From EA to Enterprise Architecture 3.0

Ken Griesi, Chief Architect, MITRE and Beryl Bellman, tenured full Professor of Communication, California State University Los Angeles
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Enterprise architecture enables enterprises to fully align business strategies with the technological infrastructures that support them. It facilitates an organization’s ability to communicate with all components that comprise it as well as strategically align with those with whom it interacts allowing, as The Open Group describes, boundary-less information flow, more effective decision support and the alignment of business, application, data and technological architectures that comprise them. This promotes change management and enterprise transformation for communication structures and practices, multimodal communications and the effective management of disruptive technologies.  

One definition of EA is it is the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of the firm’s operating model. It provides a conceptual blueprint that defines the structure and operation of an organization. The intent of EA is determining how an organization can most effectively achieve its current and future objectives. The most prevailing EA approach involves a plethora of models that shed light on various aspects of the business.

‚Äč  This new approach identifies enterprises as non-linear and highly complex systems, suggesting a rethinking about how they are to be represented within the context of EA 

Traditionally enterprise architects have tended to view the enterprise as an integrated monolith with the capacity to perform decision making from a top down perspective rather than as a federation of smaller collaborative enterprises that tend to respect autonomy as a virtue while still recognizing that their services and products supply other parts of the enterprise. Rather than focus on central command and control, enterprises are better served by “articles of federation”. This problem becomes aggravated when each of these constitutive entities “emerge” differently and march to their own idea of what response to change implies. Emergence must have to depend on clear articles of federation. Assumptions of sweeping and enterprise-wide integrated and aligned enterprises must be replaced with the practical need to limit the areas where integration and alignment is essential and areas where innovation and emergence are natural factors that drive divergence and diversity.

Introducing EA 3.0

Eric Schmidt, former CEO of Google, once said “Every two days now we create as much information as we did from the dawn of civilization up until 2003.”  With the move toward digitization and mobility, there has never been more data being generated about an enterprise than now.  Therefore, the introduction of Big Data Analytics is a game changer for EA.  Data provides EA with the pulse of the enterprise and its environment and can now be constantly synthesized to draw conclusions about an enterprise.  In this sense, EA fails when enterprises are treated as discrete systems that can be reduced into smaller problem sets, as traditional engineering approaches or some EA frameworks would assert.

This proliferation of data is changing the environment in which enterprises operate.  The ability for EAs to make sense of data with high degrees of volume, velocity, variety, and mixed veracity requires a different approach to EA than we have seen in the past.

The next evolution in Enterprise Architecture, what we are unveiling in this article as “EA 3.0”, is the application of design principles and patterns from Complex Adaptive Systems (CAS) Theory.  From the standpoint of EA 3.0, enterprises are synonymous to living organisms and are treated as complex systems that are constantly changing and adapting. In EA 3.0, enterprises are like natural systems: they respond to changes in their environment, they are opportunistic, they are susceptible to sicknesses, and they have fight or flight instincts.

Enterprises organically emerge out of the communication patterns that develop over the course of doing business and in response to the host of environmental variables in dynamically changing business landscapes. Enterprises are instances of CAS, having many interacting subcomponents whose communication patterns yield complex behaviors. These dynamic interactions at the local level lead to new emergent organizational structures. In this regard, “emergent” enterprises are inconsistent with the traditional ontological view of an organization as an objectively observable activity. This traditional “objective” view sees the enterprise as something that can be measured, labeled, classified, and related to other organizational processes. Objective and traditional EA sees the enterprise as a static “snapshot” to serve as a baseline for target architectures.

Snapshot-driven approaches to EA tend to be less than useful in driving change in complex environments. For example, consider the feasibility of an EA trying to create a snapshot of an extremely complex system like the Internet ten years from now. This is difficult or impossible to predict in advance. Moreover, the snapshot would arguably be immediately outdated and inaccurate. EA 3.0 would instead focus on the creation of simple rule sets that allow for complexity to emerge, such as: expose oneself as a node, and connect to another node.   

Contrary to the objective view is the recognition of organizations as CASs that give rise to considerations of emergence. This leads to a recently derived definition of “emergent” enterprise architectures, or EA 3.0. This new approach identifies enterprises as non-linear and highly complex systems, suggesting a rethinking about how they are to be represented within the context of EA. Moreover, this underscores the need to understand and skillfully manipulate primitives and rule sets that form the basis for an enterprise’s structure and behavior. After all, an enterprise’s architecture (its structure and behavior) exists whether or not it is acknowledged or skillfully manipulated.  

In conclusion, enterprises are becoming increasing complex particularly as the proliferation of data swells to new heights in this Information Age.  The environments in which they operate are evermore global, competitive and unforgiving.  Agility and the ability to quickly respond to change are paramount and giving rise to viral paradigm shifts such as the Agile Manifesto, which values responding to changes over following a pre-defined plan.

The need for enterprises to efficiently and effectively deal with complexity and change has, perhaps, never been more prevalent than now.  Driving relevant and timely solutions for an enterprise demands a thorough understanding of the problem space and skillful bounding of the solution space prior to engineering a solution.  Yet even as EAs faces these challenges, there are lessons to be learned from natural systems and an examination of CAS Theory. EA 3.0 stands upon the strong and steady shoulders of well-established principles that have survived the test of time. As John Zachman (2003) states, “Seven thousand years of human history would establish that the key to complexity and change is architecture.”