Compared to its bricks and mortar counterpart, enterprises architecture is a work in progress to be carried out all along enterprises life cycle; hence the need of actionable representations of environments, organization, assets, and processes.
As any artifact, actual or symbolic, models must serve some purposes which for systems architectures can be of two kind, descriptive (e.g analysis) or prescriptive (e.g design). Although that distinction often remains implicit at the systems level, it becomes critical at the enterprises level, when business objectives and organization take center stage. Compared to systems modeling, that would induce two key differences:
Architecture blueprints: enterprise architecture modeling has to be supported by a built-in distinction between business objectives and systems capabilities.
Engineering Processes: the modeling paradigm must ensure the integration of architecture blueprints and architecture changes.
Blueprints (models in systems parlance) can be characterized by a combination of targets and purposes:
Descriptive models cover all relevant aspects of environments and the ways to deal with them.
Prescriptive models aim at managed elements (organization, processes, products, or systems), and how they can be defined, designed, or built.
Predictive models add a virtual dimension to actual descriptive and prescriptive ones.
From a formal point of view these distinctions can be expressed in terms of modal logic:
Descriptive representations are meant to provide serviceable models of the business environment; such models are said to be extensional as their objective is to classify observations of objects and phenomena (or extensions) into categories. Actual descriptive (or analysis) models are used to organize the relevant features of domains; virtual ones (or analytic models) are used to extrapolate from actual observations.
Prescriptive representations go the other way as their purpose is to define presumptive artifacts or activities; they are said to be intensional as they denote sets of features meant to be supported by individuals instead of set of individuals. Actual prescriptive (or design) models deal with artifacts to be built, virtual ones with intended objectives or behaviors .
Digital Immersion & Emerging Architectures
Such a formal understanding of models has practical consequences for enterprise architecture as it provides a principled and integrated governance framework:
Strategic planning: integration of prescriptive representations between organization and systems, and of descriptive ones between expectations (business environment) and observations actual environment).
Systems engineering: integration of portfolio management and projects planning and development combining model based and agile solutions .
Business intelligence: integration of strategic and operational decision-making.
That alignment of systems and knowledge architectures is to be critical for enterprise governance, especially with regard to managing changes in digital environments.
Agility is the ability to react quickly and effectively to changes in environments. Taking cue from people, agility is conditioned by the resilience and plasticity of a backbone and the accuracy and reactivity of sensory motor connections.
On that account, the digital transformation of enterprise architectures calls into question the relevance of a flat information layer, and more generally of the traditional distinction between business and IT systems.
VALUE CHAINS & Digital Flows
To begin with connections, the generalization of digital flows is to affect the meaning of value chains, a concept introduced by Porter in 1985 as a way to chart the sequences of activities contributing to the delivery of a valuable product or service to market.
But digital transformation and the ensuing pervasiveness of software components in business processes blur the boundaries between primary and supporting activities, calling for a redefinition of the relationships between business processes and supporting systems.
In return, the generalization of homogeneous digital flows within and without enterprises could also support the exchange of combined actual and symbolic contents between business environment and operations; hence the benefits of redefining supporting activities in terms of generic capabilities binding systems to enterprises organization:
Who could use the systems: interfaces, security, confidentiality, numbers, latency, synchronization, …
What kind of objects could be managed: storage, volumes, encryption, …
How activities could be supported: representation, and management of business logic.
When processes could be executed: events, control, orchestration, choreography…
Where processes could be executed : locations, assets, communication channels.
The immediate benefit of that shift is to bring transparency to the overlaps between business processes and supporting systems. Concomitantly, it paves the way to a tighter integration of enterprise systems and knowledge architectures.
Information Layer vs Digital Backbone
Using digital flows to anchor business processes to architecture capabilities makes redundant the indiscriminate information layer often introduced between business and applications ones. Instead, a digital backbone can be set across the whole of enterprise architectures, with conceptual, logical, and digital data descriptions aligned respectively with computation independent, platform independent, and platform specific models.
The alignment of architecture layers with differentiated information processing capabilities is to become a critical asset for enterprises immersed in digital environment. To deal with changes and competitors enterprises have to combine long-term objectives relative to their business environment with direct observations from the digital one. That cannot be achieved without a distinctive management of data, information, and knowledge:
At digital level data inputs from environments are to be sorted out as observations or managed information, the former to be fed into analytic models, recorded, or deleted, the latter used to update business surrogates in line with systems models.
At business level knowledge is managed with thesauruses and semantic graphs; it is at the source of business models and objectives as well as organization, and consequently of the architecture of information systems; knowledge is also updated through analytic tools.
Enterprise architects could then manage changes with regard to business intelligence (business models and observed and managed data) and existing systems, with ontologies securing the semantic interoperability of the different representations.
Compared to brick and mortar ones, enterprise architectures come with two critical extensions, one for their ability to change, the other for the intertwine of material and symbolic components.
On that account the standard systems modeling paradigm is to fall short when enterprise changes are to be carried out in digital environments.
Problems & Solutions
Whatever the target (from concrete edifices to abstract polities), models come first in architect’s toolbox. Applied to enterprise architectures, models have to fathom different kinds of elements: physical (hardware), logical (software), human (organization), or conceptual (business).
From that point, what characterizes enterprise architectures is the mingling of physical and symbolic components, and their intrinsic evolutionary nature; problems and solutions have to be refined accordingly.
With regard to time and the ability to change, problems and solutions are defined by specific and changing contexts on one hand, shared and stable capabilities on the other hand. As for symbolic components, a level of indirection should be introduced between enterprise and physical spaces:
At enterprise level problems are defined by business environment and objectives (aka business model), and solved by organization and activities, to be translated into processes.
At system level problems are defined by processes requirements, and solved by objects representation and systems functions.
At platform level problems are defined by functional and operational requirements, and solved by applications design and configurations.
Such layered and crossed spaces are to induce two categories of feedback:
Between problems and solutions spaces, represented respectively by descriptive and prescriptive models.
Between layers and corresponding stakeholders, according to contexts, concerns, and time-frames.
Whereas facilitating that two-pronged approach is to be a primary objective of enterprise architects, the standard modeling paradigm (epitomized by languages like UML or SysML) is floundering up and down: up as it overlooks environments and organizational concerns, down by being overloaded with software concerns.
How to Sort Means (Systems) from Ends (Business)
Extending the system architecture paradigm to enterprise is the cornerstone of enterprise architecture as it provide a principled and integrated governance framework:
Business strategic planning: integration of intensional and extensional representations respectively for organisation and systems and business and physical environments.
System architecture: integration of portfolio management and projects planning and development combining model based and agile solutions .
Business intelligence: integration of strategic operational decision-making.
Bringing representations of environments, organization, and systems under a common conceptual roof is critical because planing and managing changes constitute the alpha and omega of enterprise architecture; and changes in diversified and complex organizations cannot be managed without maps.
The Matter of Change
Compared to systems architectures, change is an intrinsic aspect of enterprises architectures; hence the need for a modeling paradigm to ensure a seamless integration of blueprints and evolutionary processes.
Taking example from urbanism, the objective would be to characterize the changes with regard to scope and dependencies across maps and territories. On that account, the primary distinction should be between changes confined to either territories or maps, and changes affecting both.
Confined changes are meant to occur under the architectural floor, i.e without affecting the mapping of territories:
Territories: local changes at enterprise (e.g organisation) or systems (e.g operations) levels not requiring updates of architecture models.
Maps: local changes of domains or activities not affecting enterprise or systems elements at architecture level (e.g new features or business rules).
Conversely, changes above architectural floor whether originated in territories or maps are meant to modify the mapping relationship:
Changes in business domains (maps) induced by changes in enterprise environments (e.g regulations).
Changes in operations (systems) induced by changes in activities (e.g new channels).
That double helix of organizational, physical, and software components on one hand, models and symbolic artifacts on the other hand, is the key to agile architectures and digital transformation.
Ingrained habits die hard, especially mental ones as they are not weighted down by a mortal envelope. Fear is arguably a primary factor of persistence, if only because being able to repeat something proves that nothing bad has happened before.
Procedures epitomize that human leaning as ordered sequences of predefined activities give confidence in proportion to generality. Compounding the deterministic delusion, procedures seem to suspend time, arguably a primary factor of human anxiety.
Procedures are Dead-ends
From hourglasses to T.S. Elliot’s handful, sand materializes human double bind with time, between will of measurement and fear of ephemerality.
Procedures seem to provide a way out of the dilemma by replacing time with prefabricated frames designed to ensure that things can only happen when required. But with extensive and ubiquitous digital technologies dissolving traditional boundaries, enterprises become directly exposed to competitive environments in continuous mutation; that makes deterministic schemes out of kilter:
There is no reason to assume the permanence of initial time-frames for the duration of planned procedures.
The blending of organizations with supporting systems means that architectural changes cannot be carried out top-down lest the whole be paralyzed by the management overheads induced by cross expectations and commitments.
Unfettered digital exchanges between enterprises and their environment, combined with ubiquitous smart bots in business processes, are to require a fine grained management of changes across artefacts.
These shifts call for a complete upturn of paradigm: event driven instead of scheduled, bottom-up instead of top-down, model based instead of activity driven.
Declarative frameworks: Non Deterministic, Model Based, Agile
The procedural/declarative distinction has its origin in the imperative/declarative programming one, the principle being to specify necessary and sufficient conditions instead of defining the sequence of operations, letting programs pick the best options depending on circumstances.
Applying the principle to enterprise architecture can help to get out of a basic conundrum, namely how to manage changes across supporting systems without putting a halt to enterprise activities.
Obviously, the preferred option is to circumscribe changes to well identified business needs, and carry on with the agile development model. But that’s not always possible as cross dependencies (business, organizational, or technical) may induce phasing constraints between engineering tasks.
As notoriously illustrated by Waterfall, procedural (if not bureaucratic) schemes have for long be seen as the only way to deal with phasing constraints; that’s not a necessity: with constraints and conditions defined on artifacts, developments can be governed by their status instead of having to be hard-wired into procedures. That’s precisely what model based development is meant to do.
And since iterative development models are by nature declarative, agile and model-based development schemes may be natural bedfellows.
Epigenetics & Emerging architectures
Given their their immersion in digital environments and the primacy of business intelligence, enterprises can be seen as living organisms using information to keep an edge in competitive environments. On that account homeostasis become a critical factor, to be supported by osmosis, architecture versatility and plasticity, and traditional strategic planning.
Set on a broader perspective, the merging of systems and knowledge architectures on one hand, the pervasive surge of machine learning technologies on the other hand, introduce a new dimension in the exchange of information between enterprises and their environment, making room for emerging architectures.
Using epigenetics as a metaphor of the mechanisms at hand, enterprises would be seen as organisms, systems as organs and cells, and models (including source) as genome coded with the DNA.
According to classical genetics, phenotypes (actual forms and capabilities of organisms) inherit through the copy of genotypes and changes between generations can only be carried out through changes in genotypes. Applied to systems, it would entail that changes would only happen intentionally, after being designed and programmed into the systems supporting enterprise organization and processes.
The Extended Evolutionary Synthesis considers the impact of non coded (aka epigenetic) factors on the transmission of the genotype between generations. Applying the same principles to systems would introduce new mechanisms:
Enterprise organization and their use of supporting systems could be adjusted to changes in environments prior to changes in coded applications.
Enterprise architects could use data mining and deep-learning technologies to understand those changes and assess their impact on strategies.
Abstractions would be used to consolidate emerging designs with existing architectures.
Models would be transformed accordingly.
While applying the epigenetics metaphor to enterprise mutations has obvious limitations, it nonetheless puts a compelling light on two necessary conditions for emerging structures:
Non-deterministic mechanisms governing the way changes are activated.
A decrypting mechanism between implicit or latent contents (data from digital environments) to explicit ones (information systems).
The first condition is to be met with agile and model based engineering, the second one with deep-learning.
For all intents and purposes, digital transformation has opened the door to syntactic interoperability… and thus raised the issue of the semantic one.
To put the issue in perspective, languages combine four levels of interpretation:
Syntax: how terms can be organized.
Lexical: meaning of terms independently of syntactic constructs.
Semantic: meaning of terms in syntactic constructs.
Pragmatic: semantics in context of use.
At first, semantic networks (aka conceptual graphs) appear to provide the answer; but that’s assuming flat ontologies (aka thesaurus) within which all semantics are defined at the same level. That would go against the objective of bringing the semantics of business domains and systems architectures under a single conceptual roof. The problem and a solution can be expounded taking users stories and use cases for examples.
Crossing stories & cases
Beside the difference in perspectives, users stories and use cases stand at a methodological crossroad, the former focused on natural language, the latter on modeling. Using ontologies to ensure semantic interoperability is to enhance both traceability and transparency while making room for their combination if and when called for.
Users’ stories are part and parcel of Agile development model, their backbone, engine, and fuel. But as far as Agile is concerned, users’ stories introduce a dilemma: once being told stories are meant to be directly and iteratively put down in code; documenting them in words would bring back traditional requirements and phased development. Hence the benefits of sorting out and writing up the intrinsic elements of stories as to ensure the continuity and consistency of engineering processes, whether directly to code, or through the mediation of use cases.
To that end semantic interoperability would have to be achieved for actors, events, and activities.
Actors & Events
Whatever architectures or modeling methodologies, actors and events are sitting on systems’ fences, which calls for semantics common to enterprise organization and business processes on one side of the fence, supporting systems on the other side.
To begin with events, the distinction between external and internal ones is straightforward for use cases, because their purpose is precisely to describe the exchanges between systems and environments. Not so for users stories because at their stage the part to be played by supporting systems is still undecided, and by consequence the distinction between external and internal events.
With regard to actors, and to avoid any ambiguity, a semantic distinction could be maintained between roles, defined by organizations, and actors (UML parlance), for roles as enacted by agents interacting with systems. While roles and actors are meant to converge with analysis, understandings may initially differ across the fence between users stories and use cases, to be reconciled at the end of the day.
That would enable use cases and users stories to share overlapping yet consistent semantics for primary actors and external events:
Across stories: actors contributing to different stories affected by the same events.
Along processes: use cases set for actors and events defined in stories.
Across time-frames: actors and events first introduced by use cases before being refined by “pre-sequel” users stories.
Such ontology-based representations are to support full iterative as well as parallel developments independently of the type of methods, diagrams or documents used by projects.
Users’ stories and use cases are set in different perspectives, business processes for the former, supporting systems for the latter. As already noted, their scopes overlap for events and actors which can be defined upfront providing a double distinction between roles (enterprise view) and actors (systems view), and between external and internal events.
Activities raise more difficulties because they are meant to be defined and refined across the whole of engineering processes:
From business operations as described by users to business functions as conceived by stakeholders.
From business logic as defined in business processes to their realization as defined in diagram sequences.
From functional requirements (e.g users authentication or authorization) to quality of service.
From primitives dealing with integrity constraints to business policies managed through rules engines.
To begin with, if activities have to be consistently defined for both users’ stories and use cases, their footprint should tally the description of actors and events stipulated above; taking a leaf from Aristotle rule of the three units, activity units should therefore:
Be triggered by a single event initiated by a single primary actor.
Be located into a single physical space with all resources at hand.
Timed by a single clock controlling accesses to all resources.
On that basis, the refinement of descriptions could go according to the nature of requirements: business (users’ stories), or functional and quality of service (use cases) .
As far as ontologies are concerned, the objective is to ensure the continuity and consistency of representations independently of modeling tools and methodologies. For activities appearing in users stories and use cases, that would require:
The description of activities in relation with their business background, their execution in processes, and the corresponding functions already supported by systems.
The progressive refinement of roles (users, devices, other systems), location, and resources (objects or surrogates).
An unified definition of alternatives in stories (branches) and use cases (extension points)
The last point is of particular importance as it will determine how business and functional rules are to be defined and control implemented.
Knitting semantics: symbolic representations
The scope and complexity of semantic interoperability can be illustrated a contrario by a simple activity (checking out) described at different levels with different methods (process, use case, user story), possibly by different people at different time.
The Check-out activity is first introduced at business level (process), next a specific application is developed with agile (user story), and then extended for variants according to channels (use case).
Assuming unfettered naming (otherwise semantic interoperability would be a windfall), three parties can be mentioned under various monikers for renters, drivers, and customers.
In a flat semantic context renter could be defined as a subtype of customer, itself a subtype of party. But that option would contradict the neutrality objective as there is no reason to assume a modeling consensus across domains, methods, and time.
The ontological kernel defines parties and actors, as roles associated to agents (organization level).
Enterprises define customers as parties (business model).
Business unit can defines renters in reference to customers (business process) or directly as a subtype of role (user story).
The distinction between renters and drivers can be introduced upfront or with use cases’ actors.
That would ensure semantic interoperability across modeling paradigms and business domains, and along time and transformations.
Probing semantics: metonymies and metaphors
Once established in-depth foundations, and assuming built-in basic logic and lexical operators, semantic interoperability is to be carried out with two basic linguistic contraptions: metonymies and metaphors .
Metonymies and metaphors are linguistic constructs used to substitute a word (or a phrase) by another without altering its meaning, respectively through extensions and intensions, the former standing for the actual set of objects and behaviors, the latter for the set of features that characterize these instances.
Metonymy relies on contiguity to substitute target terms for source ones, contiguity being defined with regard to their respective extensions. For instance, given that US Presidents reside at the White House, Washington DC, each term can be used instead.
Metaphor uses similarity to substitute target terms for source ones, similarity being defined with regard to a shared subset of features, with leftovers taken out of the picture.
Compared to basic thesaurus operators for synonymy, antonymy, and homonymy, which are set at lexical level, metonymy and metaphor operate at conceptual level, the former using set of instances (extensions) to probe semantics, the latter using descriptions (intensions).
Applied to users stories and use cases:
Metonymies: terms would be probed with regard to actual sets of objects, actors, events, and execution paths (data from operations) or mined from digital environments.
Metaphors: terms for stories, cases, actors, events, and activities would be probed with regard to the structure and behavior of associated descriptions (intensions).
Compared to the shallow one set at thesaurus level for terms, deep semantic interoperability encompasses all ontological dimensions, from actual instances to categories, aspects, and concepts. As such it can take full advantage of digital transformation and deep learning technologies.
The Agile development model should not be seen as a panacea or identified with specific methodologies. Instead it should be understood as a default option to be applied whenever phased solutions can be factored out.
Alternative: When conditions cannot be met, i.e when phased solutions are required, model-based system engineering frameworks should be used to integrate business-driven projects (agile) with architecture oriented ones (phased).
“The little reed, bending to the force of the wind, soon stood upright again when the storm had passed over”
The consequences of digital environments go well beyond a simple adjustment of business processes and call for an in-depth transformation of enterprise architectures.
To begin with, the generalization of digital environments bears out the Symbolic System modeling paradigm: to stay competitive, enterprises have to manage a relevant, accurate, and up-to-date symbolic representation of their business context and concerns.
With regard to architectures, it means a seamless integration of systems and knowledge architectures.
With regard to processes it means a built-in ability to learn from environments and act accordingly.
Such requirements for resilience and adaptability in unsettled environments are characteristic of the Pagoda architecture blueprint.
As can be observed wherever high buildings are being erected on shaking grounds, Pagoda-like architectures set successive layers around a central pillar providing intrinsic strength and resilience to external upsets while allowing the floors to move with the whole or be modified independently. Applied to enterprise architectures in digital environments, that blueprint can be much more than metaphoric.
That blueprint puts a new light on model based approaches to systems engineering (MBSE):
Conceptual models, targeting enterprises organization and business independently of supporting systems.
Logical models, targeting the symbolic objects managed by supporting systems as surrogates of business objects and activities.
Physical models, targeting the actual implementation of symbolic surrogates as binary objects.
Pagoda Blueprint & Digital Environments
The Pagoda blueprint gets a new relevance in the context of digital transformation.
Moreover, the blueprint is not limited to enterprise architectures and can be applied to every kind of systems:
Devices associated to physical platforms supporting analog communication through the Internet of Things (a).
Equipements associated to physical platforms controlled by systems, supporting digital communication (b) and functional alignment (c) .
That would greatly enhance the traceability and transparency of transformations induced by the immersion of enterprises in digital environments.
Systems & Knowledge Architectures
If digitized business flows are to pervade enterprise systems and feed business intelligence (BI), systems and knowledge architectures are to be merged into a single nervous system as materialized by the Pagoda central pillar:
Ubiquitous, massive, and unrelenting digitized business flows cannot be dealt with lest a clear distinction is maintained between raw data acquired across platforms, and the information (previously data) models which ensure the continuity and consistency of systems. .
Once structured and refined, business data flows must be blended with information models sustaining systems functionalities.
A comprehensive and business driven integration of organization and knowledge could then support strategic and operational decision-making at enterprise level.
Rounding off this nervous system with a brain, ontologies would provide the conceptual frame for models representing enterprises and their environments.
Agile Architectures & Homeostasis
Homeostasis is the ability of a viable organism to learn from their environment and adapt their behavior and structures according to changes. As such homeostasis can be understood as the extension of enterprise agility set in digital environments, ensuring:
Integrated decision-making processes across concerns (business, systems, platforms), and time-frames (tactical, operational, strategic, … ).
Integrated information processing, from data-mining to knowledge management.
To that end, changes should be differentiated with regard to source (business or technology environment, organization, systems) and flows (data, information, knowledge); that would be achieved with a pagoda blueprint.
Threads of operational and strategic decision-making processes could then be weaved together, combining OODA loops at process level and economic intelligence at enterprise level.
Humans often expect concepts to come with innate if vague meanings before being compelled to withstand endless and futile controversies around definitions. Going the other way would be a better option: start with differences, weed out irrelevant ones, and use remaining ones to advance.
Concerning enterprise, it would start with the difference between business and architecture, and proceed with the wholeness of data, information, and knowledge.
Business Architecture is an Oxymoron
Business being about time and competition, success is not to be found in recipes but would depend on particularities with regard to objectives, use of resources, and timing. These drives are clearly at odds with architectures rationales for shared, persistent, and efficient structures and mechanisms. As a matter of fact, dealing with the conflicting nature of business and architecture concerns can be seen as a key success factor for enterprise architects, with information standing at the nexus.
Data as Resource, Information as Asset, Knowledge as Service
Paradoxically, the need of a seamless integration of data, information, and knowledge means that the distinction between them can no longer be overlooked.
Data is captured through continuous and heterogeneous flows from a wide range of sources.
Information is built by adding identity, structure, and semantics to data. Given its shared and persistent nature it is best understood as asset.
Knowledge is information put to use through decision-making. As such it is best understood as a service.
Ensuring the distinction as well as the integration must be a primary concern of enterprise architects.
Sustainable Success Depends on a Balancing Act
Success in business is an unfolding affair, on one hand challenged by circumstances and competition, on the other hand to be consolidated by experience and lessons learnt. Meeting challenges while warding off growing complexity will depend on business agility and the versatility and plasticity of organizations and systems. That should be the primary objective of enterprise architects.
“For things to remain the same, everything must change”
Lampedusa, “The Leopard”
Whatever the understanding of the discipline, most EA schemes implicitly assume that enterprise architectures, like their physical cousins, can be built from blueprints. But they are not because enterprises have no “Pause” and “Reset” buttons: business cannot be put on stand-by and must be carried on while work is in progress.
Systems & Enterprises
Systems are variously defined as:
“A regularly interacting or interdependent group of items forming a unified whole” (Merriam-Webster).
“A set of connected things or devices that operate together” (Cambridge Dictionary).
“A way of working, organizing, or doing something which follows a fixed plan or set of rules” (Collins Dictionary)
“A collection of components organized to accomplish a specific function or set of functions” (TOGAF from ISO/IEC 42010:2007)
While differing in focus, most understandings mention items and rules, purpose, and the ability to interact; none explicitly mention social structures or interactions with humans. That suggests where the line should be drawn between systems and enterprises, and consequently between corresponding architectures.
Architectures & Changes
Enterprises are live social entities made of corporate culture, organization, and supporting systems; their ultimate purpose is to maintain their identity and integrity while interacting with environments. As a corollary, changes cannot be carried out as if architectures were just apparel, but must ensure the continuity and consistency of enterprises’ structures and behaviors.
That cannot be achieved by off-soil schemes made of blueprints and step-by-step processes detached from actual organization, systems, and processes. Instead, enterprise architectures must be grown bottom up from actual legacies whatever their nature: technical, functional, organizational, business, or cultural.
Insofar as enterprise architectures are concerned, legacies are usually taken into account through one of three implicit assumptions:
No legacy assumptions ignore the issue, as if the case of start-ups could be generalized. These assumptions are logically flawed because enterprises without legacy are like embryos growing their own inherent architecture, and in that case there would be no need for architects.
En Bloc legacy assumptions take for granted that architectures as a whole could be replaced through some Big Bang operation without having a significant impact on business activities. These assumptions are empirically deceptive because, even limited to software architectures, Big Bang solutions cannot cope with the functional and generational heterogeneity of software components characterizing large organizations. Not to mention that enterprise architectures are much more that software and IT.
Piecemeal legacies can be seen as the default assumption, based on the belief that architectures can be re-factored or modernized step by step. While that assumption may be empirically valid, it may also miss the point: assuming that all legacies can be dealt with piecemeal rubs out the distinction pointed above between systems and enterprises.
So, the question remains of what is to be changed, and how ?
EA as a Work In Progress
As with leopard’s spots and identity, the first step would be to set apart what is to change (architectures) from what is to carry on (enterprise).
Maps and territories do provide an overview of spots’ arrangement, but they are static views of architectures, whereas enterprises are dynamic entities that rely on architectures to interact with their environment. So, for maps and territories to serve that purpose they should enable continuous updates and adjustments without impairing enterprises’ awareness and ability to compete.
That shift from system architecture to enterprise behavior implies that:
The scope of changes cannot be fully defined up-front, if only because the whole enterprise, including its organization and business model, could possibly be of concern.
Fixed schedules are to be avoided, lest each and every unit, business or otherwise, would have to be shackled into a web of hopeless reciprocal commitments.
Different stakeholders may come as interested parties, some more equal than others, possibly with overlapped prerogatives.
So, instead of procedural and phased approaches supposed to start from blank pages, EA ventures must be carried out iteratively with the planning, monitoring, assessment, and adjustment of changes across enterprises’ businesses, organizations, and systems. That can be represented as an extension of the OODA (Observation, Orientation, Decision, Action) loop:
Actual observations from operations (a)
Data analysis with regard to architectures as currently documented (b).
Changes in business processes (c).
Changes in architectures (d).
Moreover, due to the generalization of digital flows between enterprises and their environment, decision-making processes used to be set along separate time-frames (operational, tactical, strategic, …), must now be weaved together along a common time-scale encompassing internal (symbolic) as well as external (actual) events.
It ensues that EA processes must not only be continuous, but they also must deal with latency constraints.
Changes & Latency
Architectures are by nature shared across organizational units (enterprise level) and business processes (system level). As a corollary, architecture changes are bound to introduce mismatches and frictions across business-specific applications. Hence the need of sorting out the factors affecting the alignment of maps and territories:
Elapsed time between changes in territories and maps updates (a>b) depends on data analytics and operational architecture.
Elapsed time between changes in maps and revised objectives (b>c) depends on business analysis and organization.
Elapsed time between changes in objectives and their implementation (c>d) depends on engineering processes and systems architecture.
Elapsed time between changes in systems and changes in territories (d>a) depends on applications deployment and technical architectures.
On that basis it’s possible to define four critical lags:
Operational: data analytics can be impeded by delayed, partial, or inaccurate feedback from processes.
Mapping: business analysis can be impeded by delays or discrepancies in data analytics.
Engineering: development of applications can be impeded by delays or discrepancies in business analysis.
Processes: deployment of business processes can be impeded by delays in the delivery of supporting applications.
These lags condition the whole of EA undertakings because legacy structures, mechanisms, and organizations are to be continuously morphed into architectures without introducing misrepresentations that would shackle activities and stray decision-making.
EA Latency & Augmented Reality
Insofar as architectural changes are concerned, discrepancies and frictions are rooted in latency, i.e the elapsed time between actual changes in territories and the updating of relevant maps.
As noted above, these lags have to be weighted according to time-frames, from operational days to strategic years, so that the different agents could be presented with the relevant and up-to-date views befitting to each context and concerns.
That could be achieved if enterprises architectures were presented through augmented reality technologies.
Compared to virtual reality (VR) which overlooks the whole issue of reality and operates only on similes and avatars, augmented reality (AR) brings together virtual and physical realms, operating on apparatuses that weaves actual substrates, observations, and interventions with made-up descriptive, predictive, or prescriptive layers.
On that basis, users would be presented with actual territories (EA legacy) augmented with maps and prospective territories.
Composition and dynamics of maps and territories (actual and prospective) could be set and edited appropriately, subject to latency constraints.
Given the digitization of enterprises environments, engineering processes have to be entwined with business ones while kept in sync with enterprise architectures. That calls for new threads of collaboration taking into account the integration of business and engineering processes as well as the extension to business environments.
Whereas models are meant to support communication, traditional approaches are already straining when used beyond software generation, that is collaboration between humans and CASE tools. Ontologies, which can be seen as a higher form of models, could enable a qualitative leap for systems collaborative engineering at enterprise level.
Systems Engineering: Contexts & Concerns
To begin with contents, collaborations should be defined along three axes:
Requirements: business objectives, enterprise organization, and processes, with regard to systems functionalities.
Feasibility: business requirements with regard to architectures capabilities.
Architectures: supporting functionalities with regard to architecture capabilities.
Since these axes are usually governed by different organizational structures and set along different time-frames, collaborations must be supported by documentation, especially models.
In order to support collaborations across organizational units and time-frames, models have to bring together perspectives which are by nature orthogonal:
Contexts, concerns, and languages: business vs engineering.
Time-frames and life-cycle: business opportunities vs architecture stability.
That could be achieved if engineering models could be harnessed to enterprise ones for contexts and concerns. That is to be achieved through the integration of processes.
As already noted, the integration of business and engineering processes is becoming a key success factor.
For that purpose collaborations would have to take into account the different time-frames governing changes in business processes (driven by business value) and engineering ones (governed by assets life-cycles):
Business requirements engineering is synchronic: changes must be kept in line with architectures capabilities (full line).
Software engineering is diachronic: developments can be carried out along their own time-frame (dashed line).
Application-driven projects usually focus on users’ value and just-in-time delivery; that can be best achieved with personal collaboration within teams. Architecture-driven projects usually affect assets and non-functional features and therefore collaboration between organizational units.
Collaboration: Direct or Mediated
Collaboration can be achieved directly or through some mediation, the former being a default option for applications, the latter a necessary one for architectures.
Both can be defined according to basic cognitive and organizational mechanisms and supported by a mix of physical and virtual spaces to be dynamically redefined depending on activities, projects, locations, and organisation.
Direct collaborations are carried out between individuals with or without documentation:
Immediate and personal: direct collaboration between 5 to 15 participants with shared objectives and responsibilities. That would correspond to agile project teams (a).
Delayed and personal: direct collaboration across teams with shared knowledge but with different objectives and responsibilities. That would tally with social networks circles (c).
Mediated collaborations are carried out between organizational units through unspecified individual members, hence the need of documentation, models or otherwise:
Direct and Code generation from platform or domain specific models (b).
Model transformation across architecture layers and business domains (d)
Depending on scope and mediation, three basic types of collaboration can be defined for applications, architecture, and business intelligence projects.
As it happens, collaboration archetypes can be associated with these profiles.
Agile development model (under various guises) is the option of choice whenever shared ownership and continuous delivery are possible. Application projects can so be carried out autonomously, with collaborations circumscribed to team members and relying on the backlog mechanism.
Projects set across enterprise architectures cannot be carried out without taking into account phasing constraints. While ill-fated Waterfall methods have demonstrated the pitfalls of procedural solutions, phasing constraints can be dealt with a roundabout mechanism combining iterative and declarative schemes.
Engineering vs Business Driven Collaborations
With collaborative engineering upgraded at enterprise level, the main challenge is to iron out frictions between application and architecture projects and ensure the continuity, consistency and effectiveness of enterprise activities. That can be achieved with roundabouts used as a collaboration mechanism between projects, whatever their nature:
Shared models are managed at roundabout level.
Phasing dependencies are set in terms of assertions on shared models.
Depending on constraints projects are carried out directly (1,3) or enter roundabouts (2), with exits conditioned by the availability of models.
Moreover, with engineering embedded in business processes, collaborations must also bring together operational analytics, decision-making, and business intelligence. Here again, shared models are to play a critical role:
Enterprise descriptive and prescriptive models for information maps and objectives
Environment predictive models for data and business understanding.
Whereas both engineering and business driven collaborations depend on sharing information and knowledge, the latter have to deal with open and heterogeneous semantics. As a consequence, collaborations must be supported by shared representations and proficient communication languages.
Ontologies & Representations
Ontologies are best understood as models’ backbones, to be fleshed out or detailed according to context and objectives, e.g:
Thesaurus, with a focus on terms and documents.
Systems modeling, with a focus on integration, e.g Zachman Framework.
Classifications, with a focus on range, e.g Dewey Decimal System.
Meta-models, with a focus on model based engineering, e.g models transformation.
Conceptual models, with a focus on understanding, e.g legislation.
Knowledge management, with a focus on reasoning, e.g semantic web.
As such they can provide the pillars supporting the representation of the whole range of enterprise concerns:
Taking a leaf from Zachman’s matrix, ontologies can also be used to differentiate concerns with regard to architecture layers: enterprise, systems, platforms.
Last but not least, ontologies can be profiled with regard to the nature of external contexts, e.g:
Institutional: Regulatory authority, steady, changes subject to established procedures.
Professional: Agreed upon between parties, steady, changes subject to established procedures.
Corporate: Defined by enterprises, changes subject to internal decision-making.
Social: Defined by usage, volatile, continuous and informal changes.
Personal: Customary, defined by named individuals (e.g research paper).
Ontologies & Communication
If collaborations have to cover engineering as well as business descriptions, communication channels and interfaces will have to combine the homogeneous and well-defined syntax and semantics of the former with the heterogeneous and ambiguous ones of the latter.
With ontologies represented as RDF (Resource Description Framework) graphs, the first step would be to sort out truth-preserving syntax (applied independently of domains) from domain specific semantics.
On that basis it would be possible to separate representation syntax from contents semantics, and to design communication channels and interfaces accordingly.
That would greatly facilitate collaborations across externally defined ontologies as well as their mapping to enterprise architecture models.
To summarize, the benefits of ontological frames for collaborative engineering can be articulated around four points:
A clear-cut distinction between representation semantics and truth-preserving syntax.
A common functional architecture for all users interfaces, humans or otherwise.
Modular functionalities for specific semantics on one hand, generic truth-preserving and cognitive operations on the other hand.
Profiled ontologies according to concerns and contexts.
A critical fifth benefit could be added with regard to business intelligence: combined with deep learning capabilities, ontologies would extend the scope of collaboration to explicit as well as implicit knowledge, the former already framed by languages, the latter still open to interpretation and discovery.
Knowledge graphs, which have become a key component of knowlege management, are best understood as a reincarnation of ontologies.