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.