Untitled Flashcards Set

What is Organizational Knowledge:

Organizational Learning

• Creation of Organizational Knowledge

• Driver of new organizational behavior

• Sensing and responding to environment

Three major types of knowledge in an enterprise

• Structured, explicit knowledge

  • Reports, presentations

  • Formal rules

• Semi-Structured documents

  • E-mails, videos

• Unstructured, tacit knowledge

A vast majority of an organization's business content is semistructured or unstructured

Tacit knowledge - is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it, in other words knowledge that is gained through experience

Explicit knowledge -  Knowledge that can be readily articulated, codified, accessed and verbalized. It can be easily transmitted to others.

What is Knowledge Management:

Knowledge Management

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

Why is Knowledge Management Important?

• Substantial part of a firm's stock market value is related to intangible assets

• Knowledge, brands, reputations, and unique business processes

4 Stages of Knowledge Management:

1. Knowledge Acquisition

• 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 Transaction Processing Systems and External Sources

2. Knowledge Storage

• Databases

• Content and Document Management Systems

• Role of Management

3. Knowledge Dissemination

• Portals, wikis

• E-mail, instant messaging

• Search engines, collaboration tools

• A deluge of information?

  • Training programs, informal networks, and shared management experience help managers focus attention on important information.

4. Knowledge Application

• New business practices

• New products and services

• New markets
  
  
  

Types of Knowledge Management Systems:

Enterprise-Wide Knowledge Management Systems

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

• Learning and Content Management Systems

• Collaboration Tools

Knowledge Work Systems

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

Intelligent Techniques

• Diverse group of techniques used for discovering knowledge, distilling knowledge, discovering optimal solutions

Learning Management System (LMS):

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

• Support multiple modes of learning

  • CD-ROM, web-based classes, online forums, and so on

• Automates selection and administration of courses

• Assembles and delivers learning content

• Measures learning effectiveness

Massively open online courses (MOOCs)

• Web course open to large numbers of participants.

Content Management Systems:

Help capture, store, retrieve, distribute, preserve documents and semi-structured knowledge

Bring in external sources

• News feeds, research

Tools for communication and collaboration

• Blogs, wikis, and so on

Digital asset management systems

Document management

Key Problem: Developing Taxonomy (Classification or Index)

One need when collecting and storing knowledge and documents is describing a knowledge object so that it can be found later by users. Companies must decide and implement classification schemes, or taxonomies, to define categories meaningful to users, and then knowledge objects must be assigned a classification (“tagged”) so that it can be retrieved.  Some content management systems specialize in managing digital media storage and classification.

Collaboration:

Why Use Collaboration Tools?

• Communicate! Communicate! Communicate!

  • E-mail and Discussions

  • Virtual Meetings

• Store (and Find) Documents and Information

  • Content Management

  • Media and Document Management

• Make Decisions and Solve Problems

  • Group Communication

  • Shared Content

• Manage Projects

  • Schedule and Deliverables

  • Tasks and Assignments
    
    

Examples:

Microsoft SharePoint - Office 365

Google - Docs, Groups
Blackboard - Elearning

Email Do’s and Don’t’s:

DO:

• Have a clear subject line

• Use a professional salutation

• Proofread your message

• Reply to all emails

• Keep private material confidential

DONT:

• Forget a Signature Line

• Assume the recipient knows what your talking about

• “Shoot from the Lip”

Knowledge Work Systems:

Knowledge Workers

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

Three 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

Knowledge Work Systems

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

Examples of Knowledge Work Systems:

CAD (Computer-Aided Design):

• Creation of Engineering or Architectural Designs

• 3D Printing
  
  

Virtual Reality Systems

• Simulate Real-Life Environments

• 3D Medical Modeling for Surgeons

• Augmented Reality (AR) Systems

• VRML-Virtual Reality Modeling Systems

Using Intelligence Techniques for Knowledge Management:

Intelligent Techniques: Used to capture individual and collective knowledge and to extend knowledge base

• Capture tacit knowledge: Expert systems, case-based reasoning, fuzzy logic

• Knowledge discovery: Neural networks

• Generating solutions to complex problems: Genetic algorithms

• Automating tasks: Intelligent agents

Artificial Intelligence (AI) Technology:

• Computer-based systems that emulate human behavior

Capturing Knowledge: Expert Systems:

Major Characteristics:

Capture tacit knowledge in very specific and limited domain of human expertise

Capture knowledge as set of rules

Typically perform limited tasks

• Diagnosing malfunctioning machine

• Determining whether to grant credit for loan

Used for discrete, highly structured decision making

Knowledge base: Set of hundreds or thousands of rules

Inference engine: Strategy used to search knowledge base

• Forward chaining

• Backward chaining

Problems/Issues:

Most expert systems deal with problems of classification

• Have relatively few alternative outcomes 

• Possible outcomes are known in advance

Many expert systems require large, lengthy, and expensive development and maintenance efforts

• Hiring or training more experts may be less expensive

Expert systems are best used in highly structured decision-making situations.  Their key elements are their knowledge base and their inference engine.

Rules in an Expert System:
An expert system contains a number of if/then rules to be followed. The rules are interconnected, the number of outcomes is known in advance and is limited, there are multiple paths to the same outcome, and the system can consider multiple rules at a single time. The rules illustrated are for simple credit-granting expert systems.

Inference Engines in Expert Systems:

An inference engine works by searching through the rules and “firing” those rules that are triggered by facts gathered and entered by the user. Basically, a collection of rules is similar to a series of nested IF statements in a traditional software program; however, the magnitude of the statements and degree of nesting are much greater in an expert system.

Organizational Intelligence: Case-Based Reasoning:

Descriptions of past experiences of human specialists (cases), stored in knowledge base

System searches for cases with characteristics similar to new one and applies solutions of old case to new case

Successful and unsuccessful applications are grouped with case

Stores organizational intelligence

CBR found in:

Medical diagnostic systems

Customer support

How Case-Based Reasoning Works:

Case-based reasoning represents knowledge as a database of past cases and their solutions. The system uses a six-step process to generate solutions to new problems encountered by the user.

Fuzzy Logic:

Rule Based Technology that represents imprecision used in linguistic categories (e.g. cold, cool, etc.) to represent a range of values

Describe situation linguistically, then represent in a small number of rules

Used when if-then rules are extremely difficult:

• Autofocus Systems

• Detecting Medical Fraud

Machine Learning:

How computer programs improve performance without explicit programming

• Recognizing patterns

• Experience

• Prior learnings (database)

Contemporary examples

• Google searches

• Recommender systems on Amazon, Netflix

Neural Networks:

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

"Learn" patterns by searching for relationships, building models, and correcting over and over again

Humans "train" network by feeding it data inputs for which outputs are known, to help neural network learn solution by example

Used in medicine, science, and business for problems in pattern classification, prediction, financial analysis, and control and optimization

How a Neural Network Works:

A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model. In this example, the neural network has been trained to distinguish between valid and fraudulent credit card purchases.

Genetic Algorithms:

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

• Search among solution variables by changing and reorganizing component parts using processes such as inheritance, mutation, and selection

Used in optimization problems (minimization of costs, efficient scheduling, optimal jet
engine design) in which hundreds or thousands of variables exist

Able to evaluate many solution alternatives quickly

Intelligent Agents:

Work without direct human intervention to carry out repetitive, predictable tasks

• Deleting junk e-mail

• Finding cheapest airfare

Use limited built-in or learned knowledge base
Some are capable of self-adjustment, for example: Siri

Chatbots

• Natural Language Tools such as ChatGPT
  
  

Agent-based modeling applications:

• Model behavior of consumers, stock markets, and supply chains

• Predict spread of epidemics

Intelligent agents carry out repetitive tasks for a user or system. Shopping bots are an example of an intelligent agent. As an example of agent-based modeling. Siri, Apple’s “intelligent assistant,” is an example of a software program that can assist users in searching for content and answers to questions with a natural language interface.

Difficulties in AI Development:

MD Anderson Oncology Expert Advisor

• Worldwide Delivery of MD Anderson's Experience and Expertise

• Utilize IBM Watson's Intelligence and Computing Power

OOPS! After $65M, the Project is Halted

"Advanced Al technologies need to be accompanied by solution delivery methodologies, techniques and processes in order to ensure the delivery to real world Al solutions. In the case of MD Anderson, it seems that Watson's technical capabilities were far ahead and disconnected from the other aspect of the solution."

• Data Quality Issues

• Data Integration

• Speed of Development

• Regularly Train and Validate Performance.