then the IT professional services industry rapidly expanded to compete with their customers for talent in the marketplace. Even the architect role changed: an Enterprise Architect today can specialize in applications, security or data architecture among others, or focus on a specific industry such as energy, retail or healthcare.

Foote likens the fragmentation of IT jobs and skillsets that’s happening now to the emergence of IT architecture 25 years ago. Just as technical architecture practices emerged to help make sense of the disparate systems rapidly growing within companies and how best to determine the right future tech investments, a people architecture approach today helps organizations better manage an IT workforce spread through the enterprise with roles ranging from architects and analysts to a wide variety of engineers, developers and project and program managers.

“Technical architecture practices were successful because—when you did them well—companies achieved an understanding of what they have systems-wise and then connected it to where they were going and how they were going to get there, all within a process inclusive of all the various stakeholders who shared the risk in the outcome. It helped clearly define enterprise technology capabilities and gave companies more options and flexibility going forward,” according to Foote.

“Right now employers desperately need to incorporate in human capital management systems and practice the same straightforward, inclusive architecture approaches companies are already using in other areas of their businesses. This can go a long way toward not just lessening staffing shortages but also executing more predictably and being more agile in face of constant uncertainties and the accelerating pace of change. Ultimately this translates into a more effective workforce whether they are full-timers or the contingent workforce of part-timers, consultants and contractors.

“It always comes down to your people. That’s not a platitude but a fact,” insists Foote. “If you’re not competitive in today’s labor marketplace and you’re not an employer where people want to work, you’re dead.”

One industry that he says has gotten it right is the consulting industry. “After all, their assets walk out the door every night. Consulting groups within firms such as IBM and Accenture have been good at architecting their staffing because it’s their job to get out in front of what’s coming technologically. Because these firms must anticipate customer needs before they get the call to implement services, they have to be ahead of the curve in already identifying and hiring the bench strength needed to fulfill demand. They do many things right to hire, develop and keep the staff they need in place.”

Unfortunately, many companies take too much of a just-in-time approach to their workforce so they are always managing staffing from a position of scarcity rather than looking ahead, Foote says. But, this is changing, in part due to companies being tired of never having the people they need and being able to execute predictably.

The key is to put a structure in place that addresses a strategy around what a company needs and when. This applies not just to the hiring process, but also to compensation, training and advancement.

“Architecting anything allows you to be able to, in a more organized way, be more agile in dealing with anything that comes at you. That’s the beauty of architecture. You plan for the fact that you’re going to continue to scale and continue to change systems, the world’s going to continue to change, but you have an orderly way to manage the governance, planning and execution of that, the strategy of that and the implementation of decisions knowing that the architecture provides a more agile and flexible modular approach,” he said.

Foote says organizations such as The Open Group can lend themselves to facilitating People Architecture in a couple different ways. First, through extending the principles of architecture to human capital management, and second through vendor-independent, expertise and experience driven certifications, such as TOGAF® or OpenCA and OpenCITS, that help companies define core competencies for people and that provide opportunities for training and career advancement.

“I’m pretty bullish on many vendor-independent certifications in general, particularly where a defined book of knowledge exists that’s achieved wide acceptance in the industry. And that’s what you’ve got with The Open Group. Nobody’s challenging the architectural framework supremacy of TOGAF that that I’m aware of. In fact, large vendors with their own certifications participated actively in developing the framework and applying it very successfully to their business models,” he said.

Although the process of implementing People Architecture can be difficult and may take several years to master (much like Enterprise Architecture), Foote says it is making a huge difference for companies that implement it.

To learn more about People Architecture and models for implementing it, plan to attend Foote’s session at The Open Group Boston 2014 on Tuesday July 22. Foote’s session will address how architectural principles are being applied to human capital so that organizations can better manage their workforces from hiring and training through compensation, incentives and advancement. He will also discuss how career paths for EAs can be architected. Following the conference, the session proceedings will be available to Open Group members and conference attendees at www.opengroup.org.

Join the conversation – #ogchat #ogBOS

footeDavid Foote is an IT industry research pioneer, innovator, and one of the most quoted industry analysts on global IT workforce trends and multiple facets of the human side of technology value creation. His two decades of groundbreaking deep research and analysis of IT-business cross-skilling and technology/business management integration and leading the industry in innovative IT skills demand and compensation benchmarking has earned him a place on a short list of thought leaders in IT human capital management.

A former Gartner and META Group analyst, David leads the research and analytical practice groups at Foote Partners that reach 2,300 customers on six continents.

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Filed under architecture, Conference, Open CA, Open CITS, Professional Development, Standards, TOGAF®, Uncategorized

The Open Group Open Platform 3.0™ Starts to Take Shape

By Dr. Chris Harding, Director for Interoperability, The Open Group

The Open Group published a White Paper on Open Platform 3.0™ at the start of its conference in Amsterdam in May 2014. This article, based on a presentation given at the conference, explains how the definition of the platform is beginning to emerge.

Introduction

Amsterdam is a beautiful place. Walking along the canals is like moving through a set of picture postcards. But as you look up at the houses beside the canals, and you see the cargo hoists that many of them have, you are reminded that the purpose of the arrangement was not to give pleasure to tourists. Amsterdam is a great trading city, and the canals were built as a very efficient way of moving goods around.

This is also a reminder that the primary purpose of architecture is not to look beautiful, but to deliver business value, though surprisingly, the two often seem to go together quite well.

When those canals were first thought of, it might not have been obvious that this was the right thing to do for Amsterdam. Certainly the right layout for the canal network would not be obvious. The beginning of a project is always a little uncertain, and seeing the idea begin to take shape is exciting. That is where we are with Open Platform 3.0 right now.

We started with the intention to define a platform to enable enterprises to get value from new technologies including cloud computing, social computing, mobile computing, big data, the Internet of Things, and perhaps others. We developed an Open Group business scenario to capture the business requirements. We developed a set of business use-cases to show how people are using and wanting to use those technologies. And that leads to the next step, which is to define the platform. All these new technologies and their applications sound wonderful, but what actually is Open Platform 3.0?

The Third Platform

Looking historically, the first platform was the computer operating system. A vendor-independent operating system interface was defined by the UNIX® standard. The X/Open Company and the Open Software Foundation (OSF), which later combined to form The Open Group, were created because companies everywhere were complaining that they were locked into proprietary operating systems. They wanted applications portability. X/Open specified the UNIX® operating system as a common application environment, and the value that it delivered was to prevent vendor lock-in.

The second platform is the World Wide Web. It is a common services environment, for services used by people browsing web pages or for web services used by programs. The value delivered is universal deployment and access. Any person or company anywhere can create a services-based solution and deploy it on the web, and every person or company throughout the world can access that solution.

Open Platform 3.0 is developing as a common architecture environment. This does not mean it is a replacement for TOGAF®. TOGAF is about how you do architecture and will continue to be used with Open Platform 3.0. Open Platform 3.0 is about what kind of architecture you will create. It will be a common environment in which enterprises can do architecture. The big business benefit that it will deliver is integrated solutions.

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Figure 1: The Third Platform

With the second platform, you can develop solutions. Anyone can develop a solution based on services accessible over the World Wide Web. But independently-developed web service solutions will very rarely work together “out of the box”.

There is an increasing need for such solutions to work together. We see this need when looking at The Open Platform 3.0 technologies. People want to use these technologies together. There are solutions that use them, but they have been developed independently of each other and have to be integrated. That is why Open Platform 3.0 has to deliver a way of integrating solutions that have been developed independently.

Common Architecture Environment

The Open Group has recently published its first thoughts on Open Platform 3.0 in the Open Platform 3.0 White Paper. This lists a number of things that will eventually be in the Open Platform 3.0 standard. Many of these are common architecture artifacts that can be used in solution development. They will form a common architecture environment. They are:

  • Statement of need, objectives, and principles – this is not part of that environment of course; it says why we are creating it.
  • Definitions of key terms – clearly you must share an understanding of the key terms if you are going to develop common solutions or integrable solutions.
  • Stakeholders and their concerns – an understanding of these is an important aspect of an architecture development, and something that we need in the standard.
  • Capabilities map – this shows what the products and services that are in the platform do.
  • Basic models – these show how the platform components work with each other and with other products and services.
  • Explanation of how the models can be combined to realize solutions – this is an important point and one that the white paper does not yet start to address.
  • Standards and guidelines that govern how the products and services interoperate – these are not standards that The Open Group is likely to produce, they will almost certainly be produced by other bodies, but we need to identify the appropriate ones and probably in some cases coordinate with the appropriate bodies to see that they are developed.

The Open Platform 3.0 White Paper contains an initial statement of needs, objectives and principles, definitions of some key terms, a first-pass list of stakeholders and their concerns, and half a dozen basic models. The basic models are in an analysis of the business use-cases for Open Platform 3.0 that were developed earlier.

These are just starting points. The white paper is incomplete: each of the sections is incomplete in itself, and of course the white paper does not contain all the sections that will be in the standard. And it is all subject to change.

An Example Basic Model

The figure shows a basic model that could be part of the Open Platform 3.0 common architecture environment.

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Figure 2: Mobile Connected Device Model

This is the Mobile Connected Device Model: one of the basic models that we identified in the snapshot. It comes up quite often in the use-cases.

The stack on the left is a mobile device. It has a user, it has apps, it has a platform which would probably be Android or iOS, it has infrastructure that supports the platform, and it is connected to the World Wide Web, because that’s part of the definition of mobile computing.

On the right you see, and this is a frequently encountered pattern, that you don’t just use your mobile device for running apps. Maybe you connect it to a printer, maybe you connect it to your headphones, maybe you connect it to somebody’s payment terminal, you can connect it to many things. You might do this through a Universal Serial Bus (USB). You might do it through Bluetooth. You might do it by Near Field Communications (NFC). You might use other kinds of local connection.

The device you connect to may be operated by yourself (e.g. if it is headphones), or by another organization (e.g. if it is a payment terminal). In the latter case you typically have a business relationship with the operator of the connected device.

That is an example of the basic models that came up in the analysis of the use-cases. It is captured in the White Paper. It is fundamental to mobile computing and is also relevant to the Internet of Things.

Access to Technologies

This figure captures our understanding of the need to obtain information from the new technologies, social media, mobile devices, sensors and so on, the need to process that information, maybe on the cloud, to manage it and, ultimately, to deliver it in a form where there is analysis and reasoning that enables enterprises to take business decisions.

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Figure 3: Access to Technologies

The delivery of information to improve the quality of decisions is the source of real business value.

User-Driven IT

The next figure captures a requirement that we picked up in the development of the business scenario.

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Figure 4: User-Driven IT

Traditionally, you would have had the business use in the business departments of an enterprise, and pretty much everything else in the IT department. But we are seeing two big changes. One is that the business users are getting smarter, more able to use technology. The other is they want to use technology themselves, or to have business technologists closely working with them, rather than accessing it indirectly through the IT department.

The systems provisioning and management is now often done by cloud service providers, and the programming and integration and helpdesk by cloud brokers, or by an IT department that plays a broker role, rather than working in the traditional way.

The business still needs to retain responsibility for the overall architecture and for compliance. If you do something against your company’s principles, your customers will hold you responsible. It is no defense to say, “Our broker did it that way.” Similarly, if you break the law, your broker does not go to jail, you do. So those things will continue to be more associated with the business departments, even as the rest is devolved.

In short, businesses have a new way of using IT that Open Platform 3.0 must and will accommodate.

Integration of Independently-Developed Solutions

The next figure illustrates how the integration of independently developed solutions can be achieved.

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Figure 5: Architecture Integration

It shows two solutions, which come from the analysis of different business use-cases. They share a common model, which makes it much easier to integrate them. That is why the Open Platform 3.0 standard will define common models for access to the new technologies.

The Open Platform 3.0 standard will have other common artifacts: architectural principles, stakeholder definitions and descriptions, and so on. Independently-developed architectures that use them can be integrated more easily.

Enterprises develop their architectures independently, but engage with other enterprises in business ecosystems that require shared solutions. Increasingly, business relationships are dynamic, and there is no time to develop an agreed ecosystem architecture from scratch. Use of the same architecture platform, with a common architecture environment including elements such as principles, stakeholder concerns, and basic models, enables the enterprise architectures to be integrated, and shared solutions to be developed quickly.

Completing the Definition

How will we complete the definition of Open Platform 3.0?

The Open Platform 3.0 Forum recently published a set of 22 business use-cases – the Nexus of Forces in Action. These use-cases show the application of Social, Mobile and Cloud Computing, Big Data, and the Internet of Things in a wide variety of business areas.

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Figure 6: Business Use-Cases

The figure comes from that White Paper and shows some of those areas: multimedia, social networks, building energy management, smart appliances, financial services, medical research, and so on.

Use-Case Analysis

We have started to analyze those use-cases. This is an ArchiMate model showing how our first business use-case, The Mobile Smart Store, could be realized.

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Figure 7: Use-Case Analysis

As you look at it you see common models. Outlined on the left is a basic model that is pretty much the same as the original TOGAF Technical Reference Model. The main difference is the addition of a business layer (which shows how enterprise architecture has moved in the business direction since the TRM was defined).

But you also see that the same model appears in the use-case in a different place, as outlined on the right. It appears many times throughout the business use-cases.

Finally, you can see that the Mobile Connected Device Model has appeared in this use-case (outlined in the center). It appears in other use-cases too.

As we analyze the use-cases, we find common models, as well as common principles, common stakeholders, and other artifacts.

The Development Cycle

We have a development cycle: understanding the value of the platform by considering use-cases, analyzing those use-cases to derive common features, and documenting the common features in a specification.

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Figure 8: The Development Cycle

The Open Platform 3.0 White Paper represents the very first pass through that cycle, further passes will result in further White Papers, a snapshot, and ultimately The Open Platform 3.0 standard, and no doubt more than one version of that standard.

Conclusions

Open Platform 3.0 provides a common architecture environment. This enables enterprises to derive business value from social computing, mobile computing, big data, the Internet-of-Things, and potentially other new technologies.

Cognitive computing, for example, has been suggested as another technology that Open Platform 3.0 might in due course accommodate. What would that lead to? There would be additional use-cases, which would lead to further analysis, which would no doubt identify some basic models for cognitive computing, which would be added to the platform.

Open Platform 3.0 enables enterprise IT to be user-driven. There is a revolution in the way that businesses use IT. Users are becoming smarter and more able to use technology, and want to do so directly, rather than through a separate IT department. Business departments are taking in business technologists who understand how to use technology for business purposes. Some companies are closing their IT departments and using cloud brokers instead. In other companies, the IT department is taking on a broker role, sourcing technology that business people use directly.Open Platform 3.0 will be part of that revolution.

Open Platform 3.0 will deliver the ability to integrate solutions that have been independently developed. Businesses typically exist within one or more business ecosystems. Those ecosystems are dynamic: partners join, partners leave, and businesses cannot standardize the whole architecture across the ecosystem; it would be nice to do so but, by the time it was done, the business opportunity would be gone. Integration of independently developed architectures is crucial to the world of business ecosystems and delivering value within them.

Call for Input

The platform will deliver a common architecture environment, user-driven enterprise IT, and the ability to integrate solutions that have been independently developed. The Open Platform 3.0 Forum is defining it through an iterative process of understanding the content, analyzing the use-cases, and documenting the common features. We welcome input and comments from other individuals within and outside The Open Group and from other industry bodies.

If you have comments on the way Open Platform 3.0 is developing or input on the way it should develop, please tell us! You can do so by sending mail to platform3-input@opengroup.org or share your comments on our blog.

References

The Open Platform 3.0 White Paper: https://www2.opengroup.org/ogsys/catalog/W147

The Nexus of Forces in Action: https://www2.opengroup.org/ogsys/catalog/W145

TOGAF®: http://www.opengroup.org/togaf/

harding

Dr. Chris Harding is Director for Interoperability 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 Open 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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Filed under architecture, Boundaryless Information Flow™, Cloud, Cloud/SOA, digital technologies, Open Platform 3.0, Service Oriented Architecture, Standards, TOGAF®, Uncategorized