Focus: Entropy & Homeostasis

Preamble

As defined by thermodynamics entropy is a measure of the energy within a system that cannot be harnessed to useful use; cybernetics has took over, making entropy a pillar of information theory.

Figuring Digital Matter (Marcelo Cidade)

Notwithstanding the focus put on viable systems and organizations (as epitomized by the pioneering work of Stafford Beer), cybernetics’ actual imprint on corporate governance has been frustrated by the correspondence assumed between information and energy. But the immersion of enterprises into digital environments brings entropy back in front, along with a paradigmatic shift out of thermodynamics.

domain: Physics vs Economics

The second law of thermodynamics states that entropy within a system is constant, and so is information as defined by cybernetics. But economics laws, if there is such a thing, are to differ: as far as business is concerned information is not to be found in commons but comes from the processing of raw data.

As a matter of fact, thermodynamic stability is meaningless in economics because business information is not a given and uniform quantity but a fluctuating and polymorph one. It ensues that for enterprises in competitive environments measures of entropy set in isolation are pointless: taking a leaf from Lewis Carroll’s Red Queen, the entropy of any given enterprise can only be assessed in relation with its competitors.

Taking a general perspective, entropy is meant to be observed either between the two ends of communication channels, or at the boundaries of complex systems (animals, machines, or organizations). While the former can be seen as a formal coding issue, the latter is of particular relevance for enterprises immersed in digital environments.

Departing from thermodynamics view of entropy measuring a system’s thermal energy unavailable for conversion into mechanical work, economics will consider unexplained information, i.e which cannot be acted upon.

But leaving physics also means forsaking the comfort of its metrics. That’s not a problem for communication entropy, which can be dealt with as a coding issue; but the difficulty appears when counts of digits have to be replaced by measures of knowledge.

complexity: microstates vs macrostates

When there are no clear and reliable metrics to put light on it, entropy becomes a black hole defined by disorder of randomness, the former in reference to the order borne out by classifications, the latter in reference to the predictability borne out by probabilities. Since both approaches deal with the relationship between configurations and information, the issue can be summarized with:

  • A quantum of hypothetical information (dashed line).
  • The complexity of identified items (aka microstates).
  • The complexity of representations (aka macrostates).
  • The quantum of information accounted for, ordered or predicted.
How to assess what is missed ?

As stated by the basic law of thermodynamics, to be of any energetic use the internal heat of a system has to be lower than the external one. Generalized in terms of complexity, it means that the quantum of information accounted for will first increase with the number of configurations considered, reach a maximum, and then decrease until the complexity of configurations (aka internal heat) equates the complexity of the target (aka external heat).

Translated to economics, that scheme must be restated in terms of digital environments and business information.

Entropy: From data to information

The generalization of digital exchanges between enterprises and their environment points to the necessary distinction between data (exchanged flows) and information (managed assets). What happens in-between can be defined along two perspectives:

  • Representation: whether the exchanges are carried out at digital level (data observations of microstates) or in association with symbolic representations (models of macrostates) of managed information.
  • Coupling: whether the processing of data is tied to operations or carried out independently.
Entropy Layers

The issue of entropy can be restated accordingly:

  1. The direct consequence of the digital transformation is to increase osmosis between enterprises and their environment, with data flows bypassing traditional boundaries and feeding directly operational processes (bottom right).
  2. As a result, data analytics can be integrated with business processes, improving operational decision-making. In terms of entropy the outcome would be a more effective use of information models (bottom left).
  3. Next, information models (aka symbolic representations) can themselves be changed as to improve their effectiveness, i.e reduce entropy at process as well as architecture level (top left).
  4. Last but not least, deep learning could be applied to digitalized contexts (aka microstates) in order to derive new symbolic representations (aka macrostates) (top right).

On that basis entropy policies should be set at operational and strategic levels, the former dealing with processes, the latter with architectures.

Homeostasis: from Data to knowledge

As far as enterprises are concerned, homeostasis means the adaptation of organizations and systems to changes in competitive environments. To that end, policies set along the entropy paradigm could draw on improving the perception of microstates (data level) as well as the representation of macrostates (information level).

At data level, the perception of changes depends on the osmosis between systems and environments. To that end data mining is to be used to hone the sampling of populations and refine microstates.

At symbolic level (representations), the economics perspective differs from physics on two critical aspects:

  • Contrary to heat, economic information is not a constant, which means that macrostates are to be continuously updated.
  • Moreover, the economic playground is not homogenous but combines physical (e.g demographics or climate), socio-economic (e.g income or education) and symbolic (e.g politics or culture) microstates, which means that macrostates are to be differentiated.
Improving entropy

With or without differentiated macrostates, policies are to rely on two categories of models:

  • Extensional ones target environments, dealing with the analysis of business context, operations, and changes (descriptive models), as well as what should be expected (predictive models).
  • Intensional ones deal with enterprise architectures, current or planned, at processes as well as architecture level (prescriptive models).

As figured above, representations and policies are to be combined, hence the need to anchor them to enterprise architectures, as can be illustrated with the Pagoda blueprint:

Architectures & Homeostasis (crosses and numbers refer to the table above)
  • Populations of instances (microstates) are obtained from digital environments as well as from processes execution (1). Data analytics are used to improve descriptive and predictive models (operational macrostates), and consequently the osmosis between processes and environment (2).
  • Architecture improvements can be achieved through changes in prescriptive models (structural macrostates). Compared to strategic planning carried out top-down from business models and requirements, homeostasis also relies on bottom-up forces which can combine with business models and coalesce into emerging architectures (3).
  • Business and organization models are arguably a primary factor in sorting out data, respectively from environments and systems; and being by nature essentially symbolic, they are more pliable than systems models (0). Homeostasis could therefore bypass architectural macrostates, with models of business and organization emerging directly from digital environments through data mining and deep learning (4).

Such alignment of enterprise architectures and symbolic representations is to greatly enhance the transparency and traceability of digital transformations by organising changes and policies with regard to their nature (physical, functional, organizational, or business) and decision-making horizon (operational or strategic).

FURTHER READING

Squared Outline: Languages

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.

  1. 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).
  2. 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).
  3. 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).
  4. 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.

FURTHER READING

Digital Strategy

Preamble

Enterprise governance has to face combined changes in the way business times and spaces are to be taken into account. On one hand social networks put well-thought-out market segments and well planned campaigns at the mercy of consumers’ weekly whims. On the other hand traditional fences between environments and IT systems are crumbling under combined markets and technological waves.

The Strategic Objective is to Weave Systems and Knowledge Architectures (Wayne Thiebaud)

To overcome these challenges enterprises strategies should focus on four pillars:

  • Governance: the immersion of enterprises in digital environments and the crumbling of traditional fences require in-depth changes in information modeling and knowledge management schemes.
  • Data and Information: massive and continuous inflows of data calls for a
    seamless integration of data analytics (perception), information models (reasoning), and knowledge (decision-making).
  • Security & Confidentiality: new regulatory environments and costs of privacy breaches call for a clear distinction between data tied to identified individuals and information associated to designed categories.
  • Innovation: digital environments induces a new order of magnitude for the pace of technological change. Making opportunities from changes can only be achieved through collaboration mechanisms harnessing enterprise knowledge management to environments intakes.

FURTHER READING

Focus: Data vs Information

Preamble

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.


From Data Stripes to Information Structure (Victor Vasarely)

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).

Squaring the purposes of Data & Information Processing

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.

At operational level the benefits of the distinction are best expressed through what is commonly known as the OODA (Observation, Orientation, Decision, Action) loop:

  • Data is used to align operations (systems) with observations (territories).
  • Information is used to align categories (maps) with objectives.

Roles of Data (red) & Information (blue) in integrated decision-making processes

Then, the conceptual distinction between data and information is instrumental for the integration of operational and strategic decision-making processes:

  • Data analytics feeding business intelligence
  • Information modeling supporting operational assessment.

Not by chance, these distinctions can be aligned with architecture layers.

Architectures: Instances vs Categories

Blending data with information overlooks a difference of nature, the former being associated with actual instances (external observation or systems operations), the latter with symbolic descriptions (categories or types). That intrinsic difference can be aligned with architecture layers (resources are consumed, assets are managed), and decision-making processes (operations deal with instances, strategies with categories).

With regard to architectures, the relationship between instances (data) and categories (information) can be neatly aligned with capability layers, as represented by the Pagoda blueprint:

  • The platform layer deals with data reflecting observations (external facts) and actions (system operations).
  • The functional layer deals with information, i.e the symbolic representation of business and organization categories.
  • The business and organization layer defines the business and organization categories.

It must also be noted that setting apart what pertains to individual data independently of the informations managed by systems clearly props up
compliance with privacy regulations.


Architectures & Decision-making

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:

  1. Resources: data is captured through continuous and heterogeneous flows from a wide range of sources.
  2. Assets: information is built by adding identity, structure, and semantics to data.
  3. 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.

CaKe_DataInfoKnow

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.

FURTHER READING

External Links

Redeeming Conceptual Debts

Preamble

To take advantage of their immersion into digital environments enterprises have to differentiate between data (environment’s facts), information (systems’ representations), and knowledge (enterprise behavior).

Outside / Insight (Anna Hulacova)

That cannot be achieved without ironing out the semantic discrepancies between corresponding representations.

Symbolic Representations

Along with the Symbolic System modeling paradigm, the aim of computer systems is to manage the symbolic representations of business objects and processes pertaining to enterprises contexts and concerns. That view can be summarized in terms of maps and territories:

Maps and territories of systems and their environment

Behind the various labels and modus operandi, maps can be defined on three basic layers:

  • Conceptual models, targeting enterprises organization and business independently of supporting systems.
  • Logical models, targeting the symbolic objects managed by supporting systems as surrogates of business objects and activities.
  • Physical models, targeting the actual implementation of symbolic surrogates as binary objects.

Pagoda Architecture Blueprint

These maps can be aligned with commonly agreed enterprise architecture layers, respectively for organizations and processes, systems functionalities, and platforms, with a fourth added for analytical models of business environments.

Conceptual Debt

Ideally, that alignment should pave the way to the integration of systems and knowledge architectures, as represented by the Pagoda blueprint:

Insofar as systems engineering is concerned, that would require two kinds of transformations: from conceptual to logical models (aka analysis), and from logical to physical models (aka design).

While the latter is just a matter of expertise (thank to the GoF), that’s not the case for the former which has to deal with a semantic gap between descriptions of specific and changing business domains and organizations on one side, generic and stable systems architectures on the other side.

As a result, what can be termed a conceptual debt has accumulated with the the number of logical models supporting physical ones without the backing of relevant ones for business or organization. The objective is therefore to bring all models into a broader knowledge architecture.

Models & Ontologies

As introduced by Greek philosophers, ontologies are systematic accounts of whatever is known about a domain of concern. From that point, three basic observations can be made:

  1. Ontologies are made of categories of things, beings, or phenomena; as such they may range from lexicon or 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 their epistemic nature: engineering, business, politics, religions, mythologies, astrology, etc.

With regard to models, only the second observation puts ontologies apart: compared to models, ontologies are about understanding and are not necessarily driven by empirical purposes.

On that account ontologies appear as an option of choice for the integration of symbolic representations:

  • Data: instances identified at territory level, associated with terms or labels; they are mapped to business intelligence (environments) and operational (systems) models.
  • Information: categories associated with sets of instances; categories can be used for requirements analysis or software design.
  • Knowledge: ideas or concepts connect changing and overlapping sets of terms and categories; documents can be associated to any kind of item.

With models consistently mapped to ontologies, the conceptual debt could be restructured in the broader context of enterprise knowledge architecture.

Ontologies & Knowledge

As expounded by Davis, Shrobe, and Szolovits in their pivotal article, knowledge is made of five constituents:

  1. Surrogates, used as symbolic counterparts of actual objects and phenomena.
  2. Ontological commitments defining the categories of things that may exist in the domain under consideration.
  3. Fragmentary theory of intelligent reasoning defining what things can do or can be done with.
  4. Medium making knowledge understandable by computers.
  5. Medium making knowledge understandable by humans.

Points 1 and 5 are not concerned by the conceptual gap, the former being dealt with through the anchoring of identified individuals to surrogates (see below), and the latter being with human interfaces. That leaves points 2-4 as the conceptual hub where information models have to be integrated into knowledge architecture.

Assuming RDF (Resource Description Framework) graphs are used for knowledge representation (point 4), and taking a restaurant for example, the contents of information models (point 2) will be denoted by:

  • Primary nodes (rectangles), for elements specific to cooking and customers relationship management, to be decorated with features (bottom right).
  • Connection nodes (circles and arrows), for semantically neutral (aka syntactic) associations to be uniformly implemented across domains, e.g with predicate calculus (bottom left).
  • Semantic connectors supporting both syntactic and semantic associations (bottom, middle). 

Inserting information into knowledge architecture

Using ontologies to integrate models into knowledge architecture is to enable the restructuring of the conceptual debt.

Minding Semantic Gaps

Keeping with the financial metaphor, conceptual debts can be expressed in terms of spreads between models, and as such could be restructured through models transformation.

To begin with, all representations have to be anchored to environments through identified (#) instances.


Anchoring systems to their environment

Then, instances are to be associated to categories according to features
(properties or relationships) :

  • Customers, reservations, tables, and waiters are identified individuals managed through symbolic surrogates.
  • Names of dishes and ingredients do not refer to symbolic surrogates representing business objects, but are just labels pointing to recipes (documents).
  • Idem for the names of wines, except for exceptional vintages with identified bottles to be managed through symbolic surrogates.

As defined above, these models can be equivalently expressed as ontologies:

  • Properties are single-valued attributes.
  • Relationships define links between categories.
  • Aspects are structured sets of features meant to be valued through category instances.
  • Documents are contents to be accessed directly or through networks, (e.g preparations or wine reviews).
Fleshing out model backbone with features, relationships, and documents (black, italic)

It must be noted that the distinction between neutral and specific contents is not meant to be universal but be justified by pragmatic concerns, for instance:

  • Addresses are not defined as aspects but as category instances so that surrogates of actual addresses can be used to optimize deliveries.
  • Links to customers and addresses, being self-explanatory, can be defined as non specific.
  • The relationship from dishes to ingredients is structured and specific.

Sorting out truth-preserving constructs from domain specific ones is a key success factor for models transformation, and consequently debt restructuring.

Restructuring The Debts

Restructuring financial debts means redefining assets and incomes; with regard to systems it would mean reassessing architectures with regard to value chains.

To begin with, the Pagoda blueprint central pillar is to support the integration of systems and knowledge architectures and consequently the dynamic alignment of systems capabilities, meant to be stable and shared, with business opportunities, by nature changing and specific.

Then, the pairing of systems and knowledge architectures, like a DNA double helix, is to be used to restructure both technical and conceptual debts.


Pairing assets and incomes across architectures

With regard to technical debts, restructuring isn’t to present significant difficulties:

  • Pairing income flows (applications) to tangible assets (platforms) can be done at data level.
  • Model transformations between data (code) and information (models) levels can be achieved using homogeneous domain specific and programming languages.

Things are more complex with conceptual debts, for pairing as well as transformations:

  • There is no direct pairing because value chains (processes) are set across assets (organization).
  • Model transformations are to bridge the semantic gap between the
    symbolic representations of environments (knowledge) and systems (information) .

Nonetheless, these difficulties can be overcame combining integrated architectures and ontologies.

Regarding the structure of the conceptual debt, the income part is to be defined through business objectives (customers, products, channels, supply chain, etc.), and assets to be defined by corresponding enterprise architectures capabilities.

How to mind the gap between external and systems representations.

Regarding models transformations, ontologies will be used to mind the semantic gap between environments (knowledge) and systems (information) representations:

  • Power-types: describe instances of categories (age, income, education, …).
  • Specialization and generalization: defined with regard of intent, subsets for individuals (wine, gender), sub-types for aspects (temperature, serve in menu).
  • Knowledge based relationships (dashed line): used to describe objects and phenomena, actual, planned, or expected (face recognition of customers, influence of weather on dishes, association of wines and dishes, …
  • Concepts: introduced to relate information and knowledge: gourmet.
Ontological descriptions

With the backbones of symbolic representations soundly anchored to environment, it would be possible to complement functional and logical models with their conceptual counterpart and by doing so to eliminate conceptual debts. A symmetric policy could be applied to refactoring in order to redeem the technical debt associated to legacy code.

Managing Conceptual Debt

Like financial ones, conceptual debts are facts of life that have to be managed on a continuous basis, which entails:

  • A separate management of models directly tied to systems, and ontologies with broader justification.
  • A distinction between a kernel (aka knowledge engine), environment profiles, and business domains.
EA & Knowledge Management

Further Reading

EA & The Pagoda Blueprint

“The little reed, bending to the force of the wind, soon stood upright again when the storm had passed over”

Aesop

Resilience and adaptability to changing environments (Masao Ido)

Preamble

The consequences of digital environments go well beyond a simple adjustment of business processes and call for an in-depth transformation of enterprise architectures. 

To begin with, the generalization of digital environments bears out the Symbolic System modeling paradigm: to stay competitive, enterprises have to manage a relevant, accurate, and up-to-date symbolic representation of their business context and concerns. 

With regard to architectures, it means a seamless integration of systems and knowledge architectures.

With regard to processes it means a built-in ability to learn from environments and act accordingly.

Such requirements for resilience and adaptability in unsettled environments are characteristic of the Pagoda architecture blueprint.

Pagoda Blueprint

As can be observed wherever high buildings are being erected on shaking grounds, Pagoda-like architectures set successive layers around a central pillar providing intrinsic strength and resilience to external upsets while allowing the floors to move with the whole or be modified independently. Applied to enterprise architectures in digital environments, that blueprint can be much more than metaphoric.

The actual relevancy of the pagoda blueprint is best understood when the main of data, information, and knowledge is set across platforms, systems, and organization layers:


Pagoda Architecture Blueprint

That blueprint puts a new light on model based approaches to systems engineering (MBSE):

  • Conceptual models, targeting enterprises organization and business independently of supporting systems.
  • Logical models, targeting the symbolic objects managed by supporting systems as surrogates of business objects and activities.
  • Physical models, targeting the actual implementation of symbolic surrogates as binary objects.

Weaving together enterprises and knowledge architectures would greatly enhance the traceability of transformations induced by the immersion of enterprises in digital environments.

Systems & Knowledge Architectures

If digitized business flows are to pervade enterprise systems and feed decision-making processes, systems and knowledge architectures are to be merged into a single nervous system as materialized by the Pagoda central pillar:

Enterprise Nervous System
  • Ubiquitous, massive, and unrelenting digitized business flows cannot be dealt with lest a clear distinction is maintained between raw data acquired across platforms, and the information (previously data) models which ensure the continuity and consistency of systems.  .  
  • Once structured and refined, business data flows must be blended with information models sustaining systems functionalities.
  • A comprehensive and business driven integration of organization and knowledge could then support strategic and operational decision-making at enterprise level.

Rounding off this nervous system with a brain, ontologies would provide the conceptual frame for models representing enterprises and their environments.

Agile Architectures & Homeostasis

Homeostasis is the ability of a viable organism to learn from their environment and adapt their behavior and structures according to changes.
As such homeostasis can be understood as the eextension of enterprise agility set in digital environments, ensuring:

  • Integrated decision-making processes across concerns (business, systems, platforms), and time-frames (tactical, operational, strategic, … ).
  • Integrated information processing, from data-mining to knowledge management.

To that end, changes should be differentiated with regard to source (business or technology environment, organization, systems) and flows (data, information, knowledge); that would be achieved with a pagoda blueprint.

Resilience and adaptability to changes

Threads of operational and strategic decision-making processes could then be weaved together, combining OODA loops at process level and economic intelligence at enterprise level.

Further Reading