Sunday, April 8, 2012

Chapter 11 - Managing Knowledge

Communicating and sharing knowledge have become increasingly important in today’s businesses.  Knowledge is most useful and actionable when it is shared throughout the firm.  Knowledge has several dimensions that must be understood.

Knowledge is a firm asset.  It is an intangible asset and is not subject to the law of diminishing returns as are physical assets, but it experiences network effects as its value increases as more people share it.  The transformation of data into useful information and knowledge requires organizational resources.  Knowledge also has different forms.  It can be either tacit or explicit and it involves know-how, craft, and skill.  It involves knowing how to follow procedures and knowing why, not simply when, things happen.  Knowledge has location.  It is a cognitive event involving mental models and maps of individuals, having both a social and an individual basis of it.  It is “sticky” (hard to move), situated, and contextual.  Lastly, knowledge is situational.  It is conditional, knowing when to apply a procedure is just as important as knowing the procedure.  It is related to context, meaning it must be known how to use a certain tool and under what circumstances.

Organizations create and gather knowledge using a variety of organizational learning mechanisms.  Organizational learning is the process of learning to adjust the organization’s behavior to reflect learning by creating new business processes and by changing patterns of management decision making.  Organizations that can sense and respond to their environments rapidly will survive longer than organizations that have poor learning mechanisms.

Knowledge management is the set of processes developed in an organization to create, gather, store, maintain, and disseminate the firm’s knowledge.  It increases the ability of the organization to learn from its environment and to incorporate knowledge into its business processes.  To maximize the return on investment in knowledge management projects, supportive values, structures, and behavior patterns must be built.  Knowledge management involves both information systems activities and a host of enabling management and organizational activities.  Information system activities include knowledge acquisition (knowledge discovery, data mining, neural networks, genetic algorithms, knowledge workstations, expert knowledge networks), knowledge storage (document management systems, knowledge databases, expert systems), knowledge dissemination (Intranet portals, push email reports, search engines, collaboration), and knowledge application (decision support systems, enterprise applications).

Essentially there are three major types of knowledge management systems.  Enterprise-wide knowledge management systems are general-purpose firmwide efforts to collect, store, distribute, and apply digital content and knowledge.  Knowledge work systems (KWS) refer to specialized systems built for engineers, scientists, and other knowledge workers charged with discovering and creating new knowledge for a company.  Intelligent techniques have different objectives from a focus on discovering knowledge (data mining and neural networks) to distilling knowledge in the form of rules for a computer program (expert systems and fuzzy logic) to discovering optimal solutions for problems (genetic algorithms).

Enterprise-wide knowledge management systems deal with three types of knowledge:  the knowledge that exists within the firm in the form of structured text documents; the knowledge that is semistructured; and the knowledge that resides in the heads of employees where there is no formal or digital information of any kind. 

Enterprise content management systems help organizations manage structured and semistructured knowledge, providing corporate repositories of documents, reports, presentations, and best practices and capabilities for collecting and organizing email and graphic objects.  One main issue in managing knowledge is the creation of an appropriate classification scheme (known as taxonomy) to organize information into meaningful categories so that it can be easily accessed.  Once created each knowledge object needs to be “tagged” so that it can be easily retrieved.  Enterprise content management systems have this capability.  These systems also include powerful portal and collaboration technologies.  The portals can provide access to external sources of information as well as to internal knowledge resources.  Social bookmarking makes it easier to search for and share information by allowing users to save their bookmarks to Web pages on a public Web site and tag these bookmarks with keywords.  A learning management system provides tools for the management, delivery, tracking, and assessment of various types of employee learning and training.

Knowledge network systems are also known as expertise location and management systems.  They address the problem that arises when the appropriate knowledge is not in the form of a digital document but instead resides in the memory of expert individuals in the firm.  These systems provide an online directory of corporate experts in well-defined knowledge domains and use communication technologies to make it easy for employees to find the appropriate expert in a company. 

Knowledge workers include researchers, designers, architects, scientists, and engineers who primarily create knowledge and information for the organization.  They perform three key roles that are critical to the organization and to the managers who work within the organization:  they keep the organization current in knowledge as it develops in the external world; they serve as internal consultants regarding the areas of their knowledge, the changes taking place, and opportunities; and they act as change agents, evaluating, initiating, and promoting change projects.  They require highly specialized knowledge work systems with powerful graphics, analytical tools, and communications and document management capabilities. 

Knowledge work applications include CAD systems, virtual reality systems for simulation and modeling, and financial workstations.  CAD (computer-aided design) automates the creation and revision of designs, using computers and sophisticated graphics software.  Virtual reality systems 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.  Virtual Reality Modeling Language (VRML) is a standard used by these applications and is a set of specifications for interactive, 3-D modeling on the World Wide Web that can organize multiple media types to put users in a simulated real-world environment.  Augmented reality (AR) provides a live direct or indirect view of a physical real-world environment whose elements are augmented by virtual computer-generated imagery. 

Artificial intelligence (AI) technology consists of computer-based systems (both hardware and software) that attempt to emulate human behavior.  This along with database technology provides a number of intelligent techniques that organizations can use to capture individual and collective knowledge and to extend their knowledge base.  These techniques include expert systems, case-based reasoning, fuzzy logic, neural networks and data mining, genetic algorithms, and intelligent agents. 

Expert systems are used for capturing tacit knowledge and capturing the knowledge of skilled employees in the form of a set of rules in a software system that can se used by others in the organization.  However, they lack the breadth of knowledge and the understanding of fundamental principles of a human expert and they typically perform very limited tasks that can be performed by professionals in a few minutes or hours.  Yet, by capturing human expertise in limited areas, expert systems can provide benefits, helping organizations make high-quality decisions with fewer people.  These systems model human knowledge as a set of rules that collectively are called the knowledge base.  Inference engine is the strategy used to search through the knowledge base.  Two strategies commonly used are forward chaining and backward chaining.  Forward chaining begins with the information entered by the user and searches the rule base to arrive at a conclusion.  Backward chaining acts like a problem solver by beginning with a hypothesis and seeking out more information until the hypothesis is either proved or disproved.  Benefits of expert systems include improved decisions, reduced errors, reduced costs, reduced training time, and higher levels of quality and service.  But, they require large, lengthy, and expensive development efforts and the environment in which it operates is continually changing so that the expert system must also continually change.  Also, some of these systems are so complex that in a few years the maintenance costs equal the development costs.

Case-based reasoning is also used for capturing tacit knowledge.  It represents knowledge as a series of cases, and this knowledge base is continuously expanded and refined by users.  It is found in diagnostic systems in medicine or customer support where users can retrieve past cases whose characteristics are similar to the new case.  It suggests a solution or diagnosis based on the best-matching retrieved case.

Fuzzy logic is rule-based AI that tolerates imprecision by using nonspecific terms called membership functions to solve problems.  Organizations can use this logic to create software systems that capture tacit knowledge where there is linguistic ambiguity.  It also provides solutions to problems requiring expertise that is difficult to represent in the form of crisp IF-THEN rules.  Management also has found it useful for decision-making and organizational control.

Neural networks are used for knowledge discovery; solving complex, poorly understood problems for which large amounts of data have been collected.  These networks “learn” patterns from large quantities of data by sifting through data, searching for relationships, building models, and correcting over and over again the model’s own mistakes.  Neural network designers seek to put intelligence into the hardware in the form of a generalized capability to learn.  The applications are used in medicine, science, and businesses to address problems in pattern classification, prediction, financial analysis, and control and optimization.  However, neural networks cannot always explain why they arrived at a particular solution and they cannot always guarantee a completely certain solution, arrive at the same solution again with the same input data, or always guarantee the best solution.  Also, they are very sensitive and may not perform well if their training covers too little or too much data.

Genetic algorithms are useful for finding the optimal solution for a specific problem by examining a very large number of possible solutions for that problem.  They are based on techniques inspired by evolutionary biology.  They are used to solve problems that are very dynamic and complex, involving hundreds or thousands of variables or formulas. 

Intelligent agents can automate routine tasks to help firms search for and filter information for use in electronic commerce, supply chain management, and other activities.  It is software programs that work in the background without direct human intervention to carry out specific, repetitive, and predictable tasks for an individual user, business process, or software application.  These applications can be found in operating systems, application software, email systems, mobile computing software, and network tools.

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