Despite (or because) their ubiquity across powerpoint presentations, business capabilities appear under a wide range of guises. Putting them in perspectives could clarify the issue.
To begin with, business capabilities should not be confused with systems ones as they are supposed to be driven by changing environments and opportunities, in contrast to continuity, integrity, and returns on investments. Were it not for the need to juggle with both there would be no need of chief “whatever” officers.
Then, if they are to be assessed across changing contexts and concerns, business capabilities should not be tied to specific ones but focus on enterprise wherewithal :
Material: capacity and maturity of platforms with regard to changes in business processes and environments.
Functional: capacity and maturity of supporting systems with regard to changes in business processes.
Organizational: versatility and plasticity of roles and processes in dealing with changes in business opportunities.
Intelligence: information assets and ability of people to use them.
These could then be adjusted with regard to finance and time:
Strategic: assets, to be deployed and paid for across a number of business exercices.
Tactical: resources, to be deployed and paid for within single business exercices.
Particular: combined assets and resources needed to support specific value chains.
The role of enterprise architects would then to plan, assess, and manage the dynamic alignment of business and architecture capabilities.
UML Actors (aka Roles) are meant to provide a twofold description of interactions between systems and their environment: organization and business process on one hand, system and applications on the other hand.
That can only be achieved by maintaining a conceptual distinction between actual agents, able to physically interact with systems, and actors (aka roles), which are their symbolic avatars as perceived by applications.
As far as the purpose is to describe interactions, actors should be primary characterized by the nature of language (symbolic or not), and identification coupling (biological or managed):
Symbolic communication, no biological identification (systems)
Analog communication, no biological identification (active devices or equipments)
Analog communication, biological identification (live organisms)
While there has been some confusion between actors (or roles) and agents, a clear-cut distinction is now a necessity due to the centrality of privacy issues, whether it is from business or regulatory perspective.
Depending on context and purpose requirements can be understood as customary documents, contents, or work in progress.
Given that requirements are the entry point of engineering processes, nothing should be assumed regarding their form (natural, formal, or specific language), or content (business, functional, non-functional, or technical).
Depending on the language used, requirements can be directly analyzed and engineered, or may have to be first formatted (aka captured).
Requirements taxonomy should be set with regard to processes (business or architecture driven) and stakeholders (business units or enterprise architecture).
Depending on content and context, requirements can be engineered as a whole (agile development models), or set apart as to tally with external dependencies (phased development models).
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:
Digital environments and the ubiquity of software in business processes introduces a new perspective on value chains and the assessment of supporting applications.
At the same time, as software designs cannot be detached of architectures capabilities, the central question remains of allocating costs and benefits between primary and support activities .
Value Chains & Activities
The concept of value chain introduced by Porter in 1985 is meant to encompass the set of activities contributing to the delivery of a valuable product or service for the market.
Taking from Porter’s generic model, various value chains have been refined according to business specific categories for primary and support activities.
Whatever their merits, these approaches are essentially static and fall short when the objective is to trace changes induced by business developments; and that flaw may become critical with the generalization of digital business environments:
Given the role and ubiquity of software components (not to mention smart ones), predefined categories are of little use for impact analysis.
When changes in value chains are considered, the shift of corporate governance towards enterprise architecture puts the focus on assets contribution, cutting down the relevance of activities.
Hence the need of taking into account changes, software development, and enterprise architectures capabilities.
Value Added & Software Development
While the growing interest for value chains in software engineering is bound to agile approaches and business driven developments, the issue can be put in the broader perspective of project planning.
With regard to assessment,stakeholders, start with business opportunities and look at supporting systems from a black box perspective; in return, software providers are to analyze requirements from a white box perspective, and estimate corresponding development effort and time delivery.
Assuming transparency and good faith, both parties are meant to eventually align expectations and commitments with regard to features, prices, and delivery.
With regard to policies, stakeholders put the focus on returns on investment (ROI), obtained from total cost of ownership, quality of service, and timely delivery. Providers for their part try to minimize development costs while taking into account effective use of resources and costs of opportunities. As it happens, those objectives may be carried on as non-zero sum games:
Business stakeholders foretell the actualized returns (a) to be expected from the functionalities under consideration (b).
Providers consider the solutions (b’) and estimate actualized costs (a’).
Stakeholders and providers agree on functionalities, prices and deliveries (c).
Assuming that business and engineering environments are set within different time-frames, there should be room for non-zero-sum games winding up to win-win adjustments on features, delivery, and prices.
Continuous vs Phased Alignments
Notwithstanding the constraints of strategic planning, business processes are by nature opportunistic, and their ability to be adjusted to circumstances is becoming all the more critical with the generalization of digital business environments.
Broadly speaking, the squaring of supporting applications to business value can be done continuously or by phases:
Phased alignments start with some written agreements with regard to features, delivery, and prices before proceeding with development phases.
Continuous alignment relies on direct collaboration and iterative development to shape applications according to business needs.
Beyond sectarian controversies, each approach has its use:
Continuous schemes are clearly better at harnessing value chains, providing that project teams be allowed full project ownership, with decision-making freed of external dependencies or delivery constraints.
Phased schemes are necessary when value chains cannot be uniquely sourced as they take roots in different organizational units, or if deliveries are contingent on technical constraints.
In any case, it’s not a black-and-white alternative as work units and projects’ granularity can be aligned with differentiated expectations and commitments.
Work Units & Architecture Capabilities
While continuous and phased approaches are often opposed under the guises of Agile vs Waterfall, that understanding is misguided as it extends the former to a motley of self-appointed agile schemes and reduces the latter to an ill-famed archetype.
Instead, a reasoned selection of a development models should be contingent on the problems at hand, and that can be best achieved by defining work-units bottom-up with regard to the capabilities targeted by requirements:
Development patterns could then be defined with regard to architecture layers (organization and business, systems functionalities, platforms implementations) and capabilities footprint:
Phased: work units are aligned with architecture capabilities, e.g : business objects (a), business logic (b), business processes (c), users interfaces (d).
Iterative: work units are set across capabilities and defined dynamically according to development problems.
That would provide a development framework supporting the assessment of iterative as well as phased projects, paving the way for comprehensive and integrated impact analysis.
Value Chains & Architecture Capabilities
As far as software engineering is concerned, the issue is less the value chain itself than its change, namely how value is to be added along the chain.
To summarize, the transition to this layout is carried out in two steps:
Conceptually, Zachman’s original “Why” column is translated into a line running across column capabilities.
Graphically, the five remaining columns are replaced by embedded pentagons, one for each architecture layer, with the new “Why” line set as an outer layer linking business value to architectures capabilities:
That apparently humdrum transformation entails a significant shift in focus and practicality:
The focus is put on organizational and business objectives, masking the ones associated to systems and platforms layers.
It makes room for differentiated granularity in the analysis of value, some items being anchored to specific capabilities, others involving cross dependencies.
Value chains can then be charted from business processes to supporting architectures, with software applications in between.
As pointed above, the crumbling of traditional fences and the integration of enterprise architectures into digital environments undermine the traditional distinction between primary and support activities.
To be sure, business drive is more than ever the defining factor for primary activities; and computing more than ever the archetype of supporting ones. But in between the once clear-cut distinctions are being blurred by a maze of digital exchanges.
In order to avoid a spaghetti heap of undistinguished connections, value chains are to be “colored” according to the nature of links:
Between architectures capabilities: business and organization (enterprise), systems functionalities, or platforms and technologies.
Between architecture layers: engineering processes.
When set within that framework, value chains could be navigated in both directions:
For the assessment of applications developed iteratively: business value could be compared to development costs and architecture assets’ depreciation.
For the assessment of features (functional or non functional) to be shared across business applications: value chains will provide a principled basis for standard accounting schemes.
Combined with model based system engineering that could significantly enhance the integration of enterprise architecture into corporate governance.
Computation independent models (CIMs) describe organization and business processes independently of the role played by supporting systems.
Platform independent models (PIMs) describe the functionalities supported by systems independently of their implementation.
Platform specific models (PSMs) describe systems components depending on platforms and technologies.
Engineering processes can then be phased along architecture layers (a), or carried out iteratively for each application (b).
When set across activities value chains could be engraved in CIMs and refined with PIMs and PSMs(a). Otherwise, i.e with business value neatly rooted in single business units, value chains could remain implicit along software development (b).
Given the digitization of enterprises environments, engineering processes have to be entwined with business ones while kept in sync with enterprise architectures. That calls for new threads of collaboration taking into account the integration of business and engineering processes as well as the extension to business environments.
Whereas models are meant to support communication, traditional approaches are already straining when used beyond software generation, that is collaboration between humans and CASE tools. Ontologies, which can be seen as a higher form of models, could enable a qualitative leap for systems collaborative engineering at enterprise level.
Systems Engineering: Contexts & Concerns
To begin with contents, collaborations should be defined along three axes:
Requirements: business objectives, enterprise organization, and processes, with regard to systems functionalities.
Feasibility: business requirements with regard to architectures capabilities.
Architectures: supporting functionalities with regard to architecture capabilities.
Since these axes are usually governed by different organizational structures and set along different time-frames, collaborations must be supported by documentation, especially models.
In order to support collaborations across organizational units and time-frames, models have to bring together perspectives which are by nature orthogonal:
Contexts, concerns, and languages: business vs engineering.
Time-frames and life-cycle: business opportunities vs architecture stability.
That could be achieved if engineering models could be harnessed to enterprise ones for contexts and concerns. That is to be achieved through the integration of processes.
As already noted, the integration of business and engineering processes is becoming a key success factor.
For that purpose collaborations would have to take into account the different time-frames governing changes in business processes (driven by business value) and engineering ones (governed by assets life-cycles):
Business requirements engineering is synchronic: changes must be kept in line with architectures capabilities (full line).
Software engineering is diachronic: developments can be carried out along their own time-frame (dashed line).
Application-driven projects usually focus on users’ value and just-in-time delivery; that can be best achieved with personal collaboration within teams. Architecture-driven projects usually affect assets and non-functional features and therefore collaboration between organizational units.
Collaboration: Direct or Mediated
Collaboration can be achieved directly or through some mediation, the former being a default option for applications, the latter a necessary one for architectures.
Both can be defined according to basic cognitive and organizational mechanisms and supported by a mix of physical and virtual spaces to be dynamically redefined depending on activities, projects, locations, and organisation.
Direct collaborations are carried out between individuals with or without documentation:
Immediate and personal: direct collaboration between 5 to 15 participants with shared objectives and responsibilities. That would correspond to agile project teams (a).
Delayed and personal: direct collaboration across teams with shared knowledge but with different objectives and responsibilities. That would tally with social networks circles (c).
Mediated collaborations are carried out between organizational units through unspecified individual members, hence the need of documentation, models or otherwise:
Direct and Code generation from platform or domain specific models (b).
Model transformation across architecture layers and business domains (d)
Depending on scope and mediation, three basic types of collaboration can be defined for applications, architecture, and business intelligence projects.
As it happens, collaboration archetypes can be associated with these profiles.
Agile development model (under various guises) is the option of choice whenever shared ownership and continuous delivery are possible. Application projects can so be carried out autonomously, with collaborations circumscribed to team members and relying on the backlog mechanism.
Projects set across enterprise architectures cannot be carried out without taking into account phasing constraints. While ill-fated Waterfall methods have demonstrated the pitfalls of procedural solutions, phasing constraints can be dealt with a roundabout mechanism combining iterative and declarative schemes.
Engineering vs Business Driven Collaborations
With collaborative engineering upgraded at enterprise level, the main challenge is to iron out frictions between application and architecture projects and ensure the continuity, consistency and effectiveness of enterprise activities. That can be achieved with roundabouts used as a collaboration mechanism between projects, whatever their nature:
Shared models are managed at roundabout level.
Phasing dependencies are set in terms of assertions on shared models.
Depending on constraints projects are carried out directly (1,3) or enter roundabouts (2), with exits conditioned by the availability of models.
Moreover, with engineering embedded in business processes, collaborations must also bring together operational analytics, decision-making, and business intelligence. Here again, shared models are to play a critical role:
Enterprise descriptive and prescriptive models for information maps and objectives
Environment predictive models for data and business understanding.
Whereas both engineering and business driven collaborations depend on sharing information and knowledge, the latter have to deal with open and heterogeneous semantics. As a consequence, collaborations must be supported by shared representations and proficient communication languages.
Ontologies & Representations
Ontologies are best understood as models’ backbones, to be fleshed out or detailed according to context and objectives, e.g:
Thesaurus, with a focus on terms and documents.
Systems modeling, with a focus on integration, e.g Zachman Framework.
Classifications, with a focus on range, e.g Dewey Decimal System.
Meta-models, with a focus on model based engineering, e.g models transformation.
Conceptual models, with a focus on understanding, e.g legislation.
Knowledge management, with a focus on reasoning, e.g semantic web.
As such they can provide the pillars supporting the representation of the whole range of enterprise concerns:
Taking a leaf from Zachman’s matrix, ontologies can also be used to differentiate concerns with regard to architecture layers: enterprise, systems, platforms.
Last but not least, ontologies can be profiled with regard to the nature of external contexts, e.g:
Institutional: Regulatory authority, steady, changes subject to established procedures.
Professional: Agreed upon between parties, steady, changes subject to established procedures.
Corporate: Defined by enterprises, changes subject to internal decision-making.
Social: Defined by usage, volatile, continuous and informal changes.
Personal: Customary, defined by named individuals (e.g research paper).
Ontologies & Communication
If collaborations have to cover engineering as well as business descriptions, communication channels and interfaces will have to combine the homogeneous and well-defined syntax and semantics of the former with the heterogeneous and ambiguous ones of the latter.
With ontologies represented as RDF (Resource Description Framework) graphs, the first step would be to sort out truth-preserving syntax (applied independently of domains) from domain specific semantics.
On that basis it would be possible to separate representation syntax from contents semantics, and to design communication channels and interfaces accordingly.
That would greatly facilitate collaborations across externally defined ontologies as well as their mapping to enterprise architecture models.
To summarize, the benefits of ontological frames for collaborative engineering can be articulated around four points:
A clear-cut distinction between representation semantics and truth-preserving syntax.
A common functional architecture for all users interfaces, humans or otherwise.
Modular functionalities for specific semantics on one hand, generic truth-preserving and cognitive operations on the other hand.
Profiled ontologies according to concerns and contexts.
A critical fifth benefit could be added with regard to business intelligence: combined with deep learning capabilities, ontologies would extend the scope of collaboration to explicit as well as implicit knowledge, the former already framed by languages, the latter still open to interpretation and discovery.
Knowledge graphs, which have become a key component of knowlege management, are best understood as a reincarnation of ontologies.