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.
To take advantage of their immersion into digital environments enterprises have to differentiate between data (environment’s facts), information (systems’ representations), and knowledge (enterprise behavior).
That cannot be achieved without ironing out the semantic discrepancies between corresponding representations.
Behind the various labels and modus operandi, maps can be defined on three basic layers:
Conceptual models, targeting enterprises organization and business independently of supporting systems.
Logical models, targeting the symbolic objects managed by supporting systems as surrogates of business objects and activities.
Physical models, targeting the actual implementation of symbolic surrogates as binary objects.
These maps can be aligned with commonly agreed enterprise architecture layers, respectively for organizations and processes, systems functionalities, and platforms, with a fourth added for analytical models of business environments.
Ideally, that alignment should pave the way to the integration of systems and knowledge architectures, as represented by the Pagoda blueprint:
Insofar as systems engineering is concerned, that would require two kinds of transformations: from conceptual to logical models (aka analysis), and from logical to physical models (aka design).
While the latter is just a matter of expertise (thank to the GoF), that’s not the case for the former which has to deal with a semantic gap between descriptions of specific and changing business domains and organizations on one side, generic and stable systems architectures on the other side.
As a result, what can be termed a conceptual debt has accumulated with the the number of logical models supporting physical ones without the backing of relevant ones for business or organization. The objective is therefore to bring all models into a broader knowledge architecture.
Models & Ontologies
As introduced by Greek philosophers, ontologies are systematic accounts of whatever is known about a domain of concern. From that point, three basic observations can be made:
Ontologies are made of categories of things, beings, or phenomena; as such they may range from lexicon or simple catalogs to philosophical doctrines.
Ontologies are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
Ontologies are meant to be directed at specific domains of concerns, whatever their epistemic nature: engineering, business, politics, religions, mythologies, astrology, etc.
With regard to models, only the second observation puts ontologies apart: compared to models, ontologies are about understanding and are not necessarily driven by empirical purposes.
On that account ontologies appear as an option of choice for the integration of symbolic representations:
Data: instances identified at territory level, associated with terms or labels; they are mapped to business intelligence (environments) and operational (systems) models.
Information: categories associated with sets of instances; categories can be used for requirements analysis or software design.
Knowledge: ideas or concepts connect changing and overlapping sets of terms and categories; documents can be associated to any kind of item.
As expounded by Davis, Shrobe, and Szolovits in their pivotal article, knowledge is made of five constituents:
Surrogates, used as symbolic counterparts of actual objects and phenomena.
Ontological commitments defining the categories of things that may exist in the domain under consideration.
Fragmentary theory of intelligent reasoning defining what things can do or can be done with.
Medium making knowledge understandable by computers.
Medium making knowledge understandable by humans.
Points 1 and 5 are not concerned by the conceptual gap, the former being dealt with through the anchoring of identified individuals to surrogates (see below), and the latter being with human interfaces. That leaves points 2-4 as the conceptual hub where information models have to be integrated into knowledge architecture.
Assuming RDF (Resource Description Framework) graphs are used for knowledge representation (point 4), and taking a restaurant for example, the contents of information models (point 2) will be denoted by:
Primary nodes (rectangles), for elements specific to cooking and customers relationship management, to be decorated with features (bottom right).
Connection nodes (circles and arrows), for semantically neutral (aka syntactic) associations to be uniformly implemented across domains, e.g with predicate calculus (bottom left).
Semantic connectors supporting both syntactic and semantic associations (bottom, middle).
Using ontologies to integrate models into knowledge architecture is to enable the restructuring of the conceptual debt.
Minding Semantic Gaps
Keeping with the financial metaphor, conceptual debts can be expressed in terms of spreads between models, and as such could be restructured through models transformation.
To begin with, all representations have to be anchored to environments through identified (#) instances.
Then, instances are to be associated to categories according to features (properties or relationships) :
Customers, reservations, tables, and waiters are identified individuals managed through symbolic surrogates.
Names of dishes and ingredients do not refer to symbolic surrogates representing business objects, but are just labels pointing to recipes (documents).
Idem for the names of wines, except for exceptional vintages with identified bottles to be managed through symbolic surrogates.
Aspects are structured sets of features meant to be valued through category instances.
Documents are contents to be accessed directly or through networks, (e.g preparations or wine reviews).
It must be noted that the distinction between neutral and specific contents is not meant to be universal but be justified by pragmatic concerns, for instance:
Addresses are not defined as aspects but as category instances so that surrogates of actual addresses can be used to optimize deliveries.
Links to customers and addresses, being self-explanatory, can be defined as non specific.
The relationship from dishes to ingredients is structured and specific.
Sorting out truth-preserving constructs from domain specific ones is a key success factor for models transformation, and consequently debt restructuring.
Restructuring The Debts
Restructuring financial debts means redefining assets and incomes; with regard to systems it would mean reassessing architectures with regard to value chains.
To begin with, the Pagoda blueprint central pillar is to support the integration of systems and knowledge architectures and consequently the dynamic alignment of systems capabilities, meant to be stable and shared, with business opportunities, by nature changing and specific.
Then, the pairing of systems and knowledge architectures, like a DNA double helix, is to be used to restructure both technical and conceptual debts.
With regard to technical debts, restructuring isn’t to present significant difficulties:
Pairing income flows (applications) to tangible assets (platforms) can be done at data level.
Model transformations between data (code) and information (models) levels can be achieved using homogeneous domain specific and programming languages.
Things are more complex with conceptual debts, for pairing as well as transformations:
There is no direct pairing because value chains (processes) are set across assets (organization).
Model transformations are to bridge the semantic gap between the symbolic representations of environments (knowledge) and systems (information) .
Nonetheless, these difficulties can be overcame combining integrated architectures and ontologies.
Regarding the structure of the conceptual debt, the income part is to be defined through business objectives (customers, products, channels, supply chain, etc.), and assets to be defined by corresponding enterprise architectures capabilities.
Regarding models transformations, ontologies will be used to mind the semantic gap between environments (knowledge) and systems (information) representations:
Power-types: describe instances of categories (age, income, education, …).
Knowledge based relationships (dashed line): used to describe objects and phenomena, actual, planned, or expected (face recognition of customers, influence of weather on dishes, association of wines and dishes, …
Concepts: introduced to relate information and knowledge: gourmet.
With the backbones of symbolic representations soundly anchored to environment, it would be possible to complement functional and logical models with their conceptual counterpart and by doing so to eliminate conceptual debts. A symmetric policy could be applied to refactoring in order to redeem the technical debt associated to legacy code.
Managing Conceptual Debt
Like financial ones, conceptual debts are facts of life that have to be managed on a continuous basis, which entails:
A separate management of models directly tied to systems, and ontologies with broader justification.
A distinction between a kernel (aka knowledge engine), environment profiles, and business domains.
“The little reed, bending to the force of the wind, soon stood upright again when the storm had passed over”
The consequences of digital environments go well beyond a simple adjustment of business processes and call for an in-depth transformation of enterprise architectures.
To begin with, the generalization of digital environments bears out the Symbolic System modeling paradigm: to stay competitive, enterprises have to manage a relevant, accurate, and up-to-date symbolic representation of their business context and concerns.
With regard to architectures, it means a seamless integration of systems and knowledge architectures.
With regard to processes it means a built-in ability to learn from environments and act accordingly.
Such requirements for resilience and adaptability in unsettled environments are characteristic of the Pagoda architecture blueprint.
As can be observed wherever high buildings are being erected on shaking grounds, Pagoda-like architectures set successive layers around a central pillar providing intrinsic strength and resilience to external upsets while allowing the floors to move with the whole or be modified independently. Applied to enterprise architectures in digital environments, that blueprint can be much more than metaphoric.
That blueprint puts a new light on model based approaches to systems engineering (MBSE):
Conceptual models, targeting enterprises organization and business independently of supporting systems.
Logical models, targeting the symbolic objects managed by supporting systems as surrogates of business objects and activities.
Physical models, targeting the actual implementation of symbolic surrogates as binary objects.
Weaving together enterprises and knowledge architectures would greatly enhance the traceability of transformations induced by the immersion of enterprises in digital environments.
Systems & Knowledge Architectures
If digitized business flows are to pervade enterprise systems and feed decision-making processes, systems and knowledge architectures are to be merged into a single nervous system as materialized by the Pagoda central pillar:
Ubiquitous, massive, and unrelenting digitized business flows cannot be dealt with lest a clear distinction is maintained between raw data acquired across platforms, and the information (previously data) models which ensure the continuity and consistency of systems. .
Once structured and refined, business data flows must be blended with information models sustaining systems functionalities.
A comprehensive and business driven integration of organization and knowledge could then support strategic and operational decision-making at enterprise level.
Rounding off this nervous system with a brain, ontologies would provide the conceptual frame for models representing enterprises and their environments.
Agile Architectures & Homeostasis
Homeostasis is the ability of a viable organism to learn from their environment and adapt their behavior and structures according to changes. As such homeostasis can be understood as the eextension of enterprise agility set in digital environments, ensuring:
Integrated decision-making processes across concerns (business, systems, platforms), and time-frames (tactical, operational, strategic, … ).
Integrated information processing, from data-mining to knowledge management.
To that end, changes should be differentiated with regard to source (business or technology environment, organization, systems) and flows (data, information, knowledge); that would be achieved with a pagoda blueprint.
Threads of operational and strategic decision-making processes could then be weaved together, combining OODA loops at process level and economic intelligence at enterprise level.
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.
As championed by a brave writer, should we see the Web as a crib for born again narratives, or as a crypt for redundant texts.
Once Upon A Time
Borrowing from Einstein, “the only reason for time is so that everything doesn’t happen at once.” That befits narratives: whatever the tale or the way it is conveyed, stories take time. Even if nothing happens, a story must be spelt in tempo and can only be listened to or read one step at a time.
In So Many Words
Stories have been told before being written, which is why their fabric is made of words, and their motifs weaved by natural languages. So, even if illustrations may adorn printed narratives, the magic of stories comes from the music of their words.
A Will To Believe
To enjoy a story, listeners or readers are to detach their mind from what they believe about reality, replacing dependable and well-worn representations with new and untested ones, however shaky or preposterous they may be; and that has to be done through an act of will.
Stories are make-beliefs: as with art in general, their magic depends on the suspension of disbelief. But suspension is not abolition; while deeply submerged in stories, listeners and readers maintain some inward track to the beliefs they left before diving; wandering a cognitive fold between surface truths and submarine untruths, they seem to rely on a secure if invisible tether to the reality they know. On that account, the possibility of an alternative reality is to transform a comforting fold into a menacing abyss, dissolving their lifeline to beliefs. That could happen to stories told through the web.
Stories & Medium
Assuming time rendering, stories were not supposed to be affected by medium; that is, until McLuhan’s suggestion of medium taking over messages. Half a century later internet and the Web are bringing that foreboding in earnest by melting texts into multimedia documents.
Tweets and Short Message Services (SMS) offer a perfect illustration of the fading of text-driven communication, evolving from concise (160 characters) text-messaging to video-sharing.
That didn’t happen by chance but reflects the intrinsic visual nature of web contents, with dire consequence for texts: once lording it over entourages of media, they are being overthrown and reduced to simple attachments, just a peg above fac-simile. But then, demoting texts to strings of characters makes natural languages redundant, to be replaced by a web Esperanto.
Web Semantic Silos
With medium taking over messages, and texts downgraded to attachments, natural languages may lose their primacy for stories conveyed through the web, soon to be replaced by the so-called “semantic web”, supposedly a lingua franca encompassing the whole of internet contents.
As epitomized by the Web Ontology Language (OWL), the semantic web is based on a representation scheme made of two kinds of nodes respectively for concepts (squares) and conceptual relations (circles).
Concept nodes are meant to represent categories specific to domains (green, right); that tallies with the lexical level of natural languages.
Connection nodes are used to define two types of associations:
Semantically neutral constructs to be applied uniformly across domains; that tallies with the syntactic level of natural languages (blue, left).
Domain specific relationships between concepts; that tallies with the semantic level of natural languages (green, center).
The mingle of generic (syntactic) and specific (semantic) connectors induces a redundant complexity which grows exponentially when different domains are to be combined, due to overlapping semantics. Natural languages typically use pragmatics to deal with the issue, but since pragmatics scale poorly with exponential complexity, they are of limited use for semantic web; that confines its effectiveness to silos of domain specific knowledge.
But semantic silos are probably not the best nurturing ground for stories.
Stories In Cobwebs
Taking for granted that text, time, and suspension of disbelief are the pillars of stories, their future on the web looks gloomy:
Texts have no status of their own on the web, but only appear as part of documents, a media among others.
Stories can bypass web practice by being retrieved before being read as texts or viewed as movies; but whenever they are “browsed” their intrinsic time-frame and tempo are shattered, and so is their music.
If lying can be seen as an inborn human cognitive ability, it cannot be separated from its role in direct social communication; such interactive background should also account for the transient beliefs in fictional stories. But lies detached from a live context and planted on the web are different beasts, standing on their own and bereft of any truth currency that could separate actual lies from fictional ones.
That depressing perspective is borne out by the tools supposed to give a new edge to text processing:
Hyper-links are part and parcel of internet original text processing. But as far and long as stories go, introducing links (hardwired or generated) is to hand narrative threads over to readers, and by so transforming them into “entertextment” consumers.
Machine learning can do wonders mining explicit and implicit meanings from the whole of past and present written and even spoken discourses. But digging stories out is more about anthropology or literary criticism than about creative writing.
As for the semantic web, it may work as a cobweb: by getting rid of pragmatics, deliberately or otherwise, it disables narratives by disengaging them from their contexts, cutting them out in one stroke from their original meaning, tempo, and social currency.
The Deconstruction of Stories
Curiously, what the web may do to stories seems to reenact a philosophical project which gained some favor in Europe during the second half of the last century. To begin with, the deconstruction philosophy was rooted in literary criticism, and its objective was to break the apparent homogeneity of narratives in order to examine the political, social, or ideological factors at play behind. Soon enough, a core of upholders took aim at broader philosophical ambitions, using deconstruction to deny even the possibility of a truth currency.
With the hindsight on initial and ultimate purposes of the deconstruction project, the web and its semantic cobweb may be seen as the stories nemesis.
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.