System modeling is all too often a flight for abstraction, when business analysts should instead look for the proper level of representation, ie the one with the best fit to business concerns.
Modeling is synchronic: contexts must be mapped to representations (Velazquez, “Las Meninas”).
Caminao’s blog (see Topics Guide) will try to set a path to Architecture Driven System Modelling. The guiding principle is to look at systems as sets of symbolic representations and identify the core archetypes defining how they must be coupled to their actual counterparts. That would provide for lean (need-to-know specs) and fit (architecture driven) models, architecture traceability, and built-in consistency checks.
This blog is meant to be a work in progress, with the basic concepts set open to suggestions or even refutation:
All examples are taken from ancient civilizations in order to put the focus on generic problems of symbolic architectures, disregarding technologies.
Symbolic representation: a primer
Original illustrations by Albert (http://www.albertdessinateur.com/) allow for concrete understanding of requirements, avoiding the biases associated with contrived textual descriptions.
The whole of enterprises’ endeavors and behaviors cannot be coerced into models lest they inhibit their ability to navigate ill defined and shifting business environments. Enterprises immersion in digital environments is making limits all the more explicit:
On the environment side, facts, once like manna from heaven ready to be picked and interpreted, have turned into data floods swamping all recognizable models imprints
On the symbolic side, concepts, once steadily supported by explicit models and logic, are now emerging like new species from the Big Data primordial soup.
Typically, business analysts are taking the lead on both fronts toting learning machines and waving knowledge graphs. In between system architects have to deal with a two-pronged encroachment on information models.
On the one hand they have to build a Chinese wall between private data and managed information to comply with regulations
On the other hand they have to feed decision-making processes with accurate and up-to-date observations, and adjust information systems with relevant and actionable concepts.
That brings a new light on the so-called conceptual, logical, and physical “data” models as key components of enterprise architecture:
Physical data models are meant to be directly lined up with operations and digital environments
Logical models represent the categories managed by information systems and must be up to par with systems functional architecture
Conceptual models are meant to represent enterprise knowledge of business domains and objectives, as well as its embodiment in organisation and people.
Logical models (information) appear therefore as an architecture hub linking business facts (data) and concepts (knowledge), ensuring exchanges between environments and representations e.g.:
Reasoning Patterns
Deduction: matching observations (data) with models to produce new information, i.e. data with structure and semantics
Induction: making hypothesises (knowledge) about the scope of models in order to make deductions
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.
Weaving Physical and Symbolic Threads into EA Digital Fabric (Inci Eviner)
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.
A Taxonomy
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.
Enterprise Architects are to jump across loops (Bruce Beasley)
When push comes to shove, deciding on a development process is to decide between instant or delayed returns, namely focusing on users needs with agile development, or taking extended features into consideration and weighting the benefits of reuse against additional costs, e.g.:
Designs to be reused as patterns.
Profiling configurations.
Structuring business process models so that they could be designed as business functions.
Formatting business logic for automated code generation.
The intricacies of stakes and decision-making processes can be set forth by applying the Observation-Orientation-Decision-Action (OODA) loop to the four views of changes: enterprise, business domains, business applications, systems:
At enterprise level the loops are triggered by changes in business environments pertaining to business model and objectives. They are supposed to affect different business domains.
Observations: Business opportunities
Orientation: Assessment of business opportunities with regard to business objectives.
Decision: Committing resources to changes in organization and processes
Action: Achieving changes in organization and processes
Changes initiated from business domains can be derived from enterprise level or the result of more specific objectives. They are supposed to affect different applications.
Observations: Business analysis
Orientation: Functional feasibility and assessment of transformation benefits.
Decision: Committing changes in functional architecture.
Action: Development, integration, tests
Changes at application level are initiated by organizational units, business or otherwise. They are supposed to be self-contained.
Observations: Users requirements
Orientation: Engineering feasibility and assessment of development options.
Decision: Choice of a development model.
Action: Development, integration, tests
Changes at system level are initiated by organizational units, including business ones (quality of service). They are supposed to affect different applications.
Observations: Process mining and operational requirements
Orientation: Operational feasibility and assessment of configurations.
Decision: Development model.
Action: Deployment and acceptance.
Given that sizeable companies with differentiated organization and business have to manage these different threads continuously and consistently, old fashioned imperative processes can only lead to paralysis. Hence the need of a declarative approach to EA workflows.
As far as enterprise architecture is concerned, the issue of scale is fogged by two confusions: one between processes and structures, the other between space and time. That square is at the core of the discipline.
Scaling Time (Tycho Brahe)
The Matter of Time
Even before the digital unfolding of environments, everybody was to agree that business is all about timing; and yet, that critical dimension remains a side issue of most enterprise architecture frameworks, which consequently fail to deal with enterprises ability to change and adapt in competitive environments.
With regard to time, the business perspective is said to be synchronic because it must continuously tally with environments constraints, opportunities, and risks.
By contrast, the engineering perspective is said to be diachronic because once fastened to requirements, developments are supposed to proceed according their own time-span.
Mixing Timescales
For enterprise architects, pairing up business and engineering momentum may look like a Fourier transform that would decompose enterprise architecture into piecemeal capabilities to be adjusted to the flow of business circumstances. But assets being by nature discrete, changes are not easily ironed out and some mechanism is necessary to align business and engineering time-frames, the former set at enterprise level and used to align enterprise architecture capabilities with business objectives, the latter set at system level and used to manage developments.
Agile methodologies solve the problem by assuming continuous deliveries disconnected from external schedules and by folding projects into detached time warps. Along with debatable scaling attempts, definitively non agile procedures are used to carry on with agile projects at system level.
As it happens, the iterative model can be upgraded to architecture level, enabling the linking of business driven changes to systems based ones without breaking agile principles:
Projects’ scope, objectives, and invariants are set with regard to enterprise architecture capabilities.
Iterations combine requirements analysis, development, and acceptance.
Increments and deliverables are defined dynamically contingent on scope and invariants.
Exit conditions (aka deliveries) are defined with regard to quality of services and technical requirements.
So-called architecture backlogs could thus be added to coordinate self-contained developments, standalone applications as well as system business functions, e.g. (invariants are in grey):
But the coordination issue remains between architecture backlogs, and adding procedures or committees shouldn’t be an option as it would seriously curb enterprise agility. By contrast, model based solutions are to ensure a constant and consistent adaptation of enterprise architectures to their environment.
Beyond varying names, requirements have often been classified into four basic categories:
Process requirements deal with organization and business processes independently of the part played by supporting systems.
Application requirements deal with the part played by supporting systems in the realization of processes requirements..
Quality of Service requirements deal with users experience independently of symbolic contents.
Technical requirements deal with the implementation of systems functions independently of users experience.
A customary requirements taxonomy
Yet, regardless of the soundness of these categories, their effectiveness varies with contexts, and contexts have been drastically disrupted with enterprises immersion in digital environments.
Digital Disruption
With the generalization of digital environments and the ensuing intermingling of business processes and IT two established distinctions are losing their grip, first between processes and applications, and then between users and systems.
Before and after digital disruption
For processes, the blurring is to concern non deterministic operations (heuristics, non modal logic, learning, …) that used to be the prerogative of humans but are now commonly carried out by artificial brains set in applications (a), user interface (d), or elsewhere (b). As a corollary, user interfaces are losing their homogeneity, as single systems with established codes of conduct are being replaced by an undefined number of unidentified agents (c, d).
Lest they drive enterprises into dead ends requirements have to map their systems according to the new digital territories. Not surprisingly, that can be best achieved using the symbolic/non symbolic distinction.
Requirements associated with symbolic contents.. Given that symbolic expressions can be reformulated, the granularity of these requirements can always be adjusted as to fall into single domains and therefore under the authority of clearly identified owners or stakeholders.
Requirements not dealing with symbolic contents. Since they cannot be uniquely tied to symbolic flows between systems and business contexts, nothing can be assumed regarding the identity of stakeholders. Yet, as they target systems features and behaviors, they can still be associated with architecture levels: enterprise, functional, technical.
Functional Requirements
As commonly understood, functional requirements cover business concerns and the part supported by systems; as such they can be aligned with enterprise architecture capabilities, symbolic (roles, business entities, business logic), and non symbolic (physical objects and locations, time-frames, events, processes execution).
In order to deal with the blurring induced by digital flows, requirements should ensure the transparency and traceability of interactions:
Transparency: users should be continuously aware of the kind of agent in charge behind the screen.
Traceability: users should be able to ask for explanations about every outcome.
As noted above, such requirements cannot be circumscribed to users interfaces or even applications as they involve the whole of the knowledge architecture . To that end functional requirements should be organized in relation to enterprise architecture capabilities, and include the necessary extensions for knowledge architecture:
When agents/roles assignments remain open requirements should include communication (natural, symbolic, or digital) and cognitive capabilities.
Assuming that requirements are not limited to modeled information, the distinction between data, information, and knowledge should be explicit.
Likewise for non deterministic reasoning used in business logic.
Functional requirements and Capabilities
Requirements concerning the digital capabilities of entry-points and time-frames of processes execution are to be added in order to associate functional and quality of service requirements.
Non Functional Requirements
Non functional requirements (NFRs) are meant to encompass whatever remain which cannot be tied to symbolic representations.
As should be expected for leftover categories, non functional requirements are by nature a mixed bag of overlapping items straddling the line between systems and applications depending on whether they directly affect users experience (quality of service), or are of the sole concern of architects and engineers (technical requirements).
The heterogeneity of non functional requirements is compounded by the mirror impact of the digital transformation on functional ones when business requirements (e.g high-frequency trading) combine performances with “intelligent” computing (e.g. machine learning capabilities).
Quality of Service and Capabilities
Yet, that is not to say that the distinction is arbitrary; in fact it conveys an implicit policy regarding architecture capabilities: defining elapse time as functional means that high-frequency traders will be supported by their own communication network and workstations, otherwise they will be dependent upon the company technical architecture, managed by a separate organizational unit, governed by its own concerns and policies.
On that account the digital transformation may help to clarify the issue of non functional requirements because all requirements, functional or otherwise can now be framed uniformly and therefore discriminate more easily.
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.
Information layer (George Drivas)
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.
From a digital point of view value chains can be likened to nerves carrying signals between operations and organization; from a business point of view they are meant to track down the path of added value across contributing resources and assets.
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.
Redefining supporting activities in terms of enterprise architecture capabilities
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.
A digital backbone instead of an information: layer
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.
To become a discipline enterprise architecture has to contrive some shared modeling paradigm; that can be done from established ones like layers, MVC (Model-View-Controller), MDA (Model Driven Architecture), or the 4+1 views.
A Portable & Inter-operable Roof (Jonas Bendiksen)
Architecture Layers
To begin with, the basic layers of process, data, application, and technology layers can be rearranged as to make data orthogonal to other layers.
Then, Model Driven architecture (MDA) can be used as representative of model based systems engineering (MBSE) approaches:
Computation independent models (CIMs) describe organization and business processes independently of the role played by supporting systems.
Platform independent models (PIMs) describe the functionalities supported by systems independently of their implementation.
Platform specific models (PSMs) describe systems components depending on implementation platforms.
With regard to data bases modeling, there is a large consensus for the conceptual, logical, and physical distinction:
Conceptual models describe enterprises organization and business independently of supporting systems.
Logical models describe the symbolic objects managed by supporting systems as surrogates of business objects and activities.
Physical (nowadays digital) models describe the actual implementation of symbolic surrogates as binary objects.
As for systems functional modeling, the Model-View-Controller (MVC) is arguably the leading pattern whatever the guises:
Model: shared and a life-cycle independent of business processes. The continuity and consistency of business objects representation must be guaranteed independently of the applications using them.
View: what is not shared with a life-cycle set by user session. The continuity and consistency of representations is managed locally (interactions with external agents or devices independently of targeted applications.
Controller: shared with a life-cycle set by a business process. The continuity and consistency of representations is managed independently of the persistency of business objects and interactions with external agents or devices.
Services can be introduced to represent functions to be shared with no life-cycle.
Architecture engineering
Finally, the relationships between architecture blueprints and systems engineering can be derived from Philippe Kruchten’s “4+1”: View Model of Software Architecture”:
Logical view: design of software artifacts.
Process view: execution of activities.
Deployment (aka physical) view: mapping of software components across physical environments (platforms).
Development (aka implementation) view: organization of software artifacts in development environments.
A fifth view being added for use cases describing interactions between systems and environments.
At first, views at enterprise architecture level appear to be congruent with these ones, e.g: logical for data, process for applications, physical for technology. But labels may be misguiding when applied indifferently to software architecture (views) and enterprise architecture (layers): contrary to views, which are solely defined by concerns independently of targeted structures, layers reflect definitive assumptions about architectures. That confusion between views and layers, illustrated by a miscellany of tabular and pyramidal representations, would be of little consequence were the modeling of changes not a critical issue; not so for enterprise architecture.
As it happens the confusion can be worked out if views are redefined solely and comprehensibly in terms of engineering:
Domains modeling: conceptual, logical, and physical.
Applications development: business logic, systems functions, programs.
Systems deployment: locations, processes, configurations.
Enterprise organisation and operations.
That make views congruent with engineering workshops, enabling a seamless integration of enterprise architecture blueprints with evolutionary processes.
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.
Problems & Solution Spaces (Inci Eviner)
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.
Decisions are best triggered by present and concrete opportunities.
Carpe Diem (Bruno Barbey)
Decision-making is driven by rules
Boxes and arrows may help to understand issues and support decision-making processes, but decisions are more easily triggered by rules than visuals. As far as enterprise architecture is concerned, the rule of the game is the necessary collaboration across enterprise business and systems units.
Expectations Must come with commitments
Visuals presentations help with reasoning and expectations, but decisions are made when problems (expectations) and solutions (commitments) spaces can be aligned. That should be a primary argument for enterprise architecture :
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.
decisions are best triggered by present and concrete opportunities
Enterprise architecture is a continuous endeavor that can only be carried out through collaboration. As a corollary, decisions are to be made on a piecemeal basis on business driven projects involving architectural decisions, e.g. when business logic has to be factored out and business processes cut to an architecture backbone.
Assuming a common understanding of data models (conceptual, logical, physical), such business process backbones could support pragmatic, gradual, and integrated approaches to enterprise architecture.
To be brief and to the point a presentation of enterprise architecture at decision-makers is to follow three principles:
Architecture is visual issue
Schemas should contain between 8 and 12 unrelated concepts.
Semantics should be non ambiguous and specific to the issue.
(Bridget Riley)
To that end, the focus should present architecture as an activity and its outcome. Applied to enterprise, architecture has to deal with more than actual edifices and contexts, and encompasses business environments and objectives on one side, organization, processes, and assets on the other side. Moreover, enterprise architecture is a continuous and dynamic endeavor because change and exchanges are part and parcel of enterprises life-cycle:
At enterprise level, architectures are needed to map organizations and systems to changing environments.
At system level, architectures are needed to map changing organizations and processes to supporting systems.
Enterprise architecture can therefore be defined in terms of the maps and territories (outcome), and the management of changes across and between (activity).
Mapping Territories
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 maps to figure out their environments, organization, assets, and processes:
At enterprise level maps are meant to describe business environments on one hand, concepts, objectives, organization and processes on the other hand.
At operational level maps are to describe physical and digital environments on one hand, interfaces and platforms on the other hand.
Then, a third level is introduced in between to describe systems and ensure transparency and consistency with regard to territories.
Enterprise Maps & Territories
For all intents and purposes that ternary view of enterprise architectures holds true independently of labels (layers, levels, tiers, etc), or perspective (business, data, applications, technologies, etc). That genericity appears clearly when maps are aligned with traditional models hierarchy: conceptual, logical and functional, and physical.
managing changes
Taking a leaf from Stafford Beer’s view of enterprises as viable systems, their sustainability depends on a continuous and timely adaptation to their environment; that cannot be achieved without a dynamic alignment of maps and territories; hence the need for enterprise architectures to provide for:
Continuous and consistent attachments between identified elements in maps and territories.
Transparency and traceability of changes across maps and territories.
Regarding continuity, ill-famed Waterfall epitomizes the detached conception of systems engineering and its implicit assumption that requirements are enough to ensure the alignment of systems with their business environments. To be sure, the plights of Waterfall have already marked a significant rip in the detached paradigm, a rift partially patched by agile development models. But while agile, and more generally iterative schemes, are at their best for business driven application with well defined scope and ownership, they fall short for architecture oriented ones with overlapping footprint and distributed stakeholders; that’s when maps are required.
Regarding transparency, enterprise architecture misconceptions come from mirroring its physical cousin, using geometrical patterns without being specific about their constitutive elements, and consequently the dynamics of interactions.
As it happens, both flaws can be mended by generalizing to systems the well accepted view model of software architecture:
Logical view: design of software artifacts.
Process view: captures the concurrency and synchronization aspects.
Physical view: describes the mapping(s) of software artifacts onto hardware.
Development view: describes the static organization of software artifacts in development environments.
One that basis changes in systems architectures would be managed in four workshops, two focused on territories (enterprise and operations) and two on maps (domains and applications).
Mapping changes to Enterprise Architectures
Defining engineering workflows in terms of maps and territories is to provide the foundations of model based systems engineering.
