Enterprise Architecture for Digital Transformation

Lapalme has discussed “Three Schools of Thought on Enterprise Architecture” at IT Professional in 2012. Korhonen and Halén explored more on Enterprise Architecture for Digital Transformation.

Schools of Though on EA:

  • The Enterprise IT Architecting (EITA) school views enterprise architecture as “the glue between business and IT”. Focusing on enterprise IT assets, it aims at business-IT alignment, operational efficiency and IT cost reduction. It is based on the tenet that IT planning is a rational, deterministic and economic process. EA is perceived as the practice for planning and designing the architecture.
  • The Enterprise Integrating (EI) school views enterprise architecture as the link between strategy and execution. EA addresses all facets of the enterprise in order to coherently execute the strategy. The environment is seen both as a generator of forces that the enterprise is subject to and as something that can be managed. EA is utilized to enhance understanding and collaboration throughout the business.
  • The Enterprise Ecological Adaptation (EEA) school views EA as the means for organizational innovation and sustainability. The enterprise and its environment are seen as coevolving: the enterprise and its relationship to the environment can be systemically designed so that the organization is “conducive to ecological learning, environmental influencing and coherent strategy execution.” EA fosters sense making and facilitates transformation in the organization.

Level or Enterprise Architecture

  • Technical Architecture (AT) has an operational focus on reliability and present day asset utilization and is geared to present-day value realization. This is the realm of traditional IT architecture, information systems design and development, enterprise integration and solution architecture work. AT also addresses architectural work practices and quality standards, e.g. architectural support of implementation projects, development guidelines, and change management practices. In terms of organizational structure, AT would pertain to the technical level of organization, where the products are produced or services are provided.
  • Socio-Technical Architecture (AS) plays an important role as the link between strategy and execution. The business strategy is translated to a coherent design of work and the organization so that enterprise strategy may be executed utilizing all its facets, including IT. AS is about creating enterprise flexibility and capability to change rather than operational optimization: the focus on reliability is balanced with focus on validity in anticipation of changes, whose exact nature cannot be accurately predicted. AS would pertain to the managerial level of organization, where the business strategy is translated to the design of the organization.
  • Ecosystemic Architecture (AE) is an embedded capability that not only addresses the initial design and building of a robust system but also the successive designs and continual renewal of a resilient system. The architecture must allow for co-evolution with its business ecosystem, industry, markets, and the larger society. AE would pertain to the institutional level of organization, where the organization relates to its business ecosystem, industry, markets, and the larger society.

Adaptation and Maladaptation

Source: Korhonen J.J., Halén M. 2017. Enterprise Architecture for Digital TransformationIEEE 19th Conference on Business Informatics. DOI 10.1109/CBI.2017.45

Ecological Tool for Market Ecosystem

Scholl, Calinescu, Farmer (2021) illustrated how ecological tools can be used to analyse financial markets. Studying markets as complex ecosystems rather than perfectly efficient machines can help regulators guard against damaging market volatility. And they show that changes to the wealth invested via different strategies within a market ecology can help predict market malfunctions like mispricings, bubbles, and crashes.

They model different investor strategies – including non-professional investors, trend followers, and value investors – as different players within a market ecology. They find that:

  1. Just as the status and health of biological ecosystems depend on the species present and their populations, the status and health of market ecosystems depend on market strategies and the wealth invested in them.
  2. Understanding the impact of, and interactions between, different investor species can help predict market malfunctions, just as understanding the impact and interactions of different biological species can help predict ecosystem instability or collapse.
  3. Similar to how animal populations within ecosystems can fluctuate indefinitely, market prices can stray very far from equilibrium and can also fluctuate indefinitely.


  • Scholl MP, Calinescu A, Farmer JD (2021), How Market Ecology Explains Market Malfunction, Proceedings of the National Academy of Sciences, 2021 118 (26) e2015574118. DOI: 10.1073/pnas.2015574118

Complexity Economics

Arthur WB (2021) wrote a paper comparing conventional vs complexity economics.

Conventional neoclassical economics assumes:

  • Perfect rationality. It assumes agents each solve a well-defined problem using perfectly rational logic to optimize their behaviour.
  • Representative agents. It assumes, typically, that agents are the same as each other — they are ‘representative’ — and fall into one or a small number (or distribution) of representative types.
  • Common knowledge. It assumes all agents have exact knowledge of these agent types, that other agents are perfectly rational and that they too share this common knowledge.
  • Equilibrium. It assumes that the aggregate outcome is consistent with agent behaviour — it gives no incentive for agents to change their actions.

But over the past 120 years, economists such as Thorstein Veblen, Joseph Schumpeter, Friedrich Hayek, Joan Robinson, etc have objected to the equilibrium framework, each for their own reasons. All have thought a different economics was needed.

It was with this background in 1987 that the Santa Fe Institute convened a conference to bring together ten economic theorists and ten physical theorists to explore the economy as an evolving complex system.

Complexity economics sees the economy as not necessarily in equilibrium, its decision makers (or agents) as not superrational, the problems they face as not necessarily well-defined and the economy not as a perfectly humming machine but as an ever-changing ecology of beliefs, organizing principles and behaviours.

Complexity economics assumes that agents differ, that they have imperfect information about other agents and must, therefore, try to make sense of the situation they face. Agents explore, react and constantly change their actions and strategies in response to the outcome they mutually create. The resulting outcome may not be in equilibrium and may display patterns and emergent phenomena not visible to equilibrium analysis. The economy becomes something not given and existing but constantly forming from a developing set of actions, strategies and beliefs — something not mechanistic, static, timeless and perfect but organic, always creating itself, alive and full of messy vitality.

In a complex system, the actions taken by a player are channelled via a network of connections. Within the economy, networks arise in many ways, such as trading, information transmission, social influence or lending and borrowing. Several aspects of networks are interesting: how their structure of interaction or topology makes a difference; how markets self-organize within them; how risk is transmitted; how events propagate; how they influence power structures.

The topology of a network matters as to whether connectedness enhances its stability or not. Its density of connections matters, too. When a transmissible event happens somewhere in a sparsely connected network, the change will fairly soon die out for lack of onward transmission; if it happens in a densely connected network, the event will spread and continue to spread for long periods. So, if a network were to slowly increase in its degree of connection, the system will go from few, if any, consequences to many, even to consequences that do not die out. It will undergo a phase change. This property is a familiar hallmark of complexity.


  • Arthur, W.B. (2021). Foundations of complexity economicsNat Rev Phys 3, 136–145 (2021). DOI: 10.1038/s42254-020-00273-3

Three Schools of Thought on Enterprise Architecture

Three schools of thought on enterprise architecture exist, each with its own belief system (definitions, concerns, assumptions, and limitations). A novel taxonomy of these schools creates a starting point for resolving terminological challenges to help establish enterprise architecture as a discipline.

Scope & purpose:

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Brief comparison among schools of thought:

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If organisations are to survive the turbulence of today’s markets, they must learn to adapt and innovate. A survey by the Gartner and Forrester technology market consulting firms shows that current enterprise architecture practices, which are mostly based on the enterprise IT architecture school of thought, aren’t doing very well—they lack acceptance and are perceived as organisationally inconsiderate. It is recommended that the enterprises should have to move to more holistic ways of thinking if they wish to survive and flourish.

Source: James Lapalme, Three Schools of Thought on Enterprise Architecture, IT Professional Nov/Dec 2012.

Enterprise Architecture

Enterprise Architecture Framework is a set of assumptions, concepts, values, and practices that constitutes a way of looking at enterprise reality via views on (architecture) models. It offers a fundamental structure, serving as a scaffold for developing, maintaining, and using EA.

Zachman Framework

Zachman visualises the entire enterprise at one glance. All aspects are treated as equally important; should be described as simple, basic model.

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The Open Group Architecture Framework (TOGAF) is a generic yet comprehensive methodological framework for developing enterprise architecture.

TOGAF Architecture Development Methodology (ADM)

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TOGAF Architecture Content Framework

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TOGAF Architecture Capability Framework

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Gartner Methodology

Gartner Methodology essentially stems from the META Group. It does not publish a formal framework or a process to do EA. Instead, its consultants guide enterprises in setting up a process by which EA can emerge from their business strategy.

Source: Stefan Bente et. al., Collaborative Enterprise Architecture, Morgan Kaufmann, Elsevier 2012. ISBN 9780124159341.

Self-Sustainable Model Based on Big Data Flows

A Generic Platform explored below shows the main components to support heterogeneous applications, for example in a Smart City design.

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The value chain and different stakeholders:

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Representation of the data flows and rela ers in the corporate open data services framework:

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Source: Ignasi Vilajosana et.al., Bootstrapping Smart Cities through a Self-Sustainable Model Based on Big Data Flows, IEEE Communications Magazine, June 2013.

Evolutionary Architecture for Smart City

Smart cities services use components of both the ICT industry and mobile telecommunications industries. This article describes the architectural evolution required to ensure that the rollout and deployment of smart city technologies is smooth through acknowledging and integrating the strengths of both the system architectures proposed.

Here’s the pragmatic framework:

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Links to context information sources via mobile access:

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Some aspects of smart city design, and the technical impacts result from them:

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Source: Catherine E. A. Mulligan & Magnus Olsson, Architectural Implications of Smart City Business Models: An Evolutionary Perspective, IEEE Communications Magazine, July 2013.

Vertical & Horizontal Architecture for Smart City

Smart cities encompass services in diverse business and technological domains. Presently, most of these services are delivered through domain-specific, tightly coupled systems, which entail limited scalability and extensibility. Such structure is supposed to be transformed into a horizontal architecture through service-delivery platform.

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The platform is a new type of platform-as-a-service (PaaS) offering that integrates Internet of Things infrastructure and provides services for application providers to employ IoT and cloud resources on demand.

The service-delivery workflows are presented below. The work flows result in three service-delivery models: virtual verticals, third-party applications, and Internet of Things infrastructure as a service (IoT IaaS).

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Source: Fei Li et al, Web-Scale Service Delivery for Smart Cities, IEEE Internet Computing, July/Aug 2013.

Seamless Intelligence

Seamless intelligence is a configuration where everything is connected through ubiquitous networks, interfaces, and so on. While similar to previous pervasive and ubiquitous computing scenarios, seamless intelligence has deep roots in technology advancement that didn’t exist in the near past.

With pervasive computing and communication capabilities, information technology will increasingly enhance and augment human knowledge, intelligence, and connectivity. We project that by 2022 society will advance so that intelligence becomes seamless and ubiquitous for those who can afford and use state-of- the-art information technology. We expect this new reality to result from the confluence of multiple information and communication technologies. Computing devices—ranging from wearables, subdermal chips, and the computers inside our mobile devices, laptops, desktops, home servers, TV sets, and refrigerators to the computing cloud we reach via the Internet—will interconnect via different communication and networking technologies. Together, these will form an intelligent mesh: a computing and communication ecosystem that augments reality with information and intelligence gathered from our fingertips, eyes, ears, noses, and brain waves. See Figure 4 for a visual interpretation of this seamless integration.
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Source: Hasan Alkhatib et al, What Will 2022 Look Like? The IEEE CS 2022 Report, IEEE Computer Magazine, March 2015.

23 Disruptive Technology Area

Over the last two years, IEEE Computer Society technology leaders collaborated to identify important industry advances that promise to change the world by 2022. The 23 technologies provide new insights into the emergence of seamless intelligence.

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To validate the premises and conclusions made by the CS 2022 Report team, we surveyed more than 5,000 IEEE CS senior members.

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Source: Hasan Alkhatib et al, What Will 2022 Look Like? The IEEE CS 2022 Report, IEEE Computer Magazine, March 2015.