Enterprise governance has to face combined changes in the way business times and spaces are to be taken into account. On one hand social networks put well-thought-out market segments and well planned campaigns at the mercy of consumers’ weekly whims. On the other hand traditional fences between environments and IT systems are crumbling under combined markets and technological waves.
To overcome these challenges enterprises strategies should focus on four pillars:
Data and Information: massive and continuous inflows of data calls for a seamless integration of data analytics (perception), information models (reasoning), and knowledge (decision-making).
Security & Confidentiality: new regulatory environments and costs of privacy breaches call for a clear distinction between data tied to identified individuals and information associated to designed categories.
Innovation: digital environments induces a new order of magnitude for the pace of technological change. Making opportunities from changes can only be achieved through collaboration mechanisms harnessing enterprise knowledge management to environments intakes.
Distinctions must serve a purpose and be assessed accordingly. On that account, what would be the point of setting apart data and information, and on what basis could that be done.
Until recently the two terms seem to have been used indifferently; until, that is, the digital revolution. But the generalization of digital surroundings and the tumbling down of traditional barriers surrounding enterprises have upturned the playground as well as the rules of the game.
Previously, with data analytics, information modeling, and knowledge management mostly carried out as separate threads, there wasn’t much concerns about semantic overlaps; no more. Lest they fall behind, enterprises have to combine observation (data), reasoning (information), and judgment (knowledge) as a continuous process. But such integration implies in return more transparency and traceability with regard to resources (e.g external or internal) and objectives (e.g operational or strategic); that’s when a distinction between data and information becomes necessary.
Economics: Resources vs Assets
Understood as a whole or separately, there is little doubt that data and information have become a key success factor, calling for more selective and effective management schemes.
Being immersed in digital environments, enterprises first depend on accurate, reliable, and timely observations of their business surroundings. But in the new digital world the flows of data are so massive and so transient that mining meaningful and reliable pieces is by itself a decisive success factor. Next, assuming data flows duly processed, part of the outcome has to be consolidated into models, to be managed on a persistent basis (e.g customer records or banking transactions), the rest being put on temporary shelves for customary uses, or immediately thrown away (e.g personal data subject to privacy regulations). Such a transition constitutes a pivotal inflexion point for systems architectures and governance as it sorts out data resources with limited lifespan from information assets with strategic relevance. Not to mention the sensibility of regulatory compliance to data management.
Processes: Operations vs Intelligence
Making sense of data is pointless without putting the resulting information to use, which in digital environments implies a tight integration of data and information processing. Yet, as already noted, tighter integration of processes calls for greater traceability and transparency, in particular with regard to the origin and scope: external (enterprise business and organization) or internal (systems). The purposes of data and information processing can be squared accordingly:
The top left corner is where business models and strategies are meant to be defined.
The top right corner corresponds to traditional data or information models derived from business objectives, organization, and requirement analysis.
The bottom line correspond to analytic models for business (left) and operations (right).
That view illustrates the shift of paradigm induced by the digital transformation. Prior, most mappings would be set along straight lines:
Horizontally (same nature), e.g requirement analysis (a) or configuration management (b). With source and destination at the same level, the terms employed (data or information) have no practical consequence.
Vertically (same scope), e.g systems logical to physical models (c) or business intelligence (d). With source and destination set in the same semantic context the distinction (data or information) can be ignored.
The digital transformation makes room for diagonal transitions set across heterogeneous targets, e.g mapping data analytics with conceptual or logical models (e).
That double mix of levels and scopes constitutes the nexus of decision-making processes; their transparency is contingent on a conceptual distinction between data and information.
Data is used to align operations (systems) with observations (territories).
Information is used to align categories (maps) with objectives.
Then, the conceptual distinction between data and information is instrumental for the integration of operational and strategic decision-making processes:
Data analytics feeding business intelligence
Information modeling supporting operational assessment.
Not by chance, these distinctions can be aligned with architecture layers.
Architectures: Instances vs Categories
Blending data with information overlooks a difference of nature, the former being associated with actual instances (external observation or systems operations), the latter with symbolic descriptions (categories or types). That intrinsic difference can be aligned with architecture layers (resources are consumed, assets are managed), and decision-making processes (operations deal with instances, strategies with categories).
With regard to architectures, the relationship between instances (data) and categories (information) can be neatly aligned with capability layers, as represented by the Pagoda blueprint:
The platform layer deals with data reflecting observations (external facts) and actions (system operations).
The functional layer deals with information, i.e the symbolic representation of business and organization categories.
The business and organization layer defines the business and organization categories.
It must also be noted that setting apart what pertains to individual data independently of the informations managed by systems clearly props up compliance with privacy regulations.
With regard to decision-making processes, business intelligence uses the distinction to integrate levels, from operations to strategic planning, the former dealing with observations and operations (data), the latter with concepts and categories (information and knowledge).
Representations: Knowledge Architecture
As noted above, the distinction between data and information is a necessary counterpart of the integration of operational and intelligence processes; that implies in return to bring data, information, and knowledge under a common conceptual roof, respectively as resources, assets, and service:
Resources: data is captured through continuous and heterogeneous flows from a wide range of sources.
Assets: information is built by adding identity, structure, and semantics to data.
Services: knowledge is information put to use through decision-making.
Ontologies, which are meant to encompass all and every kind of knowledge, are ideally suited for the management of whatever pertains to enterprise architecture, thesaurus, models, heuristics, etc.
The significance of the distinction between data and information shows up at the two ends of the spectrum:
On one hand, it straightens the meaning of metadata, to be understood as attributes of observations independently of semantics, a dimension that plays a critical role in machine learning.
On the other hand, enshrining the distinction between what can be known of individuals facts or phenomena and what can be abstracted into categories is to enable an open and dynamic knowledge management, also a critical requisite for machine learning.
The upsurge in the scope and performances of artificial brains sometimes brings a new light on human cognition. Semantic layers and knowledge graphs offer a good example of a return to classics, in that case with Greek philosophers’ ontologies.
According to their philosophical origins, ontologies are systematic accounts of existence for whatever can make sense in an universe of discourse. From that starting point four basic observations can be made:
Ontologies are structured set of names denoting symbolic (aka cognitive) representations.
These representations can stand at different epistemic levels: terms or labels associated to representations (nothing is represented), ideas or concepts (sets of terms), instances of identified objects or phenomena, categories (sets of instances), documents.
Ontologies are solely dedicated to the validity and internal consistency of the representations. Not being concerned with external validity, As they are not meant to support empirical purposes.
Yet, assuming a distinction between epistemic levels, ontologies can be used to support both internal and external consistency of models.
That makes models a refinement of ontologies as they are meant to be externally consistent and serve a purpose.
Turning thoughts into figures faces the intrinsic constraint of dimension: two dimensional representations cannot cope with complexity.
So, lest they be limited to flat and shallow thinking, mind cartographers have to introduce the cognitive equivalent of geographical layers (nature, demography, communications, economy,…), and archetypes (mountains, rivers, cities, monuments, …)
Nodes: What’s The Map About
Nodes in maps (aka roots, handles, …) are meant to anchor thinking threads. Given that human thinking is based on the processing of symbolic representations, mind mapping is expected to progress wide and deep into the nature of nodes: concepts, topics, actual objects and phenomena, artifacts, partitions, or just terms.
It must be noted that these archetypes are introduced to characterize symbolic representations independently of domain semantics.
Connectors: Cognitive Primitives
Nodes in maps can then be connected as children or siblings, the implicit distinction being some kind of refinement for the former, some kind of equivalence for the latter. While such a semantic latitude is clearly a key factor of creativity, it is also behind the poor scaling of maps with complexity.
A way to frame complexity without thwarting creativity would be to define connectors with regard to cognitive primitives, independently of nodes’ semantics:
References connect nodes as terms.
Associations: connect nodes with regard to their structural, functional, or temporal proximity.
Analogies: connect nodes with regard to their structural or functional similarities.
At first, with shallow nodes defined as terms, connections can remain generic; then, with deeper semantic levels introduced, connectors could be refined accordingly for concepts, documentation, actual objects and phenomena, artifacts,…
Semantics: Extensional vs Intensional
Given mapping primitives defined independently of domains semantics, the next step is to take into account mapping purposes:
Extensional semantics deal with categories of actual instances of objects or phenomena.
Intensional semantics deal with specifications of objects or phenomena.
That distinction can be applied to basic semantic archetypes (people, roles, events, …) and used to distinguish actual contexts, symbolic representations, and specifications, e.g:
Car (object) refers to context, not to be confused with Car (surrogate) which specified the symbolic counterpart: the former is extensional (actual instances), the latter intensional (symbolic representations)
Maintenance Process is extensional (identified phenomena), Operation is intensional (specifications).
Reservation and Driver are symbolic representations (intensional), Person is extensional (identified instances).
It must be reminded that whereas the choice is discretionary and contingent on semantic contexts and modeling purposes (‘as-it-is’ vs ‘as-it-should-be’), consequences are not because the choice is to determine abstraction semantics.
For example, the records for cars, drivers, and reservations are deemed intensional because they are defined by business concerns. Alternatively, instances of persons and companies are defined by contexts and therefore dealt with as extensional descriptions.
Abstractions: Subsets & Sub-types
Thinking can be characterized as a balancing act between making distinctions and managing the ensuing complexity. To that end, human edge over other animal species is the use of symbolic representations for specialization and generalization.
That critical mechanism of human thinking is often overlooked by mind maps due to a confused understanding of inheritance semantics:
Strong inheritance deals with instances: specialization define subsets and generalization is defined by shared structures and identities.
Weak inheritance deals with specifications: specialization define sub-types and generalization is defined by shared features.
The combination of nodes (intension/extension) and inheritance (structures/features) semantics gives cartographers two hands: a free one for creative distinctions, and a safe one for the ensuing complexity. e.g:
Intension and weak inheritance: environments (extension) are partitioned according to regulatory constraints (intension); specialization deals with subtypes and generalization is defined by shared features.
Extension and strong inheritance: cars (extension) are grouped according to motorization; specialization deals with subsets and generalization is defined by shared structures and identities.
Intension and strong inheritance: corporate sub-type inherits the identification features of type Reservation (intension).
Mind maps built on these principles could provide a common thesaurus encompassing the whole of enterprise data, information and knowledge.
Intelligence: Data, Information, Knowledge
Considering that mind maps combine intelligence and cartography, they may have some use for enterprise architects, in particular with regard to economic intelligence, i.e the integration of information processing, from data mining to knowledge management and decision-making:
Data provide the raw input, without clear structures or semantics (terms or aspects).
Categories are used to process data into information on one hand (extensional nodes), design production systems on the other hand (intensional nodes).
Abstractions (concepts) makes knowledge from information by putting it to use.
Along that perspective mind maps could serve as front-ends for enterprise architecture ontologies, offering a layered cartography that could be organized according to concerns:
Enterprise architects would look at physical environments, business processes, and functional and technical systems architectures.
Knowledge managers would take a different perspective and organize the maps according to the nature and source of data, information, and knowledge.intelligence w
As demonstrated by geographic information systems, maps built on clear semantics can be combined to serve a wide range of purposes; furthering the analogy with geolocation assistants, layered mind maps could be annotated with punctuation marks (e.g ?, !, …) in order to support problem-solving and decision-making.
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.
An often overlooked benefit of artificial intelligence has been a renewed interest in seminal philosophical and cognitive topics; ontologies coming top of the list.
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.
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.
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.