Transcript DATA - Pakistan Engineering Council

DATA
Facts about situation
Neutral
Right data should be collected
Data should be updated regularly
Raw material extracted from
environment
Cash register
Aero plane traveling
Cooking a cake
Contextualized
Structured
Categorized
Condensed
When we add
something to data
Information
Assign value to data
Transformation
Decision making
Problem solving
A
Find and send easily
C
Format and organize
T
Current for up-dated decisions
Complete understanding
Of
Information
Absorb information
Filter
Experience
Beliefs
Brought up
Capabilities
Useful actions
Problem solving
Decision making
Information + Personal know-how
Knowledge
Dynamic
Ever changing
Why not static ?
Based on non-static things
20th Century
Information
+ Know-how
21st Century
Information +
Know-how
Know-how
?
Creation of value form information
Types of knowledge
Expressed knowledge
What we know we know
Car owner’s manual
Travelers guide
Formulas, methodologies,
documents
Expressible knowledge
What we know we know but not
noted
Family business secrets
Know - what ( How things work )
Know - what ( How things work )
+
Know - what ( How things work )
+
Know - who ( Experts )
Inexpressible knowledge
What we know that can’t be noted
Where it is present ?
Minds of people
Changing information
Changing information
+
Expertise
Changing information
+
Expertise
=
Best solution
Hat Ribbon
Knowledge assets
Knowledge assets
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Data
Knowledge assets
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Data
Information
Knowledge assets
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Data
Information
Expressed knowledge
Knowledge assets
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Data
Information
Expressed knowledge
Expressible knowledge
Knowledge assets
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Data
Information
Expressed knowledge
Expressible knowledge
Inexpressible knowledge
Management
?
Knowledge assets
Convert
information into knowledge
?
Apply knowledge
For
Value addition and competitive advantage
?
Knowledge Management
Historical overview
Historical overview
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Master to apprentice in ancient times.
Historical overview
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Master to apprentice in ancient times.
Aristotle.
Historical overview
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Master to apprentice in ancient times.
Aristotle.
Nonaka.
Historical overview
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Master to apprentice in ancient times.
Aristotle.
Nonaka.
Data storages
Historical overview
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Master to apprentice in ancient times.
Aristotle.
Nonaka.
Data storages.
Artificial intelligence. ( speech, language )
Historical overview
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Master to apprentice in ancient times.
Aristotle.
Nonaka.
Data storages.
Artificial intelligence. ( speech, language )
Expert systems. (cloning of human reasoning )
Historical overview
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Master to apprentice in ancient times.
Aristotle.
Nonaka.
Data storages.
Artificial intelligence. ( speech, language )
Expert systems. (cloning of human reasoning )
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Neural networks ( input-output pathways )
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Historical overview
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Master to apprentice in ancient times.
Aristotle.
Nonaka.
Data storages.
Artificial intelligence. ( speech, language )
Expert systems. (cloning of human reasoning )
Neural networks ( input-output pathways )
Information explosion.
SCM
Production
Apply knowledge to what we already do
Innovation
Apply knowledge to what is new
Information handlers
Information handlers
Knowledge workers
(solve problems + Add value )
Work harder
Work harder
Work smarter
KM
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( Hibbard 1997 )
Gathering firm’s collective expertise
KM
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( Hibbard 1997 )
Gathering firm’s collective expertise
Data bases
KM
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( Hibbard 1997 )
Gathering firm’s collective expertise
Data bases
Books
KM
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( Hibbard 1997 )
Gathering firm’s collective expertise
Data bases
Books
Processes
KM
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( Hibbard 1997 )
Gathering firm’s collective expertise
Data bases
Books
Processes
People’s minds
KM
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( Hibbard 1997 )
Gathering firm’s collective expertise
Data bases
Books
Processes
People’s minds
Distribute to places for biggest payoff.
KM
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( Sveiby2000 )
Art of creating value
KM
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( Sveiby2000 )
Art of creating value
From company’s intangible assets
KM
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( Brooking 1996 )
Accumulating knowledge assets
KM
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( Brooking 1996 )
Accumulating knowledge assets
Use effectively for competitive advantage.
Why KM now ?
Time span of competitive
advantage
Type writers
High speed of information
Two faces of
i
explosion
300 million pages on Internet
( 1999 )
300 million pages on Internet
( 1999 )
1 billion pages on Internet
(2002)
Knowledge is key value added in
products and services
Knowledge values more than
physical assets
Tobin’s Q formula
GM’s = $30 billion
( 2000 )
GM’s = $30 billion
( 2000 )
GM’s = $405 billion ( 1999 )
MS = $361 billion ( 2000 )
MS = $361 billion ( 2000 )
MS = $37 billion
( 2000 )
Total value
Total value – Tangible assets
Total value – Tangible assets
=
Knowledge asset
Globalization
Today’s business world not dealing
with
Consumers
Prosumer
Prosumer
Aware of their needs
Prosumer
Aware of their needs
Active
Prosumer
Aware of their needs
Active
Give feed back
No more
knowledge hoarding
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Early retirements
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Early retirements
Rapid job changes
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Early retirements
Rapid job changes
Tense working environment
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Early retirements
Rapid job changes
Tense working environment
Downsizing
Roots of KM
Roots of KM
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Business
Roots of KM
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Business
Economics
Roots of KM
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Business
Economics
Psychology
Roots of KM
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Business
Economics
Psychology
Information
KM involves
KM involves
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People
KM involves
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People
Processes
KM involves
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People
Processes
Technology
KM processes
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Creation of knowledge
KM processes
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Creation of knowledge
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Distribution of knowledge
KM processes
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Creation of knowledge
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Distribution of knowledge
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Application of knowledge
Existence before use
Creation of knowledge
Existing resources
Fostering environment
Packages
Acquire
Buy
Mergers
Social networks
Distributing knowledge
Good distribution system
Good distribution system
Early finding
Good distribution system
Early finding
Proper packaging
Good distribution system
Early finding
Proper packaging
Timely delivery
Early finding
Locate the source
Documents
Processes
People
Proper packaging
Right form
Right context
Regional
Cultural
Functional
Enable employee
to find
what he needs
Timely delivery
Push technology
Push technology
Avoid over loading
Pull technology
Pull technology
Avoid extra information
Application of knowledge
Medicine
The Holy Quran
Application
Is
Creation of value
Puzzle
Proper shape
Easy to understand
Compatible
with
work environment
Motivated employee
Suits
employees job requirement
Requirements Aligned
with
Company’s objectives
Educate employees
Tacit knowledge
50% to
95%
Fuel
for
Innovation & Creativity engine
Only competitive advantage
in
Unpredictable business
environment
Fish
Vision without action
Action without vision
Vision with action can change the
world
Arthur Barker
System analyst
System analyst
Knowledge developer
Tacit knowledge
Explicit knowledge
Nonaka’s model of KC
Nonaka’s model of KC
Tacit to tacit communication
( socialization )
Discussions
Nonaka’s model of KC
Tacit to explicit communication
( externalization )
Brainstorming
Nonaka’s model of KC
Explicit to explicit communication
( communication )
Technology
Nonaka’s model of KC
Explicit to tacit communication
( internalization )
Deduce idea from report
Evaluating expert
Levels of expertise
Levels of expertise
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Highly expert
Levels of expertise
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Highly expert
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Moderate expert
Levels of expertise
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Highly expert
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Moderate expert
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New expert
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Single experts
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More experts
Understanding expert style
Holding of sessions
Understanding experience
Language problem
Type of interviews
Arrangement of questions
Reliability of information
Response bias
Inconsistency
Ending the interview
Other techniques
On-site observation
Brain storming
Electronic brain storming
Consensus decision making
The Delphi method
Concept mapping
Black boarding
Knowledge transfer
Knowing-doing gap
Atmosphere of trust
Push reasoning before process
Do than talk
Tolerate mistakes for learning
Cooperation & Collaboration
Not
Competition
Asses job satisfaction
Collective sequential transfer
Explicit inter team transfer
Internets
Intranets
Extranet
B2B
Chat rooms
Video communication
CRM
Organizational learning
Organizational learning
Company’s collective ability
To
Organizational learning
Company’s collective ability
To
Absorb information
And
Organizational learning
Company’s collective ability
To
Absorb information
And
Learn from information
RICE model
RICE model
Responsiveness
RICE model
Responsiveness
Innovation
RICE model
Responsiveness
Innovation
Competency
RICE model
Responsiveness
Innovation
Competency
Efficiency
Creativity
Emerges when existing solutions
No longer serve the purpose
Innovation
Are responses to challenging problems