Understanding Knowledge Lecture One – Part II. Chapter 1: Understanding Knowledge 1-2 Review of Last Lecture What is Knowledge Management (KM)?Knowledge.

Understanding Knowledge

Lecture One – Part II

Chapter 1: Understanding Knowledge

1-2

Review of Last Lecture What is Knowledge Management (KM)?

What are the driving forces?

Role of KM in today’s organization

What is Knowledge Management System (KMS)?

Classification of Knowledge Management Systems

Effective Knowledge Management


1-3

In this Lecture Basic Knowledge-related

Definitions Data, Information and

Knowledge Data Processing versus

Knowledge-based Systems Types of Knowledge What makes someone an

expert (knowledge worker)?


1-4

Basic Knowledge-Related DefinitionsCommon Sense

Fact

Heuristic

Knowledge

Intelligence


1-5


Inborn ability to sense, judge, or perceive situations; grows stronger over time

Fact

Heuristic

Knowledge

Intelligence


1-6



Fact A statement that relates a certain element of truth about a subject matter or a domain

Heuristic

Knowledge

Intelligence


1-7




Heuristic A rule of thumb based on years of experience

Knowledge

Intelligence


1-8





Knowledge Understanding gained through experience; familiarity with the way to perform a task; an accumulation of facts, procedural rules, or heuristics

Intelligence


1-9





Knowledge Understanding gained through experience; familiarity with the way to perform a task; an accumulation of facts, procedural rules, or heuristics

Intelligence The capacity to acquire and apply knowledge


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Data, Information, and Knowledge Data: Unorganized and

unprocessed facts; static; a set of discrete facts about events

Information: Aggregation of data that makes decision making easier

Knowledge is derived from information in the same way information is derived from data; it is a person’s range of information


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Relationship between data, information and Knowledge

InformationData

Zero Low Medium High Very High

Value

Knowledge


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An illustration

Zero Low Medium High Very High

Value

InformationData

H T H T TH H H T H

…T T T H T

pH = 0.40pT = 0.60RH = +$10RT = -$8

nH = 40nT = 60

EV = -$0.80

Knowledge

CountingpH = nH/(nH+nT)pT = nT/(nH+nT)

EV=pH RH+ pT RT


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Relating Data, Information, and Knowledge to Events

KnowledgeKnowledge

InformationDataInformation

System

Decision

Events

Use ofinformation

Kn

ow

led

ge


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KNOWLEDGE

INFORMATION

WISDOM

Nonalgorithmic(Heuristic)

Nonprogrammable

From Data Processing to Knowledge-based SystemsFrom Data Processing to Knowledge-based Systems

DATAAlgorithmic Programmable


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Types (Categorization) of Knowledge

Shallow (readily recalled) and deep (acquired through years of experience)

Explicit (already codified) and tacit (embedded in the mind)

Procedural (repetitive, stepwise) versus Episodical (grouped by episodes or cases)


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Explicit and Tacit Knowledge

Explicit (knowing-that) knowledge: knowledge codified and digitized in books, documents, reports, memos, etc.

Tacit (knowing-how) knowledge: knowledge embedded in the human mind through experience and jobs


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Illustrations of the Different Types of Knowledge


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What makes someone an expert? An expert in a specialized area

masters the requisite knowledge The unique performance of a

knowledgeable expert is clearly noticeable in decision-making quality

Knowledgeable experts are more selective in the information they acquire

Experts are beneficiaries of the knowledge that comes from experience


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Expert’s Reasoning Methods

Reasoning by analogy: relating one concept to another Formal reasoning: using deductive or inductive methods Case-based reasoning: reasoning from relevant past cases


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Deductive and inductive reasoning Deductive reasoning:

exact reasoning. It deals with exact facts exact facts and exact and exact conclusionsconclusions

Inductive reasoning: reasoning from a set of facts or individual cases to a general general conclusionconclusion


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Human’s Learning ModelsLearning by experience: a

function of time and talent

Learning by example: more efficient than learning by experience

Learning by discovery: undirected approach in which humans explore a problem area with no advance knowledge of what their objective is.


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End of Lecture One


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You’ve just been hired by Woolworth and have been asked to bag groceries for customers….

How would you do this?


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A classic example of deductive reasoning, given by Aristotle, is All men are mortal. (major premise) Socrates is a man. (minor premise) Socrates is mortal. (conclusion)


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The wheel is round. (Or, all wheels I have seen are round)

The bird flies. (Or, all birds I have seen could fly)

to infer general propositions like:

All wheels are round.

All birds can fly.


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What is Knowledge Management? Knowledge management (KM)

may be defined simply as doing what is needed to get the most out of knowledge resources.

Related to the concept of intellectual capital (both human and structural).

KM focuses on organizing and making available important knowledge, wherever and whenever it is needed.


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Forces Driving Knowledge Management

Increasing Domain Complexity

Accelerating Market Volatility

Intensified Speed of Responsiveness

Diminishing Individual Experience


1-28

What is Knowledge Management “Systems” ?

Social/Structural mechanismsmechanisms (e.g., mentoring and retreats, etc.) for promoting knowledge sharing. Leading-edge information technologiesinformation technologies (e.g., Web-based conferencing) to support KM mechanisms.Knowledge management systems (KMS): the synergysynergy between social/structural mechanisms and latest technologies.

Understanding Knowledge Lecture One – Part II. Chapter 1: Understanding Knowledge 1-2 Review of Last Lecture What is Knowledge Management (KM)?Knowledge.

Documents

easier knowledge

knowledge data processing

high value knowledge

expert knowledge worker

information slide

heuristics intelligence

way information

aggregation of data