Archive for the ‘Enterprise Architecture’ Category

EA: Legacy & Latency

June 7, 2018

 “For things to remain the same, everything must change”

Lampedusa, “The Leopard”

Preamble

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.

hans-vredeman-de-vries-2

Dealing with EA’s Legacy: WIP or RIP ? (Hans Vredeman de Vries)

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.

EA’s Legacy

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).
DataInfoKnow_OODA

EA decision-making as an extension of the OODA loop

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.

Latency constraints can then be associated with systems engineering tasks and workshops.

DataInfoKnow_Worshops

EA changes & Latency

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.

DataInfoKnow_intervs

EA views must be set according to contexts and concerns, with relevant lags weighted appropriately.

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.

DataInfoKnow_esh3

Augmented EA: Actual territory (left), Map (center), Prospective territory (right)

Composition and dynamics of maps and territories (actual and prospective) could be set and edited appropriately, subject to latency constraints.

Further Reading

 

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GDPR Ontological Primer

May 10, 2018

Preamble

European Union’s General Data Protection Regulation (GDPR), to come into effect  this month, is a seminal and momentous milestone for data privacy .

Nothing Personal (Arthur Szyk)

Yet, as reported by Reuters correspondents, European enterprises and regulators are not ready; more worryingly, few (except consultants) are confident about GDPR direction.

Misgivings and uncertainties should come as no surprise considering GDPR’s two innate challenges:

  • Regulating privacy rights represents a very ambitious leap into a digital space now at the core of corporate business strategies.
  • Compliance will not be put under a single authority but be overseen by an assortment of national and regional authorities across the European Union.

On that account, ontologies appear as the best (if not the only) conceptual approach able to bring contexts (EU nations), concerns (business vs privacy), and enterprises (organization and systems) into a shared framework.

A workbench built with the Caminao ontological kernel is meant to explore the scope and benefits of that approach, with a beta version (Protégé/OWL 2) available for comments on the Stanford/Protégé portal using the link: Caminao Ontological Kernel (CaKe_GDPR).

Enterprise Architectures & Regulations

Compared to domain specific regulations, GDPR  is a governance-oriented regulation set across business concerns and enterprise organization; but unlike similarly oriented ones like accounting, GDPR is aiming at the nexus of business competition, namely the processing of data into information and knowledge. With such a strategic stake, compliance is bound to become a game-changer cutting across business intelligence, production systems, and decision-making. Hence the need for an integrated, comprehensive, and consistent approach to the different dimensions involved:

  • Concepts upholding businesses, organizations, and regulations.
  • Documentation with regard to contexts and statutory basis.
  • Regulatory options and compliance assessments
  • Enterprise systems architecture and operations

Moreover, as for most projects affecting enterprise architectures, carrying through GDPR compliance is to involve continuous, deep, and wide ranging changes that will have to be brought off without affecting overall enterprise performances.

Ontologies arguably provide a conclusive solution to the problem, if only because there is no other way to bring code, models, documents, and concepts under a single roof. That could be achieved by using ontologies profiles to frame GDPR categories along enterprise architectures models and components.

CakeGDPR_00.jpg

Basic GDPR categories and concepts (black color) as framed by the Caminao Kernel

Compliance implementation could then be carried out iteratively across four perspectives:

  • Personal data and managed information
  • Lawfulness of activities
  • Time and Events
  • Actors and organization.

Data & Information

To begin with, GDPR defines ‘personal data’ as “any information relating to an identified or identifiable natural person (‘data subject’)”. Insofar as logic is concerned that definition implies an equivalence between ‘data’ and ‘information’, an assumption clearly challenged by the onslaught of big data: if proofs were needed, the Cambridge Analytica episode demonstrates how easy raw data can become a personal affair. Hence the need to keep an ontological level of indirection between regulatory intents and the actual semantics of data as managed by information systems.

CakeGDPR_data

Managing the ontological gap between regulatory understandings and compliance footprints

Once lexical ambiguities set apart, the question is not so much about the data bases of well identified records than about the flows of data continuously processed: if identities and ownership are usually set upfront by business processes, attributions may have to be credited to enterprises know-how if and when carried out through data analytics.

Given that the distinctions are neither uniform, exclusive or final, ontologies will be needed to keep tabs on moves and motives. OWL 2 constructs (cf annex) could also help, first to map GDPR categories to relevant information managed by systems, second to sort out natural data from nurtured knowledge.

Activities & Purposes

Given footprints of personal data, the objective is to ensure the transparency and traceability of the processing activities subject to compliance.

Setting apart (see below for events) specific add-ons for notification and personal accesses,  charting compliance footprints is to be a complex endeavor: as there is no reason to assume some innate alignment of intended (regulation) and actual (enterprise) definitions, deciding where and when compliance should apply potentially calls for a review of all processing activities.

After taking into account the nature of activities, their lawfulness is to be determined by contexts (‘purpose limitation’ and ‘data minimization’) and time-frames (‘accuracy’ and ‘storage limitation’). And since lawfulness is meant to be transitive, a comprehensive map of the GDPR footprint is to rely on the logical traceability and transparency of the whole information systems, independently of GDPR.

That is arguably a challenging long-term endeavor, all the more so given that some kind of Chinese Wall has to be maintained around enterprise strategies, know-how, and operations. It ensues that an ontological level of indirection is again necessary between regulatory intents and effective processing activities.

Along that reasoning compliance categories, defined on their own, are first mapped to categories of functionalities (e.g authorization) or models (e.g use cases).

CakeGDPR_activ1

Compliance categories are associated upfront to categories of functionalities (e.g authorization) or models (e.g use cases).

Then, actual activities (e.g “rateCustomerCredit”) can be progressively brought into the compliance fold, either with direct associations with regulations or indirectly through associated models (e.g “ucRateCustomerCredit” use case).

CakeGDPR_activ2

Compliance as carried out through Use Case

The compliance backbone can be fleshed out using OWL 2 mechanisms (see annex) in order to:

  • Clarify the logical or functional dependencies between processing activities subject to compliance.
  • Qualify their lawfulness.
  • Draw equivalence, logical, or functional links between compliance alternatives.

That is to deal with the functional compliance of processing activities; but the most far-reaching impact of the regulation may come from the way time and events are taken into account.

Time & Events

As noted above, time is what makes the difference between data and information, and setting rules for notification makes that difference lawful. Moreover, by adding time constraints to the notifications of changes in personal data, regulators put systems’ internal events on the same standing as external ones. That apparently incidental departure echoes the immersion of systems into digitized business environments, making all time-scales equal whatever their nature. Such flattening is to induce crucial consequences for enterprise architectures.

That shift together with the regulatory intent are best taken into account by modeling events as changes in expectations, physical objects, processes execution, and symbolic objects, with personal data change belonging to the latter.

Gdpr events

Mapping internal (symbolic) and external (actual) events is a critical element of GDPR compliance

Putting apart events specific to GDPR (e.g data breaches), compliance with regard to accuracy and storage limitation regulations will require that all events affecting personal data:

  • Are set in time-frames, possibly overlapping.
  • Have notification constraints properly documented.
  • Have likelihood and costs of potential risks assessed.

As with data and activities, OWL 2 constructs are to be used to qualify compliance requirements.

Actors & Organization

GDPR introduces two specific categories of actors (aka roles): one (data subject) for natural persons, and one for actors set by organizations, either specifically for GDPR assignment, or by delegation to already defined actors.

Gdpr actors

GDPR roles can be set specifically or delegated

OWL 2 can then be used to detail how regulatory roles can be delegated to existing ones, enabling a smooth transition and a dynamic adjustment of enterprise organization with regulatory compliance.

It must be stressed that the semantic distinction between identified agents (e.g natural persons) and the roles (aka UML actors) they play in processes is of particular importance for GDPR compliance because who (or even what) is behind an actor interacting with a system is to remain unknown to the system until the actor can be authentically identified. If that ontological lapse is overlooked there is no way to define and deal with security, confidentiality or privacy regulations.

Conclusion

The use of ontologies brings clear benefits for regulators, enterprise governance, and systems architects.

Without shared conceptual guidelines chances are for the European regulatory orchestra to get lost in squabbles about minutiae before sliding into cacophony.

With regard to governance, bringing systems and regulations into a common conceptual framework is to enable clear and consistent compliance strategies and policies, as well as smooth learning curves.

With regard to architects, ontology-based compliance is to bring cross benefits and externalities, e.g from improved traceability and transparency of systems and applications.

Annex A: Mapping Regulations to Models (sample)

To begin with, OWL 2 can be used to map GDPR categories to relevant resources as managed by information systems:

  • Equivalence: GDPR and enterprise definitions coincide.
  • Logical intersection, union, complement: GDPR categories defined by, respectively, a cross, merge, or difference of enterprise definitions.
  • Qualified association between GDPR and enterprise categories.

Assuming the categories properly identified, the language can then be employed to define the sets of regulated instances:

  • Logical property restrictions, using existential and universal quantification.
  • Functional property restrictions, using joints on attributes values.

Other constructs, e.g cardinality or enumerations, could also be used for specific regulatory constraints.

Finally, some OWL 2 built-in mechanisms can significantly improve the assessment of alternative compliance policies by expounding regulations with regard to:

  • Equivalence, overlap, or complementarity.
  • Symmetry or asymmetry.
  • Transitivity
  • etc.

Annex B: Mapping Regulations to Capabilities

GDPR can be mapped to systems capabilities using well established Zachman’s taxonomy set by crossing architectures functionalities (Who,What,How, Where, When) and layers (business and organization), systems (logical structures and functionalities), and platforms (technologies).

Rules_GDPR

Regulatory Compliance vs Architectures Capabilities

These layers can be extended as to apply uniformly across external ontologies, from well-defined (e.g regulations) to fuzzy (e.g business prospects or new technologies) ones, e.g:

Ontologies, capabilities (Who,What,How, Where, When), and architectures (enterprise, systems, platforms).

Such mapping is to significantly enhance the transparency of regulatory policies.

Further Reading

External Links

Collaborative Systems Engineering: From Models to Ontologies

April 9, 2018

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.

Wang-Qingsong_scaffold

Collaboration can be personal and direct, or collective and mediated (Wang Qingsong)

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:

  1. Requirements: business objectives, enterprise organization, and processes, with regard to systems functionalities.
  2. Feasibility: business requirements with regard to architectures capabilities.
  3. Architectures: supporting functionalities with regard to architecture capabilities.
RekReuse_BFCo

Engineering Collaborations at Enterprise Level

Since these axes are usually governed by different organizational structures and set along different time-frames, collaborations must be supported by documentation, especially models.

Shared 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.
EASquare2_eam.jpg

Harnessing MBSE to EA

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.

 Processes Integration

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).
EASq2_wrkflw

Synchronic (full) vs diachronic (dashed) processes.

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.

Cycles_collabs00

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).
Cycles_collabs.jpg

Collaborations

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.

EASq2_collabs

Projects & Collaborations

As it happens, collaboration archetypes can be associated with these profiles.

Collaboration Mechanisms

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.

The OODA (Observation, Orientation, Decision, Action) loop (and avatars) can epitomize projects combining operations, data analytics, and decision-making.

EASquare2_collaMechas

Collaboration archetypes

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.

Engineering driven collaboration: roundabout and backlogs

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.
OKBI_BIDM

Business driven collaboration: operations and business intelligence

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:

KM_OntosCapabs

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).

Cross profiles: capabilities, enterprise architectures, and contexts.

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.

KM_CaseRaw

RDF graphs (top) support formal (bottom left) and domain specific (bottom right) 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.

Conclusion

To summarize, the benefits of ontological frames for collaborative engineering can be articulated around four points:

  1. A clear-cut distinction between representation semantics and truth-preserving syntax.
  2. A common functional architecture for all users interfaces, humans or otherwise.
  3. Modular functionalities for specific semantics on one hand, generic truth-preserving and cognitive operations on the other hand.
  4. Profiled ontologies according to concerns and contexts.
KM_OntosCollabs

Clear-cut distinction (1), unified interfaces architecture (2), functional alignment (3), crossed profiles (4).

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.

Further Reading

 

EA: The Matter of Layers

March 7, 2018

As the world turns digital,traditional fences between social, businesses, and systems realms are progressively crumbling. That brings new challenges for enterprises governance, in particular when manifold business stakes and IT systems are concerned.

tonyCragg_bottles
Layers & labels (T. Cragg)

Supposedly, enterprise architecture would deal with the framing of enterprises and systems concerns into a single paradigm. Yet spirited controversies persist between bottom up and top down approaches, the former trying to upgrade the footprint of IT systems to enterprise level, the latter ready to downgrade these systems to equipment level. But dissent in that case means unfinished business: like diggers tunneling from opposite directions, both groups are to succeed together or fail together. For that to be achieved common sense dictates that both teams agree on target, with each one getting its specific orientation right.

What to look for

Issue (information systems) and circumstances (digitization of business environment) put the focus on the relationship between business processes and enterprises organization and how to capture, manage, and use information.

On that account, and not surprisingly, understandings differ between EA proponents:

  • Bottom-up approaches are focused on the distinction between processes, applications, and data, overlooking key enterprise architecture concerns (a).
  • Top-down approaches come with a better understanding of EA stakes but fall short of the conceptual bridge between organization and business environments (b) .
EASquare_persp

Bottom-up (a) and top-down (b) approaches to EA

These shortcomings can be mended and approaches made to converge.

How to get there

As already noted, EA can only succeed as a discipline if systems and enterprise perspectives can be crossed, i.e if bottom-up and top-down approaches can be joined. That cannot be achieved along the outdated Process/Application/Data layers:

To begin with, the distinction between application and data, inherited from traditional programming, goes against both object-oriented design and service oriented architectures; then, processes don’t describe architectures but the way they are used.

On a broader perspective, if the impact of digitized business environments on EA is to be taken into account, data and information are to be redefined in a new paradigm, the former associated with a raw input, to be mined from the business environment and processed into the latter. It ensues that (1) data becomes irrelevant for architecture concerns and, (2) information becomes a key asset for enterprise architecture.

Merging applications and data into a logical/functional layer between business and engineering processes also critically redefines the perspective: instead of a being a collection of applications, business processes become the nexus of the architecture.

EASquare_sys

Introducing a functional layer between business and engineering processes

With a bottom-up EA perspective focused on business and engineering processes, a top-down counterpart has to be set from enterprise perspective that would ensure a meeting of minds around business processes.

That can be readily achieved by keeping processes as pivot between business environments and objectives on one side, enterprise organization on the other side:

EASquare2_eam

Processes are the nexus of enterprise and engineering concerns.

Enterprise architects could then focus on the mapping of business functions to services, the alignment of quality of services with architecture capabilities, and the flows of information across the organization.

Why It Matters

A proper understanding of architecture layers is not an academic concern to be overlooked. As a matter of fact, what is at stake is the very practical purpose of EA: display of boxes and arrows or effective handling of the spindle between business processes and architectural assets. Whereas anything will do for the former, the latter cannot be achieved without a principled and effective coupling between enterprise models and systems engineering.

Further Reading

External Links

Focus: Requirements Reuse

February 22, 2018

Preamble

Requirements is what to feed engineering processes. As such they are to be presented under a wide range of forms, and nothing should be assumed upfront about forms or semantics.

What is to be reused: Sketches or Models  ? (John Devlin)

Answering the question of reuse therefore depends on what is to be reused, and for what purpose.

Documentation vs Reuse

Until some analysis can be carried out, requirements are best seen as documents;  whether such documents are to be ephemeral or managed would be decided depending on method (agile or phased), contents (business, supporting systems, implementation, or quality of services), or purpose (e.g governance, regulations, etc).

What is to be reused.

Setting apart external conditions, requirements documentation could be justified by:

  • Traceability of decision-making linking initial requests with actual implementation.
  • Acceptance.
  • Maintenance of deliverables during their life-cycle.

Depending on development approaches, documentation could limited to archives (agile development models) or managed as intermediate products (phased development models). In the latter case reuse would entail some formatting of requirements.

The Cases for Requirements Reuse

Assuming that requirements have been properly formatted, e.g as analysis models (with technical ones managed internally at system level), reuse could be justified by changes in business, functional, or quality of services requirements:

  • Business processes are meant to change with opportunities. With requirements available as analysis models, changes would be more easily managed (a) if they could be fine-grained. Business rules are a clear example, but that could also be the case for new features added to business objects.
  • Functional requirements may change even without change of business ones, e.g if new channels and users are introduced addressing existing business functions. In that case reusable business requirements (b) would dispense with a repeat of business analysis.
  • Finally, quality of service could be affected by operational changes like localization, number of users, volumes, or frequency. Adjusting architecture capabilities would be much easier with functional (c) and business (d) requirements properly documented as analysis models.

Cases for Reuse

Along that perspective, requirements reuse appears to revolve around two pivots, documents and analysis models. Ontologies could be used to bind them.

Requirements & Ontologies

Reusing artifacts means using them in contexts or for purposes different of native ones. That may come by design, when specifications can anticipate on shared concerns, or as an afterthought, when initially unexpected similarities are identified later on. In any case, reuse policies have to overcome a twofold difficulty:

  • Visibility: business and functional analysts must be made aware of potential reuse without having to spend too much time on research.
  • Overheads: ensuring transparency, traceability, and consistency checks on requirements (documents or analysis models) cannot be achieved without costs.

Ontologies could help to achieve greater visibility with acceptable overheads by framing requirements with regard to nature (documents or models) and context:

With regard to nature, the critical distinction is between document management and model based engineering systems. When framed as ontologies, the former is to be implemented as thesaurus targeting terms and documents, the latter as ontologies targeting categories specific to organizations and business domains.

Documents, models, and capabilities should be managed separately

With regard to context the objective should be to manage reusable requirements depending on the kind of jurisdiction and stability of categories, e.g:

  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to accord.
  • 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).

Combining contexts of reuse with architectures layers (enterprise, systems, platforms) and capabilities (Who,What,How, Where, When).

Combined with artificial intelligence, ontology archetypes could crucially extend the benefits of requirements reuse, notably through the impact of deep learning for visibility.

On a broader perspective requirements should be seen as a source of knowledge, and their reuse managed accordingly.

Further Reading

Healthcare: Tracks & Stakes

February 8, 2018

Preamble

Healthcare represents at least a tenth of developed country’s GDP, with demography pushing to higher levels year after year. In principle technology could drive costs in both directions; in practice it has worked like a ratchet: upside, innovations are extending the scope of expensive treatments, downside, institutional and regulatory constraints have hamstrung the necessary mutations of organizations and processes.

Health Care Personal Assistant (Kerry James Marshall)

As a result, attempts to spread technology benefits across healthcare activities have dwindle or melt away, even when buttressed by major players like Google or Microsoft.

But built up pressures on budgets combined with social transformations have undermined bureaucratic barriers and incumbents’ estates, springing up initiatives from all corners: pharmaceutical giants, technology startups, healthcare providers, insurers, and of course major IT companies.

Yet the wide range of players’ fields and starting lines may be misleading, incumbents or newcomers are well aware of what the race is about: whatever the number of initial track lanes, they are to fade away after a few laps, spurring the front-runners to cover the whole track, alone or through partnerships. As a consequence, winning strategies would have to be supported by a comprehensive and coherent understanding of all healthcare aspects and issues, which can be best achieved with ontologies.

Ontologies vs Models

Ontologies are symbolic constructs (epitomized by conceptual graphs made of nodes and connectors) whose purpose is to make sense of a domain of discourse:

  1. Ontologies are made of categories of things, beings, or phenomena; as such they may range from simple catalogs to philosophical doctrines.
  2. Ontologies are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
  3. Ontologies are meant to be directed at specific domains of concerns, whatever they can be: politics, religion, business, astrology, etc.

That makes ontologies a special case of uncommitted models: like models they are set on contexts and concerns; but contrary to models ontologies’ concerns are detached from actual purposes. That is precisely what is expected from a healthcare conceptual framework.

Contexts & Business Domains

Healthcare issues are set across too many domains to be effectively fathomed, not to mention followed as they change. Notwithstanding, global players must anchor their strategies to different institutional contexts, and frame their policies as to make them transparent and attractive to others players. Such all-inclusive frameworks could be built from ontologies profiled with regard to the governance and stability of contexts:

  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to accord.
  • 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 set along that taxonomy of contexts could then be refined as to target enterprise architecture layers: enterprise, systems, platforms, e.g:

A sample of Healthcare profiled ontologies

Depending on the scope and nature of partnerships, ontologies could be further detailed as to encompass architectures capabilities: Who, What, How, Where, When. 

Concerns & Architectures Capabilities

As pointed above, a key success factor for major players would be their ability to federate initiatives and undertakings of both incumbents and newcomers, within or without partnerships. That can be best achieved with enterprise architectures aligned with an all-inclusive yet open framework, and for that purpose the Zachman taxonomy would be the option of choice. The corresponding enterprise architecture capabilities (Who,What, How, Where, When) could then be uniformly applied to contexts and concerns:

  • Internally across architecture layers for enterprise (business and organization), systems (logical structures and functionalities), and platforms (technologies).
  • Externally across context-based ontologies as proposed above.

The nexus between environments (contexts) and enterprises (concerns) ontologies could then be organised according to the epistemic nature of items: terms, documents, symbolic representations (aka surrogates), or business objects and phenomena.

Mapping knowledge to architectures capabilities

That would outline four basic ontological archetypes that may or may not be combined:

  • Thesaurus: ontologies covering terms, concepts.
  • Document Management: thesaurus and documents.
  • Organization and Business: ontologies pertaining to enterprise organization and business processes.
  • Engineering: ontologies pertaining to the symbolic representation (aka surrogates) of organizations, businesses, and systems.

Global healthcare players could then build federating frameworks by combining domain and architecture driven ontologies, e.g:

Building federating frameworks with modular ontologies designed on purpose.

As a concluding remark, it must be reminded that the objective is to federate the activities and systems of healthcare players without interfering with the design of their business processes or supporting systems. Hence the importance of the distinction between ontologies and models introduced above which would act as a guaranty that concerns are not mixed up insofar as ontologies remain uncommitted models.

Further Reading

External Links

Ontologies as Productive Assets

January 22, 2018

Preamble

An often overlooked benefit of artificial intelligence has been a renewed interest in seminal philosophical and cognitive topics; ontologies coming top of the list.

Ontological Questioning (The Thinker Monkey, Breviary of Mary of Savoy)

Yet that interest has often been led astray by misguided perspectives, in particular:

  • Universality: one-fits-all approaches are pointless if not self-defeating considering that ontologies are meant to target specific domains of concerns.
  • Implementation: the focus is usually put on representation schemes (commonly known as Resource Description Frameworks, or RDFs), instead of the nature of targeted knowledge and the associated cognitive capabilities.

Those misconceptions, often combined, may explain the limited practical inroads of ontologies. Conversely, they also point to ontologies’ wherewithal for enterprises immersed into boundless and fluctuating knowledge-driven business environments.

Ontologies as Assets

Whatever the name of the matter (data, information or knowledge), there isn’t much argument about its primacy for business competitiveness; insofar as enterprises are concerned knowledge is recognized as a key asset, as valuable if not more than financial ones, and should be managed accordingly. Pushing the comparison still further, data would be likened to liquidity, information to fixed income investment, and knowledge to capital ventures. To summarize, assets whatever their nature lose value when left asleep and bear fruits when kept awake; that’s doubly the case for data and information:

  • Digitized business flows accelerates data obsolescence and makes it continuous.
  • Shifting and porous enterprises boundaries and markets segments call for constant updates and adjustments of enterprise information models.

But assessing the business value of knowledge has always been a matter of intuition rather than accounting, even when it can be patented; and most of knowledge shapes up well beyond regulatory reach. Nonetheless, knowledge is not manna from heaven but the outcome of information processing, so assessing the capabilities of such processes could help.

Admittedly, traditional modeling methods are too stringent for that purpose, and looser schemes are needed to accommodate the open range of business contexts and concerns; as already expounded, that’s precisely what ontologies are meant to do, e.g:

  • Systems modeling,  with a focus on integration, e.g Zachman Framework.
  • Classifications, with a focus on range, e.g Dewey Decimal System.
  • Conceptual models, with a focus on understanding, e.g legislation.
  • Knowledge management, with a focus on reasoning, e.g semantic web.

And ontologies can do more than bringing under a single roof the whole of enterprise knowledge representations: they can also be used to nurture and crossbreed symbolic assets and develop innovative ones.

Ontologies Benefits

Knowledge is best understood as information put to use; accounting rules may be disputed but there is no argument about the benefits of a canny combination of information, circumstances, and purpose. Nonetheless, assessing knowledge returns is hampered by the lack of traceability: if a part of knowledge is explicit and subject to symbolic representation, another is implicit and manifests itself only through actual behaviors. At philosophical level it’s the line drawn by Wittgenstein: “The limits of my language mean the limits of my world”;  at technical level it’s AI’s two-lanes approach: symbolic rule-based engines vs non symbolic neural networks; at corporate level implicit knowledge is seen as some unaccounted for aspect of intangible assets when not simply blended into corporate culture. With knowledge becoming a primary success factor, a more reasoned approach of its processing is clearly needed.

To begin with, symbolic knowledge can be plied by logic, which, quoting Wittgenstein again, “takes care of itself; all we have to do is to look and see how it does it.” That would be true on two conditions:

  • Domains are to be well circumscribed. 
  • A water-tight partition must be secured between the logic of representations and the semantics of domains.

That could be achieved with modular and specific ontologies built on a clear distinction between common representation syntax and specific domains semantics.

As for non-symbolic knowledge, its processing has for long been overshadowed by the preeminence of symbolic rule-based schemes, that is until neural networks got the edge and deep learning overturned the playground. In a few years’ time practically unlimited access to raw data and the exponential growth in computing power have opened the door to massive sources of unexplored knowledge which is paradoxically both directly relevant yet devoid of immediate meaning:

  • Relevance: mined raw data is supposed to reflect the geology and dynamics of targeted markets.
  • Meaning: the main value of that knowledge rests on its implicit nature; applying existing semantics would add little to existing knowledge.

Assuming that deep learning can transmute raw base metals into knowledge gold, enterprises would need to understand, assess, and improve the refining machinery. That could be done with ontological frames.

A Proof of Concept

Compared to tangible assets knowledge may appear as very elusive, yet, and contrary to intangible ones, knowledge is best understood as the outcome of processes that can be properly designed, assessed, and improved. And that can be achieved with profiled ontologies.

As a Proof of Concept, an ontological kernel has been developed along two principles:

  • A clear-cut distinction between truth-preserving representation and domain specific semantics.
  • Profiled ontologies designed according to the nature of contents (concepts, documents, or artifacts), layers (environment, enterprise, systems, platforms), and contexts (institutional, professional, corporate, social.

That provides for a seamless integration of information processing, from data mining to knowledge management and decision making:

  • Data is first captured through aspects.
  • Categories are used to process data into information on one hand, design production systems on the other hand.
  • Concepts serve as bridges to knowledgeable information.

CaKe_DataInfoKnow

A beta version is available for comments on the Stanford/Protégé portal with the link: Caminao Ontological Kernel (CaKe).

Further Reading

External Links

Open Ontologies: From Silos to Architectures

January 1, 2018

To be of any use for enterprises, ontologies have to embrace a wide range of contexts and concerns, often ill-defined for environments, rather well expounded for systems.

Circumscribed Contexts & Crossed Concerns (Robert Goben)

And now that enterprises have to compete in open, digitized, and networked environments, business and systems ontologies have to be combined into modular knowledge architectures.

Ontologies & Contexts

If open-ended business contexts and concerns are to be taken into account, the first step should be to characterize ontologies with regard to their source, justification, and the stability of their categories, e.g:

  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to accords.
  • 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).

Assuming such an external taxonomy, the next step would be to see what kind of internal (i.e enterprise architecture) ontologies can be fitted into, as it’s the case for the Zachman framework.

The Zachman’s taxonomy is built on well established concepts (Who,What,How, Where, When) applied across architecture layers for enterprise (business and organization), systems (logical structures and functionalities), and platforms (technologies). These layers can be generalized and applied uniformly across external contexts, from well-defined (e.g regulations) to fuzzy (e.g business prospects or new technologies) ones, e.g:

Ontologies, capabilities (Who,What,How, Where, When), and architectures (enterprise, systems, platforms).

That “divide to conquer” strategy is to serve two purposes:

  • By bridging the gap between internal and external taxonomies it significantly enhances the transparency of governance and decision-making.
  • By applying the same motif (Who,What, How, Where, When) across the semantics of contexts, it opens the door to a seamless integration of all kinds of knowledge: enterprise, professional, institutional, scientific, etc.

As can be illustrated using Zachman concepts, the benefits are straightforward at enterprise architecture level (e.g procurement), due to the clarity of supporting ontologies; not so for external ones, which are by nature open and overlapping and often come with blurred semantics.

Ontologies & Concerns

A broad survey of RDF-based ontologies demonstrates how semantic overlaps and folds can be sort out using built-in differentiation between domains’ semantics on one hand, structure and processing of symbolic representations on the other hand. But such schemes are proprietary, and evidence shows their lines seldom tally, with dire consequences for interoperability: even without taking into account relationships and integrity constraints, weaving together ontologies from different sources is to be cumbersome, the costs substantial, and the outcome often reduced to a muddy maze of ambiguous semantics.

The challenge would be to generalize the principles as to set a basis for open ontologies.

Assuming that a clear line can be drawn between representation and contents semantics, with standard constructs (e.g predicate logic) used for the former, the objective would be to classify ontologies with regard to their purpose, independently of their representation.

The governance-driven taxonomy introduced above deals with contexts and consequently with coarse-grained modularity. It should be complemented by a fine-grained one to be driven by concerns, more precisely by the epistemic nature of the individual instances to be denoted. As it happens, that could also tally with the Zachman’s taxonomy:

  • Thesaurus: ontologies covering terms and concepts.
  • Documents: ontologies covering documents with regard to topics.
  • Business: ontologies of relevant enterprise organization and business objects and activities.
  • Engineering: symbolic representation of organization and business objects and activities.
KM_OntosCapabs

Ontologies: Purposes & Targets

Enterprises could then pick and combine templates according to domains of concern and governance. Taking an on-line insurance business for example, enterprise knowledge architecture would have to include:

  • Medical thesaurus and consolidated regulations (Knowledge).
  • Principles and resources associated to the web-platform (Engineering).
  • Description of products (e.g vehicles) and services (e.g insurance plans) from partners (Business).

Such designs of ontologies according to the governance of contexts and the nature of concerns would significantly reduce blanket overlaps and improve the modularity and transparency of ontologies.

On a broader perspective, that policy will help to align knowledge management with EA governance by setting apart ontologies defined externally (e.g regulations), from the ones set through decision-making, strategic (e.g plate-form) or tactical (e.g partnerships).

Open Ontologies’ Benefits

Benefits from open and formatted ontologies built along an explicit distinction between the semantics of representation (aka ontology syntax) and the semantics of context can be directly identified for:

Modularity: the knowledge basis of enterprise architectures could be continuously tailored to changes in markets and corporate structures without impairing enterprise performances.

Integration: the design of ontologies with regard to the nature of targets and stability of categories could enable built-in alignment mechanisms between knowledge architectures and contexts.

Interoperability: limited overlaps and finer granularity are to greatly reduce frictions when ontologies bearing out business processes are to be combined or extended.

Reliability: formatted ontologies can be compared to typed programming languages with regard to transparency, internal consistency, and external validity.

Last but not least, such reasoned design of ontologies may open new perspectives for the collaboration between cognitive humans and pretending ones.

Further Reading

External Links

Flawed Code vs Model in the Loop

November 27, 2017

Preamble

Repeated announces of looming software apocalypse may take some edge off vigilance, but repeated systems failures should be taken seriously, if only because they  appear to be rooted in a wide array of causes, from wrongly valued single parameters (e.g 911 threshold or Apple’s free pass for “root” users) to architecture obsolescence (e.g reservation systems.)

Spreading hazardous digits (Mona Hatoum)

Yet, if alarms are not to be ignored, prognoses should go beyond syndromes and remedies beyond sticking plaster: contrary to what is suggested by The Atlantic’s article, systems are much more than piles of code, and programming is probably where quality has been taken the most seriously.

Programs vs Systems

Whatever programmers’ creativity and expertise, they cannot tackle complexity across space, time, and languages: today’s systems are made of distributed interacting components, specified and coded in different languages, and deployed and modified across overlapping time-frames. Even without taking into account continuous improvements in quality, apocalypse is not to loom in the particulars of code but on their ways in the world.

Solutions should therefore be looked for at system level, and that conclusion can only be bolstered by the ubiquity of digitized business flows.

Systems are the New Babel

As illustrated by the windfalls benefiting Cobol old timers, language is arguably a critical factor, for the maintenance of legacy programs as well as for communication between stakeholders, users, and engineers.

So if problems can be traced back to languages, it’s where solutions are to be found: from programming languages (for code) to natural ones (for systems requirements), everything can be specified as symbolic representations, i.e models.

Model in the Loop

Models are generally understood as abstractions, and often avoided for that very reason. That shortsighted mind-set is made up for by concrete employs of abstractions, as illustrated by the Automotive industry and the way it embeds models in engineering processes.

Summarily, the Automotive’s Model in Loop (MiL) can be explained through three basic ideas:

  • Systems are to be understood as the combination of physical and software artifacts.
  • Insofar as both can be implemented as digits, they can be uniformly described as models.
  • As a consequence, analysis, design and engineering can be carried out through the iterative building, simulating, testing, and adjusting various combinations of hardware and software.

By bringing together physical components and code into a seamless digitized whole, MiL brings down the conceptual gap between actual elements and symbolic representations, aka models. And that leap could be generalized to a much wider range of systems.

Models are the New Code

Programming habits and the constraints imposed by the maintenance of legacy systems have perpetuated the traditional understanding of systems as a building-up of programs; hence the focus put on the quality of code. But when large, distributed, and perennial systems are concerned, that bottom-up mind-set falls short and brings about:

  • An exponential increase of complexity at system level.
  • Opacity and discontinued traceability at application level between current use and legacy code.

Both flaws could be corrected by combining top-down modeling and bottom-up engineering. That could be achieved with iterative processes carried out from both directions.

Model in the Loop meets Enterprise Architecture

From a formal perspective models are of two sorts: extensional ones operate bottom-up and associate sets of individuals with categories, intensional ones operate top-down and specify the features meant to be shared by all instances of a type. based on that understanding, the former can be used to simulate the behaviors of targeted individuals depending on categories, and the latter to prescribe how to create instances of types meant to implement categories.

As it happens, Model-in-loop combines the two schemes at component level:

  • Any combination of manual and automated solution can be used as a starting point for analysis and simulation (a).
  • Given the outcomes of simulation and tests, the architecture is revisited (b) and corresponding artifacts (software and hardware) are designed (c).
  • The new combination of artifacts are developed and integrated, ready for analysis and simulation (d).

Model in the Loop

Assuming that MiL bottom-up approach could be tallied with top-down systems engineering processes, it would enable a seamless and continuous integration of changes in software components and systems architectures.

Further Reading

External Links

Focus: Enterprise Architect Booklet

October 16, 2017

Objective

Given the diversity of business and organizational contexts, and EA still a fledgling discipline, spelling out a job description for enterprise architects can be challenging.

hans-vredeman-de-vries-3b

Aligning business, organization, and systems perspectives (Hans Vredeman de Vries)

So, rather than looking for comprehensive definitions of roles and responsibilities, one should begin by circumscribing the key topics of the trade, namely:

  1. Concepts : eight exclusive and unambiguous definitions provide the conceptual building blocks.
  2. Models: how the concepts are used to analyze business requirements and design systems architectures and software artifacts.
  3. Processes: how to organize business and engineering processes.
  4. Architectures: how to align systems capabilities with business objectives.
  5. Governance: assessment and decision-making.

The objective being to define the core issues that need to be addressed by enterprise architects.

Concepts

To begin with, the primary concern of enterprise architects should be to align organization, processes, and systems with enterprise business objectives and environment. For that purpose architects are to consider two categories of models:

  • Analysis models describe business environments and objectives.
  • Design models prescribe how systems architectures and components are to be developed.

Enterprise architects must focus on individuals (objects and processes) consistently identified (#) across business and system realms.

That distinction is not arbitrary but based on formal logic: analysis models are extensional as they classify actual instances of business objects and activities; in contrast, design models are intensional as they define the features and behaviors of required system artifacts.

The distinction is also organizational: as far as enterprise architecture is concerned, the focus is to remain on objects and activities whose identity (#) and semantics are to be continuously and consistently maintained across business (actual instances) and system (symbolic representations) realms:

Relevant categories at architecture level can be neatly and unambiguously defined.

  • Actual containers represent address spaces or time frames; symbolic ones represent authorities governing symbolic representations. System are actual realizations of symbolic containers managing symbolic artifacts.
  • Actual objects (passive or active) have physical identities; symbolic objects have social identities; messages are symbolic objects identified within communications. Power-types (²) are used to partition objects.
  • Roles (aka actors) are parts played by active entities (people, devices, or other systems) in activities (BPM), or, if it’s the case, when interacting with systems (UML’s actors). Not to be confounded with agents meant to be identified independently of their behavior.
  • Events are changes in the state of business objects, processes, or expectations.
  • Activities are symbolic descriptions of operations and flows (data and control) independently of supporting systems; execution states (aka modes) are operational descriptions of activities with regard to processes’ control and execution. Power-types (²) are used to partition execution paths.

Since the objective is to identify objects and behaviors at architecture level, variants, abstractions, or implementations are to be overlooked. It also ensues that the blueprints obtained remain general enough as to be uniformly, consistently and unambiguously translated into most of modeling languages.

Languages & Models

Enterprise architects may have to deal with a range of models depending on scope (business vs system) or level (enterprise and system vs domains and applications):

  • Business process modeling languages are used to associate business domains and enterprises organization.
  • Domain specific languages do the same between business domains and software components, bypassing enterprise organization and systems architecture.
  • Generic modeling languages like UML are supposed to cover the whole range of targets.
  • Languages like Archimate focus on the association between enterprises organization and systems functionalities.
  • Contrary to modeling languages programming ones are meant to translate functionalities into software end-products. Some, like WSDL (Web Service Definition Language), can be used to map EA into service oriented architectures (SOA).

Scope of Modeling Languages

While architects clearly don’t have to know the language specifics, they must understand their scope and purposes.

Processes

Whatever the languages, methods, or models, the primary objective is that architectures support business processes whenever and wherever needed. Except for standalone applications (for which architects are marginally involved), the way systems architectures support business processes is best understood with regard to layers:

  • Processes are solutions to business problems.
  • Processes (aka business solutions) induce problems for systems, to be solved by functional architecture.
  • Implementations of functional architectures induce problems for platforms, to be solved by technical architectures.

Enterprise architects should focus on the alignment of business problems and supporting systems functionalities

As already noted, enterprise architects are to focus on enterprise and system layers: how business processes are supported by systems functionalities and, more generally, how architecture capabilities are to be aligned with enterprise objectives.

Nonetheless, business processes don’t operate in a vacuum and may depend on engineering and operational processes, with regard to development for the former, deployment for the latter.

EARdmap_XProcs

Enterprise architects should take a holistic view of business, engineering, and operational processes.

Given the crumbling of traditional fences between environments and IT systems under combined markets and technological waves, the integration of business, engineering, and operational processes is to become a necessary condition for market analysis and reactivity to changes in business environment.

Hence the benefits of combining bottom-up and top-down perspectives, the former focused on business and engineering processes, the latter business models and organization.

Crossing processes and architecture perspectives

Enterprise architects could then focus on the mapping of business functions to services, the alignment of quality of services with architecture capabilities, and the flows of information across the organization.

Architecture

Blueprints being architects’ tool of choice, enterprise architects use them to chart how enterprise objectives are to be supported by systems capabilities; for that purpose:

  • On one hand they have to define the concepts used for the organization, business domains, and business processes.
  • On the other hand they have to specify, monitor, assess, and improve the capabilities of supporting systems.

In between they have to define the functionalities that will consolidate specific and possibly ephemeral business needs into shared and stable functions best aligned with systems capabilities.

MapsTerrits_Archis

The role of functional architectures is to map conceptual models to systems capabilities

As already noted, enterprise architects don’t have to look under the hood at the implementation of functions; what they must do is to ensure the continuous and comprehensive transparency between existing as well a planned business objectives and systems capabilities.

Assessment

One way or the other, governance implies assessment, and for enterprise architects that means setting apart architectural assets and business applications:

  • Whatever their nature (enterprise organization or systems capabilities), the life-cycle of assets encompasses multiple production cycles, with returns to be assessed across business units. On that account enterprise architects are to focus on the assessment of the functional architecture supporting business objectives.
  • By contrast, the assessment of business applications can be directly tied to a business value within a specific domain, value which may change with cycles. Depending on induced changes for assets, adjustments are to be carried out through users’ stories (standalone, local impact) or use cases (shared business functions, architecture impact).

Enterprise architects deal with assets, business analysts with processes.

The difficulty of assessing returns for architectural assets is compounded by cross dependencies between business, engineering, and operational processes; and these dependencies may have a decisive impact for operational decision-making.

Business Intelligence & Decision-making

Embedding IT systems in business processes is to be decisive if business intelligence (BI) is to accommodate the ubiquity of digitized business processes and the integration of enterprises with their environments. That is to require a seamless integration of data analytics and decision-making:

Data analytics (sometimes known as data mining) is best understood as a refining activity whose purpose is to process raw data into meaningful information:

  • Data understanding gives form and semantics to raw material.
  • Business understanding charts business contexts and concerns in terms of objects and processes descriptions.
  • Modeling consolidates data and business understanding into descriptive, predictive, or operational models.
  • Evaluation assesses and improves accuracy and effectiveness with regard to objectives and decision-making.

Decision-making processes in open and digitized environment are best described with the well established OODA (Observation, Orientation, Decision, Action) loop:

  1. Observation: understanding of changes in business environments (aka territories).
  2. Orientation: assessment of the reliability and shelf-life of pertaining information (aka maps) with regard to current positions and operations.
  3. Decision: weighting of options with regard to enterprise capabilities and broader objectives.
  4. Action: carrying out of decisions within the relevant time-frame.
OKBI_BIDM

Seamless integration of data analytics and decision-making.

Along that perspective data analytics and decision-making can be seen as the front-offices of business intelligence, and  knowledge management as its back-office.

More generally that scheme epitomizes the main challenge of enterprise architects, namely the continuous and dynamic alignment of enterprise organization and systems to market environment, business processes, and decision-making.

Further Reading