Business Agility vs Systems Entropy

Synopsis

As already noted, the seamless integration of business processes and IT systems may bring new relevancy to the OODA (Observation, Orientation, Decision, Action) loop, a real-time decision-making paradigm originally developed by Colonel John Boyd for USAF fighter jets.

Agility: Orientation (Lazlo Moholo-Nagy)
Agility & Orientation (Lazlo Moholo-Nagy)

Of particular interest for today’s business operational decision-making is the orientation step, i.e the actual positioning of actors and the associated cognitive representations; the point being to use AI deep learning capabilities to surmise opponents plans and misdirect their anticipations. That new dimension and its focus on information brings back cybernetics as a tool for enterprise governance.

In the Loop: OODA & Information Processing

Whatever the topic (engineering, business, or architecture), the concept of agility cannot be understood without defining some supporting context. For OODA that would include: territories (markets) for observations (data); maps for orientation (analytics); business objectives for decisions; and supporting systems for action.

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OODA loop and its actual (red) and symbolic (blue) contexts.

One step further, contexts may be readily matched with systems description:

  • Business contexts (territories) for observations.
  • Models of business objects (maps) for orientation.
  • Business logic (objectives) for decisions.
  • Business processes (supporting systems) for action.
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The OODA loop and System Perspectives

That provides a unified description of the different aspects of business agility, from the OODA loop and operations to architectures and engineering.

Architectures & Business Agility

Once the contexts are identified, agility in the OODA loop will depend on architecture consistency, plasticity, and versatility.

Architecture consistency (left) is supposed to be achieved by systems engineering out of the OODA loop:

  • Technical architecture: alignment of actual systems and territories (red) so that actions and observations can be kept congruent.
  • Software architecture: alignment of symbolic maps and objectives (blue) so that orientation and decisions can be continuously adjusted.

Functional architecture (right) is to bridge the gap between technical and software architectures and provides for operational coupling.

Business Agility: systems architectures and business operations
Business Agility: systems architectures and business operations

Operational coupling depends on functional architecture and is carried on within the OODA loop. The challenge is to change tack on-the-fly with minimum frictions between actual and symbolic contexts, i.e:

  • Discrepancies between business objects (maps and orientation) and business contexts (territories and observation).
  • Departure between business logic (objectives and decisions) and business processes (systems and actions)

When positive, operational coupling associates business agility with its architecture counterpart, namely plasticity and versatility; when negative, it suffers from frictions, or what cybernetics calls entropy.

Systems & Entropy

Taking a leaf from thermodynamics, cybernetics defines entropy as a measure of the (supposedly negative) variation in the value of the information supporting the control of viable systems.

With regard to corporate governance and operational decision-making, entropy arises from faults between environments and symbolic surrogates, either for objects (misleading orientations from actual observations) or activities (unforeseen consequences of decisions when carried out as actions).

So long as architectures and operations were set along different time-frames (e.g strategic and tactical), cybernetics were of limited relevancy. But the seamless integration of data analytics, operational decision-making, and IT supporting systems puts a new light on the role of entropy, as illustrated by Boyd’s OODA and its orientation component.

Orientation & Agility

While much has been written about how data analytics and operational decision-making can be neatly and easily fitted in the OODA paradigm, a particular attention is to be paid to orientation.

As noted before, the concept of Orientation comes with a twofold meaning, actual and symbolic:

  • Actual: the positioning of an agent with regard to external (e.g spacial) coordinates, possibly qualified with the agent’s abilities to observe, move, or act.
  • Symbolic: the positioning of an agent with regard to his own internal (e.g beliefs or aims) references, possibly mixed with the known or presumed orientation of other agents, opponents or associates.

That dual understanding underlines the importance of symbolic representations in getting competitive edges, either directly through accurate and up-to-date orientation, or indirectly by inducing opponents’ disorientation.

Agility vs Entropy

Competition in networked digital markets is carried out at enterprise gates, which puts the OODA loop at the nexus of information flows. As a corollary, what is at stake is not limited to immediate business gains but extends to corporate knowledge and enterprise governance; translated into cybernetics parlance, a competitive edge would depend on enterprise ability to export entropy, that is to decrease confusion and disorder inside, and increase it outside.

Working on that assumption, one should first characterize the flows of information to be considered:

  • Territories and observations: identification of business objects and events, collection and analysis of associated data.
  • Maps and orientations: structured and consistent description of business domains.
  • Objectives and decisions: structured and consistent description of business activities and rules.
  • Systems and actions: business processes and capabilities of supporting systems.
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Static assessment of technical and software architectures for respectively observation and decision

Then, a static assessment of information flows would start with the standing of technical and software architecture with regard to competition:

  • Technical architecture: how the alignment of operations and resources facilitate actions and observations.
  • Software architecture: how the combined descriptions of business objects and logic facilitate orientation and decision.

A dynamic assessment would be carried out within the OODA loop and deal with the role of functional architecture in support of operational coupling:

  • How the mapping of territories’ identities and features help observation and orientation.
  • How decision-making and the realization of business objectives are supported by processes’ designs.
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Dynamic assessment of decision-making and the realization of business objectives’ as supported by processes’ designs.

Assuming a corporate cousin of  Maxwell’s demon with deep learning capabilities standing at the gates in its OODA loop, his job would be to analyze the flows and discover ways to decrease internal complexity (i.e enterprise representations) and increase external one (i.e competitors’ representations).

That is to be achieved with the integration of  operational analytics, business intelligence, and decision-making.

OKBI_BIDM
Seamless integration of operational analytics, business intelligence, and decision-making.

Further Readings

EA: Entropy Antidote

Cybernetics & Governance

When seen through cybernetics glasses, enterprises are social entities whose sustainability and capabilities hang on their ability to track changes in their environment and exploit opportunities before their competitors. As a corollary, corporate governance is to be contingent on fast, accurate and purpose-driven reading of  environments on one hand, effective use of assets on the other hand.

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Entropy grows from confusion (Lazlo Moholo-Nagy)

And that will depend on enterprises’ capacity to capture data, process it into information, and translate information into knowledge supporting decision-making. Since that capacity is itself determined by architectures, a changing and competitive environment will require continuous adaptation of enterprises’ organization. That’s when disorder and confusion may increase: lest a robust and flexible organization can absorb and consolidate changes, variety will progressively clog the systems with specific information associated with local adjustments.

Governance & Information

Whatever its type, effective corporate governance depends on timely and accurate information about the actual state of assets and environments. Hence the need to assess such capabilities independently of the type of governance structure that has to be supported, and of any specific business context.

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Effective governance is contingent on the distance between actual state of assets and environment on one hand, relevant information on the other hand.

That put the focus on the processing of information flows supporting the governance of interactions between enterprises and their environment:

  • How to identify the relevant facts and monitor them as accurately and timely as required.
  • How to process external data from environment into information, and to consolidate the outcome with information related to enterprise objectives and internal states.
  • How to put the consolidated information to use as knowledge supporting decision-making.
  • How to monitor processes execution and deal with relevant feedback data.
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What is behind enterprise ability to track changes in environment and exploit opportunities.

Enterprises being complex social constructs, those tasks can only be carried on through structured organization and communication mechanisms  supporting the processing of information flows.

Architectures & Changes

Assuming that enterprise governance relies on accurate and timely information with regard to internal states and external environments, the first step would be to distinguish between the descriptions of actual contexts on one hand, of symbolic representation on the other hand.

Models are used to describe actual or symbolic objects and behaviors
Enterprise architectures can be described along two dimensions: nature (actual or symbolic), and target (objects or activities).

Even for that simplified architecture, assessing variety and information processing capabilities in absolute terms would clearly be a challenge. But assessing variations should be both easier and more directly useful.

Change being by nature relative to time, the first thing is to classified changes with regard to time-frames:

  • Operational changes are occurring, and can be dealt with, within the time-frame of processes execution.
  • Structural changes affect contexts and assets and cannot be dealt with at process level as they.

On that basis the next step will be to examine the tie-ups between actual changes and symbolic representations:

  • From actual to symbolic: how changes in environments are taken into account; how processes execution and state of assets are monitored.
  • From symbolic to actual: how changes in business models and processes design are implemented.
What moves first: actual contexts and processes or enterprise abstractions
What moves first: actual contexts and processes or enterprise abstractions

The effects of  those changes on overall governance capability will depend on their source (internal or external) and modality (planned or not).

Changes & Information Processing

As far as enterprise governance is considered, changes can be classified with regard to their source and modality.

With regard to source:

  • Changes within the enterprise are directly meaningful (data>information), purpose-driven (information>knowledge), and supposedly manageable.
  • Changes in environment are not under control, they may need interpretation (data<?>information), and their consequences or use are to be explored (information<?>knowledge).

With regard to modality:

  • Data associated with planned changes are directly meaningful (data>information) whatever their source (internal or external); internal changes can also be directly associated with purpose (information>knowledge);
  • Data associated with unplanned internal changes can be directly interpreted (data>information) but their consequences have to be analyzed (information<?>knowledge); data associated with unplanned external changes must be interpreted (data<?>information).
Changes can be classified with regard to their source (enterprise or environment) and modality (planned or not).
Changes can be classified with regard to their source (enterprise or environment) and modality (planned or not).

Assuming with Stafford Beer that viable systems must continuously adapt their capabilities to their environment, this taxonomy has direct consequences for their governance:

  • Changes occurring within planned configurations are meant to be dealt with, directly (when stemming from within enterprise), or through enterprise adjustments (when set in its environment).
  • That assumption cannot be made for changes occurring outside planned configurations because the associated data will have to be interpreted and consequences identified prior to any decision.

Enterprise governance will therefore depend on the way those changes are taken into account, and in particular on the capability of enterprise architectures to process the flows of associated data into information, and to use it to deal with variety.

EA & Models

Originally defined by thermodynamic as a measure of heat dissipation, the concept of entropy has been taken over by cybernetics as a measure of  the (supposedly negative) variation in the value of information supporting corporate governance.

As noted above, the key challenge is to manage the relevancy and timely interpretation and use of the data, in particular when new data cannot be mapped into predefined  semantic frame, as may happen with unplanned changes in contexts. How that can be achieved will depend on the processing of data and its consolidation into information as carried on at enterprise level or by business and technical units.

Given that data is captured at the periphery of systems, one may assume that the monitoring of operations performed by business and technical units are not to be significantly affected by architectures. The same assumption can be made for market research meant to be carried on at enterprise level.

Architecture Layers and Information Processing
Architecture Layers and Information Processing

Within that working assumption, the focus is to be put on enterprise architecture capability to “read” environments (from data to information), as well as to “update” itself (putting information to use as knowledge).

With regard to “reading” capabilities the primary factor will be traceability:

  • At technical level traceability between components and applications is required if changes in business operations are to be mapped to IT architecture.
  • At organizational level, the critical factor for governance will be the ability to adapt the functionalities of supporting systems to changes in business processes. And that will be significantly enhanced if both can be mapped to shared functional concepts.

Once the “readings” of external changes are properly interpreted with regard to internal assets and objectives, enterprise governance will have to decide if changes can be dealt with by the current architecture or if it has to be modified. Assuming that change management is an intrinsic part of enterprise governance, “updating” capabilities will rely on a continuous, comprehensive and consistent management of information, best achieved through models, as epitomized by the Model Driven Architecture (MDA) framework.

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Models as bridges between data and knowledge

Based on requirements capture and analysis, respective business, functional, and technical information is consolidated into models:

  • At technical level platform specific models (PSMs) provide for applications and components traceability. They support maintenance and configuration management and can be combined with design patterns to build modular software architecture from reusable components.
  • At organizational level, platform independent models (PIMs) are used to align business processes with systems functionalities. Combined with functional patterns the objective is to use service oriented architectures as a level of indirection between organization and information technology.
  • At enterprise level, computation independent models (CIMs) are meant to bring together corporate tangible and intangible assets. That’s where corporate culture will drive architectural changes  from systems legacy, environment challenges, and planned designs.

EA & Entropy

Faced with continuous changes in their business environment and competition, enterprises have to navigate between rocks of rigidity and whirlpools of variety, the former policies trying to carry on with existing architectures, the latter adding as many variants as seems to appear to business objects, processes, or channels. Meeting environments challenges while warding off growing complexity will depend on the plasticity and versatility of architectures, more precisely on their ability to “digest” the variety of data and transform it into corporate knowledge. Along that perspective enterprise architecture can be seen as a natural antidote to entropy, like a corporate cousin of  Maxwell’s demon, standing at enterprise gates and allowing changes in a way that would decrease internal complexity relative to the external one.

Further Readings

External Links