Dimensions of EA maturity

@gotze (John Gøtze) kindly sent me a copy of his 2010 paper Architecting the Firm (written with Pat Turner and Peter Bernus). My interest had been stimulated by Anders Jensen, who described the paper as follows in his blog on the Thinking Enterprise.

It has been a healthy evolution for EA to question its IT heritage and adopt a broader perspective of how commercial enterprises navigate and gravitate in their respective marketplaces. This healthy evolution has particularly been articulated by Turner, Gotze, and Bernus (2010) in their paper Architecting the Firm: Coherency and Consistency in Managing the Enterprise, which argues the key role of architecture in executive management.

The paper offers four interesting EA archetypes, which are presented as a maturity model. As I see it, the four archetypes described in the paper differ along three orthogonal dimensions: instrumentality, agency and scope.

• The first archetype ("Foundation") could be described as Invisible EA. In what sense is EA going on at all? Even the "struggle for existence" is largely invisible. 

• The second archetype ("Extended") describes EA in instrumental terms - as an instrument for managing assets.

• The third archetype ("Embedded") describes EA in terms of agency - with EA taking authority and responsibility for certain things.

• The fourth archetype ("Balanced") goes back to describing EA in instrumental terms  - but with a much broader scope of application.

However, there seems to be little evidence of a development path passing through these four archetypes in irreversible sequence, nor any reason to suppose this particular development path to be either optimal or unavoidable. For my part, I prefer not to call this kind of thing a maturity model at all. In my post on SOA maturity models, I pointed out a key difference between an evolution roadmap (which says what you can do immediately and what you may defer) and a maturity model (which says what you can't do until). A roadmap is enabling and encouraging; a maturity model is parental.



Reading the paper, I wondered how the evolution of EA within one enterprise (possibly via these four archetypes) might relate to the evolution of EA itself (into Next Generation EA). The paper outlines "the vision of the right information for the right people at the right time ... coherency of information flow". But this doesn't exactly sound like EA questioning its IT heritage does it?

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Smart Content

There are several characteristic features of so-called smart content.

• Content enhanced to be fit-to-purpose ... content that is organized and structured for customer tasks and needs, not just for the production, packaging and distribution of physical documents. (Mirko Minnich, ex Elsevier)

• Self-organizing and transparent content, organizing itself automatically depending on your context, goals, and workflow, and allowing you to see why it's doing what it's doing. (Mark Stefik, Xerox PARC)

• Granular at the appropriate level, semantically rich, useful across applications, and meaningful for collaborative interaction. (Gilbane Group)

• Has good metadata (not lots), fit for purpose, uses classifications to provide context and aid discoverability (Madi Solomon, Pearson)

And there are several characteristic technologies that are supposed to facilitate smart content, among other things. Some of these technologies are linked to Sir Tim Berners-Lee. Come on Tim!

• Semantic technologies to cross-reference and cross-polinate with other kinds of content. (Madi Solomon, Pearson)

As Natasha Fogel pointed out, "smart content is in the eye of the beholder" - in other words, the perceived smartness of content is relative to its context of use.

But in this post, I don't want to talk about the technologies themselves but about the emerging value propositions that may be supported by smart content. Last year, when he was a SVP at scientific and technical publisher Elsevier, Mirko Minnich talked about two key enablers for smart content. Firstly a value-adding process - transmuting content into scientific data, and transmuting scientific data into solutions. And secondly what he calls a product bridge, not only linking content with data but also linking the content business with the data analytics business. The product bridge appears to be a kind of platform, and Mirko was using the term "Smart Content" to refer to the platform itself as well as the content delivered on the platform.

Mirko's strategy at Elsevier represented a strong drive towards asymmetric design - in other words, recognizing that in order to deliver indirect value into a complex ecosystem you have to move away from a traditional product-based business model (in Elsevier's case, selling scientific journals) towards regarding your business as a multi-sided platform.


Mark Stefik (Xerox PARC) puts smart content into an organizational intelligence frame - the intelligence is now located (reified) in the content as well as in the people producing and consuming the content. Instead of the user asking "what content do I need", Mark wants the content to ask "who needs me?" Madi Solomon (Pearson) seems to be suggesting the exact opposite when he mentions the Big Shift from Push to Pull in his recent presentation on Smart Content. We can resolve this apparent contradiction only by understanding the intelligence as the property of the whole system rather than trying to locate it in one place - see my material on organizational intelligence.

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Sources

Seth Grimes, Six definitions of smart content (Information Week, Sept 2010)Several of the quotes above come from this article.


Technology in Publishing (Editors Update, Elsevier, Jan 2011)
Next Generation Clinical Decision Support (Elsevier Press Release, Feb 2011)

Madi Solomon, Making Information Pay (April 2011)

Five Views of Business Architecture

#OMG #BAWG The Object Management Group Business Architecture Working Group has identified five key views of a business.

1. the Business Strategy view ("What the Business Wants")
2. the Business Capabilities view ("What the Business Does")
3. the Value Stream view ("How the Business Does")
4. the Business Knowledge view ("What the Business Knows", "How the Business Thinks")
5. the Organizational view ("What the Business Is")

Source: Business Architecture Overview

While working with the CBDI Forum between 2002 and 2009, I developed an approach to business modelling for SOA, which included an emphasis on decoupling the WHAT (what the business does, what the business knows) from the HOW (how the business does). We felt that this decoupling provided the best basis for managing differentiation and integration across complex enterprises, and for achieving appropriate economics of scale and scope.

In my more recent work on Organizational Intelligence, I have sought to further decouple "What the Business Knows" from "How the Business Thinks". Different enterprises operating in the same ecosystem may be able to share a lot of common knowledge and information, but may each arrive at different judgements about What-Is-Going-On.

I shall be presenting the latest version of this schema in my Business Architecture Bootcamp and Organizational Intelligence Workshop, and explore how this schema helps to address a range of practical business problems.

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Intelligence Failure at Kodak

@mkplantes sees the demise of Kodak as an intelligence failure.

Put yourself in their Kodak leaders’ chairs for a moment and consider the four expectations of a leadership team and, more importantly, consider the speed with which they had to work though all of the expectations:

• Sense what’s going on around you? (“Digital is coming!”)
• Make sense of what you see, hear, and feel (“Film is dying, but we can’t kill it now. It’s too important!”)
• Decide on a course of action (“OMG! Nothing is as big as film is now. Let’s think about this and be careful.”)
• Act on your decisions (“Well, this is a big ship! Hard to change course overnight!”)

Kay Plantes, A sad “Kodak moment” business model failure WTN News 7 October 2011

This is effectively an OODA loop. Dr Plantes identifies a number of possible errors in this loop.

1. Incorrect estimate of the pace of change. "Successful companies often underestimate the speed of industry evolution."

2. Incorrect understanding of the value proposition from the customers' perspective. "People don’t buy film, they use film to capture the pictures they want."

3. Incorrect optimization of the basis of competition - commodity wars.

If it was a strategic error for Kodak to get caught up in a dogfight with Fuji, we should also ask how Fuji is faring? Has Fuji committed the same errors as Kodak, and is it suffering the same fate? Meanwhile, Stuart Henshall compares Kodak with HP: two inventive companies, who "failed time and time again to find a more agile footing". (HP - What's Your strategy? August 2011).

Dr Plantes complains that Kodak was focused on the product rather than the value received by its customers - in other words, a platform strategy. But Kodak has been trying to shift its business model from product to a service-oriented platform for at least five years. In November 2006, an article in BusinessWeek described this transformation, and outlined some of the big challenges then facing Kodak (Mistakes made on the road to innovation, BusinessWeek November 2006). In February 2007, Clayton Christensen and Scott D. Anthony saw the Kodak strategy as an ambitious attempt to implement Christensen's concept of disruptive innovation (Will Kodak's New Strategy Work? Forbes February 2007).


Antonio Perez (who spent much of his career at HP) has been the CEO throughout this period, and has watched the Kodak share price drop from around $25 to less than $1. We may infer that Kodak has failed to overcome the challenges identified by BusinessWeek and Christensen. But why?


What's missing from Dr Plantes' analysis is an appreciation of how these four steps operated as an effective OODA loop, with feedback and learning, rather than merely repetition. In a detailed analysis of Kodak strategy, George Mendes concludes

Kodak is an example of repeat strategic failure – it was unable to grasp the future of digital quickly enough, and even when it did so, it was implemented too slowly under a continuous change strategy and ultimately it did not fit coherently as a core competency.

George Mendes, What went wrong at Eastman Kodak (pdf), TheStrategyTank

There is a great deal on the Internet about Kodak's social media strategy - but it seems to be largely about Kodak marketing communications. Journalist Courtney Boyd Myers (@CBM) invites us to Meet the brilliant and beautiful woman behind Kodak’s social media strategy (September 2011). The woman in question is extremely photogenic and obviously good at self-promotion, but there is nothing strategic in the article. The big strategic error here is to regard social media and content management as a marketing issue, separate from the business model itself. This seems to suggest a lack of joined-up thinking - and ultimately a failure of organizational intelligence.


In 2007, Jacob McNulty thought that that instilling the elements of a learning organization would have strongly contributed to a different story for Kodak’s recent years.

A learning organization is one that learns from its mistakes and successes, spots trends in the market and acts on them by being nimble enough to do so.  A culture of learning rewards knowledge sharing which reduces the chances that you’ll be blindsided by something like digital in 2007. Kodak could have presented themselves as a picture company many years ago - whether those pictures are on film or in a file it shouldn’t matter.  Part of making that transition would require a company that is ready to learn and develop.

Jacob McNulty, Not a Kodak Moment (2007)

Other sources claim that Kodak is a learning organization. In which case, why has it failed to learn the things that matter?

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Google as a Platform (not)

Google vs Amazon (again). @davidsprott reckons Steve Yegge's rant is spot on. Steve Yegge is a software engineer who used to work for Amazon and now works for Google, despite the supposedly accidental publication of a long and opinionated rant (his words) complaining that

'[what] Google doesn't do well is Platforms. We don't understand platforms. We don't "get" platforms.'

(For more context, see Steve Yegge's second thoughts on Google+).

Waddya mean, Google doesn't get platforms? Surely Google is a major player in platform, especially in the so-called Cloud? Dion Hinchcliffe sounded fairly convinced when he was Comparing Amazon's and Google's Platform-as-a-Service (PaaS) Offerings back in April 2008.

"Amazon and Google have strategically built up an extensive set of services over the last few years and have made some very interesting assumptions that will determine who their customers are (consumers, startups, enterprises) and what type of business models can sit on top of them (advertising, subscriptions, cheapest source of outsourced computing resources). ... Google and Amazon have emerged to be the leaders in this space while Microsoft, IBM, and especially Oracle and SAP are either well behind or have unclear plans to enter the PaaS space. Both of these companies formed their DNA around the world of the Web and deeply understand how to leverage the enormous strengths of the Web platform."

So what went wrong? This guy (Yegge) sure knows one thing, says @pardhas, building platforms is not just collecting your products on one plate. For Yegge, platforms is about eating your own dogfood. Not just Amazon, but also Facebook - hey, even Microsoft understands platforms better than Google.

But there is something missing from Steve Yegge's account. He sees the (lack of) platform from an engineering perspective, but what he doesn't talk about is the enterprise/ecosystem perspective.

@davidsprott's tweet ends with the formula "SOA + platforms = competitive advantage".  David and I have written a great deal about ecosystem SOA and ecosystem architecture, and we have long credited Jeff Bezos of Amazon as someone who "gets" ecosystem.

Among other things, "getting ecosystem" means understanding the variety of ways in which the social complexity of collaborations create value for the customer, and therefore how, from the perspective of the supplier, platform architectures can capture indirect value. See Philip Boxer's presentation on supporting social complexity in collaborative enterprises, as well as my presentation on Next Generation Enterprise Architecture, both from the recent Unicom EA Forum in London.

As I pointed out in my recent VPEC-T analysis of Google, Google is adopting a positional strategy - capturing some territory and defending it against its competitors. Amazon and Apple have shifted towards open source competition on their platforms (relational strategy) while Google is still closed-source.

In his post on Tomorrow's Networked Economy, @JDeragon praises Google+ for becoming an integrated portal. Er, wasn't that AOL's strategy? And Yahoo's for that matter? Maybe Google has greater ability to execute this strategy than they did, but it still looks more like yesterday's networked economy. Have you read Kevin Kelly's book?

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For Apple's shift from product to platform, see my post on Disney, Pixar, Apple and Jobs from February 2006.

For the shift from positional stategy to relational strategy, see Philip Boxer and Bernie Cohen, Triply Articulated Modelling of the Anticipatory Enterprise and Philip Boxer, Architectures that integrate  differentiated behaviours.

For more on Ecosystem SOA and Ecosystem Architecture, please browse the Ecosystem category on this blog. Here are some further links.

Jeff Bezos Letter to Shareholders (via Geekwire) (April 2011)
Bob Ellinger, Enterprise SOA vs Ecosystem SOA (April 2011)
Vaughan Merlyn, From Enterprise Architecture to Ecosystem Architecture (July 2008)
David Sprott, Introducing Smart Ecosystem Architecture (October 2009)

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Towards a VPEC-T analysis of Google

#entarch Enterprise architects need to understand values and policies. VPEC-T is an approach that is particularly useful for situations where there are multiple conflicting values and policies, or multiple interpretations of What-Is-Going-On.

In this post, I want to look at Google. Can we infer its values and policies from its observed behaviour (over time).


We may start by asking what events we think Google is paying attention to. Here are some of the events that are available to Google.

1. You search for "XYZ"
2. You skip over the first few items, and click on the third item on the second page.
3. You look at a webpage and then come back to continue your search.
4  You rephrase and clarify your enquiry.

Google is pretty coy on its exact use of these events, but most Google-watchers assume that these events have an influence on its search algorithms and/or its advertising algorithms. In other words, we may presume that Google is generating valuable content from these events.

Google has indulged in a wide range of initiatives over the years, many of which have no obvious line to revenue. But all of them have the potential to generate vast amounts of rich content - much of it related to the observed behaviour of internet users. On this interpretation of Google's strategy, initiatives are dropped, not because they fail to generate revenue but because they fail to generate enough of the desired kind of content. Google is betting its future on building and maintaining this content through powerful positive feedback.

Google's strategy is therefore surprisingly traditional - it involves capturing some territory and defending it against its competitors. Here's an example - Google provides the Android platform to mobile device manufacturers. When Motorola wanted to use Skyhook's voice recognition instead of Google's, Google forced it to fall into line. Daniel Soar argues that this was not because Google executives feared losing revenue but because they feared losing access to an important source of content. As Soar puts it, "Google faced the unfamiliar problem of the negative feedback loop: the fewer people that used its product, the less information it would have and the worse the product would get." (Google has since bought out Motorola Mobility, which presumably resolves some of the trust issues as well.)

Daniel Soar, You can't get away from Google, London Review of Books, Vol 33 No 19, 6 October 2011


Can we understand Google's phenomenal collection and use of data as an example of organizational intelligence? Google is certainly seeking to differentiate each Internet user's experience, as well as integrate across multiple domains (web browsing, email, blogging, voice, video, satnav, and so on). Google already has an army of brilliant engineers as well as an alarmingly large carbon footprint. There is lots of evidence of Google's integrating these resources into one of the most innovative sociotechnical systems on the planet.

(By the way, when I asked Google itself about its carbon footprint, it recommended I look at a recent story in the Guardian (8 September 2011). I can see that Google has been asked this question many times before, because it pops up so quickly as an expected search term. But why should I trust Google's recommendation, and how can I ever discover what newspapers would be recommended to a browser with a different browsing history to mine?)

But a lot of this learning looks suspiciously like first-order learning. So the content gets better, based on better capture of events, but to what extent is there any systematic evolution of policies or questioning of values? There may well be some second-order or third-order learning, but it's not easy to see from the outside. There is also an important question about the relationship between Google's own ability to learn from its accumulated content, and Google's ability or willingness to provide a rich platform for learning by others in its ecosystem - in other words, a broader notion of collective intelligence.

I wonder if there are any lessons for other organizations? Sometimes firms like Amazon, Apple, Facebook and Google (Eric Schmidt's Gang of Four) seem pretty far removed from most other organizations, but their platform strategies and operating patterns will surely become increasingly relevant in other sectors. A traditional retailer may now collect and analyse a much larger quantity of data about its customers' behaviour than ever before, even if this is still several orders of magnitude less than what Google does. A traditional telecoms or media company may now see itself as a platform business in a multisided market. Therefore instead of seeing Eric Schmidt's Gang of Four as impossibly remote and mysterious organizations, populated by unbelievably talented and creative engineers, we should start to think of them as harbingers of the enterprise of the future.



See also my post Google as a Platform (not)

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Business Architecture Bootcamp

Most courses in business architecture (or any other methodology for that matter) start by teaching a set of generic principles, followed by a standard set of tasks and techniques. The tasks and techniques are then illustrated by some simplistic or contrived examples and exercises. Video store, anyone?


In my forthcoming business architecture bootcamp (Unicom Middlesex, November 22-23), I'm going to lead an exploration into some serious and non-trivial business problems, based on some real experience in a real company. The class will experiment with different ways of modelling these problems, spanning the Five Views of Business Architecture, in order to go deeper and deeper into an understanding of these problems and their architectural implications. I don't know exactly where we shall end up, but I am confident that this is a better way of learning the things that really matter to business architects.

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Early bird rates available until November 1st.

From Convenience to Consilience - “Technology Alone Is Not Enough"

Wikipedia defines Consilience as the unity of knowledge (literally a "jumping together" of knowledge), which has its roots in the ancient Greek concept of an intrinsic orderliness that governs our cosmos, inherently comprehensible by logical process.

The word appears in an appreciation of Steve Jobs by @jonahlehrer, published in the New Yorker on October 7th 2011.

What set all of Jobs’s companies apart, from Pixar to NeXT to Apple, was, indeed, an insistence that computer scientists must work together with artists and designers—that the best ideas emerge from the intersection of technology and the humanities. “One of the greatest achievements at Pixar was that we brought these two cultures together and got them working side by side,” Jobs said in 2003. ... Perhaps the clearest demonstration can be seen in the design of the Pixar campus. ...

Jobs realized, however, that it wasn’t enough to simply create a space: he needed to make people go there. As he saw it, the main challenge for Pixar was getting its different cultures to work together, forcing the computer geeks and cartoonists to collaborate. (John Lasseter, the chief creative officer at Pixar, describes the equation this way: “Technology inspires art, and art challenges the technology.”) In typical fashion, Jobs saw this as a design problem. ...

That emphasis on consilience, even if it came at the expense of convenience, has always been a defining trait of Steve Jobs. In an age of intellectual fragmentation, Jobs insisted that the best creations occurred when people from disparate fields were connected together, when our distinct ways of seeing the world were brought to bear on a singular problem. It’s what happens when a calligrapher designs a computer font and when an animator strikes up a conversation with a programmer at the bathroom sink. The Latin crest of Pixar University says it all: Alienus Non Diutius. Alone no longer.

Read more http://www.newyorker.com/online/blogs/newsdesk/2011/10/steve-jobs-pixar.html

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Steve Jobs was widely regarded as an outstanding product architect - someone with exceptional aesthetic sense and attention to detail. The creation of the iTunes ecosystem probably ranks as solution architecture rather than just product architecture. But Jonah's account emphasizes Job's exceptional ability as an enterprise architect - putting people and organizations together to achieve a classical unity of knowledge, which Jonah calls consilience. According to Jonah, it was his secret sauce.

Other commentators have said similar things. When Jobs finally resigned, John @Gruber stated that "Jobs’s greatest creation isn’t any Apple product. It is Apple itself." (Daring Fireball, August 2011) Building on this statement, Horace Dediu (@asymco) invites other companies to copy how Apple harbors the creative process and the technology processes under the same roof. If that occurs, Jobs will have left a legacy not just on his own companies (Apple, Pixar) but on all companies. (Polymath Asymco August 2011, reposted as Steve Jobs's Ultimate Lesson for Companies HBR August 2011)


See also my post Steve Jobs was not a visionary.

Learning from Stories

#entarch @thepacketrat summarizes a presentation by @beamrider9 via @cgrant @cuttertweets

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History is written by the victors. Ross Snyder, who describes himself as a code jockey at Etsy, recently gave an account of the evolution of Etsy's technical architecture from 2005 to the present. As Ross tells the story, a number of serious architectural flaws have now been sorted out. Ross attributes the former problems to trying to be too clever, and suggests that if you're doing something "clever" you're probably doing it wrong. However, he admits that future architects may well say the same thing about him.

I don't want to comment on the technical detail of the story, but I was interested in the (implied) architectural processes. And I wondered how effectively and efficiently any given architectural methodology would have either solved the problems experienced at Etsy, or avoided them in the first place. The first key question here is whether such methodologies actually help to focus the architect's attention on those issues that turned out to be critical in the Etsy case. And the second key question is whether a case study like Etsy allows us to perceive any significant difference in outcome between one methodology and another, or whether the supposed benefits of these methodologies is largely the result of Common Factors.

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Sean Gallagher, When "clever" goes wrong: how Etsy overcame poor architectural choices Ars Technica October 2011