Download PPT 11

Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition
Chapter 11
Expert system architecture, representation of
knowledge, Knowledge Acquisition, and Reasoning
11-1
Learning Objectives
• Describe the knowledge management cycle
• Describe the technologies that can be used in a
knowledge management system
• Describe the Chief Knowledge Officer CKO and
others involved in knowledge management
• Describe the role of knowledge management in
organizational activities
• Describe the different ways of evaluating intellectual
(intelligent) capital in an organization
11-2
Learning Objectives
• Describe how KMS are implemented
• Describe the roles of technology, people, and
management in knowledge management
• Describe the benefits and drawbacks of knowledge
management initiatives
• Describe how knowledge management can
transform the way an organization functions.
11-3
Opening Vignette:“MITRE Knows What It
Knows Through Knowledge Management”
11-4
Opening Vignette:“MITRE Knows What It
Knows Through Knowledge Management”
11-5
Opening Vignette:“MITRE Knows What It
Knows Through Knowledge Management”
11-6
Opening Vignette:“MITRE Knows What It
Knows Through Knowledge Management”
ENABLING TECHNOLOGIES FOR
KNOWLEDGE MANAGEMENT
Collaboration
Internet
Communication
KM LIFE-CYCLE
Create
Share
Data
Mining
Extranet
Expert
Systems
Intranet
Search
Engine
Identify
Modify
Artificial
Intelligence
feedback
Machine
Learning
Act
Databases
Apply
Measurements
Knowledge
representation
Web 2.0
Portals
CULTURE
PROCESS
PRACTICE
Web
technologies
INFLUENCING FACTORS
11-7
Opening Vignette:“MITRE Knows What It
Knows Through Knowledge Management”
11-8
Opening Vignette:“MITRE Knows What It
Knows Through Knowledge Management”
11-9
Knowledge Engineers
• Professionals who elicit knowledge from
experts
– Empathetic, patient
– Broad range of understanding, capabilities
• Integrate knowledge from various sources
– Creates and edits code
– Operates tools
• Build knowledge base
– Validates information
– Trains users
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-10
Knowledge Engineering
• Process of acquiring knowledge from
experts and building knowledge base
– Narrow perspective
• Knowledge acquisition, representation,
validation, inference, maintenance
– Broad perspective
• Process of developing and maintaining
intelligent system
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-11
Knowledge Engineering
Process
• Acquisition of knowledge
– General knowledge or metaknowledge
– From experts, books, documents, sensors, files
• Knowledge representation
– Organized knowledge
• Knowledge validation and verification
• Inferences
– Software designed to pass statistical sample
data to generalizations
• Explanation and justification capabilities
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-12
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-13
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-14
Development of a Real-Time
Knowledge-lead to success.
• Problems with fermentation process
– Quality parameters difficult to control
– Many different employees doing same task
– High turnover
• Expert system used to capture knowledge
– Expertise available 24 hours a day
• Knowledge engineers developed system by:
– Knowledge elicitation
• Interviewing experts and creating knowledge bases
– Knowledge fusion
• Fusing individual knowledge bases
– Coding knowledge base
– Testing and evaluation of system
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-15
Introduction to
Knowledge Management
• Knowledge management concepts and
definitions.
– Knowledge management
The active management of the expertise in an
organization. It involves collecting, categorizing,
and disseminating knowledge.
– Intellectual capital
The invaluable knowledge of an organization’s
employees.
11-16
Introduction to
Knowledge Management
11-17
Introduction to
Knowledge Management
Data
Processed
Information
Relevant and
Actionable
Knowledge
DEPLOYMENT CHART
Database
PHASE 1
PHASE 2
PHASE 3
PHASE 4
PHASE 5
DEPT 1
DEPT 2
DEPT 4
1
2
3
4
5
Wisdom
DEPT 3
Relevant and actionable processed-data
11-18
Introduction to
Knowledge Management
• Characteristics of knowledge
• Knowledge-based economy
The economic shift from natural resources to
intellectual assets
11-19
Introduction to
Knowledge Management
11-20
Introduction to
Knowledge Management
• Knowledge management systems
(KMS)
A system that facilitates knowledge
management by ensuring knowledge
flow from the person(s) who know to
the person(s) who need to know
throughout the organization; knowledge
evolves and grows during the process
11-21
Knowledge Management
Activities
• Knowledge management initiatives
and activities
– Most knowledge management initiatives
have one of three aims:
1.
2.
3.
To make knowledge visible
To develop a knowledge-intensive culture
To build a knowledge infrastructure
11-22
Elicitation Methods
• Manual
– Based on interview
– Track reasoning process
– Observation
• Semiautomatic
– Build base with minimal help from knowledge
engineer
– Allows execution of routine tasks with minimal
expert input
• Automatic
– Minimal input from both expert and knowledge
engineer
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-23
Manual Methods
• Interviews
– Structured
• Goal-oriented
• Walk through
– Unstructured
• Complex domains
• Data unrelated and difficult to integrate
– Semistructured
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-24
Manual Methods
• Process tracking
– Track reasoning processes
• Protocol analysis
– Document expert’s decision-making
– Think aloud process
• Observation
– Motor movements
– Eye movements
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-25
Manual Methods
•
•
•
•
•
•
•
•
•
Case analysis
Critical incident
User discussions
Expert commentary
Graphs and conceptual models
Brainstorming
Prototyping
Clustering of elements
Iterative performance review
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-26
Semiautomatic Methods
• Repertory grid analysis
– Personal construct theory
• Organized, perceptual model of expert’s knowledge
• Expert identifies domain objects and their attributes
• Expert determines characteristics and opposites for
each attribute
• Expert distinguishes between objects, creating a grid
• Expert transfer system
– Computer program that elicits information from
experts
– Rapid prototyping
– Used to determine sufficiency of available
knowledge
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-27
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-28
Semiautomatic Methods,
continued
• Computer based tools features:
– Ability to add knowledge to base
– Ability to assess, refine knowledge
– Visual modeling for construction of
domain
– Creation of decision trees and rules
– Ability to analyze information flows
– Integration tools
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-29
Automatic Methods
• Data mining by computers
• Inductive learning from existing
recognized cases
• Neural computing mimicking human
brain
• Genetic algorithms using natural
selection
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-30
Multiple Experts
• Scenarios
– Experts contribute individually
– Primary expert’s information reviewed by
secondary experts
– Small group decision
– Panels for verification and validation
• Approaches
–
–
–
–
Consensus methods
Analytic approaches
Automation of process through software usage
Decomposition
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-31
Automated Knowledge Acquisition
• Induction
– Activities
• Training set with known outcomes
• Creates rules for examples
• Assesses new cases
– Advantages
• Limited application
• Builder can be expert
– Saves time, money
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-32
Automated Knowledge
Acquisition
– Difficulties
• Rules may be difficult to understand
• Experts needed to select attributes
• Algorithm-based search process produces
fewer questions
• Rule-based classification problems
• Allows few attributes
• Many examples needed
• Examples must be cleansed
• Limited to certainties
• Examples may be insufficient
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-33
Automated Knowledge
Acquisition
• Interactive induction
– Incrementally induced knowledge
• General models
– Object Network
– Based on interaction with expert
• interviews
– Computer supported
• Induction tables
• IF-THEN-ELSE rules
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-34
Evaluation, Validation,
Verification
• Dynamic activities
– Evaluation
• Assess system’s overall value
– Validation
• Compares system’s performance to expert’s
• Concordance and differences
– Verification
• Building and implementing system correctly
• Can be automated
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-35
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-36
Production Rules
• IF-THEN
• Independent part, combined with
other pieces, to produce better result
• Model of human behavior
• Examples
– IF condition, THEN conclusion
– Conclusion, IF condition
– If condition, THEN conclusion1 (OR)
ELSE conclusion2
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-37
Artificial Intelligence Rules
• Types
– Knowledge rules
• Declares facts and relationships
• Stored in knowledge base
– Inference
• Given facts, advises how to proceed
• Part of inference engines.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-38
Artificial Intelligence Rules
• Advantages
–
–
–
–
–
Easy to understand, modify, maintain
Explanations are easy to get.
Rules are independent.
Modification and maintenance are relatively easy.
Uncertainty is easily combined with rules.
• Limitations
– Huge numbers may be required
– Designers may force knowledge into rule-based entities
– Systems may have search limitations; difficulties in
evaluation
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-39
Semantic Networks
• Graphical
depictions
• Nodes and links
• Hierarchical
relationships
between
concepts
• Reflects
inheritance
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-40
Frames
• All knowledge about object
• Hierarchical structure allows for inheritance
• Allows for diagnosis of knowledge
independence
• Object-oriented programming
– Knowledge organized by characteristics and
attributes
• Slots
• Subslots/facets
– Parents are general attributes
– Instantiated to children
• Often combined with production rules
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-41
Knowledge Relationship
Representations
• Decision tables
– Spreadsheet format
– All possible attributes compared to conclusions
• Decision trees
– Nodes and links
– Knowledge diagramming
• Computational logic
– Propositional
• True/false statement
– Predicate logic
• Variable functions applied to components of
statements
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-42
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-43
Reasoning Programs
• Inference Engine
– Algorithms
– Directs search of knowledge base
• Forward chaining
– Data driven
– Start with information, draw conclusions
• Backward chaining
– Goal driven
– Start with expectations, seek supporting evidence
– Inference/goal tree
• Schematic view of inference process
– AND/OR/NOT nodes
– Answers why and how
• Rule interpreter
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-44
Explanation Facility
• Justifier
–
–
–
–
–
–
Makes system more understandable
Exposes shortcomings
Explains situations that the user did not anticipate
Satisfies user’s psychological and social needs
Clarifies underlying assumptions
Conducts sensitivity analysis
• Types
– Why
– How
– Journalism based
• Who, what, where, when, why, how
• Why not
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-45
Generating Explanations
• Static explanation
– Preinsertion of text
• Dynamic explanation
– Reconstruction by rule evaluation
• Tracing records or line of reasoning
• Justification based on empirical
associations
• Strategic use of metaknowledge
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-46
Uncertainty
• Widespread
• Important component
• Representation
– Numeric scale
• 1 to 100
– Graphical presentation
• Bars, pie charts
– Symbolic scales
• Very likely to very unlikely
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-47
Uncertainty
• Probability Ratio
– Degree of confidence in conclusion
– Chance of occurrence of event
• Bayes Theory
– Subjective probability for propositions
• Imprecise
• Combines values
• Dempster-Shafer
– Belief functions
– Creates boundaries for assignments of
probabilities
• Assumes statistical independence
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-48
Certainty
• Certainty factors
– Belief in event based on evidence
– Belief and disbelief independent and not
combinable
– Certainty factors may be combined into
one rule
– Rules may be combined
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
11-49
Approaches to
Knowledge Management
• Process approach to knowledge
management attempts to organize
organizational knowledge through formalized
controls, processes and technologies
– Focuses on explicit knowledge and IT
• Practice approach focuses on building the
social environments or communities of
practice necessary to facilitate the sharing of
tacit understanding
– Focuses on tacit knowledge and socialization
11-50
Approaches to
Knowledge Management
• Hybrid approaches to knowledge
management
– The practice approach is used so that a
repository stores only explicit knowledge
that is relatively easy to document
– Tacit knowledge initially stored in the
repository is contact information about
experts and their areas of expertise
– Increasing the amount of tacit knowledge
over time eventually leads to the
attainment of a true process approach
11-51
Knowledge Management A Demand Led Business Activity
• Supply-driven vs. demand-driven KM
Supp
ly-dr
iven
:
Data
summarize
DIKA
R
n
Tech
ol
h
roac
p
p
a
ogy
Results
obtain
Information
contextulize
Bu s i
n e ss
-valu
e
Action
utilize
Knowledge
appr
o a ch
Dem
a
: RA
n
e
v
i
r
nd-d
KID
11-52
Approaches to
Knowledge Management
• Best practices
In an organization, the best methods
for solving problems. These are often
stored in the knowledge repository of a
knowledge management system
• Knowledge repository is the actual
storage location of knowledge in a
knowledge management system. Similar
in nature to a database, but generally
text-oriented
11-53
Approaches to
Knowledge Management
A Comprehensive View to Knowledge Repository
KNOWLEDGE UTILIZATION
KNOWLEDGE MANAGEMENT PLATFORM (KMP)
KNOWLEDGE PORTAL
(Web-based End User Interface)
Human Experts
Intelligent Broker
KNOWLEDGE CREATION
KNOWLEDGE REPOSITORY
(Knowledge / Information / Data Nuggets)
Web Crawler
Data/Text Mining Tools
Ad hoc
Search
JUN
1
5
Manual
Entries
DIVERSE INFORMATION / DATA SOURCES
(Weather / Medical Info / Finance / Agriculture / Industrial)
11-54
Approaches to
Knowledge Management
• Developing a knowledge repository
– Knowledge repositories are developed
using several different storage
mechanisms in combination
– The most important aspects and difficult
issues are making the contribution of
knowledge relatively easy for the
contributor and determining a good
method for cataloging the knowledge
11-55
Information Technology (IT) in
Knowledge Management
•
The KMS cycle
– KMS usually follow a six-step cycle:
1. Create knowledge
2. Capture knowledge
3. Improve (refine) knowledge
4. Store knowledge
5. Manage knowledge
6. Distribute (disseminate) knowledge
11-56
Information Technology (IT) in
Knowledge Management
The Cyclic Model of Knowledge Management
Capture
Knowledge
Create
Knowledge
2
1
Refine
Knowledge
6
Disseminate
Knowledge
Store
Knowledge
Manage
Knowledge
3
4
5
11-57
Information Technology (IT) in
Knowledge Management
•
Components of KMS
–
KMS are developed using three sets of core
technologies:
1. Communication
2. Collaboration
3. Storage and retrieval
– Technologies that support KM
• Artificial intelligence
• Intelligent agents
• Knowledge discovery in databases
• Extensible Markup Language (XML)
11-58
Information Technology (IT) in
Knowledge Management
• Artificial intelligence
– AI methods used in KMS:
• Assist in and enhance searching knowledge
• Help for knowledge representation (e.g., ES)
• Help establish knowledge profiles of individuals
and groups
• Help determine the relative importance of
knowledge when it is contributed to and
accessed from the knowledge repository
11-59
Information Technology (IT) in
Knowledge Management
• AI methods used in KMS:
– Scan e-mail, documents, and databases to
perform knowledge discovery, determine
meaningful relationships and rules
– Identify patterns in data (usually through neural
networks and other data mining techniques)
– Forecast future results by using data/knowledge
– Provide advice directly from knowledge by using
neural networks or expert systems
– Provide a natural language or voice command–
driven user interface for a KMS
11-60
Information Technology (IT) in
Knowledge Management
• Intelligent agents
– Intelligent agents are software systems that
learn how users work and provide
assistance in their daily tasks
– They are used to cause and identify
knowledge
• See ibm.com, gentia.com for examples
– Combined with enterprise knowledge portal
to proactively disseminate knowledge
11-61
Information Technology (IT) in
Knowledge Management
• Knowledge discovery in databases
(KDD)
A machine learning process that
performs rule instruction, or a related
procedure to establish (or create)
knowledge from large databases
– a.k.a. Data Mining (and/or Text Mining)
11-62
Information Technology (IT) in
Knowledge Management
• Model marts
Small, generally departmental repositories of
knowledge created by employing knowledgediscovery techniques on past decision
instances. Similar to data marts
• Model warehouses
Large, generally enterprise-wide repositories
of knowledge created by employing
knowledge-discovery techniques. Similar to
data warehouses
11-63
Information Technology (IT) in
Knowledge Management
• Extensible Markup Language (XML)
– XML enables standardized representations
of data structures so that data can be
processed appropriately by heterogeneous
information systems without case-by-case
programming or human intervention
• Web 2.0
– The evolution of the Web from statically
disseminating information to collaboratively
creating and sharing information
11-64
KM System Implementation
• Knowledge management products and
vendors
– Knowware
Technology tools (software/hardware products)
that support knowledge management
– Software development companies / vendors
• Collaborative computing tools
• Knowledge servers
• Enterprise knowledge portals (EKP)
An electronic doorway into a knowledge management
system…
11-65
KM System Implementation
• Software development companies / vendors
– Electronic document management (EDM)
A method for processing documents
electronically, including capture, storage,
retrieval, manipulation, and presentation
– Content management systems (CMS)
An electronic document management system
that produces dynamic versions of documents,
and automatically maintains the current set for
use at the enterprise level
11-66
KM System Implementation
• Software development tools
– Knowledge harvesting tools
– Search engines
– Knowledge management suites
– Knowledge management consulting firms
– Knowledge management ASPs
11-67
KMS Implementation
• Integration of KMS with other
business information systems
– With DSS/BI Systems
– With AI
– With databases and information systems
– With CRM systems
– With SCM systems
– With corporate intranets and extranets
11-68
Roles of People in
Knowledge Management
• Chief knowledge officer (CKO)
The person in charge of a knowledge
management effort in an organization
–
–
–
–
–
–
–
Sets KM strategic priorities
Establishes a repository of best practices
Gains a commitment from senior executives
Teaches information seekers how to better elicit it
Creates a process for managing intellectual assets
Obtain customer satisfaction information
Globalizes knowledge management
11-69
Roles of People in
Knowledge Management
• Skills required of a CKO include:
–
–
–
–
–
–
Interpersonal communication skills
Leadership skills
Business acumen
Strategic thinking
Collaboration skills
The ability to institute effective educational
programs
– An understanding of IT and its role in advancing
knowledge management
11-70
Roles of People in
Knowledge Management
• The CEO, other chief officers, and managers
– The CEO is responsible for championing a
knowledge management effort
– The officers make available the resources needed
to get the job done
• CFO ensures that the financial resources are available
• COO ensures that people begin to embed knowledge
management practices into their daily work processes
• CIO ensures IT resources are available
– Managers also support the KM efforts by providing
access to sources of knowledge
11-71
Roles of People in
Knowledge Management
• Community of practice (CoP)
A group of people in an organization
with a common professional interest,
often self-organized for managing
knowledge in a knowledge management
system
– See Application Case 11.7 as an example
of how Xerox successfully improved
practices and cost savings through CoP
11-72
Roles of People in
Knowledge Management
• KMS developers
– The team members who actually develop
the system
– Internal + External
• KMS staff
– Enterprise-wide KMS require a full-time
staff to catalog and manage the knowledge
11-73
Ensuring the Success of Knowledge
Management Efforts
• Success stories of knowledge management
– Implementing a good KM strategy can:
• Reduce…
– loss of intellectual capital
– costs by decreasing the number of times the
company must repeatedly solve the same
problem
– redundancy of knowledge-based activities
• Increase…
– productivity
– employee satisfaction
11-74
Ensuring the Success of
Knowledge Management Efforts
• MAKE: Most Admired Knowledge Enterprises
“Annually identifying the best practitioners of KM”
– Criteria (performance dimensions):
1.
2.
3.
4.
5.
6.
7.
8.
Creating a knowledge-driven corporate culture
Developing knowledge workers through leadership
Fostering innovation
Maximizing enterprise intellectual capital
Creating an environment for collaborative knowledge sharing
Facilitating organizational learning
Delivering value based on stakeholder knowledge
Transforming enterprise knowledge into stakeholders’ value
11-75
Ensuring the Success of
Knowledge Management Efforts
• MAKE: Most Admired Knowledge Enterprises
“Annually identifying the best practitioners of KM”
– 2008 Winners:
1.
2.
3.
4.
5.
6.
7.
8.
9.
McKinsey &
Company
Google
Royal Dutch Shell
Toyota
Wikipedia
Honda
Apple
Fluor
Microsoft
10.
11.
12.
13.
14.
15.
16.
17.
18.
PricewaterhouseCoopers
Ernst & Young
IBM
Schlumberger
Samsung Group
BP
Unilever
Accenture
…
11-76
Ensuring the Success of
Knowledge Management Efforts
• Useful applications of KMS
– Finding experts electronically and using
expert location systems
• Expert location systems (know-who)
Interactive computerized systems that help
employees find and connect with colleagues
who have expertise required for specific
problems—whether they are across the county
or across the room—in order to solve specific,
critical business problems in seconds
11-77
Ensuring the Success of
Knowledge Management Efforts
• Knowledge management valuation
– Financial metrics for knowledge
management valuation
• Focus knowledge management projects on
specific business problems that can be easily
quantified
• When the problems are solved, the value and
benefits of the system become apparent
11-78
Ensuring the Success of
Knowledge Management Efforts
• Knowledge management valuation
– Nonfinancial metrics for knowledge
management valuation—new ways to view
capital when evaluating intangibles:
•
•
•
•
•
•
Customer goodwill
External relationship capital
Structural capital
Human capital
Social capital
Environmental capital
11-79
Ensuring the Success of
Knowledge Management Efforts
• Causes of knowledge management failure
– The effort mainly relies on technology and
does not address whether the proposed
system will meet the needs and objectives of
the organization and its individuals
– Lack of emphasis on human aspects
– Lack of commitment
– Failure to provide reasonable incentive for
people to use the system…
11-80
Ensuring the Success of
Knowledge Management Efforts
• Factors that lead to knowledge
management success
– A link to a firm’s economic value, to
demonstrate financial viability and maintain
executive sponsorship
– A technical and organizational
infrastructure on which to build
– A standard, flexible knowledge structure to
match the way the organization performs
work and uses knowledge
11-81
Ensuring the Success of
Knowledge Management Efforts
• Factors that lead to knowledge
management success
– A knowledge-friendly culture that leads
directly to user support
– A clear purpose and language, to
encourage users to buy into the system
– A change in motivational practices, to
create a culture of sharing
– Multiple channels for knowledge transfer
11-82
Ensuring the Success of
Knowledge Management Efforts
• Factors that lead to knowledge
management success
– A significant process orientation and
valuation to make a knowledge
management effort worthwhile
– Nontrivial motivational methods to
encourage users to contribute and use
knowledge
– Senior management support
11-83
Last words on KM
• Knowledge is an intellectual asset
• IT is “just” an important enabler
• Proper management of knowledge is a
necessary ingredient for success
• Key issues:
– Organizational culture
– Executive sponsorship
– Measurement of success
11-84
• END
11-85