MIS Ch. 11 - Managing Knowledge

Knowledge Management Life Cycle

1.) Create knowledge 2.) Capture knowledge 3.) Refine knowledge 4.) Store knowledge 5.) Manage knowledge 6.) Disseminate knowledge

Three major types of knowledge management systems

1.) Enterprise-wide content management systems 2.) Knowledge network systems 3.) Learning management systems *4.) Intelligent Techniques

CAD (computer-aided-design)

Automates the creation and revision of designs, using computers and sophisticated graphics software -virtual reality systems for simulation and modeling -the designer need only make a physical prototype toward the end of the design process because the design can be easily tested and changed on the computer

Backward Chaining

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

Expert Systems

Capture knowledge of skilled employees as set of rules in software system that can be used by others in organization -intelligent technique for capturing tacit knowledge in a very specific and limited domain of human expertise -capturing human expertise in limited areas such as diagnosing a malfunctioning machine or determining whether to grant credit for a loan

Case-based Reasoning (CBR)

Descriptions of past experiences of human specialists, represented as cases, are documented and stored in a database for later retrieval when the user encounters a new case with similar parameters -uses IF-THEN-ELSE rules extracted from human experts

Intelligent Techniques

Diverse group of techniques such as data mining used for various goals: discovering knowledge, distilling knowledge, discovering optimal solutions EX: data mining, neural networks, expert systems, Case-based reasoning, fuzzy logic, genetic algorithms, intelligent agents

Knowledge Workers

Functions: Keeping organization current in knowledge, serving as internal consultants regarding their areas of expertise, acting as change agents, evaluating, initiating, and promoting change project

Enterprise-wide Knowledge Management Systems

General purpose firm-wide efforts to collect, store, distribute, and apply digital content and knowledge EX: enterprise content management systems, collaboration tools, learning management systems, knowledge network systems

Enterprise content management systems

Help organizations manage both types of information. They have capabilities for knowledge capture, storage, retrieval, distribution, and preservation to help firms improve their business processes and decisions.

Communities of Practice (COPs)

Informal social networks of professionals and employees within and outside the firm who have similar work-related activities and interests

Tacit Knowledge

Knowledge residing in the minds of employees that has not been documented personal, context-specific, and hard to formalize and communicate, consists of experience, beliefs, and skills, entirely subjective

Learning Management Systems (LMS)

Provide tools for management, delivery, tracking, and assessment of various types of employee learning and training

Data

Simple observations of states of the world Flow of events or transactions captured by an organization's systems that, by itself, is useful for transacting but little else To turn data into useful INFORMATION, a firm must expend resources to organize data into categories of understanding such as monthly, daily, regional, or store-based reports of total sales.

Knowledge Work Systems (KWS)

Specialized systems built for engineers, scientists, other knowledge workers chargers with discovering and creating new knowledge EX: (CAD), 3-D virtualization, virtual reality, investment worskstation

Wisdom

The collective and individual experience of applying knowledge to the solution of problems -where, when, how to apply knowledge

Knowledge Management

The set of business processes developed in an organization to create, store, transfer, and apply knowledge. Knowledge management increases the ability of the organization to learn from its environment and to incorporate knowledge into its business processes

Neural Networks

Used for solving complex, poorly understood problems for which large amounts of data have been collected -find patterns and relationships in massive amounts of data that would be too complicated and difficult for a human being to analyze -neural networks that have been trained to distinguish between valid and fraudulent credit card purchases

Taxonomy

appropriate classification scheme -organize information into meaningful categories so that it can be easily accessed

Organizational learning

creation of new standard operating procedures and business processes that reflect organizations' experience

Information

data endowed with relevance and purpose

Knowledge Storage

databases, document management systems

Knowledge Acquisition

documenting tacit and explicit knowledge, creating knowledge, tracking data

Knowledge Management Value Chain

each stage adds value to raw data and information: 1.) Knowledge Acquisition 2.) Knowledge Storage 3.) Knowledge Dissemination 4.) Knowledge Application *5.) Building Organizational and Management Capital

Digital asset management systems

help companies classify, store, and distribute these digital objects.

Forward Chaining

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

Structured knowledge

is explicit knowledge that exists in formal documents, as well as in formal rules that organizations derive by observing experts and their decision-making behaviors

Explicit Knowledge

knowledge that has been documented knowledge that is easily collected, organized, and transferred through digital means

Knowledge base

model human knowledge as a set of rules that collectively are called a knowledge base -rules are obtained by interviewing an expert

Knowledge discovery

neural networks and data mining are known as this -can discover underlying patterns, categories, and behaviors in large data sets that could not be discovered by managers alone or simply through experience

Massive open online courses (MOOCs)

online course made available via the Web to a very large number of participants -offered by for example UVA's Darden School of Business

Knowledge Dissemination

portals, wikis, e-mail, instant messaging, search engines, collaboration tools

Fuzzy Logic Systems

rule based technology that represents imprecision used in linguistics categories that represent range of values, describe a particular phenomenon or process linguistically and then represent that description in a small number of flexible rules

Virtual Reality Modeling Language (VRML)

set of specifications for interactive, 3-D modeling on the World Wide Web that can organize multiple media types, including animation, images, and audio to put users in a simulated real-world environment

Virtual Reality Systems

simulate real-life environments; have visualization, rendering, and simulation capabilities that go far beyond those of conventional CAD systems

Investment Workstations

streamline investment process and consolidate internal, external data for brokers, traders, portfolio managers -Bloomberg terminals -leverage the knowledge and time of its brokers, traders, and portfolio managers

Knowledge Application

to provide return on investment, organizational knowledge must become systematic part of management decision making and become situated in decision-support systems

Genetic algorithms

useful for finding the optimal solution for a specific problem by examining a very large number of possible solutions for that problem -identify the right string representing the best possible solution -worst ones are discarded, best ones survive

Knowledge

valuable information from the human mind; includes reflection, synthesis, context A firm must expend additional resources to discover patterns, rules, and contexts where knowledge works -not subject to the law of diminishing returns -more useful as more people share it -knowledge involves how to follow procedures

Intelligent Agents

work without direct human intervention to carry out specific, repetitive, and predictable tasks for user, process, or application Ex: deleting junk mail, finding cheapest airline