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Knowledge Management_pwc

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Knowledge Management_pwc

Knowledge Management and Intelligent Techniques (ITSS 3300)

Course Overview

  • Contributors: Laudon / Laudon, Kroenke / Boyle


Course Objectives

  • Evolving nature of Information Systems (IS) and Information Technology (IT)

  • Role of IS and IT in organizations

  • Key business processes modeling and application of technology

  • Application of information systems (spreadsheets, analytics) to solve business problems

  • Understanding core IS concepts:

    • Data management

    • Information technology

    • Enterprise systems

    • Information systems management

    • Business intelligence


Key IT and Business Knowledge

  • Strategic Considerations:

    • Strategy, Tactical, and Operational Management

  • Organizational Processes:

    • Internal infrastructure and external/customer-facing IT applications

    • Enterprise Applications and ERP

    • eCommerce and Social Media

  • Knowledge Management Concepts:

    • Information Security

    • Data and Databases

    • Business Intelligence and Decision Making

    • Hardware, Software, Communications, Cloud

    • Development and Project Management


Understanding Organizational Knowledge

  • Definition of Organizational Knowledge:

    • Creation of knowledge, driving new behaviors

    • Sensing and responding to the environment

  • Types of Knowledge:

    • Structured, Explicit Knowledge:

      • Reports, presentations, formal rules

    • Semi-structured Knowledge:

      • Emails, videos

    • Unstructured, Tacit Knowledge:

      • Majority of an organization’s knowledge is semi-structured/unstructured


Knowledge Management

  • Definition:

    • Set of processes for creating, storing, transferring, applying knowledge

  • Importance:

    • Intangible assets contribute to stock market value, including knowledge, brands, and unique processes


Stages of Knowledge Management

  1. Knowledge Acquisition:

    • Documenting tacit and explicit knowledge

    • Best practices, unstructured documents, expert networks

    • Tracking data from various sources

  2. Knowledge Storage:

    • Use of databases and content management systems

    • Role of management in knowledge storage

  3. Knowledge Dissemination:

    • Tools: Portals, wikis, emails, instant messaging

    • Training and management focus to counter information overload

  4. Knowledge Application:

    • Implementation of new practices, products, and market strategies


Knowledge Management Systems (KMS)

  • Enterprise-Wide Systems:

    • Firm-wide efforts to manage digital content and knowledge

    • Learning and Content Management Systems, Collaboration Tools

  • Knowledge Work Systems:

    • Specialized systems for knowledge workers to foster new knowledge

  • Intelligent Techniques:

    • Techniques for discovering and optimal solution finding


Learning Management Systems (LMS)

  • Functions:

    • Manage and track employee learning

    • Multiple learning modes: CD-ROM, web-based

    • Assemble and assess learning content

  • Massively Open Online Courses (MOOCs):

    • Accessible web courses for large participants


Content Management Systems (CMS)

  • Functions:

    • Capture and manage documents and semi-structured knowledge

    • Tools for communication (blogs, wikis)

    • Challenges in developing effective taxonomies


Collaboration Tools

  • Uses:

    • Enhance communication, document management, decision making

    • Examples: Microsoft SharePoint, Google Docs

  • Features:

    • Virtual meetings, project management, shared content


Email Etiquette

  • Do’s:

    • Clear subject lines, professional salutations, proofreading

    • Include signature lines, reply to all as necessary

  • Don’ts:

    • Forget clarity, overwhelm with jargon, lack professionalism


Knowledge Work Systems

  • Role of Knowledge Workers:

    • Researchers, designers, and their contributions

    • Functions as consultants and change agents

  • Examples of Systems:

    • CAD for design, Virtual Reality for simulations


Intelligent Techniques in Knowledge Management

  • Overview:

    • Techniques to capture knowledge and extend knowledge bases

  • Capture Tacit Knowledge:

    • Expert systems, case-based reasoning

  • Knowledge Discovery:

    • Neural networks for pattern recognition

  • Solution Generation:

    • Utilization of genetic algorithms to solve complex problems

  • Automation:

    • Intelligent agents for repetitive tasks


Expert Systems

  • Features:

    • Capture tacit knowledge in limited domains

    • Operate with a knowledge base and inference engine for decision making

  • Challenges:

    • High development/maintenance costs, limited scope of problems addressed


Case-Based Reasoning (CBR)

  • Functionality:

    • Learning from past experiences to solve new problems

  • Applications:

    • Common in medical diagnostics and customer support


Fuzzy Logic

  • Concept:

    • Handles imprecision in data and can express ranges of values

  • Examples:

    • Autofocus systems and fraud detection in medical contexts


Machine Learning

  • Definition:

    • Programs improve performance based on experience and patterns

  • Contemporary Applications:

    • Search engines, recommender systems (Amazon, Netflix)


Neural Networks

  • Functionality:

    • Recognize patterns in data too complex for humans

  • Role:

    • Trained by feeding known data and outputs

  • Applications:

    • Used in various fields for classification and financial analysis


Genetic Algorithms

  • Definition:

    • Optimize solutions using evolutionary principles

  • Applications:

    • Optimization problems with numerous variables


Intelligent Agents

  • Characteristics:

    • Operate without human supervision, handle repetitive tasks

  • Examples:

    • Siri, chatbots, may incorporate self-learning

  • Applications:

    • Consumer behavior modeling, market predictions


Challenges of AI Deployment

  • Case Study: MD Anderson Oncology Expert Advisor

    • Project halted after investment due to disconnection between advanced AI capabilities and real-world application needs

    • Data quality, integration problems, and performance training issues need addressing.