ITM 100 Chapter 11 - Knowledge Management PDF

11.1.1 Important Dimensions of Knowledge There is an important distinction between data, information, knowledge and wisdom. Data: flows of events or transactions captured by an organization’s systems that are useful for transacting. Data gets transformed into information. Information then gets tr...

11.1.1 Important Dimensions of Knowledge There is an important distinction between data, information, knowledge and wisdom. Data: flows of events or transactions captured by an organization’s systems that are useful for transacting. Data gets transformed into information. Information then gets transformed into knowledge. Knowledge: concepts, experiences 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 is knowing how and where to apply knowledge. Knowledge is both an individual and collective attribute of the firm. Knowledge is a cognitive, even a physiological, event that takes place inside people’s heads. Tacit knowledge: Knowledge residing in the minds of employees that has not been documented is called. Explicit knowledge: knowledge that has been documented. Knowledge can reside in: Email Voicemail Graphics Unstructured and structured documents Organizational Learning and Knowledge Management Like humans, organizations create and gather knowledge using a variety of organizational learning mechanisms. Organizations that learn adjust their behavior to reflect that learning by creating new business processes and by changing patterns of management decision making. This is called Organizational Learning. 1.1.2 The Knowledge Management Value Chain Knowledge management: refers to the set of business processes developed in an organization to create, store, transfer, and apply knowledge. Knowledge management increases the ability of the organization to learn from its environment and to apply knowledge to its business processes. Knowledge acquisitions - organizations acquire knowledge by discovering patterns in corporate data via: neural networks genetic algorithms natural language processing AI techniques Knowledge storage - Once they are discovered, documents, patterns, and expert rules must be stored so they can be retrieved and used by employees. Expert systems also help corporations preserve knowledge. knowledge dissemination-Portals, email, instant messaging, wikis, social business tools, and search engine technology have added to an existing array of collaboration tools for sharing calendars, documents, data, and graphics. Knowledge application: Knowledge that is not shared and applied to the practical problems facing firms and managers does not add business value. New knowledge must be built into a firm’s business processes and key application systems. Building Organizational and Management Capital: Collaboration, Communities of Practice, and Office Environments Managers can help by developing new organizational roles and responsibilities for the acquisition of knowledge, including the creation of chief knowledge officer executive and communities of practice. Communities of Practice: informal social networks of professionals and employees within and outside the firm who have similar work-related activities and interests. The activities of these communities include: Self-education and group education Conferences online newsletters, day-to-day sharing of experiences and techniques to solve specific work problems. COPs: Make it easier for people to reuse information  Lessen the learning curve Act as a spawning ground for new ideas. 11.1.3 Types of Knowledge Management Systems There are three types of knowledge management systems: Enterprise-wide knowledge management systems Knowledge work systems Intelligent techniques Enterprise-wide knowledge management systems: general-purpose firmwide efforts to collect, store, distribute, and apply digital content and knowledge. Include capabilities for: Searching for information Storing both structured and unstructured data Locating employee expertise within the firm. Support technologies: Portals Search engines Collaboration and social business tools Learning management systems. Knowledge work systems: Specialized systems built for engineers, scientists, and other knowledge workers charged with discovering and creating new knowledge for a company. - Include technologies such as CAD, visualization, simulation and virtual reality. Intelligent techniques: such as data mining, expert systems, machine learning, neural networks, natural language processing, computer vision systems, robotics, genetic algorithms, and intelligent agents. 11.2 What are artificial intelligence (AI) and machine learning? How do businesses use AI? Intelligent techniques are often described as artificial intelligence (AI). AI is the effort to design computer based systems that think like humans. 11.2.1 Evolution of AI The major driving forces of AI: the development of Big Data databases generated by the Internet, e-commerce, the Internet of Things, and social media. the drastic reduction in the cost of computer processing and the growth in the power of processors. the refinement of algorithms by tens of thousands of AI software engineers 11.2.2 Major Types of AI Artificial Intelligence is a family of programming techniques and technologies each of which has its advantages in select applications. The major types of AI include: Expert systems Machine learning Neural networks Deep learning Genetic algorithms Natural language processing Computer vision systems Robotics Intelligent agents Expert systems: 1. capture the knowledge of individual experts in an organization through in-depth interviews, and represent that knowledge as sets of rules. 2. These rules are then converted into computer code in the form of IF-THEN rules. Expert systems can walk humans through complex processes Benefits of expert systems: improved decisions reduced errors reduced costs reduced training time better quality and service. How expert systems work Expert systems model human knowledge as a set of rules that collectively are called the knowledge base. Expert systems can have from a handful to many thousands of rules. The strategy used to search through the collection of rules and formulate conclusions is called the inference engine. Expert system limitations: - Experts cannot explain how they make decisions - Aren’t good for making unstructured decisions. - do not scale well to the kinds of very large data sets produced by the Internet and the Internet of Things (IoT) - Expensive to build Machine learning Software that can identify patterns and relationships in very large data sets without specific programming although with significant human training. Makes a statistical inference. Unlike expert systems there are none. Nearly all machine learning systems involve supervised learning. Supervised learning: the system is “trained” by providing specific examples of desired inputs and outputs identified by humans in advance. - Supervised learning is one technique used to develop autonomous vehicles that need to be able to recognize objects around them. Unsupervised Learning: the same procedures are followed, but humans do not feed the system examples. Instead, the system is asked to process the development database and report whatever patterns it finds. Neural Networks A neural network is composed of interconnected units called neurons. Each neuron can take data from other neurons, and transfer data to other neurons in the system. The artificial neurons are not biological physical entities as in the human brain. But instead are software programs and mathematical models that perform the input and output function of neurons. 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 are pattern detecting software. Which is software that detects patterns. An algorithm is also called a learning rule. How it works: 1. They learn patterns from large quantities of data 2. Find pathways through the network of thousands of data 3. An algorithm or (Learning Rule) identifies the successful paths and strengthens the connection among neurons in these pathways. 4. The learning rule identifies the best pathways through the data. How humans train the neural network: One neural network comprises an input layer, processing layer and output layer. 1. Humans feed the machine a set of outcomes they want the machine to learn. It could be to identify patterns in fraudulent credit card purchases. 2. The data set is divided into two segments: a training data set and a test data set. - Training data set: trains the system. After millions of test runs the program will hopefully identify the best path through the data. - Test data set: verifies the accuracy of the system Neural networks are used in medicine, science and business to address problems in: Pattern classification Prediction Control Optimization Neural networks examples Computer vision Speech recognition Machine control diagnostics Language translation Transaction analytics Targeted online ads Deep learning neural networks Deep Learning: use multiple layers of neural networks to reveal the underlying patterns in data, and in some limited cases identify patterns without human training. The system is expected to be self taught. There is no objective, the machine just simply looks for patterns. Collections of neurons are called nodes or layers. Deep learning is still in its infancy Limitations of Neural networks and Machine Learning Require very large data sets with nonsensical data ephemeral data(data that does not last long,stock market, performance of sports teams). Only good with semistructured decisions People cannot explain how the machine came to the conclusion. AI tools are mainly used for low-level decision making not for substituting managers. Genetic Algorithms Genetic algorithms: another form of machine learning. Useful for finding the optimal solution for a specific problem by examining a very large number of alternative solutions for that problem. Their method for solving problems is based on evolutionary biology such as inheritance, mutation, selection and crossover. How it works: 1. Searches a population of randomly generated strings of binary digits to identify the right string representing the best possible solution for the problem 2. The worst ones are discarded and the better ones survive and go on to produce even better solutions. Each string corresponds to one of the variables in the problem. What is it used for: - Used to solve very complex and dynamic problems, involving hundreds or thousands of variables or formulas - The problem must be one whose range of possible solutions can be represented genetically and for which criteria can be established for evaluating fitness. Natural Language Processing Natural Language processing(NLP) makes it possible for a computer to understand and analyze language that humans use. NLP algorithms are based on machine learning including deep learning which can learn how to identify a speaker's intent. Computer Vision Systems Computer Vision Systems: deal with how computers can emulate the human visual system to view and extract information from real world images. Such systems incorporate image processing, pattern recognition and image understanding. Used in autonomous vehicles such as self-driving cars and drones. Robotics Robotics: deals with the design, construction, operation and use of movable machines that can substitute for humans along with computer systems for their control, sensory feedback and information processing. Robots cannot substitute for humans but they are programmed to perform a specific series of tasks. They are used mainly in dangerous environments such as bomb detection and deactivation. The most widespread use of robotic technology has been in manufacturing. Intelligent Agents Intelligent agents: software programs that work in the background without direct human intervention to carry out specific tasks for an individual user, business process or software application. - It uses built in learned knowledge to accomplish repetitive predictable tasks such as deleting junk mail, scheduling appointments or finding the cheapest ticket to california. - Can be shopping bots used to find products online. Although some software agents are programmed to follow a simple set of rules, others are capable of learning from experience and adjusting their behaviour using machine learning and natural language processing. Siri uses NLP to answer questions, make recommendations and perform actions. The software adapts overtime to the user’s individual preferences and personalizes results. Chatbots: software agents designed to simulate a conversation with one or more human users via textual or auditory methods. They provide automated conversations that allow users to do things like: Check the weather Manage personal finances Shop online Receive help when they have questions for customer service. 11.3 Firms must deal with three kinds of knowledge. - Structured explicit knowledge(reports and presentations) - Semistructured knowledge(email, voicemail, chat room exchanges, videos) - Tacit knowledge: knowledge that is never written down 1.3.1 Enterprise Content Management Systems Businesses today need to organize and manage both structured and semistructured knowledge assets. Structured knowledge: explicit knowledge that exists in formal documents as well as in formal rules that organizations derive by observing experts and their decision-making behaviors. According to businesses at least 80% of an organization’s business content is semi structured or unstructured. Information folders, memos, proposals, emails, graphics, electronic slide presentations Enterprise Content management: Systems that help organizations manage both semistructured and structured information. They have capabilities for: Knowledge capture Storage Retrieval Distribution Preservation to help firms improve their business processes Such systems include corporate repositories of: Documents Reports Presentations best practices as well as capabilities for collecting and organizing semistructured knowledge such as email Major enterprise content management systems also enable users to: access external sources of information, such as news feeds and research to communicate via email, chat/instant messaging, discussion groups, and videoconferencing. The leading vendors of enterprise content management software is: Open Text Corporation IBM Oracle A key problem in managing knowledge is the creation of an appropriate classification scheme, or taxonomy to organize info into meaningful categories so that it can easily be accessed. Taxonomy: method of classifying things according to a predetermined system. ECM systems have the capabilities for: Tagging Interfacing with corporate databases and content repositories Creating enterprise knowledge portals that provide a single point of access to information resources. Digital Asset Management Systems: help firms classify, store and manage unstructured digital data such as photographs, graphic images, video and audio content. 11.3.2 Locating and Sharing Expertise Enterprise content management systems, along with the systems for collaboration and social business have capabilities for locating experts and tapping their knowledge. These include online directories of corporate experts and their profiles with details about their: Job experience Projects Publications Educational degrees Repositories of expert-generated content. 1.3.3 Learning Management Systems Learning management systems: provides tools for the management, delivery, tracking and assessment of various types of employee learning. It keeps track of employee learning. LMS support multiple modes of learning such as: - Downloadable videos - Web-based classes  - Live instruction - Online and group learning in online forums and chat sessions. Businesses run their own LMS but they are also publicly available massive open online courses (MOOC). An open online course that is available online to a very large number of participants. 11.4.1 Knowledge workers and knowledge work Knowledge workers include researchers, designers, architects, scientists and engineers who create knowledge for the firm. They usually have high levels of education and members in professional organizations. 11.4.2 Requirements of Knowledge Work Systems Most knowledge workers rely on systems such as word processors, email, video conferencing, collaboration and scheduling systems which improve worker productivity in the office. Knowledge workers also require highly specialized knowledge work systems with powerful graphics, analytical tools, and communications and document management capabilities. these systems also must give the worker quick and easy access to external databases 11.4.3 Examples of Knowledge Work Systems Computer Aided Design(CAD): automates the creation and revision of designs, using computers and sophisticated graphics software. 3-D printing: also known as additive manufacturing, which uses machines to make solid objects, layer by layer, from specifications in a digital file. 3-D printing lets workers model an object on a computer and print it out with plastic, metal, or composite materials. Currently used for: Prototyping Custom manufacturing Fashioning items with small production runs Virtual reality systems: have visualization, rendering, and simulation capabilities. They use interactive graphics software to create computer-generated simulations that are so close to reality that users almost believe they are participating in a real-world situation. Augmented reality: Is a related technology for enhancing visualization by overlaying digital data and images onto a physical real-world environment. The digital technology provides additional information to enhance the perception of reality, making the surrounding real world of the user more interactive and meaningful.