Contributors: Laudon / Laudon, Kroenke / Boyle
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
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
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
Definition:
Set of processes for creating, storing, transferring, applying knowledge
Importance:
Intangible assets contribute to stock market value, including knowledge, brands, and unique processes
Knowledge Acquisition:
Documenting tacit and explicit knowledge
Best practices, unstructured documents, expert networks
Tracking data from various sources
Knowledge Storage:
Use of databases and content management systems
Role of management in knowledge storage
Knowledge Dissemination:
Tools: Portals, wikis, emails, instant messaging
Training and management focus to counter information overload
Knowledge Application:
Implementation of new practices, products, and market strategies
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
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
Functions:
Capture and manage documents and semi-structured knowledge
Tools for communication (blogs, wikis)
Challenges in developing effective taxonomies
Uses:
Enhance communication, document management, decision making
Examples: Microsoft SharePoint, Google Docs
Features:
Virtual meetings, project management, shared content
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
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
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
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
Functionality:
Learning from past experiences to solve new problems
Applications:
Common in medical diagnostics and customer support
Concept:
Handles imprecision in data and can express ranges of values
Examples:
Autofocus systems and fraud detection in medical contexts
Definition:
Programs improve performance based on experience and patterns
Contemporary Applications:
Search engines, recommender systems (Amazon, Netflix)
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
Definition:
Optimize solutions using evolutionary principles
Applications:
Optimization problems with numerous variables
Characteristics:
Operate without human supervision, handle repetitive tasks
Examples:
Siri, chatbots, may incorporate self-learning
Applications:
Consumer behavior modeling, market predictions
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.
Knowledge Management_pwc
Contributors: Laudon / Laudon, Kroenke / Boyle
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
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
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
Definition:
Set of processes for creating, storing, transferring, applying knowledge
Importance:
Intangible assets contribute to stock market value, including knowledge, brands, and unique processes
Knowledge Acquisition:
Documenting tacit and explicit knowledge
Best practices, unstructured documents, expert networks
Tracking data from various sources
Knowledge Storage:
Use of databases and content management systems
Role of management in knowledge storage
Knowledge Dissemination:
Tools: Portals, wikis, emails, instant messaging
Training and management focus to counter information overload
Knowledge Application:
Implementation of new practices, products, and market strategies
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
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
Functions:
Capture and manage documents and semi-structured knowledge
Tools for communication (blogs, wikis)
Challenges in developing effective taxonomies
Uses:
Enhance communication, document management, decision making
Examples: Microsoft SharePoint, Google Docs
Features:
Virtual meetings, project management, shared content
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
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
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
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
Functionality:
Learning from past experiences to solve new problems
Applications:
Common in medical diagnostics and customer support
Concept:
Handles imprecision in data and can express ranges of values
Examples:
Autofocus systems and fraud detection in medical contexts
Definition:
Programs improve performance based on experience and patterns
Contemporary Applications:
Search engines, recommender systems (Amazon, Netflix)
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
Definition:
Optimize solutions using evolutionary principles
Applications:
Optimization problems with numerous variables
Characteristics:
Operate without human supervision, handle repetitive tasks
Examples:
Siri, chatbots, may incorporate self-learning
Applications:
Consumer behavior modeling, market predictions
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.