Ch07 Knowledge management solutions.docx

OVERVIEW OF KM SOLUTIONS AND PROCESSES KM System Justification It involves answers to the following questions: Is existing knowledge going to be lost through retirement, transfer, or departure to other organizations? Is the proposed KM system needed in multiple locations? Are experts available and willing to support the building of the proposed KM system? Does the concerned problem needs years of proper experience and cognitive reasoning to solve? While undergoing knowledge capture, would it be possible for the expert to articulate how the problem will be solved? How critical is the knowledge that is to be captured? Are the involved tasks non algorithmic in nature? Would it possible to find a champion within the organization? Challenges in KM Systems Development Changing Organizational Culture: Involves changing people's attitudes and behaviors. Knowledge Evaluation: Involves assessing the worth of information. Knowledge Processing: Involves the identification of techniques to acquire, store, process and distribute information. Sometimes it is necessary to document how certain decisions were reached. Knowledge Implementation: An organization should commit to change, learn, and innovate. It is important to extract meaning from information that may have an impact on specific missions. Lectures learned from feedback can be stored for future to help others facing the similar problem(s). Key Differences The systems analyst gathers data and information from the users and the users depend on analysts for the solution. The knowledge developer gathers knowledge from people with known knowledge and the developer depends on them for the solution. The main interface for the systems analyst is associated with novice users who know the problem but not the solution. The main interface for the knowledge developer is associated with the knowledgeable person who knows the problem and the solution. Conventional systems development is primarily sequential, whereas KMSLC is incremental and interactive. In case of conventional systems, testing is usually done towards the end of the cycle (after the system has been built), whereas in KMSLC, the evolving system is verified and validated from the beginning of the cycle. Systems development and systems management is much more extensive for conventional information systems than it is for KMSLC. The conventional systems life cycle is usually process-driven and documentation-oriented whereas KMSLC is result-oriented. The conventional systems development does not support tools such as rapid prototyping since it follows a predefined sequence of steps KMSLC can use rapid prototyping incorporating changes on the spot. Role of Strategic Planning in KM Solutions As a consequence of evaluating the existing infrastructure, the concerned organization should develop a strategic plan which should aim at advancing the objectives of the organization with the proposed KM system in mind. Areas to be considered: Vision Resources Culture -1841596520 Forming a KM team Forming a KM team usually means Identifying the key units, branches, divisions etc. as the key stakeholders in the prospective KM system. Strategically, technically, and organizationally balancing the team size and competency. Factors impacting team success Quality and capability of team members (in terms of personality, experience, and communication skill). Size of the team. Complexity of the project. Team motivation and leadership Promising only what that can be actually delivered. Capturing Knowledge Capturing Knowledge involves extracting, analyzing and interpreting the concerned knowledge that a human expert uses to solve a specific problem. Explicit knowledge is usually captured in repositories from appropriate documentation, files etc. Tacit knowledge is usually captured from experts, and from organization's stored database(s). Interviewing is one of the most popular methods used to capture knowledge. Data mining is also useful in terms of using intelligent agents that may analyze the data warehouse and come up with new findings. In KM systems development, the knowledge developer acquires the necessary heuristic knowledge from the experts for building the appropriate knowledge base. Knowledge capture and knowledge transfer are often carried out through teams. -570865746760Knowledge capture includes determining feasibility, choosing the appropriate expert, tapping the expert’s knowledge, retapping knowledge to plug the gaps in the system, and verify/validate the knowledge base. The Role of Rapid Prototyping In most of the cases, knowledge developers use iterative approach for capturing knowledge. Foe example, the knowledge developer may start with a prototype (based on the somehow limited knowledge captured from the expert during the first few sessions). The following can turn the approach into rapid prototyping: Knowledge developer explains the preliminary/fundamental procedure based on rudimentary knowledge extracted from the expert during the few past sessions. The expert reacts by saying certain remarks. While the expert watches, the knowledge developer enters the additional knowledge into the computer-based system (that represents the prototype). The knowledge developer again runs the modified prototype and continues adding additional knowledge as suggested by the expert till the expert is satisfied. The spontaneous and iterative process of building a knowledge base is referred to as rapid prototyping. Expert Selection The expert must have excellent communication skill to be able to communicate information understandably and in sufficient detail. Some common questions that may arise in case of expert selection: How to know that the so-called expert is in fact an expert? Will he/she stay with the project till its completion? What backup would be available in case the expert loses interest or quits? How are the knowledge developer going to know what does and what does not lie within the expert's area of expertise? The Role of the Knowledge Developer The knowledge developer can be considered as the architect of the system. He/she identifies the problem domain, captures knowledge, writes/tests the heuristics that represent knowledge, and co-ordinates the entire project. Some necessary attributes of knowledge developer: Communication skills. Knowledge of knowledge capture tools/technology. Ability to work in a team with professional/experts. Tolerance for ambiguity. To be able it thinks conceptually. Ability to frequently interact with the champion, knowledge workers and knower in the organization . -635170815 Designing the KM Blueprint This phase indicates the beginning of designing the IT infrastructure/ Knowledge Management infrastructure. The KM Blueprint (KM system design) addresses a number of issues. Aiming for system interoperability/scalability with existing IT infrastructure of the organization. Finalizing the scope of the proposed KM system. Deciding about the necessary system components. Developing the key layers of the KM architecture to meet organization's requirements. These layers are: o User interface o Authentication/security layer o Collaborative agents and filtering o Application layer o Transport internet layer o Physical layer o Repositories Testing the KM System This phase involves the following two steps: Verification Procedure: Ensures that the system is right, i.e., the programs do the task that they are designed to do. Validation Procedure: Ensures that the system is the right system - it meets the user's expectations, and will be usable on demand. Implementing the KM System After capturing the appropriate knowledge, encoding in the knowledge base, verifying and validating; the next task of the knowledge developer is to implement the proposed system on a server. Implementation means converting the new KM system into actual operation. Conversion is a major step in case of implementation. Some other steps are post implementation review and system maintenance. Quality Assurance It indicates the development of controls to ensure a quality KM system. The types of errors to look for: Reasoning errors Ambiguity Incompleteness False representation Post system Evaluation Key questions to be asked in the post implementation stage: How the new system improved the accuracy/timeliness of concerned decision making tasks? Has the new system caused organizational changes? If so, how constructive are the changes? Has the new system affected the attitudes of the end users? If so, in what way? How the new system changed the cost of business operation? How significant has it been? In what ways the new system affected the relationships between end users in the organization? Do the benefit obtained from the new system justify the cost of investment? Implications for KM The managerial factors to be considered: The organization must make a commitment to user training/education prior to building the system. Top Management should be informed with cost/benefit analysis of the proposed system. The knowledge developers and the people with potential to do knowledge engineering should be properly trained. Domain experts must be recognized and rewarded. The organization needs to do long-range strategic planning. Some questions to be addressed by the management regarding systems maintenance: Who will be the in charge of maintenance? What skills the maintenance specialist needs to have? What would be the best way to train the maintenance specialist? What incentives should be provided to ensure quality maintenance? What types of support/funding will be required? What relationship should be established between the maintenance of the KM system and the IT staff of the organization? Test Your Understanding Why is it helpful to view the building of a KM system as a life cycle? It is important to have a life cycle in building knowledge management systems, because the life cycle provides structure and order to the process. Additionally, the life cycle provides a breakdown of the activities into manageable steps, good documentation for possible changes in the future, coordination of the project for a timely completion, and regular management review at each phase of the cycle. In what ways do conventional and KM systems’ development life cycles differ? How are they similar? There are many differences between the conventional and knowledge management systems’ development life cycle: A conventional system is sequential (certain steps are carried out in sequence), while the knowledge management system life cycle is incremental and interactive In the conventional system, testing generally occurs at the end of programming, while the knowledge management development life cycle provides for testing throughout various phases of system development as the system evolves The conventional system is process-driven and documentation-oriented, with emphasis on the flow of data, while the knowledge management development life cycle is result-oriented The conventional system does not support rapid prototyping or advanced languages, while the knowledge management development life cycle promotes rapid prototyping and incorporates changes on the spot Along with these differences, however, are many similarities as well: Both cycles begin with a problem and end with a solution. Both cycles require the initial gathering of information (conventional) or knowledge (KMSDLC) for the process to begin and ending up with a tested system ready for use Both the knowledge developer and the systems analyst need to choose a tool to design the system Successful KM system implementation depends on several factors. Briefly, explain each factor Level of motivation of the user. Good documentation cannot compensate for low motivation or poor attitude toward the system. Promoting motivation and commitment takes time and must be planned in advance Computer literacy and technical background of the user. A computer literate user can be easier to work with than someone who has no background at all. First-time users often require education and training before they are able to support development and use of knowledge-based system. Communication skills of the trainer. Selling people on change is sometimes considered more an art than a science. Communication skills can make the difference between a user’s acceptance or rejection of the installation. Time availability and funding for training. A training program run on a shoestring is usually a loser. Also, squeezing training time to the bare minimum often results in trainee impatience, resistance to learning, or nonuse of the system. Training should be part of the implementation phase offered around the schedule of the user. Place of training. The location of training can make a difference. On-site versus off-site training continues to be an issue with plusses and minuses for each alternative. Off-site training is generally dedicated uninterrupted learning. Its positive benefits include privacy and focus on the projects. The feasibility of off-site training depends on distance, location, and funding. In contrast, on-site training requires no out-of-town transportation or room and board expenses. Ease and duration of training. This aspect depends on the caliber of the trainer and the attitude and motivation of the trainees. “Chemistry” often affects how well all parties work with each other. Also, the training period should be reasonable and able to meet measurable goals. A long, drawn-out three-week training period does not promote the same excitement and motivation as a one-week session. Ease of access and explanatory facilities of the knowledge management system. Knowledge management systems should be easy to access and work with. A software package that provides adequate Explanations is bound to satisfy most users. The explanatory facility of the package promotes ease of use and provides convincing evidence of the integrity of the solutions provided by the system Ease of maintenance and system update. At this stage, good documentation and easy-to-follow procedures in a module-oriented knowledge management system can make the difference between easy maintenance and a “nightmare.” In this case, maintenance implies update, although update is more often considered enhancement. Payoff to the organization. A system’s benefit to the organization is usually measured in terms of cost reduction, improvement in sales or overall performance, and so on. Measurable payoff early in the development life cycle promotes successful implementation. Role of the champion. Solid top management support and a champion pushing for system adoption can make a difference between a successful and a lukewarm installation How important are organizational factors in system implementation? The primary organizational factor is top management commitment to the proposed knowledge management system. This is evident by the way it promotes the development effort through adequate funding, ensuring the availability of hardware and personnel, and allowing the champion to function within the development process. The second organizational factor is user participation in the building process. Doing so tends to increase commitment and foster a sense of ownership of the system. Other organizational factors include organizational politics and organizational climate. Politics is jockeying for leverage to influence one’s domain and control procedures, technology, or the direction of an area of operation. User readiness can also influence the success of implementation. KM SYSTEMS, SOLUTIONS, AND INFRASTRUCTURE Describe the ways to facilitate KM, along with suitable examples. KM is facilitated in a number of ways by means of KM solutions. These may be divided into four broad levels, : (1) KM Processes; (2) KM Systems; (3) KM Mechanisms and Technologies; and (4) KM infrastructure. KM Processes -- are the broad processes that aid in discovering, capturing, sharing, and applying knowledge. These include combination, socialization, externalization, internalization, exchange, directions, and routines.. For example, internalization processes benefit from simulations or experiments, which enable individuals to learn through experience, as well as from face-to-face meetings, on-the-job training, and demos. KM Systems -- are the integration of technologies and mechanisms, developed to support the above four KM processes. KM systems include expert-seeker systems, which help locate individuals possessing knowledge in a particular area, and rely on a combination of information technologies and mechanisms for classifying knowledge areas. KM Mechanisms and Technologies -- are used in KM systems, each of which utilize a combination of multiple mechanisms and multiple technologies, which again in turn could, under differing circumstances, support multiple KM systems. Examples of KM mechanisms include on- the- job training and apprenticeship, while examples of KM technologies include databases and Internet. KM Infrastructure -- reflects the long-term foundation for KM. KM mechanisms and technologies rely on the KM infrastructure for their success. Examples of KM infrastructure include the data contained in an organization’s databases and the quality of the organization’s employees (in terms of their tacit knowledge). Explain the importance of KM mechanisms and KM technologies to KM systems. Give examples of each. Both KM mechanisms and KM technologies support KM systems. Their differences however are explained below: KM mechanisms are organizational or structural means used to promote KM. They enable KM systems, and are supported by KM infrastructure. KM mechanisms may or may not utilize technology. They involve some kind of organizational arrangement or social or structural means of facilitating KM. Examples of KM Mechanisms include learning by doing, on-the-job training, learning by observation, and face-to- face meetings. More long-term KM mechanisms include the hiring of a chief knowledge officer, interdepartmental projects, traditional hierarchical relationships, organizational policies, standards, initiation, and training process for new employees, and employee rotation across departments. KM technologies support KM systems and also benefit from the KM infrastructure, especially the information technology infrastructure. KM technologies are a vital component of KM systems. Technologies that support KM include artificial intelligence (AI) technologies including case-based reasoning systems, electronic discussion groups, computer-based simulations, databases, decision support systems, enterprise resource planning systems, expert systems, management information systems, expertise locator systems, video-conferencing, and information repositories including best practices databases and Lectures learned systems. Examples of the use of KM technologies include World Bank’s use of a combination of video interviews and hyperlinks to documents and reports to systematically record the knowledge of employees that are close to retirement. Similarly, at BP, desktop video-conferencing has improved communication and enabled many problems at offshore oil fields to be solved without extensive traveling. Briefly explain the four kinds of classifications for KM systems based on the process supported. Depending on the KM process most directly supported, KM systems can be classified into four kinds: Knowledge Discovery Systems support the process of developing new tacit or explicit knowledge from data and information or from the synthesis of prior knowledge. These systems support two KM sub processes associated with knowledge discovery: combination, enabling the discovery of new explicit knowledge, and socialization, enabling the discovery of new tacit knowledge. Mechanisms and technologies can support knowledge discovery systems by facilitating combination and/or socialization. Mechanisms that facilitate combination include collaborative problem solving, joint decision making, and collaborative creation of documents. Technologies facilitating combination include knowledge discovery systems, databases, and Web-based access to data. Repositories of information, best practices, and Lectures learned also facilitate combination. Technologies can also facilitate socialization, but to a smaller extent than they can facilitate combination. Knowledge Capture Systems support the process of retrieving either explicit or tacit knowledge that resides within people, artifacts, or organizational entities. These systems can aid in the capture of knowledge that resides within or outside organizational boundaries, including within consultants, competitors, customers, suppliers, and prior employers of the organization’s new employees. Knowledge capture systems rely on mechanisms and technologies that support externalization and internalization. KM mechanisms can enable knowledge capture by facilitating externalization, or internalization. Knowledge Sharing Systems support the process through which explicit or implicit knowledge is communicated to other individuals. They do so by supporting exchange and socialization. Discussion groups or chat groups facilitate knowledge sharing by enabling an individual to explain her knowledge to the rest of the group. In addition, knowledge-sharing systems also utilize mechanisms and technologies that facilitate exchange. Some of the mechanisms that facilitate exchange are memos, manuals, progress reports, letters, and presentations. Technologies facilitating exchange include groupware and other team collaboration mechanisms, Web-based access to data, and databases, and repositories of information, including best practice databases, Lectures learned systems, and expertise-locator systems. Knowledge Application Systems support the process through which some individuals utilize knowledge possessed by other individuals without actually acquiring, or learning, that knowledge. Mechanisms and technologies support knowledge application systems by facilitating routines and direction. State the roles of (a) organizational culture and (b) organizational structure for the development of a good KM infrastructure. KM infrastructure is the foundation on which KM resides. Organization culture and organization structure are two of its main components. Organizational Culture reflects the norms and beliefs that guide the behavior of the organization’s members. It is an important enabler of KM in organizations. A supporting organization culture helps motivate employees to understand the importance and benefits from KM and to find time for it. Getting people to participate in knowledge sharing is considered the hardest part of KM, and a vital part of implementing KM is in making it a part of the organization’s culture. A KM enabling culture is one that understands the value of KM practices, has support for KM at all managerial levels, provides incentives that reward knowledge sharing, and encourages organizational interaction for the creation and sharing of knowledge. In contrast, cultures that stress individual performance and hoarding of information within units encourage limited employee interaction, and lack of an involved top management creates inhibited knowledge sharing and retention. Organizational Structure is another vital aspect on which KM depends on. Several aspects of organization structure are relevant. First, the hierarchical structure of the organization affects the people with whom each individual frequently interacts, and to or from whom he is consequently likely to transfer knowledge. Traditional reporting relationships influence the flow of data and information, the nature of groups who make decisions together, and consequently affect the sharing and creation of knowledge. By decentralizing or flattening their organization structures, companies aim to increase knowledge sharing with a larger group of individuals. Organization structures can facilitate KM through communities of practice, which is an organic and self-organized group of individuals who are dispersed geographically or organizationally but communicate regularly to discuss issues of mutual interest. They provide access to a larger group of individuals than possible within traditional departmental boundaries. Consequently, there are more numerous potential helpers, and this increases the probability that at least one of them will provide useful knowledge. Further, they also provide access to external knowledge sources. 5. In what way does information technology infrastructure contribute to KM within an organization? An organization’s information technology infrastructure greatly contributes to KM. While organizations could develop specialized IT infrastructure to pursue KM, usually the existing IT infrastructure, developed to support the organization’s information systems needs, also facilitates KM. Information technology infrastructure includes data processing, storage, and communication technologies and systems. It comprises the entire spectrum of an organization’s information systems, including transaction processing systems and management information systems. It includes databases and data warehouses, as well as enterprise resource planning systems. IT infrastructure provides capabilities in four important aspects: reach, depth, richness, and aggregation. Reach pertains to access and connection, and the efficiency of such access. Depth, in contrast, focuses on the detail and amount of information that can be effectively communicated over a medium. The richness of a medium is based on its ability to provide multiple cues, quick feedback, personalize messages, and use natural language to convey subtleties. Finally, aggregation involves the collection of large volumes of information from multiple sources for processing. Knowledge Exercises How would you develop a KM system? What are the possible mechanisms and technologies you could utilize? In developing KM systems to support KM processes, I would utilize a variety of KM mechanisms and technologies. KM mechanisms involve some kind of organizational arrangement or social or structural means of facilitating KM. The possible KM mechanisms that could be utilized are learning by doing, on-the-job training, learning by observation, and face-to-face meetings. More long-term KM mechanisms include the hiring of a chief knowledge officer, co-operative projects across departments, traditional hierarchical relationships, organizational policies, standards, initiation process for new employees, and employee rotation across department KM technologies benefit from the KM infrastructure, especially the information technology infrastructure. Examples of KM technologies are the use of a combination of video interviews and hyperlinks to documents and reports to systematically record the knowledge of employees close to retirement, desktop video- conferencing for communication and enabling problem solving at offshore locations without the need for extensive traveling. Organization that is spread across the globe? Does geographic distance hamper the utilization of these systems? In an organization spread across the globe, the use of knowledge discovery systems and knowledge capture systems do tend to get hampered to some extent due to geographic distances, but due to the increasing use of technology, these problems are getting smaller and smaller. Knowledge discovery systems support the process of developing new tacit or explicit knowledge from data and information or from the synthesis of prior knowledge. Mechanisms and technologies can support knowledge discovery systems by facilitating combination and/or socialization. Mechanisms that facilitate combination include collaborative problem solving, joint decision making, and collaborative creation of documents. In a global organization sharing documents among senior management results in the creation of new explicit knowledge, resulting in a better understanding of products and a corporate vision. Mechanisms that facilitate socialization include apprenticeships, employee rotation across areas, conferences, brainstorming retreats, cooperative projects across departments, and initiation process for new employees. In a global organization, this could become expensive, however, as it would involve the physical transfer of employees from one location to another. Technologies facilitating combination include knowledge discovery systems, databases, and Web-based access to data. Repositories of information, best practices and Lectures learned would also facilitate combination in global organizations. Technologies can also facilitate socialization, but to a smaller extent than they can facilitate combination. Some of the technologies for facilitating socialization in a global organization include video-conferencing and electronic support for communities of practice. Knowledge capture systems support the process of retrieving either explicit or tacit knowledge that resides within people, artifacts, or organizational entities. Knowledge capture systems rely on mechanisms and technologies that support externalization and internalization. Mechanisms can enable knowledge capture by facilitating externalization, i.e., the conversion of tacit knowledge into explicit form, or internalization, i.e., the conversion of explicit knowledge into tacit form. The development of models or prototypes, and the articulation of best practices or Lectures learned are some examples of mechanisms that might enable externalization in a global organization. Learning by doing, on-the-job training, learning by observation and face-to-face meetings are some of the mechanisms that might facilitate internalization in a global organization. Technologies can also support knowledge capture systems by facilitating externalization and internalization. Externalization through knowledge engineering is necessary for the implementation of intelligent technologies such as expert systems, case-based reasoning systems, and knowledge acquisition systems. Technologies that facilitate internalization include computer-based training and communication technologies.