IS300 Chap11 Note

concepts, experience, insights that provide a framework for creating, evaluating, and using info

• Collective and individual experience of applying knowledge to solve problems
• Involves where, when, and how to apply knowledge

Process in which organizations learn

Set of business processes developed in an organization to create, store, transfer, and apply knowledge

• 1.Knowledge acquisition
• 2.Knowledge storage
• 3.Knowledge dissemination
• 4.Knowledge application

• •Documenting tacit and explicit knowledge
• –Storing documents, reports, presentations, best practices
• –Unstructured documents (e.g., e-mails)
• –Developing online expert networks
• •Creating knowledge
• •Tracking data from TPS and external sources

• •Databases
• •Document management systems
• •Role of management:
• –Support development of planned knowledge storage systems
• –Encourage development of corporate
• -wide schemas for indexing documents
• –Reward employees for taking time to update and store documents properly

• •Portals
• •Push e-mail reports
• •Search engines
• •Collaboration tools
• •A deluge of information?
• –Training programs, informal networks, and shared management experience help managers focus attention on important information

• •To provide return on investment, organizational knowledge must become systematic part of management decision making and become situated in decision-support systems
• –New business practices
• –New products and services
• –New markets

• Informal social networks of professionals and employees within and outside firm who have similar work-related activities and interests
• Activities include education, online newsletters, sharing experiences and techniques
• Facilitate reuse of knowledge, discussion
• Reduce learning curves of new employees

• 1.Enterprise-wide knowledge management systems
• 2.Knowledge work systems (KWS)
• 3.Intelligent techniques

General-purpose firm-wide efforts to collect, store, distribute, and apply digital content and knowledge

Specialized systems built for engineers, scientists, other knowledge workers charged with discovering and creating new knowledge

Diverse group of techniques such as data mining used for various goals: discovering knowledge, distilling knowledge, discovering optimal solutions

• 1.Structured documents
• 2.Semistructured documents
• 3.Unstructured, tacit knowledge

Help capture, store, retrieve, distribute, preserve

Developing taxonomy

Specialized content management systems for classifying, storing, managing unstructured digital data

• Provide online directory of corporate experts in well-defined knowledge domains
• Use communication technologies to make it easy for employees to find appropriate expert in a company
• May systematize solutions developed by experts and store them in knowledge database

• Provide tools for management, delivery, tracking, and assessment of various types of employee learning and training
• Support multiple modes of learning
• Automates selection and administration of courses
• Assembles and delivers learning content
• Measures learning effectiveness

Systems for knowledge workers to help create new knowledge and integrate that knowledge into business

Researchers, designers, architects, scientists, engineers who create knowledge for the organization

• 1.Keeping organization current in knowledge
• 2.Serving as internal consultants regarding their areas of expertise
• 3.Acting as change agents, evaluating, initiating, and promoting change projects

• Substantial 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)

Creation of engineering or architectural designs

Simulate real-life environments

• a tech for enhancing visualization
• provides a live direct or indirect view of a physical real-world envir whose elements are augmented by virtual comp generated imagery

• a set of specifications for interactive, 3-D modelng on the World Wide Web that can organize multiple media types to put users in a simluated real-world envir
• platform independent, operates over a desktop comp, and requires little bandwidth

Streamline investment process and consolidate internal, external data for brokers, traders, portfolio managers

Used to capture individual and collective knowledge and to extend knowledge base

Neural networks and data mining

Computer-based systems that emulate human behavior

• Capture tacit knowledge in very specific and limited domain of human expertise
• Capture knowledge of skilled employees as set of rules in software system that can be used by others in organization
• Typically perform limited tasks that may take a few minutes or hours

Set of hundreds or thousands of rules

Strategy used to search knowledge base

Inference engine begins with information entered by user and searches knowledge base to arrive at conclusion

Begins with hypothesis and asks user questions until hypothesis is confirmed or disproved

• Descriptions of past experiences of human specialists (cases), stored in knowledge base
• System searches for cases with problem characteristics similar to new one, finds closest fit, and applies solutions of old case to new case

• Rule-based technology that represents imprecision used in linguistic categories (e.g., “cold,” “cool”) that represent range of values
• Describe a particular phenomenon or process linguistically and then represent that description in a small number of flexible rules
• Provides solutions to problems requiring expertise that is difficult to represent with IF-THEN rules

Find patterns and relationships in massive amounts of data too complicated for humans to analyze

Related AI technology allowing computers to learn by extracting information using computation and statistical methods

• Useful for finding optimal solution for specific problem by examining very large number of possible solutions for that problem
• Conceptually based on process of evolution

Genetic algorithms, fuzzy logic, neural networks, and expert systems integrated into single application to take advantage of best features of each

• Work in background to carry out specific, repetitive, and predictable tasks for user, process, or application
• Use limited built-in or learned knowledge base to accomplish tasks or make decisions on user’s behalf

• Systems of autonomous agents
• Model behavior of consumers, stock markets, and supply chains; used to predict spread of epidemics