Enterprise Architecture

Q-UEL Toolkit for Medical Decision Making :- Science of Uncertainty and Probabilities

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Quantum Universal Exchange Language

Emergent | Interoperability | Knowledge Mining | Blockchain

Q-UEL

  1. It is a toolkit / framework
  2. Is an Algorithmic Language for constructing Complex System
  3. Results into a Inferential Statistical mechanism suitable for a highly complex system – “Hyperbolic Dirac Net”
  4. Involves an approach that is based on the premise that a Highly Complex System driven by the human social structures continuously strives to achieve a higher order in the entropic journey by continuos discerning the knowledge hidden in the system that is in continuum.
  5. A System in Continuum seeking Higher and Higher Order is a Generative System.
  6. A Generative System; Brings System itself as a Method to achieve Transformation. Similar is the case for National Learning Health System.
  7. A Generative System; as such is based on Distributed Autonomous Agents / Organization; achieving Syndication driven by Self Regulation or Swarming behavior.
  8. Essentially Q-UEL as a toolkit / framework algorithmically addresses interoperability, knowledge mining and blockchain; while driving the Healthcare Eco-system into Generative Transformation achieving higher nd higher orders in the National Learning Health System.
  9. It has capabilities to facilitate medical workflow, continuity of care, medical knowledge extraction and representation from vast large sets of structured and unstructured data, automating bio-statistical reasoning leading into large data driven evidence based medicine, that further leads into clinical decision support system including knowledge management and Artificial Intelligence; and public health and epidemiological analysis.

http://www.himss.org/achieving-national-learning-health-system

GENERATIVE SYSTEM :-

https://ingine.wordpress.com/2013/01/09/generative-transformation-system-is-the-method/

A Large Chaotic System driven by Human Social Structures has two contending ways.

a. Natural Selection – Adaptive – Darwinian – Natural Selection – Survival Of Fittest – Dominance

b. Self Regulation – Generative – Innovation – Diversity – Cambrian Explosion – Unique Peculiarities – Co Existence – Emergent

Accountable Care Organization (ACO) driven by Affordability Care Act transforms the present Healthcare System that is adaptive (competitive) into generative (collaborative / co-ordinated) to achieve inclusive success and partake in the savings achieved. This is a generative systemic response contrasting the functional and competitive response of an adaptive system.

Natural selection seems to have resulted in functional transformation, where adaptive is the mode; does not account for diversity.

Self Regulation – seems like is a systemic outcome due to integrative influence (ecosystem), responding to the system constraints. Accounts for rich diversity.

The observer learns generatively from the system constraints for the type of reflexive response required (Refer – Generative Grammar – Immune System – http://www.ncbi.nlm.nih.gov/pmc/articles/PMC554270/pdf/emboj00269-0006.pdf)

From the above observation, should the theory in self regulation seem more correct and that adheres to laws of nature, in which generative learning occurs. Then, the assertion is “method” is offered by the system itself. System’s ontology has an implicate knowledge of the processes required for transformation (David Bohm – Implicate Order)

For very large complex system,

System itself is the method – impetus is the “constraint”.

In the video below, the ability for the cells to creatively create the script is discussed which makes the case for self regulated and generative complex system in addition to complex adaptive system.

 

Further Notes on Q-UEL / HDN :-

  1. That brings Quantum Mechanics (QM) machinery to Medical Science.
  2. Is derived from Dirac Notation that helped in defining the framework for describing the QM. The resulting framework or language is Q-UEL and it delivers a mechanism for inferential statistics – “Hyperbolic Dirac Net”
  3. Created from System Dynamics and Systems Thinking Perspective.
  4. It is Systemic in approach; where System is itself the Method.
  5. Engages probabilistic ontology and semantics.
  6. Creates a mathematical framework to advance Inferential Statistics to study highly chaotic complex system.
  7. Is an algorithmic approach that creates Semantic Architecture of the problem or phenomena under study.
  8. The algorithmic approach is a blend of linguistics semantics, artificial intelligence and systems theory.
  9. The algorithm creates the Semantic Architecture defined by Probabilistic Ontology :- representing the Ecosystem Knowledge distribution based on Graph Theory

To make a decision in any domain, first of all the knowledge compendium of the domain or the system knowledge is imperative.

System Riddled with Complexity is generally a Multivariate System, as such creating much uncertainty

A highly complex system being non-deterministic, requires probabilistic approaches to discern, study and model the system.

General Characteristics of Complex System Methods

  • Descriptive statistics are employed to study “WHAT” aspects of the System
  • Inferential Statistics are applied to study “HOW”, “WHEN”, “WHY” and “WHERE” probing both spatial and temporal aspects.
  • In a highly complex system; the causality becomes indeterminable; meaning the correlation or relationships between the independent and dependent variables are not obviously established. Also, they seem to interchange the position. This creates dilemma between :- subject vs object, causes vs outcomes.
  • Approaching a highly complex system, since the priori and posterior are not definable; inferential techniques where hypothesis are fixed before the beginning the study of the system become enviable technique.

Review of Inferential Techniques as the Complexity is Scaled

Step 1:- Simple System (turbulence level:-1)

Frequentist :- simplest classical or traditional statistics; employed treating data random with a steady state hypothesis – system is considered not uncertain (simple system). In Frequentist notions of statistics, probability is dealt as classical measures based only on the idea of counting and proportion. This technique is applied to probability to data, where the data sets are rather small.

Increase complexity: Larger data sets, multivariate, hypothesis model is not established, large variety of variables; each can combine (conditional and joint) in many different ways to produce the effect.

Step 2:- Complex System (turbulence level:-2)

Bayesian :- hypothesis is considered probabilistic, while data is held at steady state. In Bayesian notions of statistics, probability is of the hypothesis for a given sets of data that is fixed. That is, hypothesis is random and data is fixed. The knowledge extracted contains the more subjectivist notions of uncertainty, belief, reliability, or confidence often used in automated inference and decision support systems.

Additionally the hypothesis can be explored only in an acyclic fashion creating Directed Acyclic Graphs (DAG)

Increase the throttle on the complexity: Very large data sets, both structured and unstructured,  Hypothesis random, multiple Hypothesis possible, Anomalies can exist, There are hidden conditions, need arises to discover the “probabilistic ontology” as they represent the system and the behavior within.

Step 3: Highly Chaotic Complex System (turbulence level:-3)

Certainly DAG is now inadequate, since we need to check probabilities as correlations and also causations of the variables, and if they conform to a hypothesis producing pattern, meaning some ontology is discovered which describes the peculiar intrinsic behavior among a specific combinations of the variables to represent a hypothesis condition. And, there are many such possibilities within the system, hence  very chaotic and complex system.

Now the System itself seems probabilistic; regardless of the hypothesis and the data. This demands Multi-Lateral Cognitive approach

Telandic …. “Point – equilibrium – steady state – periodic (oscillatory) – quasiperiodic – Chaotic – and telandic (goal seeking behavior) are examples of behavior here placed in order of increasing complexity”

A Highly Complex System, demands a Dragon Slayer – Hyperbolic Dirac Net (HDN) driven Statistics (BI-directional Bayesian) for extracting the Knowledge from a Chaotic Uncertain System.

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For Who The Heck is Enterprise Architecture Not?

Thinker Practitioner

While thinking of ideas discussed in this blog, have been probing the tenants that creates the fundamental characteristics of the system such as :-

  • Division Of Labor – the most important idea that revolutionized industrialization and for rise of capitalism
  • Commodiitization vs Specialization
  • Production of cost by Economy Of Scale by Division vs Multiplexing (manual vs automation)
  • Holistic vs Reductionist
  • Organic vs Inorganic (natural dichotomy)
  • Autonomy (increased self sufficiency) vs Corporate Sovereignty
  • Natural Selection leading into Adaptive vs Self Regulation leading into Generative
  • Centralization vs Polycentrism; and Federation
  • Simplicity vs Complicated (not to be confused with Complex System)

The above premise must be used as background in probing the discipline EA – which is essentially a System Theory being an integrative of several disciplines such as sociology, economics, business management, information technology etc

Defeating Cognitive Bias and Instant Gratification

https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/art12.html

Basically have been battling within my head why this discipline Enterprise Architecture went so whacky in the corridors of corporates. I think all the below discussed stuff are symptoms. The core reasons I think is basically in the deviant behavior that corporate culture tends to promote driven by short term gains. Lets call this deviant theory (Corporate) Anomie a concept developed by Emile Durkheim, a French sociologist, who introduced this concept of Anomie in his book The Division of Labor in Society, published in 1893. Later expanded in the book Suicide published in 1897. Emile Durkheim is considered as the “father of sociology”.

1. Not for those Not solving Systemic Concerns

https://ingine.wordpress.com/2012/08/03/transformative-enterprise-architecture-framework-connecting-strategy-tactical-operational-execution-implementation/

https://ingine.wordpress.com/2007/08/22/enterprise-architecture-economic-model-system-dynamics/

Enterprise Architecture (EA) as a discipline is engaged to solve problems within systemic context. Where the (a) challenge of realizing business strategy by enabling relevant business capabilities (b) delivered by set of tactical objectives (operational) achieved by (c) making informed and decisive investments in technologies. When such systemic concerns are not being addressed, then EA is overkill. EA while it strives to solve macro concerns it does so by aligning well designed several sets of micro objectives.

2. Not for those who lack appreciation for Order and Maturity 

Generally, EA as a discipline is better desired when an enterprise strives to scale order of maturity to manage complexity by rational means. To develop organizational skills, a decisive competencies framework is desired.

https://ingine.wordpress.com/2010/03/17/enterprise-architecture-maturity-model-framework-federal/

https://ingine.wordpress.com/2012/08/01/developing-enterprise-architecture-practitioner-competamcies/

https://ingine.wordpress.com/2012/08/02/dod-ea-competencies-development-framework/

3. Not for those who cannot deal with Abstraction

https://ingine.wordpress.com/2007/08/01/teaching-an-elephant-to-dance/

EA is not for those who have not trained their mind to think in abstraction. Especially those who find themselves comfortable in dealing with physicality and forms will require head wrenching exercise to hone mind to develop abilities to deal with varieties of abstractions, while representing them with varying semantics that help in  delineation, thus leading into layered representation in terms of a framework.

“Separation of Concerns” is one such technique that achieve delineation by introduction of semantics and eventually it also helps in the design of EA Framework.

https://ingine.wordpress.com/2007/08/17/christopher-alexander-father-of-pattern-language/

https://ingine.wordpress.com/2008/03/12/ciao-interesting-pursuit-after-ea-ontology/

EA is not for those, who in abstraction find it difficult to delineate contextual from conceptual; and conceptual from logical and logical from physical.

4. Not for Reductionist; instead suited for Holistic Thinkers

https://ingine.wordpress.com/2007/08/08/the-black-swan/

https://ingine.wordpress.com/2008/03/15/six-sigma-aids-only-linear-transformation-not-non-linear-radical-transformation/

EA is not for those who are only reductionists in approach and who think everything in the world can be reduced to simple set of objects or objectives. EA is not necessarily Cartesian and certainly not for linear thinkers. EA is not about immediately discovering implementation; in fact it is delayed gratification by introducing decisive rationalizing process before subjective strategy turn into executable objective actions yielding the best business results by leverage.

5. Not for those who pursue Transformation without Goals

Although EA might help manage both organic and inorganic growth of an organization; by itself it is a disciple dealing with inconsistencies owing to structure and mechanism found within and their impact on transformation augmenting enterprise growth

https://ingine.wordpress.com/2008/12/06/enterprises-in-generative-transformation-akin-to-universe-life-cycle/

6. Not for those who pursue merely Functional Goals and NOT be concerned with Ecosystem’s Harmony 

https://ingine.wordpress.com/2007/08/03/huichols-shamanic-vision-architecture-of-the-consciousness/

EA is not a mere IT role, especially a difficult role for those who have worked only in the areas of IT infra and in delivering such services. EA is not limited only to physical layer.

EA is not for those who find it difficult to distinguish business strategy from business architecture; business architecture from application architecture; application architecture from technology (infra) architecture. And, importantly EA is an overarching and all encompassing meta-architecture inclusive of all those levels of architecture that holistically represents an enterprise in relevant abstractions.

Architecture is a holistic sum striving to achieve systemic balance by aligning function, performance and cost

Math and statistical although important, those approaches alone are not adequate to envision future capabilities for an organization. Likewise advents in technology alone do not necessarily prove transformative. Knowledge of IT architecture by itself is not transformative. Instead, EA as a discipline  requires to integrate Architecture, Capital Planning and Program Portfolio Management. All these brought together by productive Governance; even then the challenges of future remains fleeting.

7. Not for those who think Taxonomy suffices to represent EA and NOT Ontology 

https://ingine.wordpress.com/2008/12/15/implicate-order-descriptive-mechanism-form-large-systems-of-systems/

https://ingine.wordpress.com/2013/06/21/implicate-order-probabilistic-ontology-complexity-theory/

There is distinct semantics used to represent each of the layers (“separation of concerns”). Semantics introduces dimensionality to the architecture layers and they cannot be represented with limited set of semantics in limited dimensions. Each layer that is semantically different from the other requires transformation in planning and design to discover the opportunities in each layer. Ontology plays an important role in developing and assimilating ideas leading into discovering creative transformative opportunities. EA is not for those who tend to reduce everything into simple 2D representations in the hope that simplified versions help manage complexity better.

8. Not for those who are Programmatic in Approach and Not Practitioners 

Not for those who do not understand what disposes them to be credible management consultants. Furthermore, also not for those management consultants who have not gained appreciation for structure, semantics driven architecture and mechanism within; and their role together in systemic transformation, functional modernization and economic optimization.

Consultants are those who have gained immense multi-lateral experience in the industry in variety of areas especially in conducting transformation, modernization and optimization related activities. Generally individual gains such experiences driven by motivation to solve large fleeting problems those are systemic in nature. Not by pursuing opportunities where sole motivation is revenue generation no matter what.

Practitioners develop insight by assiduously probing the problem and complexity. The skills do not develop overnight. It is not swashbuckling nor shooting through the hip. It is developing opportunity by being proactive and intense probing.

There are no Outliers (myth destroyed by  Malcolm Gladwell http://en.wikipedia.org/wiki/Outliers_(book) )

“””A common theme that appears throughout Outliers is the “10,000-Hour Rule”, based on a study by Anders Ericsson. Gladwell claims that greatness requires enormous time, using the source of The Beatles’ musical talents and Gates’ computer savvy as examples.[3] The Beatles performed live in HamburgGermany over 1,200 times from 1960 to 1964, amassing more than 10,000 hours of playing time, therefore meeting the 10,000-Hour Rule. Gladwell asserts that all of the time The Beatles spent performing shaped their talent, and quotes Beatles’ biographer Philip Norman as saying, “So by the time they returned to England from Hamburg, Germany, ‘they sounded like no one else. It was the making of them.'”[3]Gates met the 10,000-Hour Rule when he gained access to a high school computer in 1968 at the age of 13, and spent 10,000 hours programming on it.[3]“””

9. Not for those who provide professional expertise as Contractor and NOT as Practitioner

https://ingine.wordpress.com/2012/08/01/developing-enterprise-architecture-practitioner-competamcies/

http://www.ipthree.org/frontpage-features/94-messageprofessionals

http://www.cos-mag.com/human-resources/hr-columns/whats-in-a-name-professional-vs-practitioner.html

It is not a contracting role – In theory contracting assumes that the client understand the requirement and they control the way project gets executed. This is obviously flawed approach.

Generally those who have thrived only in delivering IT services of operational nature are not the candidates for conducting management consulting, since most of their career has been delivering IT solutions and services for a requirement determined by the client and their consultants.

10. Not for those who do not value Professional Integrity

https://ingine.wordpress.com/2007/08/09/enterprise-architect-fighting-obfuscation/

Importantly EA is a Practitioner Discipline introducing high standards emphasizing on quality in rendering and most importantly professional ethics promoting the desired ethos for organization’s evident growth and maturity. The results of EA achieves transparency, accountability and line of sight driven by “structuralism” striving to achieve “order”.

11. Not for those who build career around Tools and NOT around Discipline Integrating Science and Art

EA is not a discipline that can be developed by building career around tools. Tools by themselves do not create Art and neither advances Science. EA is integrative of architecture, capital planning and program management. All these driven by corporate governance.

https://ingine.wordpress.com/2008/12/01/creative-people-at-work/

https://ingine.wordpress.com/2008/11/23/in-lack-of-theory-planning-will-be-along-a-straight-line/

12.Not for those who engage merely in IT Operation and Implementation

EA is integrative of strategy, operations and implementation

https://ingine.wordpress.com/2012/08/03/transformative-enterprise-architecture-framework-connecting-strategy-tactical-operational-execution-implementation/

13. Not for those who DO NOT Develop Enterprise Transition Plan and Operating Model (must skill for EA)

https://ingine.wordpress.com/2010/04/12/etp/

https://ingine.wordpress.com/2008/12/06/ea-framework-for-transition-planning/

14. Not for those who think EA and Solution Architecture are synonymous

http://blogs.msdn.com/b/gabriel_morgan/archive/2007/09/02/enterprise-architect-vs-solution-architect.aspx

http://weblog.tetradian.com/2012/09/13/linking-ea-with-sa/

 15. Not for those obsessed with Dominance and NOT Balance

EA cannot help organization achieve balance and sustainability without Governance

https://ingine.wordpress.com/2008/02/22/governance-managing-conflict-change-the-intrinsic-duality-of-an-enterprise/

16. Not for those who engage Masquerading Solution Facades and Intellectual Contrives 

EA improves “Loss of Innocence” while it DEFEATS Solutions that do not align with context.

https://ingine.wordpress.com/2007/08/01/teaching-an-elephant-to-dance/

 17. Not for those who think Service Oriented Architecture, Correlation Architecture, etc by themselves constitute EA

https://ingine.wordpress.com/2007/07/30/ea-framework-to-accomodate-soa-style/

 18. Not for those who merely think Strategy alone and NOT Innovation help create Newer Opportunities

Innovation to create newer opportunities while achieving higher order in capabilities and business sustainability are key to ensure system harmony against the challenges of existing and ensuing complexities.

https://ingine.wordpress.com/2012/12/20/moving-from-darwinian-adaptive-to-generative-transformation/

List for NOT’s can be endless….

…N. Is Certainly for System Thinkers

Implicate Order as Ontology For Complex System – Creating Generative Transformation

For a messy complex system - undergoing generative transformation - Implicate Order provides the direction.

For a messy complex system – undergoing generative transformation – Implicate Order provides the direction.

Implicate Order as an Ontology v1.1

 

When Cartesian Breaks Down - Implicate Order Reins

When Cartesian Breaks Down – Implicate Order Reins

Big Data is conundrum unless Knowledge Management adds Semantics

Key to big data analytics, is to discover the underlying patterns in the businesses behaviors. When complexities and randomness increases, the interacting underlying patterns act to form generative orders, which become the emergent patterns.

Key to big data related architecture is to understand generative order and emergent architecture.

Generative Pattern
http://c2.com/cgi/wiki?GenerativePattern

Generative Order, Randomness, Emergent
https://ingine.wordpress.com/2007/08/17/christopher-alexander-father-of-pattern-language/

Complexity, Structure, Emergent Architecture
Complexity does not require a designer, rather randomness through recursive patterns generate complexities…the result is emergent. The system unassisted by outside sources, by increase in inherent randomness seeks order from generative process, hence it emerges. The architecture or structure of such system is termed “Emergent”

http://m.youtube.com/index?desktop_uri=%2F&gl=US#/watch?v=yeKWDOJvK2o

User-Centric Enterprise Architecture: Enterprise Architecture Design

By Andy Blumenthal

User-Centric Enterprise Architecture: Enterprise Architecture Design.

User-centric Enterprise Architecture provides information to decision-makers using design thinking, so as to make the information easy to understand and apply to planning and investment decisions.

Some examples of how we do this:

  1. Simplifying complex information by speaking the language of the business (and not all techie).
  2. Unifying disparate information to give a holistic view that breaks the traditional vertical (or functional) views and instead looks horizontally across the organization to foster enterprise solutions where we build once and reuse multiple times.
  3. Visualizing information to condense lots of information and tell a story—as the saying goes, a picture is worth a thousand words.
  4. Segmenting end-users and tailoring EA information products to the different user groups which we do with profiles geared to executive decision makers, models for mid-level managers, and inventories for the analysts.

Interestingly enough, in the summer issue of MIT Sloan Management Review, there is an article called “How to Become a Better Manager…By thinking Like a Designer.”

Here are some design pointers from the experts that you can use to aid your enterprise architectures (they are written to parallel the principles from User-centric EA, as I have previously described above):

  1. Embrace simplicity—“people often confuse simplicity…with simplistic….it takes courage to be simple…and the simplest solution is often the best.”
  2. Look for patterns in the data—“good problem solvers become proficient at identifying patterns.” Further, designers seek “harmony to bring together hierarchy, balance, contrast, and clear space in a meaningful way.”
  3. Apply visual thinking—often managers…rely heavily on data and information to tell the story and miss the opportunity to create context and meaning,” instead managers need to “think of themselves as designers, visual thinkers or storytellers.”
  4. Presenting clearly to specific end-users—“good design is about seeing and communicating clearly.” Moreover, it’s about “seeing things from the clients point of view…designers learn pretty quickly that is not about Me, it’s about You.”

MIT Sloan states “we have come to realize over the past few years that design-focused organizations do better financially than their less design-conscious competitors…design is crafting communications to answer audience needs in the most effective way.

This is a fundamental lesson: organizations that apply the User-centric Enterprise Architecture design approach will see superior results than legacy EA development efforts that built “artifacts” made up primarily of esoteric eye charts that users could not readily understand and apply.