Transcript PPT09 Know Mgmt - ExSys

Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition
Chapter 9
Knowledge Management
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang , arafatmy
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Learning Objectives
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Define knowledge.
Learn the characteristics of knowledge management.
Describe organizational learning.
Understand the knowledge management cycle.
Understand knowledge management system technology and how it
is implemented.
Learn knowledge management approaches.
Understand the activities of the CKO and knowledge workers.
Describe the role of knowledge management in the organization.
Be able to evaluate intellectual capital.
Understand knowledge management systems implementation.
Illustrate the role of technology, people, and management with
regards to knowledge management.
Understand the benefits and problems of knowledge management
initiatives.
Learn how knowledge management can change organizations.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Knowledge Mgmt
Basically KM is collaborative computing
at the Organization level.
The goal is to capture, store, maintain,
and deliver useful knowledge in a
meaningful form to anyone who
needs it anyplace and anytime within
an org.
Knowledge = Intellectual Capital
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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The Importance
of Managing Knowledge
• Intellectual capital is appreciable assets, most
assets depreciate.
• Knowledge work is increasing in importance as
much as the increase in service Knowledge economy.
• Employees with the most intellectual capital have
become volunteers to improve B.P.‫االبداع‬
• Most managers ignore intellectual capital and lose
out on the benefits of its use.
• Employees with the most intellectual capital are
often the least appreciated.
• Knowledge mismanagement.
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Encourage Knowledge
SHARING
• Discouragement due to org. cultural or
technical barriers.
• Educate people on the value of knowledge.
• Refurbish reward and recognition system.
• Act as a role model for sharing.
• Make it a job requirement.
• Make the tech. work for people; don’t expect the
people to work for the tech. (machines in front of machines)
• Educate people about the value in the know.
• It is OK to make mistakes, “No one is perfect”
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Why people do not share
KNOWLWDGE
It can cripple an org. As people refuse to share
what they know, a situation created by Org.
Culture and Barriers. Why people (hide away)
their knowledge .?
• Knowledge as a source of power and will
guarantee job security.
• People wont get credit for sharing knowledge.
• People don’t have time, or know how to share.
• People don’t know the value or how much they
know.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Siemens Knows What It Knows Through
Knowledge Management
• Knowledge management
– Community of interest
• Repositories (storehouse)
• Communities of practice
• Informal knowledge-sharing techniques (Social Networks)
– Employee initiated
• Created ShareNet
– Easy to share knowledge
– Incentives for posting
– Internal evangelists responsible for training,
monitoring, and assisting users
– Top management support
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Knowledge Management
• Process to help organization identify,
select, organize, disseminate, transfer
information
• Structuring enables problem-solving,
dynamic learning, strategic planning,
decision-making and reduce redundancy
• Leverage value of intellectual capital
through reuse
• The age of knowledge worker.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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9.3Knowledge
• Data = collection of facts, measurements,
statistics
• Information = organized data
• Knowledge = contextual, relevant, actionable info.
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Strong experiential and reflective elements
Good leverage and increasing returns
Dynamic
Branches and fragments with growth
Knowledge is valuable when it is shared.
Uncertain value in sharing
Evolves over time with experience
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Knowledge
• Explicit (Leaky) knowledge
– Objective, rational, technical
– Policies, goals, strategies, papers, reports
– Codified
– Leaky knowledge
• Tacit (sticky) knowledge
– Subjective, cognitive, experiential learning
– Highly personalized
– Difficult to formalize
– Cumulative store of the experiences.
– Within the brain of individuals or embedded in the
group interaction.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Knowledge Management
• Systematic and active management
of ideas, information, and knowledge
residing within organization’s
employees
• Knowledge management systems
– Use of technologies to manage
knowledge
– Used with turnover, change, downsizing
– Provide consistent levels of service
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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9.4 Organizational
Learning
• Learning organization
– Ability to learn from past
– To improve, organization must learn
– 3 Issues:
• Meaning, Management, Measurement
– 5 Activities:
• Problem-solving, Creative experimentation, learning from past,
learning from acknowledged best practices, transfer of knowledge
within organization
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• Organizational memory
way to save and share.
Must have organizational memory to have a learning Org.
Categories ( Well )
1- Individual 2-information 3-Culture 4-Transformation 5-Structural
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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“Generally when a technology project fails, it is because
the technology doesn’t match the organization’s culture”
• Organizational learning
– Develop new knowledge that have the potential to influence
behavior.
– Corporate memory is critical for success.
– Org. Learning Process:
 Know. Acquisition.
 Know. Sharing.
 Know. Utilization.
• Organizational culture
– Pattern of shared basic assumptions
– Can cause KM success or failure.
– Difficult to measure the impact of culture on org. (Strong culture
produce strong results(ROI, Net income, increase in stock price)
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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KSF
To create an enterprise with a
culture of continuous change
where employees are not
threatened by change but are
encouraged by it because they
believe it will improve their
quality of life.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Knowledge Management
Initiatives
• Aims
– Make knowledge visible
– Develop knowledge intensive culture
– Build knowledge infrastructure
• Surrounding processes
– Creation of knowledge
– Sharing of knowledge
– Seeking out knowledge
– Using knowledge
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Knowledge Management
Initiatives
• Knowledge creation
– Generating new ideas, routines, insights
– Modes
• Socialization, externalization, internalization,
combination
• Knowledge sharing
– Willing explanation to another directly or
through an intermediary
• Knowledge seeking
– Knowledge sourcing
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Core competency linked to
Tacit and Explicit Knowledge
Explicit
Knowledge
Policies, Patents,
Decisions,
strategies
Convert tacit know. To
measurable Explicit knowledge
Process of explication may
generate tacit knowledge
Tacit
Knowledge
Expertise, Knowhow, Org. Culture,
Values
Core
Competencies
of the
organization
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Approaches to Knowledge
Management
1. Process Approach (consultants/Insurance)
– Codify and store knowledge strategy.
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Assumes that most knowledge is explicit.
People 2 document, and People talk.
Formalized controls, processes, technologies
Forces individuals into fixed patterns of thinking.
Knowledge is typically static in nature,
Fails to capture most tacit knowledge
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Approaches to Knowledge
Management cont.
2. Practice Approach
– Assumes that most knowledge is tacit
• Articulated expressed knowledge, not easily captured
• Knowledge is typically dynamic in nature
• Informal systems
– Social events, communities of practice, person-toperson contacts
• Challenge to make tacit knowledge explicit, capture it,
add to it, transfer it
• Encourage people to generate and innovate ideas.
• Can result in inefficiency for the abundance of ideas
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Approaches to Knowledge
Management
cont.
3. Hybrid Approach
– Practice approach initially used to store explicit
knowledge
– Tacit knowledge primarily stored as contact information
– Best practices captured and managed
• Best practices
– Methods that effective organizations use to operate and
manage functions
• Knowledge repository
– Place for capture and storage of knowledge
– Different storage mechanisms depending upon data
captured
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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9.6 Knowledge Management
System Cycle
• Creates knowledge
through new ways of doing
things
• Identifies and captures new
knowledge
• Places knowledge into
context so it is usable
• Stores knowledge in
repository
• Reviews for accuracy and
relevance
• Makes knowledge
available at all times to
anyone
Disseminate
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
9-21
Components of Knowledge
Management Systems
KMS are developed using 3 sets of technologies:
1. Communication
• Access knowledge
• Communicates with others
2. Collaboration
• Perform group work (formal, Informal)
• Synchronous or asynchronous
• Same place/different place
3. Storage and retrieval
• Capture, storing, retrieval, and management of both
explicit and tacit knowledge through collaborative
systems
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Components of Knowledge
Management Systems
• Supporting technologies
– Artificial intelligence
• KM is a systems often have AI methods embedded in them
• Expert systems, neural networks, fuzzy logic, intelligent
agents
– Intelligent agents
• Systems that learn how users work and provide assistance
– Knowledge discovery in databases
• Process used to search for and extract information
– Internal = data and document mining
– External = model marts and model warehouses
– XML
• Extensible Markup Language
• Enables standardized representations of data
• Better collaboration and communication through portals
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Knowledge Management
System Implementation
• Challenge to identify and integrate components
– Early systems developed with networks, groupware,
databases
• Knowware is Technology tools that support KM
1. Collaborative computing tools
– Groupware
2. Knowledge servers
3. Enterprise knowledge portals
4. Document management systems DMS
1. Content Management Systems
5. Knowledge harvesting tools
6. Search engines
7. Knowledge management suites
– Complete out-of-the-box solutions
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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9.7 Knowledge Management
System Implementation
• Implementation
– Software development companies.
– Information systems vendors.
Consulting firms.
– Application Service Providers ASP
Outsourcing,
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Knowledge Management System
Integration
• Integration with enterprise and
information systems
• DSS/BI
– Integrates models and activates them for specific
problem
• Artificial Intelligence
– Expert system = if-then-else rules
– Natural language processing = understanding
searches
– Artificial neural networks = understanding text
– Artificial intelligence based tools = identify and
classify expertise
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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Knowledge Management System
Integration
• Database
– Knowledge discovery in databases
• CRM
– Provide tacit knowledge to users
• Supply chain management systems
– Can access combined tacit and explicit knowledge
• Corporate intranets and extranets
– Knowledge flows more freely in both directions
– Capture knowledge directly with little user involvement
– Deliver knowledge when system thinks it is needed
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
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9.8 Role of PEOPLE in KM
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Chief knowledge officer
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Senior level
Sets strategic priorities
Defines area of knowledge based on organization mission and goals
Creates infrastructure
Identifies knowledge champions
Manages content produced by groups
Adds to knowledge base
CEO
– Champion knowledge management
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Upper management
– Ensures availability of resources to CKO
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Communities of practice
Knowledge management system developers
– Team members that develop system
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Knowledge management system staff
– Catalog and manage knowledge
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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9.9 Valuation ensuring success KM
• Asset-based approaches
– Identifies intellectual assets
– Focuses on increasing value
• Knowledge linked to applications and
business benefits approaches
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Balanced scorecard (KSF)
Economic value added
Inclusive valuation methodology
Return on management ratio
Knowledge capital measure
• Estimated sale price approach
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Metrics
• Financial
– ROI
– Perceptual, rather than absolute
– Intellectual capital not considered an asset
• Non-financial
– Value of intangibles
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External relationship linkages capital
Structural capital
Human capital
Social capital
Environmental capital
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Factors Leading to Success and
Failure of Systems
Success
– Companies must assess need
– System needs technical and organizational infrastructure
to build on
– System must have economic value to organization
– Senior management support
– Organization needs multiple channels for knowledge
transfer
– Appropriate organizational culture
Failure
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System does not meet organization’s needs
Lack of commitment
No incentive to use system
Lack of integration
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
9-31
End of ch9 KM
Than You
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
9-32