Archive for the ‘Data Mining’ Category

Ontologies as Productive Assets

January 22, 2018


An often overlooked benefit of artificial intelligence has been a renewed interest in seminal philosophical and cognitive topics; ontologies coming top of the list.

Ontological Questioning (The Thinker Monkey, Breviary of Mary of Savoy)

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.

Ontologies Benefits

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.

Further Reading

External Links


Open Ontologies: From Silos to Architectures

January 1, 2018

To be of any use for enterprises, ontologies have to embrace a wide range of contexts and concerns, often ill-defined for environments, rather well expounded for systems.

Circumscribed Contexts & Crossed Concerns (Robert Goben)

And now that enterprises have to compete in open, digitized, and networked environments, business and systems ontologies have to be combined into modular knowledge architectures.

Ontologies & Contexts

If open-ended business contexts and concerns are to be taken into account, the first step should be to characterize ontologies with regard to their source, justification, and the stability of their categories, e.g:

  • Social: No authority, volatile, continuous and informal changes.
  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to established procedures.
  • Corporate: Defined by enterprises, periodic, changes subject to internal decision-making.
  • Personal: Customary, defined by named individuals (e.g research paper).

Assuming such an external taxonomy, the next step would be to see what kind of internal (i.e enterprise architecture) ontologies can be fitted into, as it’s the case for the Zachman framework.

The Zachman’s taxonomy is built on well established concepts (Who,What,How, Where, When) applied across architecture layers for enterprise (business and organization), systems (logical structures and functionalities), and platforms (technologies). These layers can be generalized and applied uniformly across external contexts, from well-defined (e.g regulations) to fuzzy (e.g business prospects or new technologies) ones, e.g:

Ontologies, capabilities (Who,What,How, Where, When), and architectures (enterprise, systems, platforms).

That “divide to conquer” strategy is to serve two purposes:

  • By bridging the gap between internal and external taxonomies it significantly enhances the transparency of governance and decision-making.
  • By applying the same motif (Who,What, How, Where, When) across the semantics of contexts, it opens the door to a seamless integration of all kinds of knowledge: enterprise, professional, institutional, scientific, etc.

As can be illustrated using Zachman concepts, the benefits are straightforward at enterprise architecture level (e.g procurement), due to the clarity of supporting ontologies; not so for external ones, which are by nature open and overlapping and often come with blurred semantics.

Ontologies & Concerns

A broad survey of RDF-based ontologies demonstrates how semantic overlaps and folds can be sort out using built-in differentiation between domains’ semantics on one hand, structure and processing of symbolic representations on the other hand. But such schemes are proprietary, and evidence shows their lines seldom tally, with dire consequences for interoperability: even without taking into account relationships and integrity constraints, weaving together ontologies from different sources is to be cumbersome, the costs substantial, and the outcome often reduced to a muddy maze of ambiguous semantics.

The challenge would be to generalize the principles as to set a basis for open ontologies.

Assuming that a clear line can be drawn between representation and contents semantics, with standard constructs (e.g predicate logic) used for the former, the objective would be to classify ontologies with regard to their purpose, independently of their representation.

The governance-driven taxonomy introduced above deals with contexts and consequently with coarse-grained modularity. It should be complemented by a fine-grained one to be driven by concerns, more precisely by the epistemic nature of the individual instances to be denoted. As it happens, that could also tally with the Zachman’s taxonomy:

  • Thesaurus: ontologies covering terms and concepts.
  • Documents: ontologies covering documents with regard to topics.
  • Business: ontologies of relevant enterprise organization and business objects and activities.
  • Engineering: symbolic representation of organization and business objects and activities.

Ontologies: Purposes & Targets

Enterprises could then pick and combine templates according to domains of concern and governance. Taking an on-line insurance business for example, enterprise knowledge architecture would have to include:

  • Medical thesaurus and consolidated regulations (Knowledge).
  • Principles and resources associated to the web-platform (Engineering).
  • Description of products (e.g vehicles) and services (e.g insurance plans) from partners (Business).

Such designs of ontologies according to the governance of contexts and the nature of concerns would significantly reduce blanket overlaps and improve the modularity and transparency of ontologies.

On a broader perspective, that policy will help to align knowledge management with EA governance by setting apart ontologies defined externally (e.g regulations), from the ones set through decision-making, strategic (e.g plate-form) or tactical (e.g partnerships).

Open Ontologies’ Benefits

Benefits from open and formatted ontologies built along an explicit distinction between the semantics of representation (aka ontology syntax) and the semantics of context can be directly identified for:

Modularity: the knowledge basis of enterprise architectures could be continuously tailored to changes in markets and corporate structures without impairing enterprise performances.

Integration: the design of ontologies with regard to the nature of targets and stability of categories could enable built-in alignment mechanisms between knowledge architectures and contexts.

Interoperability: limited overlaps and finer granularity are to greatly reduce frictions when ontologies bearing out business processes are to be combined or extended.

Reliability: formatted ontologies can be compared to typed programming languages with regard to transparency, internal consistency, and external validity.

Last but not least, such reasoned design of ontologies may open new perspectives for the collaboration between cognitive humans and pretending ones.

Further Reading

External Links

Deep Blind Testing

March 21, 2017


Tests are meant to ensure that nothing will go amiss. Assuming that expected hazards can be duly dealt with beforehand, the challenge is to guard against unexpected ones.

Unexpected Outcome (Ariel Schlesinger)

That would require the scripting of every possible outcomes in an unlimited range of unknown circumstances, and that’s where Deep Learning may help.

What to Look For

As Donald Rumsfeld once famously said, there are things that we know we don’t know, and things we don’t know we don’t know; hence the need of setting things apart depending on what can be known and how, and build the scripts accordingly:

  • Business requirements: tests can be designed with respect to explicit specifications; yet some room should also be left for changes in business circumstances.
  • Functional requirements: assuming business requirements are satisfied, the part played by supporting systems can be comprehensively tested with respect to well-defined boundaries and operations.
  • Quality of service: assuming business and functional requirements are satisfied, tests will have to check how human interfaces and resources are to cope with users behaviors and expectations which, by nature, cannot be fully anticipated.
  • Technical requirements: assuming business and functional requirements are satisfied as well as users’ expectations for service, deployment, maintenance, and operations are to be tested with regard to feasibility and costs.

Automated testing has to take into account these differences between scope and nature, from bounded and defined specifications to boundless, fuzzy and changing circumstances.

Automated Software Testing

Automated software testing encompasses two basic components: first the design of test cases (events, operations, and circumstances), then their scripted execution. Leading frameworks already integrate most of the latter together with the parts of the former targeting technical aspects like graphical user interfaces or system APIs. Artificial intelligence (AI) and machine learning (ML) have also been tried for automated test generation, yet with a scope limited by dependency on explicit knowledge, and consequently by the need of some “manual” teaching. That hurdle may be overcame by the deep learning ability to get direct (aka automated) access to implicit knowledge.

Reconnaissance: Known Knowns

Systems are designed artifacts, with the corollary that their components are fully defined and their behavior predictable. The design of technical test cases can therefore be derived from what is known of software and systems architectures, the former for test units, the latter for integration and acceptance tests. Deep learning could then mine recorded log-files in order to identify critical cases’ events and circumstances.

Exploration: Known Unknowns

Assuming that applications must be tested for use during their expected shelf life, some uncertainty has to be factored in for future business circumstances. Yet, assuming applications are designed to meet specific business objectives, such hypothetical circumstances should remain within known boundaries. In that context deep learning could be applied to exploration as well as policies:

  • Compared to technical test cases that can rely on the content of systems log-files, business and functional ones have to look outside and mine raw data from business environments.
  • In return, the relevancy of observations can be assessed with regard to business objectives, improved, and feed the policy module in charge of defining test cases.

Blind Errands: Unknown Unknowns

Even with functional and technical capabilities well-tested and secured, quality of service may remain contingent on human quirks: instinctive or erratic behaviors that could thwart the best designed handrails. On one hand, and due to their very nature, such hazards are not to be easily forestalled by reasoned test cases; but on the other hand they don’t take place in a void but within known functional circumstances. Given that porosity of functional and cognitive layers, the validity of functional test cases may be compromised by unfathomable cognitive associations, and that could open the door to unmanageable regression. Enter deep learning and its ability to extract knowledge from insignificance.

Compared to business and functional test cases, hazards are not directly related to business activities. As a consequence, the learning process cannot be guided by business and functional test cases but has to chart unpredictable human behaviors. As it happens, that kind of learning combining random simulation with automated reinforcement is what makes the specificity of deep learning.

From Non-regression to Self-improvement

As a conclusion, if non-regression is to be the cornerstone of quality management, test cases are to be set along clear swim-lanes: business logic (independently of systems), supporting systems functionalities (for shared applications), users interfaces (for non shared interactions). Then, since test cases are also run across swim-lanes, it opens the door to feedback, e.g unit test cases reassessed directly from business rules independently of systems functionalities, or functional test cases reassessed from users’ behaviors.

Considering that well-defined objectives, sound feedback mechanisms, and the availability of massive data from systems logs (internal) and business environment (external) are the main pillars of deep learning technologies, their combination in integrated frameworks could result in a qualitative leap toward self-improving automated test cases.

Further Reading


NIEM & Information Exchanges

January 24, 2017


The objective of the National Information Exchange Model (NIEM) is to provide a “dictionary of agreed-upon terms, definitions, relationships, and formats that are independent of how information is stored in individual systems.”

(Alfred Jensen)

NIEM’s model makes no difference between data and information (Alfred Jensen)

For that purpose NIEM’s model combines commonly agreed core elements with community-specific ones. Weighted against the benefits of simplicity, this architecture overlooks critical distinctions:

  • Inputs: Data vs Information
  • Dictionary: Lexicon and Thesaurus
  • Meanings: Lexical Items and Semantics
  • Usage: Roots and Aspects

That shallow understanding of information significantly hinders the exchange of information between business or institutional entities across overlapping domains.

Inputs: Data vs Information

Data is made of unprocessed observations, information makes sense of data, and knowledge makes use of information. Given that NIEM is meant to be an exchange between business or institutional users, it should have no concern with data mining or knowledge management.

Data is meaningless, information meaning is set by semantic domains.

As an exchange, NIEM should have no concern with data mining or knowledge management.

The problem is that, as conveyed by “core of data elements that are commonly understood and defined across domains, such as person, activity, document, location”, NIEM’s model makes no explicit distinction between data and information.

As a corollary, it implies that data may not only be meaningful, but universally so, which leads to a critical trap: as substantiated by data analytics, data is not supposed to mean anything before processed into information; to keep with examples, even if the definition of persons and locations may not be specific, the semantics of associated information is nonetheless set by domains, institutional, regulatory, contractual, or otherwise.

Data is meaningless, information meaning is set by semantic domains.

Data is meaningless, information meaning is set by semantic domains.

Not surprisingly, that medley of data and information is mirrored by NIEM’s dictionary.

Dictionary: Lexicon & Thesaurus

As far as languages are concerned, words (e.g “word”, “ξ∏¥” ,”01100″) remain data items until associated to some meaning. For that reason dictionaries are built on different levels, first among them lexical and semantic ones:

  • Lexicons take items on their words and gives each of them a self-contained meaning.
  • Thesauruses position meanings within overlapping galaxies of understandings held together by the semantic equivalent of gravitational forces; the meaning of words can then be weighted by the combined semantic gravity of neighbors.

In line with its shallow understanding of information, NIEM’s dictionary only caters for a lexicon of core standalone items associated with type descriptions to be directly implemented by information systems. But due to the absence of thesaurus, the dictionary cannot tackle the semantics of overlapping domains: if lexicons alone can deal with one-to-one mappings of items to meanings (a), thesauruses are necessary for shared (b) or alternative (c) mappings.


Shared or alternative meanings cannot be managed with lexicons

With regard to shared mappings (b), distinct lexical items (e.g qualification) have to be mapped to the same entity (e.g person). Whereas some shared features (e.g person’s birth date) can be unequivocally understood across domains, most are set through shared (professional qualification), institutional (university diploma), or specific (enterprise course) domains .

Conversely, alternative mappings (c) arise when the same lexical items (e.g “mole”) can be interpreted differently depending on context (e.g plastic surgeon, farmer, or secret service).

Whereas lexicons may be sufficient for the use of lexical items across domains (namespaces in NIEM parlance), thesauruses are necessary if meanings (as opposed to uses) are to be set across domains. But thesauruses being just tools are not sufficient by themselves to deal with overlapping semantics. That can only be achieved through a conceptual distinction between lexical and semantic envelops.

Meanings: Lexical Items & Semantics

NIEM’s dictionary organize names depending on namespaces and relationships:

  • Namespaces: core (e.g Person) or specific (e.g Subject/Justice).
  • Relationships: types (Counselor/Person) or properties (e.g PersonBirthDate).

NIEM’s Lexicon: Core (a) and specific (b) and associated core (c) and specific (d) properties

But since lexicons know only names, the organization is not orthogonal, with lexical items mapped indifferently to types and properties. The result being that, deprived of reasoned guidelines, lexical items are chartered arbitrarily, e.g:

Based on core PersonType, the Justice namespace uses three different schemes to define similar lexical items:

  • “Counselor” is described with core PersonType.
  • “Subject” and “Suspect” are both described with specific SubjectType, itself a sub-type of PersonType.
  • “Arrestee” is described with specific ArresteeType, itself a sub-type of SubjectType.

Based on core EntityType:

  • The Human Services namespace bypasses core’s namesake and introduces instead its own specific EmployerType.
  • The Biometrics namespace bypasses possibly overlapping core Measurer and BinaryCaptured and directly uses core EntityType.
Lexical items are meshed disregarding semantics

Lexical items are chartered arbitrarily

Lest expanding lexical items clutter up dictionary semantics, some rules have to be introduced; yet, as noted above, these rules should be limited to information exchange and stop short of knowledge management.

Usage: Roots and Aspects

As far as information exchange is concerned, dictionaries have to deal with lexical and semantic meanings without encroaching on ontologies or knowledge representation. In practice that can be best achieved with dictionaries organized around roots and aspects:

  • Roots and structures (regular, black triangles) are used to anchor information units to business environments, source or destination.
  • Aspects (italics, white triangles) are used to describe how information units are understood and used within business environments.
nformation exchanges are best supported by dictionaries organized around roots and aspects

Information exchanges are best supported by dictionaries organized around roots and aspects

As it happens that distinction can be neatly mapped to core concepts of software engineering.

P.S. Thesauruses & Ontologies

Ontologies are systematic accounts of existence for whatever is considered, in other words some explicit specification of the concepts meant to make sense of a universe of discourse. From that starting point three basic observations can be made:

  1. Ontologies are made of categories of things, beings, or phenomena; as such they may range from simple catalogs to philosophical doctrines.
  2. Ontologies are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
  3. Ontologies are meant to be directed at specific domains of concerns, whatever they can be: politics, religion, business, astrology, etc.

With regard to models, only the second one puts ontologies apart: contrary to models, ontologies are about understanding and are not supposed to be driven by empirical purposes.

On that basis, ontologies can be understood as thesauruses describing galaxies of concepts (stars) and features (planets) held together by semantic gravitation weighted by similarity or proximity. As such ontologies should be NIEM’s tool of choice.

Further Reading

External Links

New Year: 2016 is the One to Learn

December 15, 2016

Sometimes the future is best seen through rear-view mirrors; given the advances of artificial intelligence (AI) in 2016, hindsight may help for the year to come.


Deep Mind Learning (J.Bosh)

Deep Learning & the Depths of Intelligence

Deep learning may not have been discovered in 2016 but Google’s AlphaGo has arguably brought a new dimension to artificial intelligence, something to be compared to unearthing the spherical Earth.

As should be expected for machines capabilities, artificial intelligence has for long been fettered by technological handcuffs; so much so that expert systems were initially confined to a flat earth of knowledge to be explored through cumbersome sets of explicit rules. But exponential increase in computing power has allowed neural networks to take a bottom-up perspective, mining for implicit knowledge hidden in large amount of raw data.

Like digging tunnels from both extremities, it took some time to bring together top-down and bottom-up schemes, namely explicit (rule-based) and implicit (neural network-based) knowledge processing. But now that it comes to fruition, the alignment of perspectives puts a new light on the cognitive and social dimensions of intelligence.

Intelligence as a Cognitive Capability

Assuming that intelligence is best defined as the ability to solve problems, the first criterion to consider is the type of input (aka knowledge) to be used:

  • Explicit: rational processing of symbolic representations of contexts, concerns, objectives, and policies.
  • Implicit: intuitive processing of factual (non symbolic) observations of objects and phenomena.

That distinction is broadly consistent with the one between humans, seen as the sole symbolic species with the ability to reason about explicit knowledge, and other animal species which, despite being limited to the processing of implicit knowledge, may be far better at it than humans. Along that understanding, it would be safe to assume that systems with enough computing power will sooner or later be able to better the best of animal species, in particular in the case of imperfect inputs.

Intelligence as a Social Capability

Alongside the type of inputs, the second criterion to be considered is obviously the type of output (aka solution). And since classifications are meant to be built on purpose, a typology of AI outcomes should focus on relationships between agents, humans or otherwise:

  • Self-contained: problem-solving situations without opponent.
  • Competitive: zero-sum conflictual activities involving one or more intelligent opponents.
  • Collaborative: non-zero-sum activities involving one or more intelligent agents.

That classification coincides with two basic divides regarding communication and social behaviors:

  1. To begin with, human behavior is critically different when interacting with living species (humans or animals) and machines (dumb or smart). In that case the primary factor governing intelligence is the presence, real or supposed, of beings with intentions.
  2. Then, and only then, communication may take different forms depending on languages. In that case the primary factor governing intelligence is the ability to share symbolic representations.

A taxonomy of intelligence with regard to cognitive (reason vs intuition) and social (symbolic vs non-symbolic) capabilities may help to clarify the role of AI and the importance of deep learning.

Between Intuition and Reason

Google’s AlphaGo astonishing performances have been rightly explained by a qualitative breakthrough in learning capabilities, itself enabled by the two quantitative factors of big data and computing power. But beyond that success, DeepMind (AlphaGo’s maker) may have pioneered a new approach to intelligence by harnessing both symbolic and non symbolic knowledge to the benefit of a renewed rationality.

Perhaps surprisingly, intelligence (a capability) and reason (a tool) may turn into uneasy bedfellows when the former is meant to include intuition while the latter is identified with logic. As it happens, merging intuitive and reasoned knowledge can be seen as the nexus of AlphaGo decisive breakthrough, as it replaces abrasive interfaces with smart full-duplex neural networks.

Intelligent devices can now process knowledge seamlessly back and forth, left and right: borne by DeepMind’s smooth cognitive cogwheels, learning from factual observations can suggest or reinforce the symbolic representation of emerging structures and behaviors, and in return symbolic representations can be used to guide big data mining.

From consumers behaviors to social networks to business marketing to supporting systems, the benefits of bridging the gap between observed phenomena and explicit causalities appear to be boundless.

Further Reading

External Links

Business Agility vs Systems Entropy

November 28, 2016


As already noted, the seamless integration of business processes and IT systems may bring new relevancy to the OOAD (Observation, Orientation, Decision, Action) loop, a real-time decision-making paradigm originally developed by Colonel John Boyd for USAF fighter jets.

Agility: Orientation (Lazlo Moholo-Nagy)

Agility & Orientation (Lazlo Moholo-Nagy)

Of particular interest for today’s business operational decision-making is the orientation step, i.e the actual positioning of actors and the associated cognitive representations; the point being to use AI deep learning capabilities to surmise opponents plans and misdirect their anticipations. That new dimension and its focus on information brings back cybernetics as a tool for enterprise governance.

In the Loop: OOAD & Information Processing

Whatever the topic (engineering, business, or architecture), the concept of agility cannot be understood without defining some supporting context. For OODA that would include: territories (markets) for observations (data); maps for orientation (analytics); business objectives for decisions; and supporting systems for action.

OODA loop and its actual (red) and symbolic (blue) contexts.

OODA loop and its actual (red) and symbolic (blue) contexts.

One step further, contexts may be readily matched with systems description:

  • Business contexts (territories) for observations.
  • Models of business objects (maps) for orientation.
  • Business logic (objectives) for decisions.
  • Business processes (supporting systems) for action.

The OODA loop and System Perspectives

That provides a unified description of the different aspects of business agility, from the OODA loop and operations to architectures and engineering.

Architectures & Business Agility

Once the contexts are identified, agility in the OODA loop will depend on architecture consistency, plasticity, and versatility.

Architecture consistency (left) is supposed to be achieved by systems engineering out of the OODA loop:

  • Technical architecture: alignment of actual systems and territories (red) so that actions and observations can be kept congruent.
  • Software architecture: alignment of symbolic maps and objectives (blue) so that orientation and decisions can be continuously adjusted.

Functional architecture (right) is to bridge the gap between technical and software architectures and provides for operational coupling.

Business Agility: systems architectures and business operations

Business Agility: systems architectures and business operations

Operational coupling depends on functional architecture and is carried on within the OODA loop. The challenge is to change tack on-the-fly with minimum frictions between actual and symbolic contexts, i.e:

  • Discrepancies between business objects (maps and orientation) and business contexts (territories and observation).
  • Departure between business logic (objectives and decisions) and business processes (systems and actions)

When positive, operational coupling associates business agility with its architecture counterpart, namely plasticity and versatility; when negative, it suffers from frictions, or what cybernetics calls entropy.

Systems & Entropy

Taking a leaf from thermodynamics, cybernetics defines entropy as a measure of the (supposedly negative) variation in the value of the information supporting the control of viable systems.

With regard to corporate governance and operational decision-making, entropy arises from faults between environments and symbolic surrogates, either for objects (misleading orientations from actual observations) or activities (unforeseen consequences of decisions when carried out as actions).

So long as architectures and operations were set along different time-frames (e.g strategic and tactical), cybernetics were of limited relevancy. But the seamless integration of data analytics, operational decision-making, and IT supporting systems puts a new light on the role of entropy, as illustrated by Boyd’s OODA and its orientation component.

Orientation & Agility

While much has been written about how data analytics and operational decision-making can be neatly and easily fitted in the OODA paradigm, a particular attention is to be paid to orientation.

As noted before, the concept of Orientation comes with a twofold meaning, actual and symbolic:

  • Actual: the positioning of an agent with regard to external (e.g spacial) coordinates, possibly qualified with the agent’s abilities to observe, move, or act.
  • Symbolic: the positioning of an agent with regard to his own internal (e.g beliefs or aims) references, possibly mixed with the known or presumed orientation of other agents, opponents or associates.

That dual understanding underlines the importance of symbolic representations in getting competitive edges, either directly through accurate and up-to-date orientation, or indirectly by inducing opponents’ disorientation.

Agility vs Entropy

Competition in networked digital markets is carried out at enterprise gates, which puts the OODA loop at the nexus of information flows. As a corollary, what is at stake is not limited to immediate business gains but extends to corporate knowledge and enterprise governance; translated into cybernetics parlance, a competitive edge would depend on enterprise ability to export entropy, that is to decrease confusion and disorder inside, and increase it outside.

Working on that assumption, one should first characterize the flows of information to be considered:

  • Territories and observations: identification of business objects and events, collection and analysis of associated data.
  • Maps and orientations: structured and consistent description of business domains.
  • Objectives and decisions: structured and consistent description of business activities and rules.
  • Systems and actions: business processes and capabilities of supporting systems.

Static assessment of technical and software architectures for respectively observation and decision

Then, a static assessment of information flows would start with the standing of technical and software architecture with regard to competition:

  • Technical architecture: how the alignment of operations and resources facilitate actions and observations.
  • Software architecture: how the combined descriptions of business objects and logic facilitate orientation and decision.

A dynamic assessment would be carried out within the OODA loop and deal with the role of functional architecture in support of operational coupling:

  • How the mapping of territories’ identities and features help observation and orientation.
  • How decision-making and the realization of business objectives are supported by processes’ designs.

Dynamic assessment of decision-making and the realization of business objectives’ as supported by processes’ designs.

Assuming a corporate cousin of  Maxwell’s demon with deep learning capabilities standing at the gates in its OODA loop, his job would be to analyze the flows and discover ways to decrease internal complexity (i.e enterprise representations) and increase external one (i.e competitors’ representations).

That is to be achieved with the integration of  operational analytics, business intelligence, and decision-making.


Seamless integration of operational analytics, business intelligence, and decision-making.

Further Readings

Brands, Bots, & Storytelling

May 2, 2016

As illustrated by the recent Mashable “pivot”, meaningful (i.e unbranded) contents appear to be the main casualty of new communication technologies. Hopefully (sic), bots may point to a more positive perspective, at least if their want for no no-nonsense gist is to be trusted.

(Latifa Echakhch)

Could bots repair gibberish ? (Latifa Echakhch)

The Mashable Pivot to “branded” Stories

Announcing Mashable recent pivot, Pete Cashmore (Mashable ‘s founder and CEO) was very candid about the motives:

“What our advertisers value most about
 Mashable is the same thing that our audience values: Our content. The
 world’s biggest brands come to us to tell stories of digital culture, 
innovation and technology in an optimistic and entertaining voice. As 
a result, branded content has become our fastest growing revenue 
stream over the past year. Content is now at the core of our ad 
offering and we plan to double down there.


Also revealing was the semantic shift in a single paragraph: from “stories”, to “stories told with an optimistic and entertaining voice”, and finally to “branded stories”; as if there was some continuity between Homer’s Iliad and Outbrain’s gibberish.

Spinning Yarns

From Lacan to Seinfeld, it has often been said that stories are what props up our world. But that was before Twitter, Facebook, YouTube and others ruled over the waves and screens. Nowadays, under the combined assaults of smart dummies and instant messaging, stories have been forced to spin advertising schemes, and scripts replaced  by subliminal cues entangled in webs of commercial hyperlinks. And yet, somewhat paradoxically, fictions may retrieve some traction (if not spirit) of their own, reprieved not so much by human cultural thirst as by smartphones’ hunger for fresh technological contraptions.

Apps: What You Show is What You Get

As far as users are concerned, apps often make phones too smart by half: with more than 100 billion of apps already downloaded, users face an embarrassment of riches compounded by the inherent limitations of packed visual interfaces. Enticed by constantly renewed flows of tokens with perfunctory guidelines, human handlers can hardly separate the wheat from the chaff and have to let their choices be driven by the hypothetical wisdom of the crowd. Whatever the outcomes (crowds may be right but often volatile), the selection process is both wasteful (choices are ephemera, many apps are abandoned after a single use, and most are sparely used), and hazardous (too many redundant dead-ends open doors to a wide array of fraudsters). That trend is rapidly facing the physical as well as business limits of a zero-sum playground: smarter phones appear to make for dumber users. One way out of the corner would be to encourage intelligent behaviors from both parties, humans as well as devices. And that’s something that bots could help to bring about.

Bots: What You Text Is What You Get

As software agents designed to help people find their ways online, bots can be differentiated from apps on two main aspects:

  • They reside in the cloud, not on personal devices, which means that updates don’t have to be downloaded on smartphones but can be deployed uniformly and consistently. As a consequence, and contrary to apps, the evolution of bots can be managed independently of users’ whims, fostering the development of stable and reliable communication grammars.
  • They rely on text messaging to communicate with users instead of graphical interfaces and visual symbols. Compared to icons, text put writing hands on driving wheels, leaving much less room for creative readings; given that bots are not to put up with mumbo jumbo, they will prompt users to mind their words as clearly and efficiently as possible.

Each aspect reinforces the other, making room for a non-zero playground: while the focus on well-formed expressions and unambiguous semantics is bots’ key characteristic, it could not be achieved without the benefits of stable and homogeneous distribution schemes. When both are combined they may reinstate written languages as the backbone of communication frameworks, even if it’s for the benefits of pidgin languages serving prosaic business needs.

A Literary Soup of Business Plots & Customers Narratives

Given their need for concise and unambiguous textual messages, the use of bots could bring back some literary considerations to a latent online wasteland. To be sure, those considerations are to be hard-headed, with scripts cut to the bone, plots driven by business happy ends, and narratives fitted to customers phantasms.

Nevertheless, good storytelling will always bring some selective edge to businesses competing for top tiers. So, and whatever the dearth of fictional depth, the spreading of bots scripts could make up some kind of primeval soup and stir the emergence of some literature untainted by its fouled nourishing earth.

Further Readings

Agile Collaboration & Social Creativity

February 22, 2016

Open-plan offices and social networks are often seen as significant factors of collaboration and innovation, breeding and nurturing the creativity of knowledge workers, weaving their ideas into webs of truths, and molding their minds into some collective intelligence.

Brains need some breathing space

Open-plan offices, collaboration, and knowledge workers creativity

Yet, as creativity comes with agility, knowledge workflows should give brains enough breathing space lest they get more pressure than pasture.

Collaboration & Thinking Flows

Collaboration is a means to an end. To be of any use exchanges have to be fed with renewed ideas and assumptions, triggering arguments and adjustments, and opening new perspectives. If not they may burn themselves out with hollow considerations blurring clues and expectations, clogging the channels, and finally stemming the thinking flows.

Taking example from lean manufacturing, the first objective should be to streamline knowledge workflows as to eliminate swirling pools of squabbles, drain stagnant puddles of stale thoughts, and gear collaboration to flowing knowledge streams. As illustrated by flood irrigation, the first step is to identify basin levels.

Dunbar Numbers & Collaboration Basins

Studying the grooming habits of social primates, psychologist Robin Dunbar came to the conclusion that the size of social circles that individuals of a living species can maintain is set by the size of brain’s neocortex. Further studies have confirmed Dunbar’s findings, with the corresponding sizes for humans set around 10 for trusted personal groups and 150 for untried social ones. As it happens, and not by chance, those numbers seem to coincide with actual observations: the former for personal and direct collaboration, the latter for social and mediated collaboration.

Based on that understanding, the objective would be to organize knowledge workflows across two primary basins:

  • On-site and face-to-face collaboration with trusted co-workers. Corresponding interactions would be driven by personal dispositions and attitudes.
  • On-line and networked collaboration with workers, trusted or otherwise. Corresponding interactions would be based on shared interests and past exchanges.

Knowledge Workflows

The aim of knowledge workflows is to process data into information and put it to use. That is to be achieved by combining different kinds of tasks, in particular:

  • Data and information management: build the symbolic descriptions of contexts, concerns, and means.
  • Objectives management: based on a set of symbolic descriptions, identify and refine opportunities together with the ways to realize them.
  • Tasks management: allocate rights and responsibilities across organizations and collaboration frames, public and shallow or personal and deep.
  • Flows management: monitor and manage actual flows, publish arguments and propositions, consolidate decisions, …

Taking into account constraints and dependencies between the tasks, the aims would be to balance creativity and automation while eliminating superfluous intermediate products (like documents or models) or activities (e.g unfocused meetings).

With regard to dependencies, KM tasks are often intertwined and cannot be carried out sequentially; moreover, as illustrated by the impact of “creative accounting” on accounted activities, their overlapping is not frozen but subject to feedback, changes and adjustments.

With regard to automation, three groups are to be considered: the first requires only raw processing power and can be fully automated; the second also involves some intelligence that may be provided by smart systems; and the third calls for decision-making that can only be done by human agents entitled by the organization.

At first sight some lessons could be drawn from lean manufacturing, yet, since knowledge processes are not subject to hardware constraints, agile approaches should provide a more informative reference.

Iterative Knowledge Processing

A simple preliminary step is to check the applicability of agile principles by replacing “software” by “knowledge”. Assuming that ground is secured, the core undertaking is to consider what would become of cycles and iterations when applied to knowledge processing:

  • Cycle invariants: tasks would be iterated on given sets of symbolic descriptions applied to the state of affairs (contexts, concerns, and means).
  • Iterations content: based on those descriptions data would be processed into information, changes would be monitored, and possibilities explored.
  • Exit condition: cycles would complete with decisions committing changes in the state of affairs that would also entail adjustments or changes in symbolic descriptions.

That scheme meets three of the basic tenets of the agile paradigm, i.e open scope (unknowns cannot be set in advance), continuity of delivery (invariants are defined and managed by knowledge workers), and users in driving seats (through exit conditions). Yet it still doesn’t deal with creativity and the benefits of collaboration for knowledge workers.

Thinking Space & Pace

The scope of creativity in processes is neatly circumscribed by the nature of flows, i.e the possibility to insert knowledge during the processing: external for material flows (e.g in manufacturing), internal for symbolic flows (e.g in software engineering and knowledge processing).

Yet, whereas both software engineering and knowledge processes come with some built-in capability to redefined their symbolic flows on-the-fly, they don’t grant the same room to creativity. Contrary to software engineering projects which have to close their perspectives on the delivery of working products, knowledge processes are meant to keep them open to new understandings and opportunities. For the former creativity is the means to an end, for the latter it’s the end in itself, with collaboration as means.

Such opposite perspectives have direct consequences for two basic agile collaboration mechanisms: backlog and time-boxing:

  • Backlogs are used to structure and manage the space under exploration. But contrary to software processes whose space is focused and structured by users’ needs, knowledge processes are supposed to play on workers’ creativity to expand and redefine the range under consideration.
  • Time-boxes are used to synchronize tasks. But with creativity entering the fray, neither space granularity or thinking pace can be set in advance and coerced into single-sized boxes. In that case individuals must remain in full control of the contents and stride of their thinking streams.

It ensues that when creativity is the primary success factor standard agile collaboration mechanisms are falling short and intelligent collaboration schemes are to be introduced.

Creativity & Collaboration Tiers

The synchronization of creative activities has to deal with conflicting objectives:

  • On one hand the mental maps of knowledge workers and the stream of their thoughts have to be dynamically aligned.
  • On the other hand unsolicited face-to-face interactions or instant communications may significantly impair the course of creative thinking.

When activities, e.g software engineering, can be streamlined towards the delivery of clearly defined outcomes, backlogs and time-boxes can be used to harness workers’ creativity. When that’s not the case more sophisticated collaboration mechanisms are needed.

Assuming that mediated collaboration has a limited impact on thinking creativity (emails don’t have to be answered, or even presented, instantly), the objective is to steer knowledge workflows across a two-tiered collaboration framework: one personal and direct between knowledge workers, the other social and mediated through enterprise or institutional networks.

On the first tier knowledge workers would manage their thinking flows (content and tempo) independently, initiating or accepting personal collaboration (either through physical contact or some kind of instant messaging) depending on their respective “state of mind”.

The second tier would be for social collaboration and would be expected to replace backlogs and time-boxing. Proceeding from the first to the second tier would be conditioned by workers’ needs and expectations, triggered on their own initiative or following prompts.

From Personal to Collective Thinking

The challenging issue is obviously to define and implement the mechanisms governing the exchanges between collaboration tiers, e.g:

  • How to keep tabs on topics and contents to be safeguarded.
  • How to mediate (i.e filter and time) the solicitations and contribution issued by the social tier.
  • How to assess the solicitations and contribution issued by individuals.
  • How to assess and manage knowledge deemed to remain proprietary.
  • How to identify and manage knowledge workers personal and social circles.

Whereas such issues are customary tackled by various AI systems (knowledge management, decision-making, multi-players games, etc), taken as a whole they bring up the question of the relationship between personal and collective thinking, and as a corollary, the role of organization in nurturing corporate innovation.

Conclusion: Collaboration Spaces vs Panopticon

As illustrated by the rising of futuristic headquarters, leading technology firms have been trying to tackle these issues by redefining internal architecture as collaboration spaces. Compared to traditional open spaces, such approaches try to fuse physical and digital spaces into overlapping layers of collaboration spaces, using artificial intelligence to harness cooperation.

Yet, lest uniform and comprehensive transparency brings the worrying shadow of a panopticon within which everyone can be unknowingly observed, working spaces have to be designed as to enhance collaboration without trespassing on privacy.

That could be achieved with a layered transparency set along the nature of collaboration:

  • Immediate and personal: working cells regrouping 5 to 10 workstations earmarked for a task and used indifferently by teams members.
  • Delayed and personal: open physical spaces accommodating working cells, with instant messaging and geo-localization; spaces are hinged on domains and focused on shared knowledge.
  • On-line and networked: digital spaces merging physical spaces and organizational structures.

That mix of physical and virtual spaces could be dynamically redefined depending on activities, projects, location, and organisation.

Further Readings

External Links

Operational Intelligence & Decision Making

January 18, 2016


According to a leading tools provider operational intelligence (OI) is the ability to “discover and analyze relationships between business events and corresponding IT events”.

Sigmar Polke

Operational Decision-making (Sigmar Polke)

From a marketing perspective, the moniker suggests some kind of cross-breeding between operational research, artificial intelligence, and real-time analytics. Yet, behind vendor dressing, problems and policies remain the ones traditionally dealt with by decision-making and knowledge management, and as far as marketing is concerned, pitches will hardly affect the assessment of field professionals.

Nevertheless, functional pitches may have a deeper influence if they try to outline the aims of operational intelligence to the people directly involved, affecting the way problems are understood and dealt with. That may be the case if business and system events are seemed to be put on a par: overlooking the directed dependency between actual events and their systems counterparts can critically hamper the very capabilities of systems decision-making.

Facts, Data, & Information

The new connected world of human brains and smart things have scaled down space and time by orders of magnitude, up to the point that events seem to come out as soon as they happen, wherever that may be. Facts and updates, that once were incoming as discrete and manageable batches of information, are now bursting continuously and massively as seamless streams of data that have to be processed on-the-fly into information lest they be cannibalized by ambient noise. That new configuration blurs the distinction between operational data (pushed, shallow, transient) and underlying information (pulled, deep, persistent), making it unworkable, if not meaningless altogether.

Taking inventories decisions as an example, traditional schemes rely on periodic readings of actual inventories and sales crossed with market foresight. Now, with on-line sales and the internet of things, real-time data can be used to build on-the-fly indicators whose biases and inaccuracy would be dynamically readjusted on the basis of information built on hindsight. At any given time (t), decision-makers will be presented with actual observations (a),  initial estimations of previous observations (b1, b2), and revised estimations of previous observations (c).


At any given time (t), decision-makers are presented with actual observations (a), initial estimations of previous observations (b1, b2), and revised estimations of previous observations (c).

Set along this framework, the debate about big data can be misleading as it puts the focus on the quantity of data feeding the processes, overlooking the process itself and the distinction between data, information, and knowledge.

Information, Knowledge, & Decision-making

Generally speaking, the distinction between data and information can be set with reference to time and context, data being instant and standalone, and information associated to a shelf life and domain. With regard to decision-making, it would mean that data can be directly used within the context of the current activities and circumstances; e.g, whereas on-line sales data may (or may not) be directly (i.e despite inaccuracies and biases) used to allocate inventories across depots, it has to be “mined” into consolidated information before being used in the broader perspective of inventories planning.

Compared to the transition between data and information, which is carried out by adding time and context, the one between information and knowledge is best understood in terms of decision-making.

Information is obtained by anchoring data to time-frames and contexts, knowledge is acquired by putting information to use.

Information is obtained by anchoring data to time-frames and contexts, knowledge is acquired by putting information to use.

Decisions are best defined as commitments set against some unknown circumstances: somebody, somewhere, or sometime. First, it ensues that decision-making calls for specific and timed information that has to be maintained up-to-date until decisions are taken. Then, taking decisions introduces some irreversible change in the state of affairs or expectations, making potentially obsolete all relevant information. So it may be argued that decisions is what transform information into knowledge.

Operational Intelligence: Objectives & Tools

Assuming decisions mark the nexus between information and knowledge, operational intelligence could be defined as the ability to put information to use, that ability being supported by the analysis of the relationships between business events and corresponding IT events.

Far from being academic, that distinction is essentially pragmatic as it marks the boundary between OI objectives and tools capabilities:

  • The aim of OI is to make sense (and profit) from the dynamic relationship between business (aka external) events on one hand, business objectives and enterprise capabilities on the other hand.
  • The role of supporting tools is to define and manage IT (aka internal) events used to reflect external ones and analyze them.
Whereas business events (red) represent change in the state of affairs, IT events (blue) only represent changes in associated information.

Whereas business events (red) represent change in the state of affairs, IT events (blue) only represent changes in associated information.

Since IT events are artifacts built on purpose there isn’t much to discover or analyze about them; not to mention the fact that confusing business events and their IT shadows is bound to undermine the whole decision-making process. So what is at stake for OI is how to design IT events as to timely and accurately trail the relevant business events.

Operational Intelligence & Actual Knowledge

As already noted, operational intelligence (OI) is about decision-making, which entails changing the state of objects, processes, or expectations. Compared to knowledge management (KM) which may or may not be time-related, OI is inherently bound to the actual state of affairs: on one hand it relies on specific and timed information, on the other hand it renders that information obsolete when it triggers decisions.

At the risk of oversimplification, operational intelligence can first be understood as a combination of traditional disciplines:

  • Data-mining is to filter facts and events, capture data, and analyze it into information.
  • Knowledge management chart information with regard to business objectives and enterprise capabilities.
  • Decision-making manage time-stamps and plan commitments subject to accuracy and likelihood.

But the specificity of operational intelligence is to be found in the way these functions are intertwined and cross-fed by operational concerns.

To begin with, data mining can be dynamically adjusted depending on what is needed for decision-making, and when. As a corollary, with the benefits of data so cooked in advance, some decisions can be taken directly, bypassing the mediation (and delays) of information processing. From a cognitive point of view that would be the equivalent of non symbolic (aka implicit) knowledge to be processed by neuronal networks.

Parceling out OI objectives

Decision-making and differentiated knowledge management

Conversely, information processing could benefit from operational feedback so that knowledge management would be driven by business value, and the supporting information weighted by timing and shelf-life considerations. Whereas part of it could be done through implicit connections, it would be more comprehensively and explicitly achieved through symbolic representations.

Operational Intelligence: Signals vs Symbols

Assuming that intelligence is the ability to figure out situations and solve problems, one may conclude that it is inherently operational. Along the same reasoning, if knowledge is information put to use, it may be implicit as well as explicit.

Nonetheless, the merit of operational intelligence is to bring to a single functional roof symbolic and non symbolic knowledge, the former explicit, using mediation of semantic constructs and used to weight information and support managed decisions, the latter implicit, using direct associations between actual objects or phenomena, and supporting automated decisions.

Further Readings

Data Mining & Requirements Analysis

October 24, 2015


Data mining explores business opportunities and competitive advantage, requirements analysis considers supporting applications. Both use models, the former’s are predictive and ephemeral, the latter’s descriptive (or prescriptive) and perennial.

(Andreas Gursky)

Data mining: sorting business wheat from world chaff (Andreas Gursky)

As the generalization of digitized environment calls for more integration of business and software engineering processes, understanding the relationship between data mining and requirements analysis could significantly improve processes maturity and agility.

Data vs Requirements Analysis

Nowadays the success of a wide range of enterprises critically depends on two achievements:

  1. Mapping business models to changing environments by sorting through facts, capturing the relevant data, and processing the whole into meaningful and up to date information. That can be achieved through analysis models meant to described business expectations with regard to supporting systems.
  2. Putting that information into effective use through business processes and supporting systems. That is done by systems architecture and design models meant to prescribe how to build software artifacts.

From data analysis to systems requirements and software design

Those challenges are converging: under the pressure of markets forces and technological advances most of traditional fences between business channels and IT systems are crumbling, putting the focus on the functional integration between data mining and production systems. That’s where predictive models can help by anchoring descriptive models to moving markets and by cross-feeding analysis and operations. How that can be achieved has been the bread and butter of good corporate governance for some time, but there has been less interest for the third branch, namely how data analysis (predictive models) could “inform” business requirements (descriptive models).

From Data to Information

Facts are not given but must be captured through a symbolic description of actual observations. That entails some observer set on task using a mix of conceptual and technical apparatus. Data mining and requirements analysis are practical realizations of that process:

  • Data mining relies on analytic tools to extract revealing information that could be used to chart opportunities along business models.
  • Requirements analysis relies on business processes and users’ practice to extract symbolic descriptions that will be used to build models of supporting applications.

If both walk the path from data to information, their objectives are different: the former’s is to improve business decisions by making sense of actual observations; the latter’s is to build system surrogates from the symbolic descriptions of actual business objects and activities.

Anchors & Structures: Plasticity of  Business Entities

Perhaps paradoxically, business agility calls for terra firma because nimble trades must be rooted in corporate identity and business continuity. As a consequence, the first step of requirements analysis should be to associate individuals business objects or activities with stable and consistent identification mechanisms, and to group them with regard to that mechanism:

  • External entities with natural (person) or designed identity (car).
  • Symbolic entities for roles (customer) or commitments (maintenance contract).
  • Actual activities (promotion campaign) and events (sale) or business logic (promotion).


Conversely, as the aim of data analysis is to explore every business angle, individual observations are supposed to be moved across groups; yet, since the units identified by data analysis will have to be aligned with the ones described by requirements analysis, moves must also keep track of identities. That dilemma between continuity of identified structures on one side, plasticity of functional aspects on the other side, can be illustrated by banks which, in response to marketing requirements, had to shift from account (internal identification) to customer (external identification) based systems.

From account (left) to customer (right) centered systems

It’s easier to market insurance from customer centered systems (right) than from account centered ones (left)

That challenge can be overcome by linking the identification of symbolic entities to external anchors.

Profiles & Features: Versatility of Business Opportunities

As noted above, requirements and data analysis are set on the same road but driven by different forces: the former tries to group individuals with regard to identification mechanisms before fleshing them out with relevant features; the latter tries to group individuals with given identities according to features and opportunity profiles. Yet, what could appear as collision courses may become a meeting of minds if both courses are charted with regard to variants analysis.

From the requirements perspective the primary concern is to distinguish between structural and functional variants:

  • Structural variants are bound to identities, i.e set up-front for the respective life-cycle of individual business objects or transactions. As a consequence they cannot be changed without undermining business continuity. Moreover, being part and parcel of descriptors (e.g  types and use cases) their change will affect engineering processes.
  • Functional variants may vary during the respective life-cycle of individual business objects or transactions. As a consequence they can be changed without undermining business continuity, and changes in descriptors (e.g partitions and scenarii) can be managed without affecting engineering processes.

From the data mining perspective the objective is to improve the benefits of information systems for decision-making processes:

  • Static: how to classify individuals as to reduce the uncertainty of predictions
  • Dynamic: how to classify business options as to reduce the uncertainty of decisions.

Since those objectives are set for individuals, constraints on continuity and consistency can be dealt with independently of the description of symbolic surrogates.

Identified individuals with profiles for customers (a), their behaviors (b), and conciliatory gestures (c)

Identified individuals with profiles for customers (a), their behaviors (b), and promotional gestures (c)

It ensues that perspectives can be adjusted by factoring out the constraints of continuity and consistency for business objects (e.g cars), agents (e.g customer) and processes (e.g repair). Profiles for agents (a), behaviors (b), and business options (c) could then be freely explored and tailored with regard to changes in business environment and objectives.

Applying Data Analysis to Requirements

Not surprisingly data analysis techniques can be used to adjust perspectives. For that purpose a sample of individuals (business objects and operations) representing the population targeted by requirements would have to be submitted to basic mining routines. Borrowing a catalog from F. Provost & T. Fawcett:

  1. Classification: estimates the probability for each individual (objects or operations) to belong to a set of classes; can be used to assess the closeness of the variants (respectively power-types or execution paths) identified by requirements analysis.
  2. Regression: reverse classification; estimates how much of individual features valuations can be explained by the proposed classifications.
  3. Similarity: a shallow version of classification; can be used to assess the distance between variants and consolidate the proposed classifications.
  4. Clustering: a deep version of classification; can be used to distinguish between shallow and natural classifications.
  5. Co-occurrence: deals with behavioral variants; can be used to distinguish between functional and structural classifications.
  6. Profiling: reverse of co-occurrence; can be used to consolidate functional and structural classifications.
  7. Links prediction: can be used to define relationships.
  8. Data reduction: eliminate redundant individuals; can be used to consolidate requirements and refine tests scenarii.
  9. Causal modeling: brings together business logic (events and rules) and users decisions; should provide the backbone of tests scenarii.

Besides the direct benefits for requirements, such procedures may help to bridge the span between data and requirements analysis and significantly improve processes’ capability and maturity level.

Business Objectives & Enterprise Architecture Capabilities

Data mining being first and foremost about competitive edge, it relies on a timely and effective coupling between enterprises capabilities and business opportunities. But the dilemma between continuity and plasticity described above for business objects and processes reappears at enterprise level: how to conciliate architecture, by nature perennial, with the agility needed to make the best of changing and competitive environments ?

As architectural big bang is arguably a last resort option, answers to that question must be progressive and local: if changes are to be swift and pertinent they must be both circumscribed and leveraged to the relevant parts of architecture. Taking an (amended) leaf of the Zachman framework, its sixth column (“Why” ) could be reset as a line for business and operational objectives that would cross the original five columns instead of the architecture layers. Using a pentagonal representation of enterprise architecture, that line would be set as circling the outer range.

Enterprise Architecture and the loci of change

It is worth to note that setting objectives on a line crossing the columns of capabilities instead of a column crossing the lines of layers means that objectives are set at enterprise level and their cascading impact traced and managed through layers.

Conceptual Models & Business Contexts

But even that updated framework doesn’t take into account the fundamental changes in business environments. Once secure behind organizational and technical fences, enterprises must now navigate through open digitized business environments and markets. For business processes it means a seamless integration with supporting applications; for corporate governance it means keeping track of heterogeneous and changing business contexts and concerns while assessing the capability of organizations and systems to cope, adjust, and improve.

As long as environments were a hotchpotch of actual and symbolic artifacts the pros and cons of integration could be balanced. But the generalization of digital flows and transactions has upended the balance: there is no more room or time for latency and enterprises must bring all symbolic representations (business, organization, and systems) under a common conceptual roof:


Conceptual models as bridges between business processes, and systems.

A canonical approach would be to introduce a conceptual indexing scheme open to extensions but with its footprint defined by business processes and systems functionalities. That would ensure a better integration of processes and supporting engineering but will do nothing for the modeling gap between enterprise architecture and external contexts. That could be achieved with ontologies.

Conceptual Loop: Ontologies & Business Intelligence

As far as data mining is concerned, three kinds of operations are to be considered:

  • Data understanding gives form and semantics to raw material.
  • Business understanding charts business contexts and concerns in terms of objects and processes descriptions.
  • Modeling consolidate data and business understanding into descriptive, predictive, and operational models.

The aim of data mining is to refine raw data into meaningful information

While traditional approaches fall short when tasked with iterative modeling of unstructured data, ontologies may fare better because their explicit aim is only to describe what could exist in a domain of discourse:

  1. They are made of categories of things, beings, or phenomena; as such they may range from simple catalogs to philosophical doctrines.
  2. They are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
  3. They are meant to be directed at specific domains of concerns, whatever they can be: politics, religion, business, astrology, etc.

With regard to models, only the second point puts ontologies apart: contrary to models, ontologies are about understanding and are not supposed to be driven by empirical purposes. It ensues that ontologies can be understood as conceptual (aka canonical) models, used as such for business analysis and extended with purposes for systems analysis and design.

In addition to the integration with enterprise architectures, ontologies benefits for business intelligence would be twofold.

On one side, and whatever their use, ontologies could be aligned with the nature of contexts and their impact on business and enterprise governance e.g:

  • Social: pragmatic semantics, no authority, volatile, continuous and informal changes.
  • Institutional: mandatory semantics sanctioned by regulatory authority, steady, changes subject to established procedures.
  • Professional: agreed upon semantics between parties, steady, changes subject to established procedures.
  • Corporate: enterprise defined semantics, changes subject to internal decision-making.
  • Personal: customary semantics defined by named individuals.

Ontologies, capabilities (Who,What,How, Where, When), and architectures (enterprise, systems, platforms).

On the other side ontologies could be defined according to the nature of targeted items, namely terms, documents, symbolic representations, or actual objects and phenomena. That would outline four basic concerns that may or may not be combined:

  • Thesaurus: ontologies covering terms and concepts.
  • Document Management: ontologies covering documents with regard to topics.
  • Organization and Business: ontologies pertaining to enterprise organization, objects and activities.
  • Engineering: ontologies pertaining to the symbolic representation of products and services.

Ontologies: Purposes & Targets

On a broader perspective, ontologies could be influential in spanning the gap between explicit and implicit knowledge. 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, ontologies would be decisive in framing the understanding, assessment, and improvement of the processes.

Operational Loop: Business Intelligence & Decision-making

Once carried out separately and periodically, decision-making is to be carried out iteratively at operational, tactical, and strategic level; while each level is to be set along its own time-frames, all are to rely on data-mining, with cycles following the same pattern:

  1. Observation: understanding of changes in business opportunities.
  2. Orientation: assessment of the reliability and shelf-life of pertaining information with regard to current positions and operations.
  3. Decision: weighting of options with regard to enterprise capabilities and broader objectives.
  4. Action: carrying out of decisions within the relevant time-frame.

Given business new playground, decision-making processes have to weave together material and digitized flows, actual contexts (aka territories) and symbolic descriptions (maps), and overlapping time-frames (operational tactical, strategic). That operational loop could then be coupled with the broader one of business intelligence:


Integration of  operational analytics, business intelligence, and decision-making.

Selected Readings


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IT Modernization < V.Hanniet

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IT Modernization < V. Hanniet

software model driven approaches

Caminao's Ways

Do systems know how symbolic they are ?