Aggregated enterprise architecture wisdom
Since the release of ArchiMate 3.0 last June, my colleagues and I have written a series of blog posts about combining ArchiMate with other standards, methods and modeling techniques. This post summarizes what we have shown you.
The rise of mobile networks, improved wireless tech, and rapid sensor innovation over the past 10 years has enabled companies to use internet-connected sensors and actuators to improve business operations and transform products. The ever-increasing num…
I’m pleased to announce that Forrester’s cloud computing predictions for 2017 published this morning!
Check out Predictions 2017: Customer-Obsessed Enterprises Launch Cloud’s Second Decade. Our cloud team has gathered ten key developments in cloud computing that will shape this industry in 2017 — and what you should do about them today.
Cloud computing has been the most exciting and disruptive force in the tech market in the last decade, and it will continue to disrupt traditional computing models at least through 2020. Starting in 2017, large enterprises will move to cloud in a big way, and that will super-charge the market. We predict the influx of enterprise dollars will push the global public cloud market to $236B in 2020, up from $146B in 2017.
Cloud platforms from the global megacloud providers like Amazon Web Services, Microsoft, IBM, Google, Salesforce, Oracle, Centurylink and SAP will set the pace, accelerating adoption of private cloud and hosted private cloud as well. In 2017, you need to:
In the full report, we break down the 10 things you need to know about cloud computing in 2017, and actions to take for each. This essential reading highlights these 10 trends:
For years legacy modernization has been the Cinderella of IT. Everyone knows legacy systems are a massive drag on the business, but there has rarely been a compelling business case to justify the perceived cost and risk involved in modernization. But things are changing! In a recent report [1] McKinsey said, “Outdated IT systems are often the biggest Achilles’ heel for established companies seeking to compete successfully against upstarts. To obtain the same cost and performance benefits that online companies enjoy, established companies need an IT architecture that is modular, simple, customer-centric, and configurable—and they need it quickly.”
In a recent Gartner survey [2], 45 percent of respondents with knowledge of their organization’s software strategy indicated that one of the current top five IT project priorities is “application modernization of installed on-premises core enterprise applications” and a further 41 percent indicated that “extending capabilities of core enterprise applications” is a top five priority,
Gartner predicts [2] that by 2020, 75 percent of application purchases supporting digital business will be “build,” not “buy.” Gartner’s research shows that many organizations already favour a new kind of “build” that does not include out-of-the-box solutions, but instead is a combination of application components that are differentiated, innovative and not standard software or software with professional services (for customization and integration requirements), or solutions that are increasingly sourced from start-ups, disrupters or specialized local providers.
Forrester have said recently [3] that application development is the key strategy for firms transforming to digital. As I said last year [4] “most . . . innovating companies are . . . demonstrating the extraordinary innovation, productivity and quality that can be achieved by a convergence of business and IT skills and expertise . . . changing from command and control to delegated responsibility development models. Yet Forrester also advise that there are insufficient developers to meet demand, and that innovation in development tools has not met market demand.
Further Agile methods are not the panacea they are cracked up to be. In 2014 I observed [5], “The most common concern our customers voiced . . was the unexpected outcomes of Agile projects. They don’t talk about failure as such. But they do talk about loss of consistency; inability to govern; lack of coordination and the increasing time to market.” While there has been a rush by consultants to promote and enterprises to adopt scaled agile frameworks, I continue to observe projects struggling to disprove Newton’s third law, as they attempt to manage hugely complex efforts with yesterday’s life cycle tooling and technology.
And it’s here that we need to get over the Cinderella syndrome and embrace the idea that modernization is not business as usual. Modernization applies to everything you do – including how you build systems. As Jason Bloomberg said [6] last year, “For specific legacy capabilities to properly support the digital transformation, we need a better approach for abstracting legacy assets to drive agility, an architectural approach freed from middleware and laser focused on the business agility drivers from the digital transformation initiative.” In order to modernize you don’t just need more developers or partners as Forrester suggest; you need to reinvent the business process and solutions delivery activity.
The diagram below illustrates a dynamic systems model for factory based Agile Modernization.
Essentially modernization needs to become business as usual; every program, project and increment must be progressively modernizing the legacy backend as necessary, in an inherently Agile modernization process. Product and Modernization backlogs must have equal priority. The delivery tooling and process needs to be common to forward engineering and modernization, and Oh by the way, needs to be massively productive and high quality. A good way to do this is to use an Agile factory approach that a) restructures and rationalizes applications into services, b) separates everything that is common or reusable, or should be standardized into the development platform, c) manages development artifacts dependency and d) allows and leaves the technology binding as late as possible. For discussion of how this Agile modernization works see recent blog [7] – Scaled Agile Factory.
[1] Two ways to modernize IT systems for the digital era, McKinsey
[2] Gartner Says Modernization and Digital Transformation Projects Are Behind Growth in Enterprise Application Software Market
[3] Digital Transformation and the Modernization Imperative, Forrester
[4] Modernizing the modernization strategy, David Sprott’s Blog
[5] Understanding Agile Adoption Failure, David Sprott’s Blog
[6] Two Digital Transformation Time Bombs, Jason Bloomberg, Wired
[7] Scaled Agile Factory, David Sprott’s Blog
Spraint is a quaint English word for otter droppings. Analysis of otter populations and their dietary habits can be performed by analysis of their spraint.I see the same kind of analysis being required in systems that send their “spraint” – often in th…
Spraint is a quaint English word for otter droppings. Analysis of otter populations and their dietary habits can be performed by analysis of their spraint.I see the same kind of analysis being required in systems that send their “spraint” – often in th…
UK Court News. Uber has lost a test case in the UK courts, in which it argued that its drivers were self-employed and therefore not entitled to the minimum wage or any benefits. Why is this ruling not quite as straightforward as it seems? To answer this question, we have to look at the mathematics of two-sided or multi-sided platforms.
Platforms exist in two states – growth and steady-state. A mature steady-state platform maintains a stable and sustainable balance between supply and demand. But to create a platform, you have to build both supply and demand at the same time. Innovative platforms such as Uber are oriented towards expansion and growth – recruiting new passengers and new drivers, and launching in new cities.
| New Passengers | “Every week in London, 30,000 people download Uber to their phones and order a car for the first time. The technology company, which is worth $60bn, calls this moment “conversion”. It sets great store on the first time you use its service … With Uber, the feeling should be of plenty, and of assurance: there will always be a driver when you need one.” (Knight) |
| New Drivers | “They make it sound so simple: Sign up to drive with Uber and soon you’ll be earning an excellent supplementary income! That’s the central message in Uber’s ongoing multi-platform marketing campaign to recruit new drivers.” (McDermott) |
| New Cities | “Uber has deployed its ride-hailing platform in 400 cities around the world since its launch in San Francisco on 31 May 2010, which means that it enters a new market every five days and eight hours. … To take over a city, Uber flies in a small team, known as “launchers” and hires its first local employee, whose job it is to find drivers and recruit riders.” (Knight) |
But here’s the problem. In order to encourage passengers to rely on the service, Uber needs a surfeit of drivers. If passengers want instant availability of drivers (plenty, assurance, there will always be a driver when you need one), then Uber has to maintain a pool of under-utilized drivers. (Knowles)
Simple mathematics tells us that if Uber takes on far more drivers than it really needs, some of them won’t earn very much. Furthermore, people with little experience of this kind of work may underestimate the true costs involved, and may have an unrealistic idea of the amounts they can earn: Uber has no obvious incentive to disillusion them. (This is an example of Asymmetric Information.) Even if the average earnings of Uber drivers are well above the minimum wage, as Uber claims, it is not the average that matters here but the distribution.
The myth is that these are drivers who can choose whether to provide a service or not, so they are free agents. Libertarians wax lyrical about the “gig economy” and the benefits to passengers. However, the UK courts have judged that Uber drivers work under a series of constraints, and are therefore to be classified as “workers” for the purposes of various regulations, including minimum wage and other benefits.
Uber has announced its intention to appeal the UK judgement. But if the judgement stands, what are the implications for Uber? Firstly, Uber’s overall costs are likely to increase, and Uber will undoubtedly find a way either to pass these costs onto the passengers or to pass them back to the drivers in some other form. But more interestingly, Uber now has a financial incentive to balance supply and demand more fairly, and to avoid taking on too many drivers.
Uber sometimes argues it is merely a technology company, and is not in the transportation business. Dismissing this argument, the UK courts quoted a previous judgement from the North California District Court:
“Uber does not simply sell software; it sells rides. Uber is no more a ‘technology company’ than Yellow Cab is a ‘technology company’ because it uses CB radios to dispatch taxi cabs.”
However, Uber’s undoubted technological know-how should enable it to develop (and monetize) appropriate technologies and algorithms to manage a two-sided platform in a more balanced way.
Why is the Uber ruling not quite as straightforward as it seems? @richardveryard @ricphillips pic.twitter.com/OXIgTHvA6z— Jeffrey Newman (@JeffreyNewman) October 29, 2016
Update: similar concerns have been raised about Amazon delivery drivers. I have previously praised Amazon on this blog for its pioneering understanding of platforms, so let’s hope that both Amazon and Uber can create platforms that are fair to drivers as well as its customers.
Mr Y Aslam, Mr J Farrar and Others -V- Uber (Courts and Tribunals Judiciary, 28 October 2016)
Sarah Butler, Uber driver tells MPs: I work 90 hours but still need to claim benefits (Guardian, 6 February 2017)
Tom Espiner and Daniel Thomas, What does Uber employment ruling mean? (BBC News, 28 October 2016)
David S. Evans, The Antitrust Economics of Multi-Sided Platform Markets (Yale Journal on Regulation, Vol 20 Issue 2, 2003). Multisided Platforms, Dynamic Competition and the Assessment of Market Power for Internet-Based Firms (CPI Antitrust Chronicle, May 2016)
Sam Knight, How Uber Conquered London (Guardian, 27 April 2016)
Kitty Knowles, 10 of the biggest complaints about Uber – from Uber drivers (The Memo, 5 November 2015)
Barry Levine, Uber opens up its API – and creates a new platform (VentureBeat, 20 August 2014)
John McDermott, I’ve done the (real) math: No way an Uber driver makes minimum wage (We Are Mel, 17 May 2016)
Hilary Osborne, Uber loses right to classify UK drivers as self-employed (Guardian, 28 October 2016)
Aaron Smith, Gig Work, Online Selling and Home Sharing (Pew Research Center, 17 November 2016)
Ciro Spedaliere, How to start a multi-sided platform (30 June 2015)
Amazon drivers ‘work illegal hours’ (BBC News, 11 November 2016)
See further discussion with @wimrampen and others on Storify: Uber Mathematics – A Discussion
Related Posts
Uber Mathematics 2 (Dec 2016) Uber Mathematics 3 (Dec 2016)
Uber’s Defeat Device and Denial of Service (March 2017)
Updated 6 February 2017
By Loren K. Baynes, Director, Global Marketing Communications, The Open Group In the City of Lights, The Open Group hosted the last quarterly event of 2016, October 24-27. With the Eiffel Tower and Arc de Triomphe as backdrops, 200 attendees … Continue reading →
Link: http://eapj.org/enterprise-architecture-ea-a-want-or-need/ From Enterprise Architecture Professional Journal 00
The term “strategy” is perhaps one of the most misused, and misunderstood concepts in business literature. In this series of blog posts, we refer to strategy as positioning the firm with respect to its environment. We endeavor to answer the questions: how can we (a) improve the process of strategic management through the use of models, and (b) improve the execution/implementation of strategies with Enterprise Architecture Management?
Algorithms have been getting a bad press lately, what with Cathy O’Neil’s book and Zeynap Tufekci’s TED talk. Now the German Chancellor, Angela Merkel, has weighed into the debate, calling for major Internet firms (Facebook, Google and others) to ma…
Algorithms have been getting a bad press lately, what with Cathy O’Neil’s book and Zeynap Tufekci’s TED talk. Now the German Chancellor, Angela Merkel, has weighed into the debate, calling for major Internet firms (Facebook, Google and others) to make their algorithms more transparent.
There are two main areas of political concern. The first (raised by Mrs Merkel) is the control of the news agenda. Politicians often worry about the role of the media in the political system when people only pick up the news that fits their own point of view, but this is hardly a new phenomenon. Even in the days before the Internet, few people used to read more than one newspaper, and most people would prefer to read the newspapers that confirm their own prejudices. Furthermore, there have been recent studies that show that even when you give different people exactly the same information, they will interpret it differently, in ways that reinforce their previous beliefs. So you can’t blame the whole Filter Bubble thing on Facebook and Google.
But they undoubtedly contribute further to the distortion. People get a huge amount of information via Facebook, and Facebook systematically edits out the uncomfortable stuff. It aroused particular controversy recently when its algorithms decided to censor a classic news photograph from the Vietnam war.
Update: Further criticism from Tufekci and others immediately following the 2016 US Election
2016 was a close election where filter bubbles & algorithmic funneling was weaponized for spreading misinformation. https://t.co/QCb4KG1gTV pic.twitter.com/cbgrj1TqFb— Zeynep Tufekci (@zeynep) November 9, 2016
The second area of concern has to do with the use of algorithms to make critical decisions about people’s lives. The EU regards this as (among other things) a data protection issue, and privacy activists are hoping for provisions within the new General Data Protection Regulation (GDPR) that will confer a “right to an explanation” upon data subjects. In other words, when people are sent to prison based on an algorithm, or denied a job or health insurance, it seems reasonable to allow them to know what criteria these algorithmic decisions were based on.
Reasonable but not necessarily easy. Many of these algorithms are not coded in the old-fashioned way, but developed using machine learning. So the data scientists and programmers responsible for creating the algorithm may not themselves know exactly what the criteria are. Machine learning is basically a form of inductive reasoning, using data about the past to predict the future. As Hume put it, this assumes that “instances of which we have had no experience resemble those of which we have had experience”.
In a Vanity Fair panel discussion entitled “What Are They Thinking? Man Meets Machine,” a young black woman tried unsuccessfully to explain the problem of induction and biased reasoning to Sebastian Thrun, formerly head of Google X.
At the end of the panel on artificial intelligence, a young black woman asked Thrun whether bias in machine learning “could perpetuate structural inequality at a velocity much greater than perhaps humans can.” She offered the example of criminal justice, where “you have a machine learning tool that can identify criminals, and criminals may disproportionately be black because of other issues that have nothing to do with the intrinsic nature of these people, so the machine learns that black people are criminals, and that’s not necessarily the outcome that I think we want.”
In his reply, Thrun made it sound like her concern was one about political correctness, not unconscious bias. “Statistically what the machines do pick up are patterns and sometimes we don’t like these patterns. Sometimes they’re not politically correct,” Thrun said. “When we apply machine learning methods sometimes the truth we learn really surprises us, to be honest, and I think it’s good to have a dialogue about this.”
In other words, Thrun assumed that whatever the machine spoke was Truth, and he wasn’t willing to acknowledge the possibility that the machine might latch onto false patterns. Even if the algorithm is correct, it doesn’t take away the need for transparency; but if there is the slightest possibility that the algorithm might be wrong, the need for transparency is all the greater. And evidence is that some of the algorithms are grossly wrong.
In this post, I’ve talked about two of the main concerns about algorithms – firstly the news agenda filter bubble, and secondly the critical decisions affecting individuals. In both cases, people are easily misled by the apparent objectivity of the algorithm, and are often willing to act as if the algorithm is somehow above human error and human criticism. Of course algorithms and machine learning are useful tools, but an illusion of infallibility is dangerous and ethically problematic.
Rory Cellan-Jones, Was it Facebook ‘wot won it’? (BBC News, 10 November 2016)
Ethan Chiel, EU citizens might get a ‘right to explanation’ about the decisions algorithms make (5 July 2016)
Kate Connolly, Angela Merkel: internet search engines are ‘distorting perception’ (Guardian, 27 October 2016)
Bryce Goodman, Seth Flaxman, European Union regulations on algorithmic decision-making and a “right to explanation” (presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY)
Mike Masnick, Activists Cheer On EU’s ‘Right To An Explanation’ For Algorithmic Decisions, But How Will It Work When There’s Nothing To Explain? (Tech Dirt, 8 July 2016)
Fabian Reinbold, Warum Merkel an die Algorithmen will (Spiegel, 26 October 2016)
Nitasha Tiku, At Vanity Fair’s Festival, Tech Can’t Stop Talking About Trump (BuzzFeed, 24 October 2016) HT @noahmccormack
Julia Carrie Wong, Mark Zuckerberg accused of abusing power after Facebook deletes ‘napalm girl’ post (Guardian, 9 September 2016)
New MIT technique reveals the basis for machine-learning systems’ hidden decisions (Kutzweil News, 31 October 2016) HT @jhagel
Video: When Man Meets Machine (Vanity Fair, 19 October 2016)
See Also
The Problem of Induction (Stanford Encyclopedia of Philosophy, Wikipedia)
Related Posts
The Shelf-Life of Algorithms (October 2016)
Weapons of Math Destruction (October 2016)
Updated 10 November 2016