Archive for the ‘Enterprise governance’ Category

EA’s Merry-go-round

June 14, 2017

Preamble

All too often EA is planned as a big bang project to be carried out step by step until completion. That understanding is misguided as it confuses EA with IT systems and implies that enterprises could change their architectures as if they were apparel.

EA is a never-ending endeavor (Robert Doisneau)

But enterprise architecture is part and parcel of enterprises, a combination of culture, organization, and systems; whatever the changes, they must keep the continuity, integrity, and consistency of the whole.

Work Units

Compared to usual projects, architectural ones are not meant to address business needs but architecture capabilities. Taking a leaf from the Zachman Framework, those capabilities can be organized around five pillars supporting enterprise, systems, and platform architectures:

  • Who: enterprise roles, system users, platform entry points.
  • What: business objects, symbolic representations, objects implementation.
  • How: business logic, system applications, software components.
  • When: processes synchronization, communication architecture, communication mechanisms.
  • Where: business sites, systems locations, platform resources.

These capabilities are used by business, engineering, and operational processes.

Enterprise architecture capabilities

Defining architectural projects and work units bottom-up from capabilities will bring clear benefits.

Contrary to top-down (aka activity based) ones, bottom-up schemes don’t rely on one-fits-all procedures; as a consequence work units can be directly defined by capabilities and therefore mapped to engineering workshops:

Iterative development of architecture capabilities across workshops

Moreover, dependency constraints can be directly defined as declarative assertions attached to capabilities and managed dynamically instead of having to be hard-wired into phased processes.

That approach is to ensure two agile conditions critical for the development of architectural features:

  • Shared ownership: lest the whole enterprise be paralyzed by decision-making procedures, work units must be carried out under the sole responsibility of project teams.
  • Continuous delivery: architecture driven developments are by nature transverse but the delivery of building blocs cannot be put off by the decision of all parties concerned; instead it should be decoupled from integration.

Enterprise architecture projects could then be organized as a merry-go-round of capabilities based work units to be added, developed, and delivered according to needs and time-frames.

Work units & Time Frames

Enterprise architecture is about governance more than engineering. As such it has to ensure continuity and consistency between business objectives and strategies on one side, engineering resources and projects on the other side.

Assuming that capability-based work units will do the job for internal dependencies (application contents and engineering), the problem is to deal with external ones (business objectives and enterprise organization) without introducing phased processes. Beyond differences in monikers, such dependencies can generally be classified along three reasoned categories:

  • Operational: whatever can be observed and acted upon within a given envelope of assets and capabilities.
  • Tactical: whatever can be observed and acted upon by adjusting assets, resources and organization without altering the business plans and anticipations.
  • Strategic: decisions regarding assets, resources and organization contingent on anticipations regarding business environments.

Architectural projects could then be managed as a dynamic backlog of self-contained work units continuously added (a) or delivered (b).

EA projects: a merry-go-round of work units.

The role of enterprise architects will then to manage the deployment of updated architecture capabilities according to their respective time-frames.

Further Reading

Beans must be Counted, one way And the other

May 2, 2017

Preamble

Conversations across software engineering forums sometimes reveal unexpected views, as it’s the case for the benefits of accountability.

Counting Paper Beans (Pieter Brueghel the Younger)

One would assume that competition would impel enterprises to scrutiny with regard to resources employed and product outcomes, pushing for the assessment of internal activities based on some agreed metrics. And yet, now and again, software development is viewed as a boutique occupation, if not an art pursuit, carried out by creative craftsmen for enlightened if demanding patrons; a vocation too distinctive to be gauged by common yardsticks.

Difficulties of Oversight

Setting apart creative delusions, the assessment of software development is effectively confronted with rational as well as practical obstacles.

To begin with rationality, and unlike traditional products, there is no market pricing mechanism that could match software development costs with customers’ value. As a consequence business stakeholders and systems engineers prefer to play safe and keep their respective assessments on the opposed banks of the customer/provider divide.

As for the practicality of assessments, the choice is between idiosyncratic approaches (e.g users’ points) and reasoned ones (essentially function points). The former ones being by nature specific and subject to changes in business opportunities, whereas the latter ones are being plagued by implementation plights that make them both costly and unreliable.

Yet, the diluting of IT systems in business environments is making that conundrum irrelevant: the fusing of business processes and supporting software is blanketing the discontinuities between business value and development costs.

Perils of Oversight

Given the digital integration between systems and business environments and the part played by software in production, marketing and operations, enterprises can no longer ignore the economics of software development.

As far as enterprises are concerned, economics use prices for two key purposes, external and internal.

With regard to their business environment, enterprises need metrics to price the resources they could buy and the products they could sell; their competitive edge fully depends on the thoroughness and accuracy of both.

With regard to their internal governance, enterprises need metrics to gauge the efficiency of their factors and the maturity of their processes, and allocate resources accordingly. That internal assessment is the basis of their versatility and plasticity:

  • Confronted to continuous, frequent, and often abrupt changes in business environments, enterprise must be able to adapt their activities without having to change its architectures. That cannot be achieved without timely and accurate assessments of the way their resources are put to use.
  • Conversely, enterprises may have to change their architectures without affecting their performances; that cannot be achieved without a comprehensive and accurate assessments of alternative options, organizational as well as technical.

To summarize, the spread and intricacy of software footprint over both sides of the crumbling fences between enterprise systems and business environments makes software economics a necessary component of enterprises governance, so a tally of software beans should not be an option.

Further Reading

Squaring EA Governance

April 18, 2017

Preamble

Enterprise governance has to face combined changes in the way business times and spaces are to be taken into account. On one hand social networks put well-thought-out market segments and well planned campaigns at the mercy of consumers’ weekly whims. On the other hand traditional fences between environments and IT systems are crumbling under combined markets and technological waves.

Jasenka Tucan-Vaillant

Squaring Governance in Space and Time (Jasenka Tucan-Vaillant)

So, despite (or because of) the exponential ability of intelligent systems to learn from circumstances, enterprise governance is not to cope with such dynamic complexities without a reliable compass set with regard to key primary factors: time-frames of concerns; control of processes; administration of artifacts.

Concerns & Time-frames

Confronted to massive and continuous waves of stochastic data flows, the priority is to position external events and decision-making with regard to business and assets time-frames:

  • Business value is to be driven by market opportunities which cannot be coerced into predefined fixed time-frames.
  • Assets management is governed by continuity and consistency constraints on enterprise identity, objectives, and investments along time.

Governance Square and its four corners

Enterprises, once understood as standalone entities, must now be redefined as living organisms in continuous adaptation with their environment. Governance schemes must therefore be broadened to business environments and layered as to take into account the duality of time-frames: operational for business value, strategic for assets.

Control of processes and administration of artifacts can then be defined accordingly.

Time & Control: Processes

Architectures being by nature shared and persistent, their layers are meant to reflect different time-frames, from operational cycles to long-term assets:

  • At enterprise level the role of architectures is to integrate shared assets and align various objectives set along different time-frames. At this level it’s safe to assume some cross dependencies between processes, which would call for phased governance.
  • By contrast, business units are meant to be defined as self-governing entities pursuing specific objectives within their own time-frame. From a competitive perspective markets opportunities and competitors moves are best assumed unpredictable, and processes best governed by circumstances.

Enterprise Processes have to align business and engineering objectives

Processes can then be defined vertically (business or Systems) as well as horizontally (enterprise architecture or application development), and governance set accordingly:

  • At enterprise level processes are phased: stakeholders and architects plan and manage the development and deployment of assets (organization and systems).
  • At business units level processes are lean and just-in-time: business analysts and software engineers design and develop applications supporting users needs as defined by users stories or use cases.

Models are then to be introduced to describe shared assets (organization and systems) across the enterprise. They may also support business analysis and software engineering.

Spaces & Administration: Models and Artifacts

Whatever the targets and terminologies, architecture is best defined as a relationship between concrete territories (processes and systems) and abstract maps (blueprints or models).

Carrying on with the four corners of governance square:

  • Business analysts are to set users’ narratives (concrete) in line with the business plots (blueprints) set by stakeholders.
  • Software engineers designing applications (concrete) in line with systems functional architectures (blueprints).

Enterprise Architecture uses maps to manage territories

As for the overlapping of business and development time-frames, the direct mapping between concrete business and system corners (e.g though agile development) is to facilitate the governance of integrated actual and numeric flows across business and systems.

Conclusion: A Compass for Enterprise Architects

Behind turfs perimeters and jobs descriptions, roles and responsibilities involved in enterprise architecture can be summarized by four drives:

  • Business stakeholders (top left): adjust organization as to maximize the versatility and plasticity of architectures.
  • Business analysts (bottom left): define business processes with regard to broader objectives and engineering efficiency.
  • Software engineers (bottom right): maximize the value for users and the quality of applications.
  • Systems architects (top right): dynamically align systems with regard to business models and engineering processes.

Orientation should come before job descriptions

Whereas roles and responsibilities will generally differ depending on enterprise environment, business, and culture, such a compass would ensure that the governance of enterprise architectures hinges on reliable pillars and is driven by clear principles.

Further Reading

Deep Blind Testing

March 21, 2017

Preamble

Tests are meant to ensure that nothing will go amiss. Assuming that expected hazards can be duly dealt with beforehand, the challenge is to guard against unexpected ones.

Unexpected Outcome (Ariel Schlesinger)

That would require the scripting of every possible outcomes in an unlimited range of unknown circumstances, and that’s where Deep Learning may help.

What to Look For

As Donald Rumsfeld once famously said, there are things that we know we don’t know, and things we don’t know we don’t know; hence the need of setting things apart depending on what can be known and how, and build the scripts accordingly:

  • Business requirements: tests can be designed with respect to explicit specifications; yet some room should also be left for changes in business circumstances.
  • Functional requirements: assuming business requirements are satisfied, the part played by supporting systems can be comprehensively tested with respect to well-defined boundaries and operations.
  • Quality of service: assuming business and functional requirements are satisfied, tests will have to check how human interfaces and resources are to cope with users behaviors and expectations which, by nature, cannot be fully anticipated.
  • Technical requirements: assuming business and functional requirements are satisfied as well as users’ expectations for service, deployment, maintenance, and operations are to be tested with regard to feasibility and costs.

Automated testing has to take into account these differences between scope and nature, from bounded and defined specifications to boundless, fuzzy and changing circumstances.

Automated Software Testing

Automated software testing encompasses two basic components: first the design of test cases (events, operations, and circumstances), then their scripted execution. Leading frameworks already integrate most of the latter together with the parts of the former targeting technical aspects like graphical user interfaces or system APIs. Artificial intelligence (AI) and machine learning (ML) have also been tried for automated test generation, yet with a scope limited by dependency on explicit knowledge, and consequently by the need of some “manual” teaching. That hurdle may be overcame by the deep learning ability to get direct (aka automated) access to implicit knowledge.

Reconnaissance: Known Knowns

Systems are designed artifacts, with the corollary that their components are fully defined and their behavior predictable. The design of technical test cases can therefore be derived from what is known of software and systems architectures, the former for test units, the latter for integration and acceptance tests. Deep learning could then mine recorded log-files in order to identify critical cases’ events and circumstances.

Exploration: Known Unknowns

Assuming that applications must be tested for use during their expected shelf life, some uncertainty has to be factored in for future business circumstances. Yet, assuming applications are designed to meet specific business objectives, such hypothetical circumstances should remain within known boundaries. In that context deep learning could be applied to exploration as well as policies:

  • Compared to technical test cases that can rely on the content of systems log-files, business and functional ones have to look outside and mine raw data from business environments.
  • In return, the relevancy of observations can be assessed with regard to business objectives, improved, and feed the policy module in charge of defining test cases.

Blind Errands: Unknown Unknowns

Even with functional and technical capabilities well-tested and secured, quality of service may remain contingent on human quirks: instinctive or erratic behaviors that could thwart the best designed handrails. On one hand, and due to their very nature, such hazards are not to be easily forestalled by reasoned test cases; but on the other hand they don’t take place in a void but within known functional circumstances. Given that porosity of functional and cognitive layers, the validity of functional test cases may be compromised by unfathomable cognitive associations, and that could open the door to unmanageable regression. Enter deep learning and its ability to extract knowledge from insignificance.

Compared to business and functional test cases, hazards are not directly related to business activities. As a consequence, the learning process cannot be guided by business and functional test cases but has to chart unpredictable human behaviors. As it happens, that kind of learning combining random simulation with automated reinforcement is what makes the specificity of deep learning.

From Non-regression to Self-improvement

As a conclusion, if non-regression is to be the cornerstone of quality management, test cases are to be set along clear swim-lanes: business logic (independently of systems), supporting systems functionalities (for shared applications), users interfaces (for non shared interactions). Then, since test cases are also run across swim-lanes, it opens the door to feedback, e.g unit test cases reassessed directly from business rules independently of systems functionalities, or functional test cases reassessed from users’ behaviors.

Considering that well-defined objectives, sound feedback mechanisms, and the availability of massive data from systems logs (internal) and business environment (external) are the main pillars of deep learning technologies, their combination in integrated frameworks could result in a qualitative leap toward self-improving automated test cases.

Further Reading

 

Focus: Business Cases for Use Cases

February 27, 2017

Preamble

As originally defined by Ivar Jacobson, uses cases (UCs) are focused on the interactions between users and systems. The question is how to associate UC requirements, by nature local, concrete, and changing, with broader business objectives set along different time-frames.

Sigmar-Polke-Hope-Clouds

Cases, Kites, and Clouds (Sigmar Polke)

Backing Use Cases

On the system side UCs can be neatly traced through the other UML diagrams for classes, activities, sequence, and states. The task is more challenging on the business side due to the diversity of concerns to be defined with other languages like Business Process Modeling Notation (BPMN).

Use cases at the hub of UML diagrams

Use Cases contexts

Broadly speaking, tracing use cases to their business environments have been undertaken with two approaches:

  • Differentiated use cases, as epitomized by Alister Cockburn’s seminal book (Readings).
  • Business use cases, to be introduced beside standard (often renamed as “system”) use cases.

As it appears, whereas Cockburn stays with UCs as defined by Jacobson but refines them to deal specifically with generalization, scaling, and extension, the second approach introduces a somewhat ill-defined concept without setting apart the different concerns.

Differentiated Use Cases

Being neatly defined by purposes (aka goals), Cockburn’s levels provide a good starting point:

  • Users: sea level (blue).
  • Summary: sky, cloud and kite (white).
  • Functions: underwater, fish and clam (indigo).

As such they can be associated with specific concerns:

Cockburn’s differentiated use cases

  • Blue level UCs are concrete; that’s where interactions are identified with regard to actual agents, place, and time.
  • White level UCs are abstract and cannot be instanciated; cloud ones are shared across business processes, kite ones are specific.
  • Indigo level UCs are concrete but not necessarily the primary source of instanciation; fish ones may or may not be associated with business functions supported by systems (grey), e.g services , clam ones are supposed to be directly implemented by system operations.

As illustrated by the example below, use cases set at enterprise or business unit level can also be concrete:

Example with actors for users and legacy systems (bold arrows for primary interactions)

UC abstraction connectors can then be used to define higher business objectives.

Business “Use” Cases

Compared to Cockburn’s efficient (no new concept) and clear (qualitative distinctions) scheme, the business use case alternative adds to the complexity with a fuzzy new concept based on quantitative distinctions like abstraction levels (lower for use cases, higher for business use cases) or granularity (respectively fine- and coarse-grained).

At first sight, using scales instead of concepts may allow a seamless modeling with the same notations and tools; but arguing for unified modeling goes against the introduction of a new concept. More critically, that seamless approach seems to overlook the semantic gap between business and system modeling languages. Instead of three-lane blacktops set along differentiated use cases, the alignment of business and system concerns is meant to be achieved through a medley of stereotypes, templates, and profiles supporting the transformation of BPMN models into UML ones.

But as far as business use cases are concerned, transformation schemes would come with serious drawbacks because the objective would not be to generate use cases from their business parent but to dynamically maintain and align business and users concerns. That brings back the question of the purpose of business use cases:

  • Are BUCs targeting business logic ? that would be redundant because mapping business rules with applications can already be achieved through UML or BPMN diagrams.
  • Are BUCs targeting business objectives ? but without a conceptual definition of “high levels” BUCs are to remain nondescript practices. As for the “lower levels” of business objectives, users’ stories already offer a better defined and accepted solution.

If that makes the concept of BUC irrelevant as well as confusing, the underlying issue of anchoring UCs to broader business objectives still remains.

Conclusion: Business Case for Use Cases

With the purposes clearly identified, the debate about BUC appears as a diversion: the key issue is to set apart stable long-term business objectives from short-term opportunistic users’ stories or use cases. So, instead of blurring the semantics of interactions by adding a business qualifier to the concept of use case, “business cases” would be better documented with the standard UC constructs for abstraction. Taking Cockburn’s example:

Abstract use cases: no actor (19), no trigger (20), no execution (21)

Different levels of abstraction can be combined, e.g:

  • Business rules at enterprise level: “Handle Claim” (19) is focused on claims independently of actual use cases.
  • Interactions at process level: “Handle Claim” (21) is focused on interactions with Customer independently of claims’ details.

Broader enterprise and business considerations can then be documented depending on scope.

Further Reading

External Links

New Year: 2016 is the One to Learn

December 15, 2016

Sometimes the future is best seen through rear-view mirrors; given the advances of artificial intelligence (AI) in 2016, hindsight may help for the year to come.

(J.Bosh)

Deep Mind Learning (J.Bosh)

Deep Learning & the Depths of Intelligence

Deep learning may not have been discovered in 2016 but Google’s AlphaGo has arguably brought a new dimension to artificial intelligence, something to be compared to unearthing the spherical Earth.

As should be expected for machines capabilities, artificial intelligence has for long been fettered by technological handcuffs; so much so that expert systems were initially confined to a flat earth of knowledge to be explored through cumbersome sets of explicit rules. But exponential increase in computing power has allowed neural networks to take a bottom-up perspective, mining for implicit knowledge hidden in large amount of raw data.

Like digging tunnels from both extremities, it took some time to bring together top-down and bottom-up schemes, namely explicit (rule-based) and implicit (neural network-based) knowledge processing. But now that it comes to fruition, the alignment of perspectives puts a new light on the cognitive and social dimensions of intelligence.

Intelligence as a Cognitive Capability

Assuming that intelligence is best defined as the ability to solve problems, the first criterion to consider is the type of input (aka knowledge) to be used:

  • Explicit: rational processing of symbolic representations of contexts, concerns, objectives, and policies.
  • Implicit: intuitive processing of factual (non symbolic) observations of objects and phenomena.

That distinction is broadly consistent with the one between humans, seen as the sole symbolic species with the ability to reason about explicit knowledge, and other animal species which, despite being limited to the processing of implicit knowledge, may be far better at it than humans. Along that understanding, it would be safe to assume that systems with enough computing power will sooner or later be able to better the best of animal species, in particular in the case of imperfect inputs.

Intelligence as a Social Capability

Alongside the type of inputs, the second criterion to be considered is obviously the type of output (aka solution). And since classifications are meant to be built on purpose, a typology of AI outcomes should focus on relationships between agents, humans or otherwise:

  • Self-contained: problem-solving situations without opponent.
  • Competitive: zero-sum conflictual activities involving one or more intelligent opponents.
  • Collaborative: non-zero-sum activities involving one or more intelligent agents.

That classification coincides with two basic divides regarding communication and social behaviors:

  1. To begin with, human behavior is critically different when interacting with living species (humans or animals) and machines (dumb or smart). In that case the primary factor governing intelligence is the presence, real or supposed, of beings with intentions.
  2. Then, and only then, communication may take different forms depending on languages. In that case the primary factor governing intelligence is the ability to share symbolic representations.

A taxonomy of intelligence with regard to cognitive (reason vs intuition) and social (symbolic vs non-symbolic) capabilities may help to clarify the role of AI and the importance of deep learning.

Between Intuition and Reason

Google’s AlphaGo astonishing performances have been rightly explained by a qualitative breakthrough in learning capabilities, itself enabled by the two quantitative factors of big data and computing power. But beyond that success, DeepMind (AlphaGo’s maker) may have pioneered a new approach to intelligence by harnessing both symbolic and non symbolic knowledge to the benefit of a renewed rationality.

Perhaps surprisingly, intelligence (a capability) and reason (a tool) may turn into uneasy bedfellows when the former is meant to include intuition while the latter is identified with logic. As it happens, merging intuitive and reasoned knowledge can be seen as the nexus of AlphaGo decisive breakthrough, as it replaces abrasive interfaces with smart full-duplex neural networks.

Intelligent devices can now process knowledge seamlessly back and forth, left and right: borne by DeepMind’s smooth cognitive cogwheels, learning from factual observations can suggest or reinforce the symbolic representation of emerging structures and behaviors, and in return symbolic representations can be used to guide big data mining.

From consumers behaviors to social networks to business marketing to supporting systems, the benefits of bridging the gap between observed phenomena and explicit causalities appear to be boundless.

Further Reading

External Links

iStar and the Requirements Conundrum

December 12, 2016

Synopsis

Whenever software engineering problems are looked at, the blame is generally put on requirements, with each side of the business/system divide holding the other responsible.

rockwell_runaway

iStar modeling put the focus on communication (N. Rockwell)

The iStar approach tries to tackle the problem with a conceptual language focused on interactions between business processes and supporting systems.

Dilemma

Conceptual approaches to requirements try to breach the dilemma between phased and agile development schemes: the former takes for granted that requirements can be fully and definitively set upfront; the latter takes a more pragmatic path and tries to reconcile business and system analysts through direct and continuous collaboration.

Setting apart frictions between specific methods, the benefits of agile principles and practices are now well-recognized, contingent on the limits of agile scope. Summarily, agile development is at its best when requirements capture and analysis can be weaved with development and tests. The question remains of what happens when requirements are to be dealt with separately.

The iStar’s answer shares with agile a focus on collaboration and doesn’t take side for business (e.g users’ stories) or systems (e.g use cases). Instead, iStar modeling language is meant to support a conceptual description of interactions between business processes and supporting systems in terms of actors’ goals and commitments, and the associated dependencies.

Actors & Goals

The defining aspect of the iStar modeling approach is to replace one-sided perspectives (business or system) by a systemic one focused on the interactions between agents. The interactive part of a requirement will therefore comprise three basic items:

  • A primary actor trigger an interaction in order to meet some goal; e.g a car owner want his car repaired.
  • Secondary actors may be involved during the ensuing exchanges: e.g body shop, appraiser, insurance company.
  • Functions to be performed: actual task; e.g appraise damages; qualification (soft goal), e.g fair appraisal; and resources, e.g premium payment.
Actors & dependencies

Actors & Dependencies

Dependencies Semantics

The factual description of interactions is both detailed and enriched by elements set within a broader scope:

  • Goal (strong) dependency: assertions about actual state of affairs: object, activity, or expectations.
  • Soft-goal dependency: assertions about expected outcomes.
  • Task dependency: organizational, functional, or technical constraints pertaining to the execution of activities.
  • Resource dependency: constraints or conditions on the availability of inputs, actual or symbolic.

It would be tempting to generalize the strong/soft distinction to dependencies as to make use of modal logic, strong dependencies associated with deontic rules, soft dependencies with alethic ones. That would .

iStar & Caminao

Since iStar modeling categories are directly aligned with UML Use Cases, they can easily mapped to core Caminao stereotypes for actors, objects, events, and activities.

Actors & dependencies

iStar with Caminao Stereotypes

Interestingly, the iStar strong/soft distinction could translate to the actual/symbolic one which constitute the conceptual backbone of the Caminao paradigm.

Assessment

From the business perspective, iStar must be credited with two critical tenets:

  • The focus on interactions between agents is essential for business and system analysts to collaborate. Such benefits appear clearly for the definition of primary and secondary roles (aka actors), intents (business) and capabilities (supporting environments).
  • The distinction between strong and soft goals, even if the logical basis remains unexploited.

Yet, the system perspective lacks a functional dimension, e.g:

  • Architecture levels (enterprise and organization, systems and functionalities, platforms and technologies) are not taken into consideration, nor the nature of capabilities, e.g strategic and operational.
  • The strong/soft dependencies distinction is not explicitly associated with systems capabilities.

On the whole these pros and cons reflect iStar’s declared intent on conceptual modeling; as a corollary these flaws mark also the limits of conceptual modeling when it is detached from the symbolic description of supporting systems functionalities.

Nonetheless, as illustrated by the research quoted below, iStar remains a sound basis for the specification of interactions between users and systems, either as use cases or users’ stories.

Further Reading

External Links

Business Agility vs Systems Entropy

November 28, 2016

Synopsis

As already noted, the seamless integration of business processes and IT systems may bring new relevancy to the OOAD (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: OOAD & 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.

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

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.
cccc

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).

Further Readings

Business Agility & the OODA Loop

November 21, 2016

Preamble

The OOAD (Observation, Orientation, Decision, Action) loop is a real-time decision-making paradigm developed in the sixties by Colonel John Boyd from his experience as fighter pilot and military strategist.

(Moholy Nagy)

How to get inside opponent’s loop (Lazlo Moholy-Nagy)

The relevancy of OODA for today’s operational decision-making comes from the seamless integration of IT systems with business operations and the resulting merits of agile development processes.

Business: End of Discrete Time-Frames

Business governance was used to be phased: analyze the market, select opportunities, build capabilities, launch operations. No more. With the melting of the fences between actual and symbolic realms, periodic transitional events have lost most of their relevancy. Deprived of discrete and robust time-frames, the weaving of observed facts with business plans has to be managed on the fly. Success now comes from continuous readiness, quicker tempo, and the ability to operate inside adversaries’ time-scales, for defense (force competitor out of favorable position) as well as offense (get a competitive edge). Hence the reference to dogfights.

Dogfights & Agile Primacy

John Boyd train of thoughts started with the observation that, despite the apparent superiority of the soviet Mig 15 on US F-86 during the Korea war, US fighters stood their ground. From that factual observation it took Boyd’s comprehensive engineering work to demonstrate that as far as dogfights were concerned fast transients between maneuvers (aka agility) was more important than technical capabilities. Pushed up Pentagon’s reluctant ladders by Boyd’s sturdy determination, that conclusion have had wide-ranging consequences in the design of USAF fighters and pilots formation for the following generations. Its influence also spread to management, even if theories’ turnover is much faster there, and shelf-life much shorter.

Nowadays, with the accelerated integration of business processes with IT systems, agility is making a comeback from the software engineering corner. Reflecting business and IT convergence, principles like iterative development, just-in-time delivery, and lean processes, all epitomized by the agile software development model, are progressively mingling into business practices with strong resemblances to dogfights; and the resemblances are not only symbolic.

IT Systems & Business Competition

While some similarities between dogfights and business competition may seem metaphorical, one critical aspect is all too real, namely the increasing importance of supporting machines, IT systems or fighter jets.

Basically, IT systems, like fighters’ electronics, are tasked to observe environments, analyse changes in relation to position and objectives, and support decision-making. But today’s systems go further with two qualitative leaps:

  • The seamless integration of physical and symbolic flows let systems manage some overlapping between supporting decisions and carrying out actions.
  • Due to their artificial intelligence capabilities, systems can learn on-the-job and improve their performances in real-time feedback loops.

When combined, these two trends have drastic impact on the way machines can support human activities in real-time competitive situations. More to the point, they bring new light on business agility.

Business Agility

As illustrated by the radical transformation of fighter cockpits, the merging of analog and digital flows leaves little room for human mediation: data must be processed into information and presented instantly along two critical dimensions, one for decision-making, the other for information life-cycle:

  • Man/Machine interfaces have to materialize the merging of actual and symbolic realms as to support just-in-time decision-making.
  • The replacement of phased selected updates of environment data by continuous changes in raw and massive data means that the status of information has to be incorporated with the information itself, yet without impairing decision-making.

Beyond obvious differences between dogfights and business competition, that double exigence is to characterize business agility:

  1. Instant understanding of changes in business opportunities (Observation) .
  2. Simultaneous assessment of the reliability and shelf-life of pertaining information with regard to current positions and operations (Orientation).
  3. Weighting of options with regard to enterprise capabilities and broader objectives (Decision).
  4. Carrying out of decisions within the relevant time-span (Action).

That understanding of business agility is to be compared with its development and architecture cousins. Yet it doesn’t seem to add much to data analytics and operational decision-making. That is until the concept of orientation is reassessed.

Agility & Orientation: Task vs Tack

To begin with basics, the concept of Orientation comes with a twofold meaning, actual and symbolic:

  • Actual: a position with regard to external (e.g spacial) coordinates, possibly qualified with abilities to observe, move, or act.
  • Symbolic: a position with regard to internal (e.g beliefs or aims) references, possibly mixed with known or presumed orientation of other agents, opponents or associates.

When business is considered, data analytics is supposed to deal comprehensively and accurately with markets’ actual orientations. But the symbolic facet is left largely unexplored.

Boyd’s contribution is to bring together both aspects and combine them into actual practice, namely how to foretell the tack of your opponents from their actual tracks as well as their surmised plans, while fooling them about your own moves, actual or planned.

Such ambitions once out of reach, can now be fulfilled due to the combination of big data, artificial intelligence, and the exponential growth on computing power.

Further Readings

 

Business Problems shouldn’t sleep with IT Solutions

October 8, 2016

Preamble

The often mentioned distinction between problem and solution levels may make sense from an analyst’s particular point of view, whether business or system.  But blending problems and solutions independently of their nature becomes a serious over simplification for enterprise architects considering that one of their prime responsibility is to keep apart business problems from IT solutions.

(Mircea Cantor)

Functional problem with technical solution (Mircea Cantor)

That issue is relevant from engineering as well as business perspective.

Engineering View: Problem Levels & Architecture Layers

As long as computers are used to solve problems the only concern is to find the best solution, and the only architecture of concern is software’s.

But enterprise architects have to deal with systems, not computers, namely how to best serve business objectives with corporate resources, across business units and along business cycles. For that purpose resources (financial, human, technical) and their use are to be layered according to the nature of problems and solutions: business processes (enterprise), supporting functionalities (systems), and technologies (platforms).

From an engineering perspective, the intended congruence between problems levels and architecture layers can be illustrated with the OMG’s model driven architecture (MDA) framework:

  • Computation independent models (CIMs) deal with business processes solutions, to be translated into functional problems for supporting systems.
  • Platform independent models (PIMs) deal with functional solutions, to be translated into technical problems for supporting platforms.
  • Platform specific models (PSMs) deal with technical solutions, to be implemented as code.
MDA layers correspond to a clear hierarchy of problems and solutions

MDA layers can be mapped to a clear hierarchy of problems and solutions

Along that understanding, architectures can be seen as solutions, and the primary responsibility of enterprise architects is to see that problems/solutions brace remain in their respective swim-lanes.

Business View: Business Value & Enterprise Assets

Whereas the engineering perspective may appear technical or specific to a model based approach, the same issue is all the more significant when expressed with regard to business concerns and corporate governance. In that case the critical distinction is between business value and assets:

  • Business value: Problems are set by business opportunities, and solutions by processes and applications. The critical factor is reactivity and time-to-market.
  • Assets: Problems are set by business objectives and strategy, and solutions are to be supported by organization and systems capabilities. The critical factor is reuse and ROI.
Decision-making must distinguish between business opportunities and enterprise governance

Decision-making must distinguish between business opportunities and enterprise governance

If opportunities are to be seized and operations managed on the fly  yet tally with strategic decisions, respective problems and solutions should be kept apart. Juggling with their dynamic alignment is at the core of enterprise architects’ job description.

Enterprise Architects & Governance

Engineering and business perspectives are not to be seen as the terms of an alternative to be picked by enterprise architects. As a matter of fact they must be crossed and governance policies selected depending on the point of view:

  • Looking at EA from an engineering perspective,  the business one will focus on systems governance and assets management as epitomized by model based systems engineering schemes.
  • Looking at EA from a business perspective, the engineering one will focus on lean and just-in-time solutions, as epitomized by agile development models.

As far as governance of large and complex corporate entities, supposedly EA’s primary target, must deal with tactical, operational, and strategic concerns, the nexus between business and engineering perspectives is where enterprise architects are to stand.