The aim of an EA must be to define, implement and refine the overall architecture on a continuous basis, not necessarily eliminate any kind of business complexity, but understands and leverage complexity accordingly.
Complexity has increased exponentially. Imagine the complexity that comes in due to these characteristics (less structure, rules
and regulations, diversity, ambiguity, unpredictability, lack of linearity and
increased flux) working and impacting together.
1. Complexity Classification & Theory
There is a long and strong tradition of complexity theory
and many attempts at its application, all of which suffer from an overly
intellectual love of high-level distinctions and theory for its own sake. In
business context, how to understand both productive and unproductive
complexity at a more practical way, and how to leverage it in emergent digital
enterprise:
- Descriptive complexity or Kolmogorov–Chaitin complexity, algorithmic entropy, or program-size complexity of an object, such as a piece of text, is a measure of the computational resources needed to specify the object
- Nonlinearity Complexity: Linear thinking & view is normality in the industrial era while segregation and division are a major theme then, even now, however, digitalization means accelerated changes.
- Emerging Complexity: It’s not a simple matter of accounting for rates of change, but of understanding how interactions between entities, it's about hyper-connection and hyper-complexity. It's digital normal in which EA should really master
- Productive complexity such as design complexity or highly productive complexity -Employees interacting as they create value from intangible knowledge-based assets, invisible but powerful learning agile culture, cross-silo business collaboration, or macro-systematic complexity such as regulations
- There is an unproductive complexity of bureaucracy: Silo walls between functions, and confusing matrix designs, resistance to change, workforce constraints, slow decision-making, complex administrative processes and competing incentives; growing misalignment between the needs of the organization and the processes supporting it.. etc.
Complexity theory is not a practice in any managerial sense,
but a theoretical framework which helps to break out of the view of the world
as a plan-able, logic system. Mathematical applications of the theory in
modeling financial and physical systems belong to the domain of science and
makes few objective contributions to the more interesting fields of complexity
in politics, social systems, business engagement, among others. One
application has been taken over by Fredmund Malik, one of the great systems
theorists with his Syntegration process.
It is elegant, theoretically beautiful, but incredibly complex as a facilitation
process.
2. How EA Leverage Complexity
The problem is businesses have taken complexity as the new
norm instead of understanding that most of the complexity being created and in
reality it is really very simple. People over time have created complexity by
dividing functions and now we need to get them back within the whole.
- Understanding (and leveraging on)
complexity. This is why it is very important to understand what the "Value" of an Enterprise Architecture can do for the organization. But the next question for the EA is what the boundaries are for the enterprise. EA needs to help business understand many important aspects of the business which are non-linear and that’s hard for many managers to accept, especially with respect to accelerating returns...
- Communication, Process (Methods), Ontology (Taxonomy, Meta-models), and Governance, are plausible ways to understand complexity. Communication is one of the means to leverage complexity, while management processes and governance are disciplines to manage complexity.
- Modeling is the principal research tool for complex systems. Complexity science furnishes us with the concepts and tools for building multi-level representations of the world and for making sense of the dynamics of emergence. The dynamics of emergence is predicated on microdiversity, and fine-grained representations are essentially descriptive models of the detailed complexity of the business/world and its dynamics. Thus it is through exploratory modeling that we discover how the complex business/world works, and how macro-level properties and behaviors of systems emerge from micro-level diversity and dynamics.
- Agile as Practice in Managing Complexity: Agile could be seen as the social practice of complexity theory. Agile is focused on the practice of complex human responses to complexity in social environments. Effective Agile practices take solid enterprise architecture / framework / standards/process / methodology / configuration management et
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