Knowledge Management Principles

Important Dimensions of Knowledge

• Knowledge is distinct from data, information, and wisdom.
• Data are events/transactions captured by organizational systems.
• Information is transformed data.
• Knowledge combines concepts, experience, and insights.
• Wisdom applies knowledge to problem-solving.
• Knowledge is a cognitive and physiological event within individuals and also collective within an organization.
• Tacit knowledge is undocumented employee knowledge.
• Explicit knowledge is documented knowledge.
• Knowledge can reside in various formats (email, voicemail, graphics).
• Knowledge is managed through organizational learning.
• Organizations adjust their behavior based on learning, which is called organizational learning; this includes changes to business processes and managerial decision-making.

The Knowledge Management Value Chain

• Knowledge management involves creating, storing, transferring, and applying knowledge within an organization.
• Organizations acquire knowledge by identifying patterns from corporate data sources such as neural networks, genetic algorithms, natural language processing, and AI techniques.
• Stored knowledge includes documents, patterns, and expert rules, using tools like expert systems, portals etc.
• Knowledge dissemination involves sharing knowledge through email, instant messaging, wikis, and social media.
• Knowledge application is crucial as knowledge not applied adds no business value.

Building Organizational and Management Capital

• Managers create new roles (e.g., chief knowledge officer) and communities of practice (COPs) to improve knowledge acquisition.
• COPs are informal networks of employees with shared work-related activities and interests.
• COP activities include self-education, group learning, conferences, online newsletters, and day-to-day knowledge sharing.
• COPs help reuse information efficiently.

Types of Knowledge Management Systems

• Enterprise-wide knowledge management systems are used for general-purpose knowledge management across an entire organization.
• Knowledge work systems aid specialized knowledge workers.

Artificial Intelligence (AI) and Machine Learning

• AI encompasses computer-based systems designed to mimic human thought processes.
• Machine Learning is a subset of AI where software detects patterns in large datasets with minimal specific programming.
• AI is driven by Big Data, reduced cost of computer processing, and refined algorithms.
• Types of AI include: expert systems, machine learning, neural networks, deep learning, genetic algorithms, natural language processing, computer vision, robotics, and intelligent agents.

Expert Systems

• Capture expert knowledge through interviews and represent it as rules (IF-THEN).
• Expert systems help with complex processes, decision-making, error reduction, cost reduction, and improved training.
• Expert systems model human knowledge using a knowledge base and an inference engine to formulate conclusions.

Machine Learning

• Machine learning identifies patterns in large datasets without specific programming, making statistical inferences.
• Supervised learning trains systems using input-output examples, while unsupervised learning processes data without pre-defined examples.

Neural Networks

• Neural networks detect patterns in data and learn through algorithms that strengthen connections between data points; they are used for pattern classification, optimization, and prediction.
• Humans feed outcome examples to neural networks to help them learn, and this is done by dividing a data set.
• The data set is split into a training data set and test data set.

Deep Learning

• Deep learning uses multiple layers of neural networks to identify underlying patterns in complex data.
• The system is expected to learn patterns without explicit programming.

Genetic Algorithms

• Based on evolutionary biology, genetic algorithms search for optimal solutions.
• They use populations of strings to represent possible solutions.
• Optimization processes continue with mutation, selection, and crossover steps.
• Solutions evolve over time leading to the best possible outcome.

Natural Language Processing (NLP)

• NLP allows computers to interpret and understand human language.
• NLP algorithms are often based on machine learning, including deep learning, to determine intent.

Computer Vision Systems

• These systems imitate human vision to extract information from images.
• Applications include autonomous vehicles and image understanding.

Robotics

• Robotics involves building and using machines to perform tasks that substitute human labor.
• Robots excel in dangerous or highly repetitive tasks.

Intelligent Agents

• Intelligent agents are software programs that automate tasks for users, businesses, or software applications.
• Examples include spam filtering, appointment scheduling, and online shopping tools.

Enterprise Content Management Systems (ECMs)

• ECM systems manage both structured and unstructured knowledge assets for businesses.
• They store, retrieve, distribute, and preserve knowledge while automating and helping with processes like knowledge capture.
• ECM systems help preserve valuable knowledge and documents.
• Businesses use these to improve business processes and are commonly found in large corporate settings.
• Examples of the content maintained include documents, reports, presentations, best practices, and other information.

Learning Management Systems (LMSs)

• Learning management systems help businesses manage, deliver, track, and assess employee learning.
• They offer different delivery methods like downloadable videos and web-based classes.

Knowledge Work Systems Examples

• Computer-aided design (CAD) systems automate design creation and revision.
• 3-D printing creates solid objects from digital files.
• Virtual reality (VR) and augmented reality (AR) enhance visualization and interaction with information. These systems are typically used by engineers, scientists, and knowledge workers tasked with creating knowledge.