The whole of enterprises’ endeavors and behaviors cannot be coerced into models lest they inhibit their ability to navigate ill defined and shifting business environments. Enterprises immersion in digital environments is making limits all the more explicit:
On the environment side, facts, once like manna from heaven ready to be picked and interpreted, have turned into data floods swamping all recognizable models imprints
On the symbolic side, concepts, once steadily supported by explicit models and logic, are now emerging like new species from the Big Data primordial soup.
Typically, business analysts are taking the lead on both fronts toting learning machines and waving knowledge graphs. In between system architects have to deal with a two-pronged encroachment on information models.
On the one hand they have to build a Chinese wall between private data and managed information to comply with regulations
On the other hand they have to feed decision-making processes with accurate and up-to-date observations, and adjust information systems with relevant and actionable concepts.
That brings a new light on the so-called conceptual, logical, and physical “data” models as key components of enterprise architecture:
Physical data models are meant to be directly lined up with operations and digital environments
Logical models represent the categories managed by information systems and must be up to par with systems functional architecture
Conceptual models are meant to represent enterprise knowledge of business domains and objectives, as well as its embodiment in organisation and people.
Logical models (information) appear therefore as an architecture hub linking business facts (data) and concepts (knowledge), ensuring exchanges between environments and representations e.g.:
Deduction: matching observations (data) with models to produce new information, i.e. data with structure and semantics
Induction: making hypothesises (knowledge) about the scope of models in order to make deductions
The way tests are designed and executed is being doubly affected by development methods and AI technologies. On one hand well-founded approaches (e.g test-driven development) are often confined to faith-based niches; on the other hand automated schemes and agile methods push many testers out of their comfort zone.
Resetting the issue within a knowledge-based enterprise architecture would pave the way for sound methods and could open new doors for their users.
Tests can be understood in terms of preventive and predictive purposes, the former with regard to actual products, the latter with regard to their forthcoming employ. On that account policies are to distinguish between:
Test plans, to be derived from requirements.
Test cases, to be collected from environments.
Test execution, to be run and monitored in simulated environments.
The objective is to cross these pursuits with knowledge architecture layers.
Test plans are derived from business requirements, either on their own at process level (e.g as users stories or activities), or combined with functional requirements at application level (e.g as use cases). Both plans describe sequences of actions meant to be performed by organizational entities identified at enterprise level. Circumstances are then specified with regard to quality of service and technical requirements.
Test cases’ backbones are built from business scenarii fleshed out with instances mimicking identified entities from business environment, and hypothetical decisions taken by entitled users. The generation of actual instances and decisions could be automated depending on the thoroughness and consistency of business requirements.
To be actually tested, business scenarii have to be embedded into functional ones, yet the distinction must be maintained between what pertains to business logic and what pertains to the part played by supporting systems.
By contrast, despite being built from functional scenarii, integration and acceptance ones are meant to be blind to business or functional contents, and cases can therefore be generated independently.
Unit and components tests are the building blocks of all test cases, the former rooted in business requirements, the latter in functional ones. As such they can be used to a built-in integration of tests and development.
TDD in the loop
Whatever its merits for phased projects, the development V-model suffers from a structural bias because flaws rooted in requirements, arguably the most damaging, tend to be diagnosed after designs are encoded. Test driven development (TDD) takes a somewhat opposite approach as code specifications are governed by testability. But reversing priorities may also introduce reverse issues: where the V-model’s verification and validation come too late and too wide, TDD’s may come hasty and blinkered, with local issues masking global ones. Applying the OODA (Observation, Orientation, Decision, Action) loop to test cases offers a way out of the dilemma:
Observation (West): test and assessment for component, integration, and acceptance test cases.
Orientation (North): assessment in the broader context of requirements space (business, functional, Quality of Service), or in the local context of application (East).
Decision (East): confirm or adjust the development paths with regard to functional scenarii, development backlog, or integration constraints.
Action (South): develop code at unit, component, or process levels.
As each station is meant to deal with business, functional, and operational test cases, the challenge is to ensure a seamless integration and reuse across iterations and layers.
managing Tests cases
Whatever the method, tests plans are meant to mirror requirements scope and structure. For architecture oriented projects, tests should be directly aligned with the targeted capabilities of architecture layers:
For business driven projects, test plans should be set along business scenarii, with development units and associated test cases defined with regard to activities. When use cases, which cover the subset of activities supported by systems, are introduced upfront for both business and functional requirements, test plans should keep the distinction between business and functional requirements.
All things considered, test cases are to be comprehensively and consistently run against requirements distinct in goals (business vs architecture), layers (business, functions, platforms), or formalism (text, stories, use cases, …).
In contrast, test cases are by nature homogeneous as made of instances of objects, events, and behaviors; ontologies can therefore be used to define and manage these instances directly from models. The example below make use of instances for types (propulsion, body), car model (Renault Clio), and car (58642).
The primary and direct benefit of representing test cases as instances in ontologies is to ensures a seamless integration and reuse of development, integration, and acceptance test cases independently of requirements context.
But the ontological approach have broader and deeper consequences: by defining test cases as instances in line with environment data, it opens the door to their enrichment through deep-learning.
knowledgeable test cases
Names may vary but tests are meant to serve a two-facet objective: on one hand to verify the intrinsic qualities of artefacts as they are, independently of context and usage; on the other hand to validate their features with regard to extrinsic circumstances, present or in a foreseeable future.
That duality has logical implications for test cases:
The verification of intrinsic properties can be circumscribed and therefore by carried out based on available information, e.g: design, programing language syntax and semantics, systems configurations, etc.
The validation of functional features and behaviors is by nature open-ended with regard to scope and time-frame; it ensues that test cases have to rely on incomplete or uncertain information.
Without practical applications that distinction has been of little consequence, until now: while the digital transformation removes the barriers between test cases and environment data, the spreading of machine learning technologies multiplies the possibilities of exchanges.
Along the traditional approach, test cases relies on three basic sources of information:
Syntax and semantics of programing languages are used to check software components (a)
Logical and functional models (including patterns) are used to check applications designs (b).
Requirements are used to check applications compliance (c).
With barriers removed, test cases as instances can be directly aligned with environment data, opening doors to their enrichment, e.g:
Random data samples can be mined from environments and used to deal with human instinctive or erratic behaviors. By nature knee jerks or latent behavior cannot be checked with reasoned test cases, yet they neither occur in a void but within known operational of functional or circumstances; data analytics can be used to identify these quirks (d).
Systems being designed artifacts, components are meant to tally with models for structures as well as behaviors. Crossing operational data with design models will help to refine and hone integration and acceptance test cases (e).
Whereas integration tests put the focus on models and code, acceptance tests also involve the mapping of models to business and organizational concepts. As a corollary, test cases are to rely on a broader range of knowledge: external regulations, mined from environments, or embedded in organization through individual and collective skills (f).
Given the immersion of enterprises in digital environments, and assuming representing test cases as ontological instances, these are already practical opportunities. But the real benefits of knowledge based test cases are to come from leveraging machine learning technologies across enterprise and knowledge architectures.
As a capability of live organisms, languages are best understood in terms of communication.
That understanding is of particular interest for enterprises immersed in digital environments inhabited by hybrids with deep learning capabilities.
Languages begin with the need of direct (here) and immediate (now) communication. While there is no time for explanations, messages must convey some meaning, if only to distinguish friends from foes. Hence the use of signs pointing to categories of objects or phenomena. That’s the language lexical layer linking instantly observations (data) to information (bottom right).
Rules governing the combination of signs follow soon because more has to be communicated about circumstances and what is to be done with. That’s the language syntactic layer linking observations (data) to current information (top right).
The breakthrough comes with symbolic representation: once disentangled from immediate circumstances, communications can encompass whatever is deemed relevant in contexts and concerns; That’s the language semantic layer that weave together information and knowledge (top left).
The cognitive ability to “manipulate” symbolic representations (aka models) independently of circumstances opens the door to any kind of constructions. That’s the language pragmatic layer meant to put knowledge to actual use (bottom left).
That functional taxonomy can be usefully applied to the digital transformation of enterprise architectures, the first layer aligned with data, the second and third with information, and the fourth with knowledge.
The digital transformation induces fundamental changes for the exchanges between enterprises and their environment.
To begin with, their immersion into digital environments means that the traditional fences surrounding their IT systems are losing their relevance, being bypassed by massive data flows to be processed without delay.
Then, the induced osmosis upturns the competition playground and compels drastic changes in governance: less they fall behind, enterprises have to redefine their organization, systems, and processes.
Strategic thinking is first and foremost making differences with regard to markets, resources and assets, and time-frames. But what makes the digital revolution so disruptive is that it resets the ways differences are made:
Markets: the traditional distinctions between products and services are all but forgotten.
Resources and assets: with software, smart or otherwise, now tightly mixed in products fabric, and business processes now driven by knowledge, intangible assets are taking the lead on conventional ones.
Time-frames: strategies have for long been defined as a combination of anticipations, objectives and policies whose scope extends beyond managed horizons. But digital osmosis and the ironing out of markets and assets traditional boundaries are dissolving the milestones used to draw horizons perspectives.
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 induce 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 information 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.
That approach has been tested with the Caminao ontological kernel using OWL2; a beta version is available for comments on the Stanford/Protégé portal with the link: Caminao Ontological Kernel (CaKe_).
Conclusion: From Metadata to Machine Learning
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.
Assuming that intelligence is the capacity to learn, three kind of motifs should be considered:
Data, for facts from physical and symbolic environments
Information, for categories giving structure and meaning to data
Knowledge, for concepts adding purposes to information
These motifs serve three kinds of motives: communication between agents; representation of contexts, concerns, and conversations; imagination of alternatives realities.
That taxonomy neatly coincides with Spinoza’s philosophical one: data for senses and beliefs, information for reason, and knowledge for judgment..
Intelligence can thus be characterized by four cognitive operations:
Deduction: using observations (data) and models (information) to establish new facts
Induction: making hypothesises (knowledge) on the matching between models (information) and observations (data)
Abduction: assessing inductions
Intuition: making hypothesises (knowledge) from direct observations
Induction, deduction, and abduction may arguably be performed by artificial brains providing traceability; that’s not the case for intuition because it involves accountability.
The 3*4 taxonomy of intelligence’s motifs and threads can then be used to characterize brains’ capabilities in terms of problem solving.
To begin with the representation of issues:
Symbolic representation can rely on functions (data), models (information), and semantic or conceptual graphs (explicit knowledge)
Alternatively, non symbolic representation can start with digits (data), documents (information), and neural networks (implicit knowledge)
Problem solving itself will make use of:
Symbolic representation: deduction (data), induction (information), and abduction (knowledge)
Non symbolic representation: data analytics (data), pattern matching (information), and Machine learning (implicit knowledge)
Capabilities can then be used to draw a line between natural and artificial brains.
Artificial vs Natural Brains
The enterprises immersion in digital environments combined with the ubiquity of Artificial intelligence and Machine learning technologies have put traceability and accountability on top of design issues; hence the need to set the limits of artificial brains:
Artificial brains can build and process symbolic and non symbolic representations
Artificial brains can solve problems applying logic and abstraction or data analytics, respectively to symbolic and non symbolic representations
Like natural ones, artificial brains combine sensory-motor capabilities with purely cognitive ones
Like human ones, and apart from sensory-motor capabilities, artificial brains support a degree of cognitive plasticity and versatility between symbolic and non symbolic representation and processing.
While artificial brains’ capabilities appear to match human ones, that’s not the case fall when shortfalls in traceability (e.g., with abduction or intuitions) must be supplemented by judgements’ accountability.
Artificial General Intelligence
While still an open-ended field of research, the debate about Artificial ‘General’ Intelligence (AGI) can be summarized in terms of scope or agents.
Regarding scope the debate turns around the ability of Machine learning technologies to:
Reproduce human implicit knowledge and intuition
Deal consciously with actual and virtual realities
As both issues remain shrouded in the mystery of individual consciousness, it might be worth to consider the collective dimension of general intelligence. Applying the principle to enterprises:
Motifs: intelligence rooted in operations (facts), organization (concepts) and systems (categories)
Motives: intelligence driven by business processes (communication), information management (representation), and planning and decision-making (imagination)
Implicit knowledge and intuition would be rooted in data and process mining, and weaved with the fabric of consciousness built from a continuous adjustment of managed (actual) and planned (virtual) realities.
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.
“For things to remain the same, everything must change”
Lampedusa, “The Leopard”
Whatever the understanding of the discipline, most EA schemes implicitly assume that enterprise architectures, like their physical cousins, can be built from blueprints. But they are not because enterprises have no “Pause” and “Reset” buttons: business cannot be put on stand-by and must be carried on while work is in progress.
Systems & Enterprises
Systems are variously defined as:
“A regularly interacting or interdependent group of items forming a unified whole” (Merriam-Webster).
“A set of connected things or devices that operate together” (Cambridge Dictionary).
“A way of working, organizing, or doing something which follows a fixed plan or set of rules” (Collins Dictionary)
“A collection of components organized to accomplish a specific function or set of functions” (TOGAF from ISO/IEC 42010:2007)
While differing in focus, most understandings mention items and rules, purpose, and the ability to interact; none explicitly mention social structures or interactions with humans. That suggests where the line should be drawn between systems and enterprises, and consequently between corresponding architectures.
Architectures & Changes
Enterprises are live social entities made of corporate culture, organization, and supporting systems; their ultimate purpose is to maintain their identity and integrity while interacting with environments. As a corollary, changes cannot be carried out as if architectures were just apparel, but must ensure the continuity and consistency of enterprises’ structures and behaviors.
That cannot be achieved by off-soil schemes made of blueprints and step-by-step processes detached from actual organization, systems, and processes. Instead, enterprise architectures must be grown bottom up from actual legacies whatever their nature: technical, functional, organizational, business, or cultural.
Insofar as enterprise architectures are concerned, legacies are usually taken into account through one of three implicit assumptions:
No legacy assumptions ignore the issue, as if the case of start-ups could be generalized. These assumptions are logically flawed because enterprises without legacy are like embryos growing their own inherent architecture, and in that case there would be no need for architects.
En Bloc legacy assumptions take for granted that architectures as a whole could be replaced through some Big Bang operation without having a significant impact on business activities. These assumptions are empirically deceptive because, even limited to software architectures, Big Bang solutions cannot cope with the functional and generational heterogeneity of software components characterizing large organizations. Not to mention that enterprise architectures are much more that software and IT.
Piecemeal legacies can be seen as the default assumption, based on the belief that architectures can be re-factored or modernized step by step. While that assumption may be empirically valid, it may also miss the point: assuming that all legacies can be dealt with piecemeal rubs out the distinction pointed above between systems and enterprises.
So, the question remains of what is to be changed, and how ?
EA as a Work In Progress
As with leopard’s spots and identity, the first step would be to set apart what is to change (architectures) from what is to carry on (enterprise).
Maps and territories do provide an overview of spots’ arrangement, but they are static views of architectures, whereas enterprises are dynamic entities that rely on architectures to interact with their environment. So, for maps and territories to serve that purpose they should enable continuous updates and adjustments without impairing enterprises’ awareness and ability to compete.
That shift from system architecture to enterprise behavior implies that:
The scope of changes cannot be fully defined up-front, if only because the whole enterprise, including its organization and business model, could possibly be of concern.
Fixed schedules are to be avoided, lest each and every unit, business or otherwise, would have to be shackled into a web of hopeless reciprocal commitments.
Different stakeholders may come as interested parties, some more equal than others, possibly with overlapped prerogatives.
So, instead of procedural and phased approaches supposed to start from blank pages, EA ventures must be carried out iteratively with the planning, monitoring, assessment, and adjustment of changes across enterprises’ businesses, organizations, and systems. That can be represented as an extension of the OODA (Observation, Orientation, Decision, Action) loop:
Actual observations from operations (a)
Data analysis with regard to architectures as currently documented (b).
Changes in business processes (c).
Changes in architectures (d).
Moreover, due to the generalization of digital flows between enterprises and their environment, decision-making processes used to be set along separate time-frames (operational, tactical, strategic, …), must now be weaved together along a common time-scale encompassing internal (symbolic) as well as external (actual) events.
It ensues that EA processes must not only be continuous, but they also must deal with latency constraints.
Changes & Latency
Architectures are by nature shared across organizational units (enterprise level) and business processes (system level). As a corollary, architecture changes are bound to introduce mismatches and frictions across business-specific applications. Hence the need of sorting out the factors affecting the alignment of maps and territories:
Elapsed time between changes in territories and maps updates (a>b) depends on data analytics and operational architecture.
Elapsed time between changes in maps and revised objectives (b>c) depends on business analysis and organization.
Elapsed time between changes in objectives and their implementation (c>d) depends on engineering processes and systems architecture.
Elapsed time between changes in systems and changes in territories (d>a) depends on applications deployment and technical architectures.
On that basis it’s possible to define four critical lags:
Operational: data analytics can be impeded by delayed, partial, or inaccurate feedback from processes.
Mapping: business analysis can be impeded by delays or discrepancies in data analytics.
Engineering: development of applications can be impeded by delays or discrepancies in business analysis.
Processes: deployment of business processes can be impeded by delays in the delivery of supporting applications.
These lags condition the whole of EA undertakings because legacy structures, mechanisms, and organizations are to be continuously morphed into architectures without introducing misrepresentations that would shackle activities and stray decision-making.
EA Latency & Augmented Reality
Insofar as architectural changes are concerned, discrepancies and frictions are rooted in latency, i.e the elapsed time between actual changes in territories and the updating of relevant maps.
As noted above, these lags have to be weighted according to time-frames, from operational days to strategic years, so that the different agents could be presented with the relevant and up-to-date views befitting to each context and concerns.
That could be achieved if enterprises architectures were presented through augmented reality technologies.
Compared to virtual reality (VR) which overlooks the whole issue of reality and operates only on similes and avatars, augmented reality (AR) brings together virtual and physical realms, operating on apparatuses that weaves actual substrates, observations, and interventions with made-up descriptive, predictive, or prescriptive layers.
On that basis, users would be presented with actual territories (EA legacy) augmented with maps and prospective territories.
Composition and dynamics of maps and territories (actual and prospective) could be set and edited appropriately, subject to latency constraints.