Developing a biological understanding of organizational knowledge

Access my research papers from Google Citations

A unique area in the state space of the Mandlebrot set

An attractor

Presentation for Knowledge Management Leaders Forum, 24 July 2013

Attribution CC BY

William P. Hall

President Kororoit Institute Proponents and Supporters Assoc., Inc. - http://kororoit.org Principal EA Principals – http://eaprincipals.com william-hall@bigpond.com http://www.orgs-evolution-knowledge.net

definition

My Background

Early life: physics / natural history / cytogenetics / evolutionary biology (PhD Harvard, 1973)

1981-1989: Computer literacy journalism, technical communication, commercial software, banking

1990-2007: Documentation and knowledge management systems analyst and designer for Tenix Defence/$ 7 BN ANZAC Ship Project

– Tenix grew to be Australia’s largest defence engineering prime contractor and then failed.

– How did Tenix succeed and why did it fail?

2001-now: Researcher trying to understand what organizational knowledge is and why organizations have such major problems managing and applying it

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Company formed 1987

Oct. 1989 won $7 BN stringently fixed price contract to build 10 frigates for Aus. & NZ , with many difficult warranty/ liquidated damages milestones.

Project completed 2007 – every ship delivered on-time

– on-budget

– company profit

– happy customers

Mid 2004 began a $500 M fixed-price “Protector” contract to build 7 ships to commercial standards for New Zealand, to be completed in 2007

By 2007 only one ship delivered (with substantial defects). Tenix costs were so far above contract value that Tenix auctioned Defence assets to highest bidder

Management assumed Tenix knew how to build ships. Policies blocked social transfer of personal knowledge re problems/solutions from ANZAC staff to new staff hired for Protector.

Success & failure of Tenix Defence

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10

Hall, W.P., Richards, G., Sarelius, C., Kilpatrick, B. 2008. Organisational management of project and technical knowledge over fleet lifecycles. Australian Journal of Mechanical Engineering. 5(2):81-95.

Hall, W.P., Nousala, S., Kilpatrick B. 2009. One company – two outcomes: knowledge integration vs corporate disintegration in the absence of knowledge management. VINE: The journal of information and knowledge management systems 39(3), 242-258

My own major success in the company

Delivery of engineering tech data and maintenance knowledge into relationally based maintenance management system

– (2000 ship specific maintenance routines x 10 ships) + (engineering technical data on all systems/components x 10 ships) Correct and consistent for the points of use

Applicable/Effective

Available to those who need it, when and where it is needed

Useable

– Conventional authoring and information systems could not meet requirement Cost blowout to fix

Schedule slippage

Liquidated damages (multi $M)

– Implemented structured authoring and product data management

Solution substantially improved quality of data, information and knowledge delivered for substantially reduced labour cost

– 80% reduction in number of documents managed

– 98% reduction in documents delivered

– further 50-70% reduction in managed text down the line

10 ships delivered on time, on budget, for corporate profit 4

Savings: $50 M - $100 M ??

How Tenix closed the ANZAC knowledge building circles

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Developing the biological understanding

Only scratching the surface - see papers for details

Human biology – Origins of culture and social organization

– Adaptation

– Genetic vs cultural heredity (knowledge transfer)

Karl Popper’s evolutionary epistemology

Autopoiesis

Foundations of organizational knowledge

Enterprise Knowledge Architecture 6

Biological basis of human individual and

social knowledge

Basically we are bipedal apes who

became top predators on the African savannah

Hall, W.P. 2013. Evolutionary origins of Homo sapiens.

Extract from Application Holy Wars or a New Reformation: A fugue on the theory of knowledge [in preparation] - http://tinyurl.com/kqrcxsf

We are all apes

Our close primate cousins, orangutans, gorillas, chimpanzees and bonobos live in organized social groups that make and use tools

– Orangutans live in small single mum families but are effective tool users and teachers

Another video shows mother taking boat to raid a fish trap for a meal

– Chimpanzees work in larger social groups with a lot of interaction

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Attenborough: Amazing DIY Orangutans - BBC Earth - http://tinyurl.com/avl8yby

Charlotte Uhlenbroek Chimpanzees' sophisticated use of tools - BBC wildlife – http://tinyurl.com/lj8ejt2

Comparative genomics provides detailed geneologies

9 From Locke et al. (2011)

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Our family tree

White et al’s (2009) depiction of the adaptive plateaus achieved by the different species grade shifts in the Pliocene radiation of hominins as our ancestors became more adapted to more open and arid environments. CLCA = chimpanzee-human last common ancestor.

CLCA was a forest ape using simple natural and biodegradable tools to increase dietary range probably a lot like today’s chimps and bonobos

Changing climates broke up forest into grassy woodlands. Ardipithecus adapted by developing bipedal locomotion and use of tools for self-protection and to harvest wider dietary range.

Australopithecus became a successful savannah dweller Homo became top carnivore in Africa and Eurasia

Grave risk of predation by big cats & other carnivores on savanna

Gangs of chimps can cooperate to deter cats

Anthropoid apes aren’t the only primate tool users – See Capuchin nut-cracking industry - http://tinyurl.com/mky2b3l

Pleiocene climate change forced some apes onto a savanna – a tough neighbourhood to survive in!

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From Tattersall (2010) Masters of the Planet, p. 49

see Kortlandt 1980. How might early hominids have defended themselves against large predators and food competitors? Journal of Human Evolution 9, 79-112 – http://tinyurl.com/l5z5vu2

Cultural knowledge transfer and adaptation to the savanna opened new worlds

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Guthrie (in Roebroeks 2007) speculated that a tiny technological improvement was all that was needed for a more effective defence than waving big sticks

– Any cat running into a thorn branch will have its eyes torn to shreds. Cats “know” this

– Easy step from thorn branch to spear for hunting

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Increasing tool complexity in archaeological record

• Development of increasingly complex stone tools (after Stout 2011), correlates with increasing brain capacity (and more social intelligence?)

• Exponential growth in technology continues up to today with development of cognitive tools: speech, writing, printing, computers and the internet.

• Today computing technology is growing hyper-exponentially

See extract from my draft book

Genetic vs cultural heredity (mechanisms for knowledge transfer)

Shared heritage defines the species/group

Adaptation = change through time

Natural selection eliminates entities with maladaptive genes/knowledge

– Genetic heritage slow to change)

– Cultural heritage (can lead to more rapid change) More plastic but may not durable unless reenforced

Can be shared laterally

Tacit vs explicit sharing & transfer

Capacity for language is very recent

Linguistically expressed language can be criticized & peer reviewed

Self-selection / criticism to eliminate errors – Memory of and learning from history

– Speech, writing

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Karl Popper’s Evolutionary Epistemology

In his later work, Popper applied

evolutionary biology to his theory of knowledge

• Popper, K.R. 1972. Objective Knowledge – an Evolutionary

Approach. Oxford University Press / Routledge. • Popper, K.R. 1994. Knowledge and the Body-Mind Problem –

in Defence of Interaction. Routledge. • Hall, W.P. 2003. Managing maintenance knowledge in the

context of large engineering projects - Theory and case study. Journal of Information and Knowledge Management, Vol. 2, No. 2 - http://tinyurl.com/3yqh8j

Sources for evolutionary approach to epistemology

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Charles Darwin (1859) On the Origin of Species

Konrad Lorenz – 1973 Nobel Prize (animal cognition / knowledge)

Donald T. Campbell (1960, 1974)

– (1960) Blind Variation and Selective Retention…. (paper)

– (1974) Evolutionary Epistemology (chapter in Schilpp)

Sir Karl R. Popper ( 1972 – knowledge is solutions to problems)

– (1972) Objective Knowledge – An Evolutionary Approach

– (1974) “The main task of the theory of knowledge is to understand it as continuous with animal knowledge; and … its discontinuity – if any – from animal knowledge” p 1161, “Replies to my Critics”

– (1994) Knowledge and the Body-Mind Problem

Knowledge revolutions

– Thomas Kuhn (1960) The Structure of Scientific Revolutions

– Stephen J. Gould (and Eldridge 1972) - Punctuated equilibria

Karl Popper's first great idea from Objective Knowledge: Knowledge = solutions to problems

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Pn a real-world problem faced by a living entity

TS a tentative solution/theory. Tentative solutions are varied through serial/parallel iteration

EE a test or process of error elimination

Pn+1 changed problem as faced by an entity incorporating a surviving solution

The whole process is iterated

All knowledge claims are constructed, cannot be proven to be true

TSs may be embodied as “structure” in the “knowing” entity, or

TSs may be expressed in words as hypotheses, subject to objective criticism; or as genetic codes in DNA, subject to natural selection

Objective expression and criticism lets our theories die in our stead

Through cyclic iteration, sources of errors are found and eliminated

Solutions/theories become more reliable as they survive repetitive testing

Surviving TSs are the source of all knowledge!

Karl Popper, Objective Knowledge – An Evolutionary Approach (1972), pp. 241-244

Popper's second great idea: “three worlds” ontology

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Energy flow Thermodynamics

Physics Chemistry

Biochemistry

Cybernetic self-regulation

Cognition Consciousness

Tacit knowledge

Genetic heredity Recorded thought Computer memory Logical artifacts Explicit knowledge

Reproduce/Produce

Develop/Recall

World 1 – External Reality

World 2 Organismic/personal/ situational/subjective/tacit knowledge in world 2 emerges from world 1

World 3 The world of “objective” knowledge

“living knowledge”

“codified knowledge”

The real world

Why people who should know better ignore Popper

Popper’s intellectual arrogance – Ignored or denigrated those he disagreed with (e.g., Polanyi)

– Irritated many academic philosophers (ref Wittgenstein & Polanyi affairs), especially ex positivists and constructivists

Popper’s “negative attitude towards definitions” facilitated misunderstanding (reaction to Wittgenstein)

– Undefined language created paradigmatic barriers between schools

Popper’s use of ‘objective’ in the title Objective Knowledge apparently caused those who believed knowledge could not be objective (i.e., “objective” in that it was verifiably true) to reject the book without reading it (ref constructivists)

– Popper used “objective” in the very different sense that knowledge could be objectively codified in tangible objects, e.g: "the world of the logical contents of books, libraries, computer

memories, and suchlike" (1972: p. 74)

our theories, conjectures, guesses (and, if we like, the logical content of our genetic code)" (1972: p. 73) 19

The autopoietic organization

Knowledge and life are inseparable. With a proper definition of life,

knowledge-based organizations are seen to be living

• Maturana, H.R., Varela, F.J. 1980. Autopoiesis and Cognition – the Realization of the Living. Kluwer.

• Nelson, R.R., Winter, S.G. 1982. An Evolutionary Theory of Economic Change, Harvard Uinv. Press.

• Kauffman, S.A. 1993. The Origins of Order – Self-organization and Selection in Evolution. Oxford Univ. Press

• Hall, W.P. 2005. Biological nature of knowledge in the learning organization. The Learning Organization 12(2):169-188.

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What makes a system living?

Autopoiesis – Self-regulating, self-sustaining, self-(re)producing dynamic entity

– Fundamentally cyclical, continuation depends on the structure of the state in the previous instant to produce autopoiesis in the next instant (ref Popper; Maturana & Varela)

– Selective survival builds knowledge into the system one problem solution at a time

Imperatives for continuation of autopoiesis Constraints and boundaries, regulations determine what is physically allowable

Energy (exergy)

Component recruitment

Materials

Observation

s

Entropy/Waste

Products

Departures

Actions

ProcessesProcesses

"universal" laws governing component interactions determine physical capabilities

The entity's imperatives and goals

The entity's history and present circumstances

HIGHER LEVEL SYSTEM / ENVIRONMENT

SUBSYSTEMS / COMPONENTS

Constraints and boundaries, regulations determine what is physically allowable

Energy (exergy)

Component recruitment

Materials

Observation

s

Entropy/Waste

Products

Departures

Actions

ProcessesProcesses

"universal" laws governing component interactions determine physical capabilities

The entity's imperatives and goals

The entity's history and present circumstances

HIGHER LEVEL SYSTEM / ENVIRONMENT

SUBSYSTEMS / COMPONENTS

0 0 0 0 0 0 0 00 0 1 2 1 0 0 00 0 1 1 2 1 0 00 1 3 5 3 2 0 00 1 1 3 2 2 0 00 1 2 3 2 1 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0

0 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 1 2 1 0 00 0 0 1 1 2 1 00 0 1 3 5 3 2 00 0 1 1 3 2 2 00 0 1 2 3 2 1 00 0 0 0 0 0 0 0

1-1 1-2 1-3 1-4

2-1

0 0 0 0 0 0 0 00 0 1 2 1 0 0 00 0 1 1 2 1 0 00 1 3 5 3 2 0 00 1 1 3 2 2 0 00 1 2 3 2 1 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0

0 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 1 2 1 0 00 0 0 1 1 2 1 00 0 1 3 5 3 2 00 0 1 1 3 2 2 00 0 1 2 3 2 1 00 0 0 0 0 0 0 0

1-1 1-2 1-3 1-4

2-1

Self producing entity in Conway’s Game of Life cellular automaton

Autopoietic system

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Spontaneous co-emergence of autopoiesis and knowledge

The dynamic vectors of the present instant result from causal events in past instants as reflected in the adjacent possibles of the immediately prior instant

– Historical connections (heritage) determine the vectors in state space of the present instant.

Attractor basins: convergent paths may become coherently autopoietic, such that the ensemble structure of a convergent state in one instant generates an ensemble structure that remains convergent in the next instant.

Chaos: divergent paths lead to incoherent structures that dis-integrate and lose the historical thread of successful autopoiesis

Ensembles that remain convergent through the selective elimination of divergent outcomes retain structural knowledge that solved a problem of survival to retain convergent structure

Hall, W.P., Else, S., Martin, C., Philp, W. 2011. Time-based frameworks for valuing knowledge: maintaining strategic knowledge. Kororoit Institute Working Papers No. 1: 1-28. Hall, W.P. 2011. Physical basis for the emergence of autopoiesis, cognition and knowledge. Kororoit Institute Working Papers No. 2: 1-63

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Knowledge-based “adaptive” systems exist at several hierarchical levels of structural organization

– Nation – State – Council – Community group – Person – Body cell

For effective action, flows of knowledge, decision and action must pass through several hierarchical levels

Scalability and the complex hierarchy

Hall, W.P. 2006 Emergence and growth of knowledge and diversity in hierarchically complex living systems. Workshop "Selection, Self-Organization and Diversity CSIRO Centre for Complex Systems Science and ARC Complex Open Systems Network, Katoomba, NSW, Australia 17-18 May 2006.

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Six necessary and sufficient criteria for recognizing an autopoietic system

Bounded – System components identifiably demarcated from environment

– E.g., organizational badges, logos, reception desks, gates, etc.

Complex – separate and functionally different subsystems exist within boundary)

Mechanistic – System dynamics driven by self-sustainably regulated economic cash flows or

dissipative “metabolic” processes

Self-defining – System demarcation intrinsically produced

– E.g., employment policies, procedures, etc.

Self-producing – System intrinsically produces own components

– E.g., recruitment & training programs

Autonomous – self-produced components are necessary and sufficient to produce the

system.

Foundations of organizational knowledge

Understanding organizational knowledge and how to manage it flows naturally

from the biological point of view

• Hall, W.P., Dalmaris, P., Nousala, S. 2005. A biological

theory of knowledge and applications to real world organizations. Knowledge Management in Asia Pacific, Wellington, N.Z. 28-29 November 2005

• Vines, R., Hall, W.P. 2011. Exploring the foundations of organizational knowledge. Kororoit Institute Working Papers No. 3: 1-39

Personal vs organizational knowledge

Important difference – individual knowledge (in any form) is known only by a person – organizational knowledge is available and physically or socially

accessible to those who may apply it for organizational needs – Even where explicit knowledge exists, individual knowledge may be

required to access it within a useful response time. People know:

– what knowledge the organization needs, – who may know the answer, – where in the organization explicit knowledge may be found, – why the knowledge is important or why it was created, – when the knowledge might be needed, and – how to apply the knowledge

This human knowledge is critical to the organization Snowden, D. 2002. Complex acts of knowing: paradox and

descriptive self-awareness. J. Knowledge Management 6:100-111 – Personal knowledge is volunteered; it cannot be conscripted. – People always know more than can be told, and will tell more than

can be written down. – People only know what they know when they need to know it. 26

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OODA system of systems in the socio-technical knowledge-based organization

ORIENT (PROCESS)

PEOPLE

CULTURE & PARADIGMS

INFRASTRUCTURE

“ CORPORATE MEMORY ”

SENSE

ANALYSIS SYNTHESIS

PEOPLE PEOPLE

GENETIC HERITAGE

DATA CONTENT LINKS

RELATIONS

ANNOTA -

TIONS

OBSERVE DECIDE, ACT

DOCS RECORDS

Boyd 1996 see Osinga, F.P.B. (2005) Science, Strategy and War: the strategic theory of John Boyd. Eburon Academic Publishers, Delft, Netherlands [also Routledge, Taylor and Francis Group (2007)] - http://tinyurl.com/26eqduv

Personal (i.e., human) knowledge

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●Sense making – W2 process

constructing tacit understanding in context

– We only know what we know when we need to know it

Nickols, F. 2000. The knowledge in knowledge management (KM). in J.W. Cortada and J.A. Woods, eds. The Knowledge Management Yearbook 2001-2002. Butterworth-Heinemann

(W2) (W2) (W3)

(W2) (W2/W3)

Building and processing knowledge in the organization / community

IFK(W2)

FK

CK

EK}

Semantics of explicit

knowledge are only

available via World 2

processes

Code:

EK – Explicit Knowledge

CK – Common Knowledge

FK – Formal Knowledge

IFK – Integrated Formal

Knowledge

For the purposes of this diagram

CK and FK are expressions

of explicit knowledge (EK)

WORLD 1

WORLD 2PERSONAL

KNOWLEDGE

WORLD 3

KNOWLEDGE

BUILDING

PROCESSES

KNOWING

ORGANIZATION

(including organizational tacit knowledge)

ENVIRONMENTAL

CONTEXTS

SEMIPERMEABLE

BOUNDARY●

●

DRIVE & ENABLE

ANTICIPATE & INFLUENCE

OBSERVE, TEST & MAKE SENSE

KNO

WLED

GE FLO

WS

& EXC

HAN

GESIFK

(W2)

FK

CK

EK}

Semantics of explicit

knowledge are only

available via World 2

processes

Code:

EK – Explicit Knowledge

CK – Common Knowledge

FK – Formal Knowledge

IFK – Integrated Formal

Knowledge

For the purposes of this diagram

CK and FK are expressions

of explicit knowledge (EK)

WORLD 1

WORLD 2PERSONAL

KNOWLEDGE

WORLD 3

KNOWLEDGE

BUILDING

PROCESSES

KNOWING

ORGANIZATION

(including organizational tacit knowledge)

ENVIRONMENTAL

CONTEXTS

SEMIPERMEABLE

BOUNDARY●

●

DRIVE & ENABLE

ANTICIPATE & INFLUENCE

OBSERVE, TEST & MAKE SENSE

KNO

WLED

GE FLO

WS

& EXC

HAN

GES

Vines, R., Hall, W.P. 2011. Exploring the foundations of organizational knowledge.

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Cyclic interactions between personal (W2) and explicit (W3) knowledge

30

31

Formal organizational knowledge from personal knowledge

Personal

Accessible and shared in group Organizational

Error reduction in new knowledge claims

Kn

ow

ledg

e q

ualit

y a

ssura

nce

th

rou

gh

criticis

m a

nd

re

alit

y te

stin

g

WORLD 3

Formal

knowledge

WORLD 3

Explicit

knowledge

WORLD 3

Common

knowledge

Kn

ow

ledg

e e

xch

an

ge

Review

processing

Error reduction in new knowledge claims

Kn

ow

ledg

e q

ualit

y a

ssura

nce

th

rou

gh

criticis

m a

nd

re

alit

y te

stin

g

WORLD 3

Formal

knowledge

WORLD 3

Explicit

knowledge

WORLD 3

Common

knowledge

Kn

ow

ledg

e e

xch

an

ge

Review

processing

32

Creating and building knowledge is cyclical

Knowledge is solutions to problems of living – Iterated cycles of creation and destruction (Boyd, Osinga)

Creation = assembly of sense data and information to suggest claims about the world

Destruction = testing and criticizing claims against the world to eliminate those claims that don’t work

– Popper: solutions are those claims which prove to work (at least most of the time) Knowledge is mentally constructed Cannot logically prove that any claimed solution is actually true All claims must be considered to be tentative (i.e., potentially

fallible) Follow tested claims until they are replaced by something that

works better

Knowledge building cycles are endlessly iterated and may exist at several hierarchical levels of organization

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Hierarchy of knowledge building cycles

3 stages in building reliable knowledge – Personal/individual

– Group/team

– Peer review/formal publication

W1

Context

Individual

NOOSPHERE

Peer review / formalization

Rework

Publication

Group/team

review/extension

W1

Context

Individual

NOOSPHERE

Peer review / formalization

Rework

Publication

Group/team

review/extension

world knowledge-base

application of existing knowledge

Knowledge construction cycle

Vines et al. 2011 Hall, Nousala 2010 Nousala et al. 2010 Hall et al. 2010

Putting theory into practice

Understanding how to manage

organizational knowledge flows naturally from the biological point of view

• Hall, W.P., Dalmaris, P., Nousala, S. 2005. A biological

theory of knowledge and applications to real world organizations. Knowledge Management in Asia Pacific, Wellington, N.Z. 28-29 November 2005

• Vines, R., Hall, W.P. 2011. Exploring the foundations of organizational knowledge. Kororoit Institute Working Papers No. 3: 1-39

Enterprises exist in contexts that must be addressed as imperatives if they are to survive

Enterprises are living entities

No enterprise or subsidiary component should be considered in isolation from its existential contexts

– What are its imperatives for continued existence? to maintain survival and wellbeing

to maintain resource inputs necessary to survival

to produce and distribute goods necessary to survival

to survive environmental changes

to minimize risk

to maintain future wellbeing

– Organizational systems satisfying imperatives must track continually changing contexts with observations, decisions and actions

35

36

Building and maintaining an adaptive KM architecture to meet organizational imperatives

DRIVERS ENABLERS &

IMPEDIMENTS PEOPLE PROCESS

STRATEGY DEVELOPMENT

STRATEGICREQUIREMENTS

OBSERVATIONOF CONTEXT & RESULTS

ORIENTATION & DECISIONENACTED

STRATEGY

In competitionWin more contracts

Perform better on contracts won

Minimise losses to risks and liabilities

Meet statutory and regulatory requirements

Operational Excellence

Customer satisfaction

Stakeholder intimacy

Service deliveryGrowthSustainability ProfitabilityRisk mitigation

Knowledge auditKnowledge mapping

Business disciplines

Technology & systems

Information disciplines

Incentives & disincentives

Etc.

Internal / external communication

TaxonomiesSearching & retrieval

Business process analysis & reengineering

Tracking and monitoring

Intelligence gathering

QA / QC

Strategic management

Architectural role

Communities of Practice

Corporate communications

HR practicesCompetitive intelligence

IT strategyEtc.

… ITERATION …

Where to next?

Developing an enterprise KNOWLEDGE architecture

See EA Principals (http://eaprincipals.com)

An EA definition from Gartner

Enterprise architecture is the process of translating business vision and strategy into effective enterprise change by creating, communicating and improving the key principles and models that describe the enterprise's future state and enable its evolution.

The scope of the enterprise architecture includes the people, processes, information and technology of the enterprise, and their relationships to one another and to the external environment.

Enterprise architects compose holistic solutions that address the business challenges of the enterprise and support the governance needed to implement them.

(http://www.e.govt.nz/enterprise-architecture)

Note focus on information management.

Powerful methodology but needs to focus on knowledge in the biological organization 38

What is an EA Framework?

Framework (from American Heritage Dictionary) 1. A structure for supporting or enclosing something else,

especially a skeletal support used as the basis for something being constructed.

2. An external work platform; a scaffold. 3. A fundamental structure, as for a written work. 4. A set of assumptions, concepts, values, and practices that

constitutes a way of viewing reality.

EA Framework – All of these definitions apply to the kinds of frameworks

used in EA. – The value of a framework is determined by the degree to

which it provides positively useful guidance to the architect, minus the degree to which adherence to its strictures limits the architect’s ability to see and think about possibly bigger pictures.

– Frameworks may be applied at several architectural levels.

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Enterprise solutions architecture builds models & blueprints

Identifies interrelationships among – Components forming the “business” – Data and information systems – Supporting technologies

Two perspectives joined by modeling relationships – “As is” – Understanding and modeling how to get from the before to the after – “To be”

Primary components of models – Business architecture (how the business works) – Applications architecture (software) – Technology architecture (hardware) – Solutions architecture (how it all fits together to deliver business

outcomes) – To add: knowledge architecture (data, information, knowledge

requirements to support business decisions & processes) Other components

– Security architecture – Human capital architecture 40

Where EA’s approaches apply

Problem Space {Modeling Space} Solution Space

– EA should begin with understanding problems the enterprise faces (i.e., “business” problems) and then design deliverable solutions (in the solution space) to solve them

– The Architect works in a Modeling Space between the Problem Space and Solution Space to rationally define the problems and to identify and specify interventions that map back to provide practical solutions in the problem space (all too often seen to be IT implementations)

Root causes of problems are often related to personnel, processes, or knowledge – not just technological inefficiency.

– IT implementations focused on technology often fail to solve original problems and may create new ones

– The wise architect is aware of and considers non-technological issues in proposed solutions 41

TOGAF provides a proven methodology

The Open Group Architecture Framework Phases:

– Preliminary Charter & mobilization

– A. Architectural vision scope, stakeholders, vision & approvals

– B. Business architecture business architecture to support agreed

vision – C. Information systems architecture

includes data and application architectures

– D. Technology architecture – E. Opportunities & solutions

delivery vehicles and implementation planning

– F. Migration planning sequence of transition architectures with

implementation & migration plans – G. Implementation governance – H. Architecture change management – Requirements management (throughout) 42

Community knowledge management

See EA Principals (http://eaprincipals.com)

New tools extending human cognition introduce radical capabilities for knowledge infrastructure

“Instant” observation/communication/decision/action possible

– Every smart phone in a hand is an intelligent sensing node also capable of organizing and supporting action

– Polling & voting (e.g., SurveyMonkey) – Acting (e.g., Mechanical Turk)

Crowd sourcing tools for assembling knowledge – wiki – databases

Unlimited access to knowledge resources – cloud computing – Google Scholar / Google Translate

> 50% world knowledge available free-on-line via author archiving > 95% available via research library subscriptions

– University of Melbourne accesses 105,000 eJournals – Scholar offers direct access from search result to university

subscription

Etc. – beyond imagining 44

Sample community action groups

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Click picture to open link

See: Hall, W.P., Nousala, S., Best, R. 2010. Free technology for the support of community action groups: theory, technology and practice.

Some references on relevant technology for building knowledge infrastructures for sustainability

Hall, W.P., Nousala, S., Best, R., Nair, S. 2012. Social networking tools for knowledge-based action groups. (in) Computational Social Networks - Part 2: Tools, Perspectives and Applications, (eds) Abraham, A., Hassanien, A.-E. Springer-Verlag, London, pp. 227-255

Nousala, S., Hall, W.P., Hadgraft, R. 2011. Socio-technical systems for connecting social knowledge and the governance of urban action. 15th WMSCI, CENT Symposium, July 19-22, 2011, Orlando, Florida, USA.

Vines, R., Hall, W.P., McCarthy, G. 2011. Textual representations and knowledge support-systems in research intensive networks. (in) Cope, B., Kalantzis, M., Magee, L. (eds). Towards a Semantic Web: Connecting Knowledge in Academic Research. Oxford: Chandos Press, pp. 145-195.

Hall, W.P., Nousala, S., Best, R. 2010. Free technology for the support of community action groups: theory, technology and practice. Knowledge Cities World Summit, 16-19, November 2010, Melbourne, Australia

Hall, W.P., Nousala, S. 2010. What is the value of peer review – some sociotechnical considerations. Second International Symposium on Peer Reviewing, ISPR 2010 June 29th - July 2nd, 2010 – Orlando, Florida, USA

Hall, W.P., Nousala, S., Vines, R. 2010. Using Google’s apps for the collaborative construction, refinement and formalization of knowledge. ICOMP'10 - The 2010 International Conference on Internet Computing July 12-15, Las Vegas, Nevada, USA

Nousala, S., Miles, A., Kilpatrick, B., Hall, W.P. 2009. Building knowledge sharing communities using team expertise access maps (TEAM). International Journal of Business and Systems Research 3(3), 279-296.

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