Chapter 11
Describe the important dimensions of knowledge.
- KNOWLEDGE IS A FIRM ASSET - KNOWLEDGE HAS DIFFERENT FORMS - KNOWLEDGE HAS A LOCATION - KNOWLEDGE IS SITUATIONAL
Describe the stages in the knowledge management value chain.
1. KNOWLEDGE ACQUISITION - business analytics - data mining 2. KNOWLEDGE STORAGE - content management systems - knowledge databases 3. KNOWLEDGE DISSEMINATION - portals - search engines 4. KNOWLEDGE APPLICATION - decision support systems - enterprise applications Each stage adds value to raw data and information as they are transformed into usable knowledge.
Define artificial intelligence (AI) and the major AI techniques.
ARTIFICIAL INTELLIGENCE (AI): Effort to develop computer-based systems that can think and behave like humans. Major types of AI techniques - expert systems - machine learning - neural networks and deep learning - genetic algorithms - natural language processing - computer vision systems - robotics - intelligent agents
Describe how the following systems support knowledge work: CAD, virtual reality, and augmented reality.
COMPUTER AIDED DESIGN (CAD): Information system that automates the creation and revision of designs using sophisticated graphics software. - Using a CAD workstation, the designer need only make a physical prototype toward the end of the design process because the design can be easily tested and changed on the computer. - The ability of CAD software to provide design specifications for the tooling and manufacturing processes also saves a great deal of time and money while producing a manufacturing process with far fewer problems. VIRTUAL REALITY: Interactive graphics software and hardware that create computer-generated simulations that provide sensations that emulate real-world activities. AUGMENTED REALITY: A technology for enhancing visualization. Provides a live direct or indirect view of a physical real-world environment whose elements are augmented by virtual computer-generated imagery.
Define and describe computer vision systems, natural language processing systems, and robotics and give examples of their applications in organizations.
COMPUTER VISION SYSTEMS: Systems that try to emulate the human visual system to view and extract information from real world images. - An example is Facebook's facial recognition tool called DeepFace, which is nearly as accurate as the human brain in recognizing a face. NATURAL LANGUAGE PROCESSING SYSTEMS: AI technique for enabling a computer to understand and analyze natural language as opposed to language formatted to be understood by computers. - You can see natural language processing at work in leading search engines such as Google, spam filtering systems, and text mining sentiment analysis ROBOTICS: Use of machines that can substitute for human movements as well as computer systems for their control, sensory feedback, and information processing. - They are often are used in dangerous environments (such as bomb detection and deactivation), manufacturing processes, military operations (drones), and medical procedures (surgical robots).
Distinguish between data, knowledge, and wisdom and between tacit knowledge and explicit knowledge.
DATA: Flows of events or transactions captured by an organization's systems that are useful for transacting but little else. KNOWLEDGE: Concepts, experience, and insight that provide a framework for creating, evaluating, and using information. WISDOM: The collective and individual experience of applying knowledge to the solution of problems. Wisdom involves where, when, and how to apply knowledge. TACIT KNOWLEDGE: Knowledge residing in the minds of employees that have not been documented. EXPLICIT KNOWLEDGE: The knowledge that has been documented.
Define and describe the various types of enterprise-wide knowledge management systems and explain how they provide value for businesses.
ENTERPRISE CONTENT MANAGEMENT: Enterprise content management (ECM) systems help organizations manage both types of information. They have capabilities for knowledge capture, storage, retrieval, distribution, and preservation to help firms improve their business processes and decisions.
Define an expert system, describe how it works, and explain its value to business.
EXPERT SYSTEM: Knowledge-intensive computer program that captures the expertise of a human in limited domains of knowledge. Expert systems capture the knowledge of individual experts in an organization through in-depth interviews, and represent that knowledge as sets of rules. These rules are then converted into computer code in the form of IF-THEN rules. Such programs are often used to develop apps that walk users through a process of decision making. Expert systems provide benefits such as improved decisions, reduced errors, reduced costs, reduced training time, and better quality and service.
Define and describe genetic algorithms, and intelligent agents. Explain how each works and the kinds of problems for which each is suited.
GENETIC ALGORITHMS: Problem-solving methods that promote the evolution of solutions to specified problems using the model of living organisms adapting to their environment. - Searches a population of randomly generated strings of binary digits to identify the right string representing the best possible solution for the problem. - As the solutions alter and combine, the worst ones are discarded and the better ones go on to produce better solutions. Genetic algorithms are used to solve problems that are very dynamic and complex, involving hundreds or thousands of variables or formulas. INTELLIGENT AGENT: Software program that uses a built-in or learned knowledge base to carry out specific, repetitive, and predictable tasks for an individual user, business process, or software application. The agent uses a limited built-in or learned knowledge base to accomplish tasks or make decisions on the user's behalf, such as deleting junk email, scheduling appointments, or finding the cheapest airfare to California.
Define knowledge management and explain its value to businesses.
KNOWLEDGE MANAGEMENT: The set of processes developed in an organization to create, gather, store, maintain, and disseminate the firm's knowledge. Knowledge management increases the ability of the organization to learn from its environment and to incorporate knowledge into its business processes.
Define knowledge work systems and describe the generic requirements of knowledge work systems.
KNOWLEDGE WORKERS: Researchers, designers, architects, scientists, and engineers who primarily create knowledge and information for the organization. THREE KEY ROLES OF KNOWLEDGE WORKERS - Keeping the organization current in knowledge as it develops in the external world—in technology, science, social thought, and the arts - Serving as internal consultants regarding the areas of their knowledge, the changes taking place, and opportunities - Acting as change agents, evaluating, initiating, and promoting change projects REQUIREMENTS OF KNOWLEDGE WORK SYSTEMS - Powerful graphics - Analytical tools - Communications and document management capabilities - Sufficient computing power to handle sophisticated graphics or complex calculations - Provide quick and easy access to external databases - User-friendly interfaces
Define machine learning, explain how it works, and give some examples of the kinds of problems it can solve.
MACHINE LEARNING: Software that can identify patterns and relationships in very large data sets without explicit programming although with significant human training. In machine learning, there are no experts, and there is no effort to write computer code for rules reflecting an expert's understanding. Instead, ML begins with very large data sets with tens to hundreds of millions of data points and automatically finds patterns and relationships by analyzing a large set of examples and making a statistical inference. Example: Facebook ad display uses prior user behavior information, information supplied by advertisers, and user activity on apps and other websites that Facebook can track. It uses that data to estimate within seconds the probability that any specific user will actually click on the ad.
Define neural networks and deep learning neural networks, describing how they work and how they benefit organizations.
NEURAL NETWORK: Algorithms loosely based on the processing patterns of the biological brain that can be trained to classify objects into known categories based on data inputs. Neural networks find patterns and relationships in very large amounts of data that would be too complicated and difficult for a human being to analyze by using machine learning algorithms and computational models that are loosely based on how the biological human brain is thought to operate. Neural networks learn patterns from large quantities of data by sifting through the data, and ultimately finding pathways through the network of thousands of neurons. DEEP LEARNING: "Deep learning" neural networks are more complex, with many layers of transformation of the input data to produce a target output. Used almost exclusively for pattern detection on unlabeled data where the system is not told what to look for specifically but to simply discover patterns in the data. The system is expected to be self-taught. Neural network applications in medicine, science, and business address problems in pattern classification, prediction, and control and optimization.
Describe the role of the following in facilitating knowledge management: taxonomies, MOOCs, and learning management systems.
TAXONOMY: Method of classifying things according to a predetermined system. - Once the categories for classifying knowledge has been created, each knowledge object needs to be "tagged," or classified so that it can be easily retrieved. MASSIVE OPEN ONLINE COURSE (MOOC): Online course made available via the web to very large numbers of participants. - Companies view MOOCs as a new way to design and deliver online learning where learners can collaborate with each other, watch short videos, and participate in threaded discussion groups. LEARNING MANAGEMENT SYSTEM: A learning management system (LMS) provides tools for the management, delivery, tracking, and assessment of various types of employee learning and training.
