The seamless integration of enterprise systems into digital business environments calls for a resetting of value chains with regard to enterprise architectures, and more specifically supporting assets.
Concerning value chains, the traditional distinction between primary and supporting activities is undermined by the generalization of digital flows, rapid changes in business environments, and the ubiquity of software agents. As for assets, the distinction could even disappear due to the intertwining of tangibles resources with organization, information, and knowledge .
These difficulties could be overcome by bypassing activities and drawing value chains directly between business processes and systems capabilities.
From Activities to Processes
In theory value chains are meant to track down the path of added value across enterprise architectures; in practice their relevancy is contingent on specificity: fine when set along silos, less so if set across business functions. Moreover, value chains tied to static mappings of primary and support activities risk losing their grip when maps are redrawn, which is bound to happen more frequently with digitized business environments.
These shortcomings can be fixed by replacing primary activities by processes and support ones by system capabilities, and redefining value chains accordingly.
From Processes to Functions & Capabilities
Replacing primary and support activities with processes and functions doesn’t remove value chains primary issue, namely their path along orthogonal dimensions.
That’s not to say that business processes cannot be aligned with self-contained value chains, but insofar as large and complex enterprises are concerned, value chains are to be set across business functions. Thus the benefit of resetting the issue at enterprise architecture level.
Borrowing EA description from the Zachman framework, the mapping of processes to capabilities is meant to be carried out through functions, with business processes on one hand, architectures capabilities on the other hand.
If nothing can be assumed about the number of functions or the number of crossed processes, EA primary capabilities can be clearly identified, and functions classified accordingly, e.g: boundaries, control, entities, computation. That classification (non exclusive, as symbolized by the crossed pentagons) coincides with that nature of adjustments induced by changes in business environments:
Diversity and flexibility are to be expected for interfaces to systems’ clients (users, devices, or other systems) and triggering events, as to tally with channels and changes in business and technology environments.
Continuity is critical for the identification and semantics of business objects whose consistency and integrity have to be maintained along time independently of users and processes.
In between, changes in processes control and business logic should be governed by business opportunities independently of channels or platforms.
Processes, primary or otherwise, would be sliced according to the nature of supporting capabilities e.g: standalone (a), real-time (b), client-server (c), orchestration service (d), business rules (e), DB access (f).
Value chains could then be attached to business processes along these functional guidelines.
Tying Value Chains to Processes
Bypassing activities is not without consequences for the meaning of value chains as the original static understanding is replaced by a dynamic one: since value chains are now associated to specific operations, they are better understood as changes than absolute level. That semantic shift reflects the new business environment, with manufacturing and physical flows having been replaced by mixed (SW and HW) engineering and digital flows.
Set in a broader economic perspective, the new value chains could be likened to a marginal version of returns on capital (ROC), i.e the delta of some ratio between value and contributing assets.
Digital business environments may also made value chains easier to assess as changes can be directly traced to requirements at enterprise level, and more accurately marked across systems functionalities:
Logical interfaces (users or systems): business value tied to interactions with people or other systems.
Physical interfaces (devices): business value tied to real-time interactions.
Business logic: business value tied to rules and computations.
Information architecture: business value tied to systems information contents.
Processes architecture: business value tied to processes integration.
Platform configurations: business value tied to resources deployed.
The next step is to frame value chains across enterprise architectures in order to map values to contributing assets.
Assets & Organization
Value chains are arguably of limited use without weighting assets contribution. On that account, a major (if underrated) consequence of digital environments is the increasing weight of intangible assets brought about by the merge of actual and information flows and the rising importance of economic intelligence.
For value chains, that shift presents a double challenge: first because of the intrinsic difficulty of measuring intangibles, then because even formerly tangible assets are losing their homogeneity.
Redefining value chains at enterprise architecture level may help with the assessment of intangibles by bringing all assets, tangible or otherwise, into a common frame, reinstating organization as its nexus:
From the business perspective, that framing restates the primacy of organization for the harnessing of IT benefits.
From the architecture perspective, the centrality of organization appears when assets are ranked according to modality: symbolic (e.g culture), physical (e.g platforms), or a combination of both.
On that basis enterprise organization can be characterized by what it supports (above) and how it is supported (below). Given the generalization of digital environments and business flows, one could then take organization and information systems as proxies for the whole of enterprise architecture and draw value chains accordingly.
Value Chains & Assets
Trendy monikers may differ but information architectures have become a key success factor for enterprises competing in digital environments. Their importance comes from their ability to combine three basic functions:
Mining the continuous flows of relevant and up-to-date data.
Analyzing and transforming data, feeding the outcome to information systems
Putting that information to use in operational and strategic decision-making processes.
A twofold momentum is behind that integration: with regard to feasibility, it can be seen as a collateral benefit of the integration of actual and digital flows; with regard to opportunity, it can give a decisive competitive edge when fittingly carried through. That makes information architecture a reference of choice for intangible assets.
Insofar as enterprise architecture is concerned, value chains can then be threaded through three categories of assets:
Frames are meant to bring light on artifacts, not to cover them with veils; on that account, frameworks’ primary justification should be their leverage on engineering practices.
It ensues that enterprises frameworks should only be considered in relation to actual contents (organization, artifacts, processes, …), and their benefits for core governance issues, in particular measurements, processes integration, and economic intelligence.
The least frameworks should do for enterprise architects is to provide common bearings at both application and enterprise levels:
Application level: a consolidated scale for the mapping of value points, function points, and development costs.
Enterprise level: a common basis for a reliable assessment of systems capabilities and processes maturity.
A detailed account of how a framework would support such bearings should therefore be a prerequisite.
Cornerstone: Business & Engineering Processes Integration
Except for the Zachman framework, providers haven’t much to say about how actual business and engineering processes are to be hung to the proposed framework, and more generally about the continuity with existing methods, tools, and practices.
Conversely, being specific about the hinges linking it to processes is to give credence to a framework leveraging impact:
Business: leveraged agility from the harnessing of users’ stories to business functions.
Engineering: leveraged transparency, traceability and reuse from model based systems engineering (MBSE).
As the cornerstone of any enterprise framework, integration is to support their ultimate purpose, namely economic intelligence.
Deal Breaker: Economic Intelligence
Economic intelligence is usually understood as a merge of data analytics, information architecture, business intelligence, and decision-making; as such it can be seen as a primary justification for enterprise architecture frameworks.
As a corollary, one would expect frameworks to be specific with regard to the integration of data analytics, information processing, and knowledge management:
To begin with, no framework should be considered without being explicit about the ways such integration could be achieved.
Then, integration schemes should aim at being neutral with regard to engineering or support environments deployed at architecture level.
When options are considered, that should be taken as a deal breaker.
Assuming that expectations about measurements and processes could be reasonably borne out, decisions would rest on economic intelligence strategies.
Depending on the weight of information systems legacy, full neutrality may deem a too ambitious strategy. In that case an enterprise architecture framework could be built bottom up from MBSE environments.
Given that proviso, a neutral framework designed on purpose would bring much more leverage due to the generalization of well-defined slots and interfaces for business and engineering platforms; that would also facilitate modernization and consolidate quality assurance across enterprise architectures.
Finally, for EA frameworks to ensure consistent metrics, processes integration, and economic intelligence, knowledge management must encompass a wide range of concerns (business, engineering, regulations, …), contexts (environments, enterprise, systems), and resources (texts, models, people, …); that can be best achieved with profiled ontologies.
Humans often expect concepts to come with innate if vague meanings before being compelled to withstand endless and futile controversies around definitions. Going the other way would be a better option: start with differences, weed out irrelevant ones, and use remaining ones to advance.
Concerning enterprise, it would start with the difference between business and architecture, and proceed with the wholeness of data, information, and knowledge.
Business Architecture is an Oxymoron
Business being about time and competition, success is not to be found in recipes but would depend on particularities with regard to objectives, use of resources, and timing. These drives are clearly at odds with architectures rationales for shared, persistent, and efficient structures and mechanisms. As a matter of fact, dealing with the conflicting nature of business and architecture concerns can be seen as a key success factor for enterprise architects, with information standing at the nexus.
Data as Resource, Information as Asset, Knowledge as Service
Paradoxically, the need of a seamless integration of data, information, and knowledge means that the distinction between them can no longer be overlooked.
Data is captured through continuous and heterogeneous flows from a wide range of sources.
Information is built by adding identity, structure, and semantics to data. Given its shared and persistent nature it is best understood as asset.
Knowledge is information put to use through decision-making. As such it is best understood as a service.
Ensuring the distinction as well as the integration must be a primary concern of enterprise architects.
Sustainable Success Depends on a Balancing Act
Success in business is an unfolding affair, on one hand challenged by circumstances and competition, on the other hand to be consolidated by experience and lessons learnt. Meeting challenges while warding off growing complexity will depend on business agility and the versatility and plasticity of organizations and systems. That should be the primary objective of enterprise architects.
“For things to remain the same, everything must change”
Lampedusa, “The Leopard”
Whatever the understanding of the discipline, most EA schemes implicitly assume that enterprise architectures, like their physical cousins, can be built from blueprints. But they are not because enterprises have no “Pause” and “Reset” buttons: business cannot be put on stand-by and must be carried on while work is in progress.
Systems & Enterprises
Systems are variously defined as:
“A regularly interacting or interdependent group of items forming a unified whole” (Merriam-Webster).
“A set of connected things or devices that operate together” (Cambridge Dictionary).
“A way of working, organizing, or doing something which follows a fixed plan or set of rules” (Collins Dictionary)
“A collection of components organized to accomplish a specific function or set of functions” (TOGAF from ISO/IEC 42010:2007)
While differing in focus, most understandings mention items and rules, purpose, and the ability to interact; none explicitly mention social structures or interactions with humans. That suggests where the line should be drawn between systems and enterprises, and consequently between corresponding architectures.
Architectures & Changes
Enterprises are live social entities made of corporate culture, organization, and supporting systems; their ultimate purpose is to maintain their identity and integrity while interacting with environments. As a corollary, changes cannot be carried out as if architectures were just apparel, but must ensure the continuity and consistency of enterprises’ structures and behaviors.
That cannot be achieved by off-soil schemes made of blueprints and step-by-step processes detached from actual organization, systems, and processes. Instead, enterprise architectures must be grown bottom up from actual legacies whatever their nature: technical, functional, organizational, business, or cultural.
Insofar as enterprise architectures are concerned, legacies are usually taken into account through one of three implicit assumptions:
No legacy assumptions ignore the issue, as if the case of start-ups could be generalized. These assumptions are logically flawed because enterprises without legacy are like embryos growing their own inherent architecture, and in that case there would be no need for architects.
En Bloc legacy assumptions take for granted that architectures as a whole could be replaced through some Big Bang operation without having a significant impact on business activities. These assumptions are empirically deceptive because, even limited to software architectures, Big Bang solutions cannot cope with the functional and generational heterogeneity of software components characterizing large organizations. Not to mention that enterprise architectures are much more that software and IT.
Piecemeal legacies can be seen as the default assumption, based on the belief that architectures can be re-factored or modernized step by step. While that assumption may be empirically valid, it may also miss the point: assuming that all legacies can be dealt with piecemeal rubs out the distinction pointed above between systems and enterprises.
So, the question remains of what is to be changed, and how ?
EA as a Work In Progress
As with leopard’s spots and identity, the first step would be to set apart what is to change (architectures) from what is to carry on (enterprise).
Maps and territories do provide an overview of spots’ arrangement, but they are static views of architectures, whereas enterprises are dynamic entities that rely on architectures to interact with their environment. So, for maps and territories to serve that purpose they should enable continuous updates and adjustments without impairing enterprises’ awareness and ability to compete.
That shift from system architecture to enterprise behavior implies that:
The scope of changes cannot be fully defined up-front, if only because the whole enterprise, including its organization and business model, could possibly be of concern.
Fixed schedules are to be avoided, lest each and every unit, business or otherwise, would have to be shackled into a web of hopeless reciprocal commitments.
Different stakeholders may come as interested parties, some more equal than others, possibly with overlapped prerogatives.
So, instead of procedural and phased approaches supposed to start from blank pages, EA ventures must be carried out iteratively with the planning, monitoring, assessment, and adjustment of changes across enterprises’ businesses, organizations, and systems. That can be represented as an extension of the OODA (Observation, Orientation, Decision, Action) loop:
Actual observations from operations (a)
Data analysis with regard to architectures as currently documented (b).
Changes in business processes (c).
Changes in architectures (d).
Moreover, due to the generalization of digital flows between enterprises and their environment, decision-making processes used to be set along separate time-frames (operational, tactical, strategic, …), must now be weaved together along a common time-scale encompassing internal (symbolic) as well as external (actual) events.
It ensues that EA processes must not only be continuous, but they also must deal with latency constraints.
Changes & Latency
Architectures are by nature shared across organizational units (enterprise level) and business processes (system level). As a corollary, architecture changes are bound to introduce mismatches and frictions across business-specific applications. Hence the need of sorting out the factors affecting the alignment of maps and territories:
Elapsed time between changes in territories and maps updates (a>b) depends on data analytics and operational architecture.
Elapsed time between changes in maps and revised objectives (b>c) depends on business analysis and organization.
Elapsed time between changes in objectives and their implementation (c>d) depends on engineering processes and systems architecture.
Elapsed time between changes in systems and changes in territories (d>a) depends on applications deployment and technical architectures.
On that basis it’s possible to define four critical lags:
Operational: data analytics can be impeded by delayed, partial, or inaccurate feedback from processes.
Mapping: business analysis can be impeded by delays or discrepancies in data analytics.
Engineering: development of applications can be impeded by delays or discrepancies in business analysis.
Processes: deployment of business processes can be impeded by delays in the delivery of supporting applications.
These lags condition the whole of EA undertakings because legacy structures, mechanisms, and organizations are to be continuously morphed into architectures without introducing misrepresentations that would shackle activities and stray decision-making.
EA Latency & Augmented Reality
Insofar as architectural changes are concerned, discrepancies and frictions are rooted in latency, i.e the elapsed time between actual changes in territories and the updating of relevant maps.
As noted above, these lags have to be weighted according to time-frames, from operational days to strategic years, so that the different agents could be presented with the relevant and up-to-date views befitting to each context and concerns.
That could be achieved if enterprises architectures were presented through augmented reality technologies.
Compared to virtual reality (VR) which overlooks the whole issue of reality and operates only on similes and avatars, augmented reality (AR) brings together virtual and physical realms, operating on apparatuses that weaves actual substrates, observations, and interventions with made-up descriptive, predictive, or prescriptive layers.
On that basis, users would be presented with actual territories (EA legacy) augmented with maps and prospective territories.
Composition and dynamics of maps and territories (actual and prospective) could be set and edited appropriately, subject to latency constraints.
European Union’s General Data Protection Regulation (GDPR), to come into effect this month, is a seminal and momentous milestone for data privacy .
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.
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.
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.
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).
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).
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.
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.
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.
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.
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).
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:
Such mapping is to significantly enhance the transparency of regulatory policies.
Given the digitization of enterprises environments, engineering processes have to be entwined with business ones while kept in sync with enterprise architectures. That calls for new threads of collaboration taking into account the integration of business and engineering processes as well as the extension to business environments.
Whereas models are meant to support communication, traditional approaches are already straining when used beyond software generation, that is collaboration between humans and CASE tools. Ontologies, which can be seen as a higher form of models, could enable a qualitative leap for systems collaborative engineering at enterprise level.
Systems Engineering: Contexts & Concerns
To begin with contents, collaborations should be defined along three axes:
Requirements: business objectives, enterprise organization, and processes, with regard to systems functionalities.
Feasibility: business requirements with regard to architectures capabilities.
Architectures: supporting functionalities with regard to architecture capabilities.
Since these axes are usually governed by different organizational structures and set along different time-frames, collaborations must be supported by documentation, especially models.
In order to support collaborations across organizational units and time-frames, models have to bring together perspectives which are by nature orthogonal:
Contexts, concerns, and languages: business vs engineering.
Time-frames and life-cycle: business opportunities vs architecture stability.
That could be achieved if engineering models could be harnessed to enterprise ones for contexts and concerns. That is to be achieved through the integration of processes.
As already noted, the integration of business and engineering processes is becoming a key success factor.
For that purpose collaborations would have to take into account the different time-frames governing changes in business processes (driven by business value) and engineering ones (governed by assets life-cycles):
Business requirements engineering is synchronic: changes must be kept in line with architectures capabilities (full line).
Software engineering is diachronic: developments can be carried out along their own time-frame (dashed line).
Application-driven projects usually focus on users’ value and just-in-time delivery; that can be best achieved with personal collaboration within teams. Architecture-driven projects usually affect assets and non-functional features and therefore collaboration between organizational units.
Collaboration: Direct or Mediated
Collaboration can be achieved directly or through some mediation, the former being a default option for applications, the latter a necessary one for architectures.
Both can be defined according to basic cognitive and organizational mechanisms and supported by a mix of physical and virtual spaces to be dynamically redefined depending on activities, projects, locations, and organisation.
Direct collaborations are carried out between individuals with or without documentation:
Immediate and personal: direct collaboration between 5 to 15 participants with shared objectives and responsibilities. That would correspond to agile project teams (a).
Delayed and personal: direct collaboration across teams with shared knowledge but with different objectives and responsibilities. That would tally with social networks circles (c).
Mediated collaborations are carried out between organizational units through unspecified individual members, hence the need of documentation, models or otherwise:
Direct and Code generation from platform or domain specific models (b).
Model transformation across architecture layers and business domains (d)
Depending on scope and mediation, three basic types of collaboration can be defined for applications, architecture, and business intelligence projects.
As it happens, collaboration archetypes can be associated with these profiles.
Agile development model (under various guises) is the option of choice whenever shared ownership and continuous delivery are possible. Application projects can so be carried out autonomously, with collaborations circumscribed to team members and relying on the backlog mechanism.
Projects set across enterprise architectures cannot be carried out without taking into account phasing constraints. While ill-fated Waterfall methods have demonstrated the pitfalls of procedural solutions, phasing constraints can be dealt with a roundabout mechanism combining iterative and declarative schemes.
Engineering vs Business Driven Collaborations
With collaborative engineering upgraded at enterprise level, the main challenge is to iron out frictions between application and architecture projects and ensure the continuity, consistency and effectiveness of enterprise activities. That can be achieved with roundabouts used as a collaboration mechanism between projects, whatever their nature:
Shared models are managed at roundabout level.
Phasing dependencies are set in terms of assertions on shared models.
Depending on constraints projects are carried out directly (1,3) or enter roundabouts (2), with exits conditioned by the availability of models.
Moreover, with engineering embedded in business processes, collaborations must also bring together operational analytics, decision-making, and business intelligence. Here again, shared models are to play a critical role:
Enterprise descriptive and prescriptive models for information maps and objectives
Environment predictive models for data and business understanding.
Whereas both engineering and business driven collaborations depend on sharing information and knowledge, the latter have to deal with open and heterogeneous semantics. As a consequence, collaborations must be supported by shared representations and proficient communication languages.
Ontologies & Representations
Ontologies are best understood as models’ backbones, to be fleshed out or detailed according to context and objectives, e.g:
Thesaurus, with a focus on terms and documents.
Systems modeling, with a focus on integration, e.g Zachman Framework.
Classifications, with a focus on range, e.g Dewey Decimal System.
Meta-models, with a focus on model based engineering, e.g models transformation.
Conceptual models, with a focus on understanding, e.g legislation.
Knowledge management, with a focus on reasoning, e.g semantic web.
As such they can provide the pillars supporting the representation of the whole range of enterprise concerns:
Taking a leaf from Zachman’s matrix, ontologies can also be used to differentiate concerns with regard to architecture layers: enterprise, systems, platforms.
Last but not least, ontologies can be profiled with regard to the nature of external contexts, e.g:
Institutional: Regulatory authority, steady, changes subject to established procedures.
Professional: Agreed upon between parties, steady, changes subject to established procedures.
Corporate: Defined by enterprises, changes subject to internal decision-making.
Social: Defined by usage, volatile, continuous and informal changes.
Personal: Customary, defined by named individuals (e.g research paper).
Ontologies & Communication
If collaborations have to cover engineering as well as business descriptions, communication channels and interfaces will have to combine the homogeneous and well-defined syntax and semantics of the former with the heterogeneous and ambiguous ones of the latter.
With ontologies represented as RDF (Resource Description Framework) graphs, the first step would be to sort out truth-preserving syntax (applied independently of domains) from domain specific semantics.
On that basis it would be possible to separate representation syntax from contents semantics, and to design communication channels and interfaces accordingly.
That would greatly facilitate collaborations across externally defined ontologies as well as their mapping to enterprise architecture models.
To summarize, the benefits of ontological frames for collaborative engineering can be articulated around four points:
A clear-cut distinction between representation semantics and truth-preserving syntax.
A common functional architecture for all users interfaces, humans or otherwise.
Modular functionalities for specific semantics on one hand, generic truth-preserving and cognitive operations on the other hand.
Profiled ontologies according to concerns and contexts.
A critical fifth benefit could be added with regard to business intelligence: combined with deep learning capabilities, ontologies would extend the scope of collaboration to explicit as well as implicit knowledge, the former already framed by languages, the latter still open to interpretation and discovery.