Knowledge management Knowledge Management (KM) comprises a range of practices used in an organisation to identify, create, represent, distribute and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organisational processes or practice. An established discipline since 1991 (see Nonaka 1991), KM includes courses taught in the fields of business administration, information systems, management, and library and information sciences (Alavi & Leidner 1999). More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy. Many large companies and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their 'Business Strategy', 'Information Technology', or 'Human Resource Management' departments (Addicott, McGivern & Ferlie 2006). Several consulting companies also exist that provide strategy and advice regarding KM to these organisations. KM efforts typically focus on organisational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, and continuous improvement of the organisation. KM efforts overlap with Organisational Learning, and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge. KM efforts can help individuals and groups to share valuable organisational insights, to reduce redundant work, to avoid reinventing the wheel per se, to reduce training time for new employees, to retain intellectual capital as employees turnover in an organisation, and to adapt to changing environments and markets (McAdam & McCreedy 2000)(Thompson & Walsham 2004). KM efforts have a long history, to include on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training and mentoring programs. More recently, with increased use of computers in the second half of the 20th century, specific adaptations of technologies such as knowledge bases, expert systems, knowledge repositories, group decision support systems, intranets and computer supported cooperative work have been introduced to further enhance such efforts. In 1999, the term personal knowledge management was introduced which refers to the management of knowledge at the individual level (Wright 2005). More recently with the advent of the Web 2.0, the concept of knowledge management has evolved towards a vision more based on people participation and emergence. This line of evolution is termed Enterprise 2.0 (McAfee 2006). However, there is still a debate (and discussions even in Wikipedia (Lakhani & McAfee 2007)) whether Enterprise 2.0 is just a fad, it brings something new, or if it is indeed the future of knowledge management (Davenport 2008). Dimensions Different frameworks for distinguishing between knowledge exist. One proposed framework for categorising the dimensions of knowledge distinguishes between tacit knowledge and explicit knowledge. Tacit knowledge represents internalised knowledge that an individual may not be consciously aware of how he or she accomplishes particular tasks. At the opposite end of the spectrum, explicit knowledge represents knowledge that the individual holds consciously in mental focus, in a form that can easily be communicated to others. (Alavi & Leidner 2001). Early research suggested that a successful KM effort needs to convert internalised tacit knowledge into explicit knowledge in order to share it, but the same effort must also permit individuals to internalise and make personally meaningful any codified knowledge retrieved from the KM effort. Subsequent research into KM suggested that a distinction between tacit knowledge and explicit knowledge represented an oversimplification and that the notion of explicit knowledge is self-contradictory. Specifically, for knowledge to be made explicit, it must be translated into information (i.e., symbols outside of our heads) (Serenko & Bontis 2004). Later on, Ikujiro Nonaka proposed a model (SECI for Socialization, Externalization, Combination, Internalization) which considers a spiraling knowledge process interaction between explicit knowledge and tacit knowledge (Nonaka & Takeuchi 1995). In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 'reinternalised' into implicit knowledge. A second proposed framework for categorising the dimensions of knowledge distinguishes between embedded knowledge of a system outside of a human individual (e.g., an information system may have knowledge embedded into its design) and embodied knowledge representing a learned capability of a human body’s nervous and endocrine systems (Sensky 2002). A third proposed framework for categorising the dimensions of knowledge distinguishes between the exploratory creation of "
(i.e., innovation) vs. the transfer or exploitation of "
within a group, organisation, or community. Collaborative environments such as communities of practice or the use of social computing tools can be used for both knowledge creation and transfer .  Strategies Knowledge may be accessed at three stages: before, during, or after KM-related activities. Different organisations have tried various knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans. Considerable controversy exists over whether incentives work or not in this field and no consensus has emerged. One strategy to KM involves actively managing knowledge (push strategy). In such an instance, individuals strive to explicitly encode their knowledge into a shared knowledge repository, such as a database, as well as retrieving knowledge they need that other individuals have provided to the repository . Another strategy to KM involves individuals making knowledge requests of experts associated with a particular subject on an ad hoc basis (pull strategy). In such an instance, expert individual(s) can provide their insights to the particular person or people needing this (Snowden 2002). Other knowledge management strategies for companies include: rewards (as a means of motivating for knowledge sharing) storytelling (as a means of transferring tacit knowledge) cross-project learning after action reviews knowledge mapping (a map of knowledge repositories within a company accessible by all) communities of practice best practice transfer competence management (systematic evaluation and planning of competences of individual organization members) proximity & architecture (the physical situation of employees can be either conducive or obstructive to knowledge sharing) master-apprentice relationship collaborative technologies (groupware, etc) knowledge repositories (databases, etc) measuring and reporting intellectual capital (a way of making explicit knowledge for companies) knowledge brokers (some organizational members take on responsibility for a specific "
and act as first reference on whom to talk about a specific subject) social software (wikis, social bookmarking, blogs, etc)  Motivations A number of claims exist as to the motivations leading organisations to undertake a KM effort . Typical considerations driving a KM effort include: Making available increased knowledge content in the development and provision of products and services Achieving shorter new product development cycles Facilitating and managing innovation and organisational learning Leveraging the expertise of people across the organisation Increasing network connectivity between internal and external individuals Managing business environments and allowing employees to obtain relevant insights and ideas appropriate to their work Solving intractable or wicked problems Managing intellectual capital and intellectual assets in the workforce (such as the expertise and know-how possessed by key individuals) Debate exists whether KM is more than a passing fad, though increasing amount of research in this field may hopefully help to answer this question, as well as create consensus on what elements of KM help determine the success or failure of such efforts (Wilson 2002) .  Technologies Early KM technologies included online corporate yellow pages as expertise locators and document management systems. Combined with the early development of collaborative technologies (in particular Lotus Notes), KM technologies expanded in the mid-1990s. Subsequent KM efforts leveraged semantic technologies for search and retrieval and the development of e-learning tools for communities of practice  (Capozzi 2007). More recently, development of social computing tools (such as blogs and wikis) have allowed more unstructured, self-governing or ecosystem approaches to the transfer, capture and creation of knowledge, including the development of new forms of communities, networks, or matrixed organisations. However such tools for the most part are still based on text and code, and thus represent explicit knowledge transfer. These tools face challenges in distilling meaningful re-usable knowledge and ensuring that their content is transmissible through diverse channels (Andrus 2005). Information relates to description, definition, or perspective (what, who, when, where). Knowledge comprises strategy, practice, method, or approach (how). Wisdom embodies principle, insight, moral, or archetype (why). A collection of data is not information. A collection of information is not knowledge. A collection of knowledge is not wisdom. A collection of wisdom is not truth. Just doing anything that may have a positive impact on worker Effectiveness while calling that thing "
Knowledge has two basic definitions of interest. The first pertains to a defined body of information. Depending on the definition, the body of information might consist of facts, opinions, ideas, theories, principles, and models (or other frameworks). Clearly, other categories are possible, too. Subject matter (e.g., chemistry, mathematics, etc.) is just one possibility. Knowledge also refers to a person’s state of being with respect to some body of information. These states include ignorance, awareness, familiarity, understanding, facility, and so on. Need knowledge of management Marketplaces are increasingly competitive and the rate of innovation is rising. Reductions in staffing create a need to replace informal knowledge with formal methods. Competitive pressures reduce the size of the work force that holds valuable business knowledge. The amount of time available to experience and acquire knowledge has diminished. Early retirements and increasing mobility of the work force lead to loss of knowledge. There is a need to manage increasing complexity as small operating companies are trans-national sourcing operations. Changes in strategic direction may result in the loss of knowledge in a specific area. What Is Knowledge Management? Knowledge Management is one of the hottest topics today in both the industry world and information research world. In our daily life, we deal with huge amount of data and information. Data and information is not knowledge until we know how to dig the value out of of it. This is the reason we need knowledge management. Unfortunately, there's no universal definition of knowledge management, just as there's no agreement as to what constitutes knowledge in the first place. We chose the following definition for knowledge management for its simplicity and broad context. Simple Definition:Knowledge Management (KM) refers to a multi-disciplined approach to achieving organizational objectives by making the best use of knowledge. KM focuses on processes such as acquiring, creating and sharing knowledge and the cultural and technical foundations that support them. Knowledge Management may be viewed in terms of: People – how do you increase the ability of an individual in the organisation to influence others with their knowledge Processes – Its approach varies from organization to organization. There is no limit on the number of processes Technology – It needs to be chosen only after all the requirements of a knowledge management initiative have been established. Or Culture –The biggest enabler of successful knowledge-driven organizations is the establishment of a knowledge-focused culture Structure – the business processes and organisational structures that facilitate knowledge sharing Technology – a crucial enabler rather than the solution. What Is Knowledge Management Related To? Knowledge management draws from a wide range of disciplines and technologies: Cognitive science Expert systems, artificial intelligence and knowledge base management systems (KBMS) Computer-supported collaborative work (groupware) Library and information science Technical writing Document management Decision support systems Semantic networks Relational and object databases Simulation Organizational science object-oriented information modeling electronic publishing technology, hypertext, and the World Wide Web; help-desk technology full-text search and retrieval performance support systems Although around 20 kinds of disciplines and study areas were listed above, there is no way to include all of the related subjects to knowledge management. The History of Knowledge Management 1. 70's, A number of management theorists have contributed to the evolution of knowledge management Peter Drucker: information and knowledge as organizational resources Peter Senge: "
Leonard-Barton: well-known case study of "
Chaparral Steel "
, a company having knowledge management strategy 2. 80's, Knowledge (and its expression in professional competence) as a competitive asset was apparent Managing knowledge that relied on work done in artificial intelligence and expert systems Knowledge management-related articles began appearing in journals and books 3. 90's until now, A number of management consulting firms had begun in-house knowledge management programs Knowledge management was introduced in the popular press, the most widely read work to date is Ikujiro Nonaka’s and Hirotaka Takeuchi’s The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation (1995) The International Knowledge Management Network(IKMN) went online in 1994 Knowledge management has become big business for such major international consulting firms as Ernst & Young, Arthur Andersen, and Booz-Allen & Hamilton The Value of Knowledge Management Some benefits of KM correlate directly to bottom-line savings, while others are more difficult to quantify. In today's information-driven economy, companies uncover the most opportunities — and ultimately derive the most value — from intellectual rather than physical assets. To get the most value from a company's intellectual assets, KM practitioners maintain that knowledge must be shared and serve as the foundation for collaboration. Yet better collaboration is not an end in itself; without an overarching business context, KM is meaningless at best and harmful at worst. Consequently, an effective KM program should help a company do one or more of the following: Foster innovation by encouraging the free flow of ideas Improve decision making Improve customer service by streamlining response time Boost revenues by getting products and services to market faster Enhance employee retention rates by recognizing the value of employees' knowledge and rewarding them for it Streamline operations and reduce costs by eliminating redundant or unnecessary processes These are the most prevalent examples. A creative approach to KM can result in improved efficiency, higher productivity and increased revenues in practically any business function. Knowledge Management Today According to a recent IDC report, knowledge management is in a state of high growth, especially among the business and legal services industries. As the performance metrics of early adopters are documenting the substantial benefits of knowledge management, more organizations are recognizing the value of leveraging organizational knowledge. As a result, knowledge management consulting services and technologies are in high demand, and knowledge management software is rapidly evolving. Knowledge Management Drivers The main drivers behind knowledge management efforts are: Knowledge Attrition: Despite the economic slowdown, voluntary employee turnover remains high. A recent survey by the global consulting firm Drake Beam Morin revealed an average voluntary employee turnover rate of 20 percent with 81 percent of organizations citing employee turnover as a critical issue. Estimated annual costs of employee turnover was a staggering $129 million per organization. Much of this cost is due to knowledge attrition, which can be effectively minimized using knowledge management techniques.
Knowledge Merging: Since 1980, the annual value of mergers has risen 100 fold reaching a cumulative $15 trillion in 1999. Over 32,000 deals were announced, triple the number of 10 years earlier and more than 30 times as many as in 1981. The recent frenzy of corporate mergers coupled with the increased need to integrate global corporate communications requires the merging of disparate and often conflicting knowledge models.
Content Management: The explosion of digitally stored business-critical data is widely documented. Forester Research estimates that online storage for Global 2,500 companies will grow from an average of 15,000 gigabytes per company in 1999 to 153,000 gigabytes by 2003, representing a compound annual growth rate of 78%. As the volume of digital information expands, the need for its logical organization is critical for purposes of information retrieval, sharing and reuse.
E-Learning: As the economy becomes more global and the use of PCs more pervasive, there has been a dramatic increase in e-learning, also known as computer based training. E-learning is closely linked to and overlapping with, but not equal to knowledge management. E-learning can be an effective medium for knowledge management deliverables. KM Objectives The graph below shows the results of a recent IDC study in which corporations cited various objectives for knowledge management efforts: Activities related to these objectives include: creating knowledge sharing networks that facilitate a corporate knowledge culture, developing knowledge leaders, optimizing intellectual capital by producing knowledge management solutions such as codification strategies and knowledge bases, and estimating revenue and efficiency gains resulting from knowledge management in terms of return on investment (ROI). KM ROI Although 65% of organizations that are currently implementing KM initiatives have not measured the impact of their performance, large revenue gains and efficiency improvements have been recorded by numerous major corporations. For instance: Ford Motor Company accelerated its concept-to-production time from 36 months to 24 months. The flow on value of this has been estimated at US $1.25 billion, The Dow Chemical Company saved $40 million a year in the re-use of patents, Chase Manhattan, one of the largest banks in the US, used Customer relationship management KM initiatives to increase its annual revenue by 15%, and Pfizer credits KM practices for discovering the hidden benefits of the Viagra drug. Technologies That Support Knowledge Management The following diagram reflects the main technologies that currently support knowledge management systems.
These technologies roughly correlate to four main stages of the KM life cycle: Knowledge is acquired or captured using intranets, extranets, groupware, web conferencing, and document management systems.
An organizational memory is formed by refining, organizing, and storing knowledge using structured repositories such as data warehouses.
Knowledge is distributed through education, training programs, automated knowledge based systems, expert networks.
Knowledge is Applied or leveraged for further learning and innovation via mining of the organizational memory and the application of expert systems such as decision support systems. All of these stages are enhanced by effective workflow and project management. Present and Future State of KM Currently, communities of practice such as the Knowledge Management Network and the development of standards and best practices are in a mature stage of development. KM curricula such as certification, corporate training and university graduate certificate programs are on the rise. Techniques such as data mining and text mining that use KM for competitive intelligence and innovation are in the early stages of development. Finally, organizations are investing heavily in ad hoc KM software that facilitates organizational knowledge. The chart below estimates the state of their current and future KM activities. The Future of Knowledge Management In the next several years ad-hoc software will develop into comprehensive, knowledge aware enterprise management systems. KM and E-learning will converge into knowledge collaboration portals that will efficiently transfer knowledge in an interdisciplinary and cross functional environment. Information systems will evolve into artificial intelligence systems that use intelligent agents to customize and filter relevant information. New methods and tools will be developed for KM driven E-intelligence and innovation. The Effect of Knowledge Management on Databases Multiple corporate databases will merge into large, integrated, multidimensional knowledge bases that are designed to support competitive intelligence and organizational memory. These centralized knowledge repositories will optimize information collection, organization, and retrieval. They will offer knowledge enriching features that support the seamless interoperability and flow of information and knowledge. These features may include: the incorporation of video and audio clips, links to external authoritative sources, content qualifiers in the form of source or reference metadata, and annotation capabilities to capture tacit knowledge. Content will be in the form of small reusable learning objects and associated metadata that provides contextual information to assist KM reasoning and delivery systems. The Implications of Knowledge Management For... Database Users: From business class users to the general public, database users will enjoy a new level of interaction with the KM system including just-in-time knowledge that delivers precise relevant information on demand and in context. More complex, smart systems will translate to optimal usability and less time spent searching for relevant information. For example, data analysts will enjoy simplified access and more powerful tools for data exploitation. The use of knowledge bases can reduce customer service costs by providing customers with easy access to 24/7 self service via smart systems that reduce the need to contact customer service or technical support staff. Database users may even create customized views of knowledge bases that support their needs. Database Developers: The design and development of knowledge based systems will be considerably more complex than current database development methods. Developers must consider the overall technical architecture of the corporation to ensure seamless interoperability. The use of standardized metadata and methods will also facilitate both intra-corporate and inter-corporate interoperability. Making effective physical storage and platform choices will be equally more complex. Both knowledge base developers and administrators must understand the role of the knowledge base in the overall KM system. Database Administrators: Database Administrators will evolve into Knowledge Managers. The knowledge base will store and maintain corporate memory and Knowledge Managers will become the gatekeepers of corporate knowledge. The lines between technical roles such as Web Developer, Data Analyst or Systems Administrator will blur as these systems merge into and overlap with KM systems. DBAs will need to have some knowledge about each of these disciplines. General Public: Even if they are not interacting directly with a knowledge base, the general public will benefit from the secondary effects of improved customer service due to faster access to more accurate information by service providers. Summary Organizations are realizing that intellectual capital or corporate knowledge is a valuable asset that can be managed as effectively as physical assets in order to improve performance. The focus of knowledge management is connecting people, processes and technology for the purpose of leveraging corporate knowledge. The database professionals of today are the Knowledge Managers of the future, and they will play an integral role in making these connections possible.