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Chapter 11 Managing Knowledge (What are the business benefits of using…
Chapter 11
Managing Knowledge
What is the role of knowledge management systems in business?
Set of business processes developed in an organization to create, store, transfer, and apply knowledge
Important dimensions of knowledge
is a firm asset
intangible asset
requires organizational resources
experiences network effects
its value increases as more People share it
has a location
Cognitive event
there is social and individual basis of knowledge
sticky "hard to moove"
has different forms
Can be tacit or explicit
involves knowing
how to follow procedures
why and not just simply when things happened
how, craft and skill
is situational
is related to context: Knowing circumstances to use certain tool
is conditional: Knowing when to apply procedure
Organizational learning
The knowledge management value chain
Knowledge acquisition
Knowledge Discovery
Data minig
Neural networks
Genetic algorithms
knowledge workstations
Expert knowledge networks
Knowledge storage
Content management systems
Knowledge databases
Expert systems
Knowledge dissemination
Intranet portals
Search Engines
Collaboration and social business tools
Knowledge application
Decision support systems
Enterprise applications
Types of knowledge management systems
Enterprise-wide knowledge management systems
Knowledge work systems (KWS)
Intelligent techniques
What types of systems are used for enterprise-wide knowledge management and how do they provide value for businesses?
Three major types of knowledge
Structured documents
Unstructured, tacit knowledge
Semistructured documents
Enterprise content management systems
Help capture, store, retrieve, distribute, preserve
importance of
taxonomy
to organize information and meaningful knowledge
Digital asset management systems
Locating and sharing expertise
Provide online directory of corporate experts in well-defined knowledge domains
Search tools enable employees to find appropriate expert in a company
Social networking and social business tools for finding knowledge outside the firm
Learning management systems (LMS)
Support multiple modes of learning
Provide tools for management, delivery, tracking, and assessment of employee learning and training
Massively open online courses (MOOCs)
What are the major types of knowledge work systems and how do they provide value for firms?
Knowledge workers and knowledge work
include Researchers, designers, architects, scientists, engineers who create knowledge for the organization
Key roles
Keeping organization current in knowledge
Serving as internal consultants regarding their areas of expertise
Acting as change agents, evaluating, initiating, and promoting change projects
Requirements of knowledge work systems
Sufficient computing power for graphics, complex calculations
Powerful graphics and analytical tools
Communications and document management
Access to external databases
User-friendly interfaces
Optimized for tasks to be performed (design engineering, financial analysis)
Examples of knowledge work systems
CAD (computer-aided design)
Virtual reality systems
use virtual reality modeling language (VRML)
Investment workstations
Intelligent techniques: Used to capture individual and collective knowledge and to extend knowledge base
Artificial intelligence (AI) technology
Computer-based systems that emulate human behavior
Expert systems
Augmented reality (AR)
What are the business benefits of using intelligent techniques for knowledge management?
Capturing knowledge: expert systems
inteligente technique for caputring tacit knowledge in a very specific and limited domain of human expertise
How expert systems work
work as a Knowledge base
use inference engine to search through the knowledge base
Forward chaining
Backward chaining
Fuzzy logic sytems
Machine learning
Is a study of how computer programs improve performance without explicit programming
Recognizing patterns
Experience
Prior learnings (database)
Contemporary examples
Google searches
Recommender systems on Amazon, Netflix
IBM Watson
Neural networks
“Learn” patterns by searching for relationships, building models, and correcting over and over again
Find patterns and relationships in massive amounts of data too complicated for humans to analyze
Used in medicine, science, and business for problems in pattern classification, prediction, financial analysis, and control and optimization
Humans “train” network by feeding it data inputs for which outputs are known, to help neural network learn solution by example
Genetic algorithms
Useful for finding optimal solution for specific problem
Conceptually based on process of evolution
Used in optimization problems
Able to evaluate many solution alternatives quickly
Organizational Intelligence: case-base reasoning (
CBR
)
Descriptions of past experiences of human specialists (cases), stored in knowledge base
2- System searches for cases
3-System asks user aditional questions
1 - user describes the problem
4-System finds closest fit
5-System modifies the Solution to better fit the problem
6- system stores problema and successfull Solution in database
Intelligent agents
Work without direct human intervention to carry out specific, repetitive, and predictable tasks
Use limited built-in or learned knowledge base
Agent-based modeling applications
Hybrid AI systems
Intelligent Agents