lecture 1 parttwo
DESCRIPTION
knowledgeTRANSCRIPT
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Understanding Knowledge
Lecture One – Part II
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Chapter 1: Understanding Knowledge
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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
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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)?
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Basic Knowledge-Related DefinitionsCommon Sense
Fact
Heuristic
Knowledge
Intelligence
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Basic Knowledge-Related DefinitionsCommon Sense
Inborn ability to sense, judge, or perceive situations; grows stronger over time
Fact
Heuristic
Knowledge
Intelligence
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Chapter 1: Understanding Knowledge
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Basic Knowledge-Related DefinitionsCommon Sense
Inborn ability to sense, judge, or perceive situations; grows stronger over time
Fact A statement that relates a certain element of truth about a subject matter or a domain
Heuristic
Knowledge
Intelligence
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Chapter 1: Understanding Knowledge
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Basic Knowledge-Related DefinitionsCommon Sense
Inborn ability to sense, judge, or perceive situations; grows stronger over time
Fact A statement that relates a certain element of truth about a subject matter or a domain
Heuristic A rule of thumb based on years of experience
Knowledge
Intelligence
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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
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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.