Knowledge management




Knowledge management


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Knowledge management


Advanced Knowledge: Advanced knowledge is the type of knowledge that is more likely to generate sustainable competitive advantage. For instance, there are world class consumer electronics companies galore but Sony is ahead of them because it has developed unique capabilities in miniaturization. Similarly, in the software industry, IBM has developed an advanced knowledge of middleware. (See Core Knowledge, Innovative Knowledge)

Agent: Agents are software programs that search for available information and filter incoming information based on specified characteristics. Intelligent agents can work without direct human intervention to carry out specific, repetitive and predictable tasks.  Agents support gathering, delivering, categorizing, profiling information, or notifying the knowledge seeker about the existence of changes in an area of interest. Many agents can perceive, reason and act in the environments in which they operate. Some agents can learn from past mistakes. Essentially, an agent uses a limited built-in or learned knowledge base to execute tasks or take decisions. Agents can be programmed to execute various tasks - delete junk email, schedule appointments or search for the lowest airfare.  Agents can be of three types – static in the client, static in the server and mobile. The most useful are the mobile agents that can move from one server to another to locate information. Such agents can either report results periodically or if they find something relevant. According to Amrit Tiwana , agents embody the push model. They can disseminate news, bulletins, warnings and notifications. Agents operate in asynchronous mode. They can monitor information at the source without being dependent on the system from which they originate. Agent technology has grown in sophistication and capabilities in recent years. In supply chain management, agents can improve the coordination among different entities. For example, P&G has been using agents to cut logistics costs by optimizing scheduling processes. (See Knowledge Base)

Agile Methodology: Processes are meant to impose discipline on the way people do their work in an organization. The danger with such methodologies is that they may stifle creativity. Agile methodology is a useful compromise between no process and too much process. Agile methods are adaptive and thrive on change. They are people oriented rather than process oriented. Agile methods take into account that a process cannot compensate for the skills of team members. The role of a process is to support the team.  While managing knowledge, too much of a process orientation may sometimes backfire. The “practice” of knowledge workers, i.e how they actually do their work, is as important as “process” which is about how they should be doing their work.  Agile methodology is a term associated with Martin Fowler (For more information, visit his website:


Application Service Provider (ASP): This is a business that delivers and manages applications and computer services from a few centers to multiple users using the Internet or a private network. Instead of buying software, customers can effectively rent the same. The payment may be on subscription or transaction basis. The customer typically interacts with a single entity, not an array of technologies and service vendors. ASP contracts typically guarantee a level of service and support to ensure that the software is working and available at all times.


Articulation:  This is the process by which tacit knowledge is converted into explicit knowledge. Articulation, also called externalization, is one of the four components of the Socialization, Externalization, Combination and Internalization (SECI) model developed by the Japanese scholars, Hirotaka Takeuchi and Inkujiro Nonaka. Making tacit knowledge explicit is one of the major challenges of Knowledge Management. Figurative language and symbolism can greatly facilitate the process of articulation.

Artificial Intelligence (AI):  Much work has been done to make computers develop the intelligence of human beings. Artificial Intelligence (AI) involves the elimination or reduction of human involvement by extracting people’s knowledge and having the computer make or support important decisions. Despite lacking the flexibility, breadth and generality of human intelligence, AI can be used to capture, codify and extend organizational knowledge. AI can be used to generate solutions to specific problems that are too complex to be analyzed by human beings on their own.  But AI has not taken off as rapidly as expected for various reasons. It is not that easy to extract knowledge from the brains of experts. Knowledge also changes more rapidly than the design of such systems can cope with. So AI often complements, rather than replaces human experts. (See Genetic Algorithms, Neural Networks, Case Based reasoning, Fuzzy Logic)

Asynchronous Communication: Asynchronous communication means the transmission and receipt of a message not occurring simultaneously. A good example is email. Blogging is also an example of asynchronous communication. Asynchronous communication is non intrusive but it lacks interactivity. It is often the interaction of messages and ideas that leads to rich knowledge sharing and knowledge creation.
Automated Decision Making: Use of computers in decision making. These systems are taking over previously human made decisions in various areas of management. Essentially, computers make decisions on the basis of pre-specified business rules. Yield management systems that automate pricing are common in the airline industry. In the financial services industry, program trading of equities and currencies is taking off.  Automated credit approval is quite common in case of banks and mortgage companies. (See Decision support systems)


Autonomy: Autonomy is a necessary condition for knowledge creation. Autonomy encourages people to pursue new ideas, work on them and develop new knowledge. When autonomy is limited, the culture can get stifling and people will not take the initiative to share ideas, chase opportunities and create knowledge. Workers in different departments will share knowledge with each other in a seamless manner only when there is autonomy. Without autonomy, silos will be created within the organization.


Ba: A term developed by the famous Japanese management guru, Ikujiro Nonaka. Knowledge cannot be created in a vacuum. Knowledge needs a context to be created. It needs a space where information is given meaning through interpretation. Ba is a useful concept in this regard.

Ba is a shared context, in which knowledge is shared, created and utilized, through human interactions. Ba provides the energy, quality and space to perform the individual knowledge conversions and to move along the knowledge spiral.

Ba can be built by providing physical space such as meeting rooms, cyberspace such as computer networks or mental space such as common goals to foster interactions. A Ba must have the right mix of people with different backgrounds and viewpoints to make the shared context a rich one. The challenge for leaders is to locate the right people.

When participants come together in a Ba, they must suspend judgment of the objective meaning and see things as they are. This allows tacit knowledge to be articulated without any pre conceived notions. Then, they must reflect on what the thing means to them and put the meaning into words. Finally, they must reflect on whether this meaning can be universally applied to other situations.

Love, care, trust and commitment form the foundation of knowledge creation. A Ba needs all of these. A Ba needs to be a self organizing place with intention, direction and interest. Without intention, energy in Ba cannot be directed effectively. Only chaos rules. The energy of Ba is given by its self organizing nature. To be effective, Ba requires creative chaos and redundancy. Creative chaos results when challenging goals are set and employees are forced to question conventional assumptions. Redundancy results when people are given more information than they need. This generates more ideas, leading to more alternatives.

Ba need not be limited to a single organization. It can cross the organizational boundary and exist in the form of a joint venture with a supplier, as an alliance with a competitor, as a relationship with a customer or as a tie up with a local university. (See Redundancy)

Benchlearning: A structured approach to learning from others, and improving. Developed by Bengt Karlof and his colleagues, it goes beyond benchmarking. Focused on quantitative comparisons, benchmarking tends to downplay the key role of knowledge transfer. (See Benchmarking).

Benchmarking: The process of identifying who is the very best, who is setting  the standard and defining that standard. Benchmarking is a systematic process for comparing the performance of an activity or process across industries or organizations or departments and then introducing necessary improvements. Benchmarking starts with some fundamental questions. Who has the best CRM? Who has the highest quality levels? Who has the most robust delivery process? Who provides the best after sales practice? Who has the most agile supply chain? Who manages customer relationships best? Who has the highest quality levels? Much of the early work in benchmarking was done in the area of manufacturing. Now benchmarking is applied almost anywhere.

Benchmarking can be both internal, i.e. within the organization, and external, i.e. across organizations. External benchmarking can provide models of excellence. However, this may be little compared with the vast amount of untapped knowledge already residing inside organizations, which can be tapped through internal benchmarking. Vibrant mechanisms for internal benchmarking represent one of the most tangible manifestations of knowledge management. They are also tangible evidence of a learning organization—one that can analyze, reflect, learn, and change, based on experience.
(See Best Practices, Bench Learning)

Benefits Tree: A diagrammatic depiction that explains the linkages between knowledge processes and business outcomes. Such a tree is helpful in justifying investments in knowledge management. More importantly, a benefits tree can help ensure that KM efforts are focused on achieving the desired outcomes.

Best Practices: Sharing of best practices within an organization is an important area of KM.  Such knowledge sharing enables lagging departments to catch up with leaders. For example, the Ispat group, global leaders in the steel industry, has driven up productivity by systematic sharing of best practices in their plants across the world. At a higher level, a best practice is the distillation of accumulated wisdom about the most effective way to carry out a business activity or process. Viewed this way, arriving at a best practice involves comparison with other firms within the industry and sometimes across industries. For example, Toyota has established best practices in the area of lean manufacturing, Dell in supply chain management and McKinsey in tacit knowledge sharing.

What exactly constitutes a best practice? According to Carla O’Deli and C. Jackson Grayson , labeling any practice as best immediately raises a hue and cry in the organization. Not only is "best" a moving target but it is also contextual. Arriving at a working definition of best practice can help create a shared language across the organization. As the term 'best' seems to imply that no further improvements are possible, the term good practice is often preferred. Some companies have thought through carefully while dealing with this definitional issue. The oil company, Chevron has adopted a simple definition of best practices: Any practice, knowledge, know-how, or experience that has proven to be valuable or effective within one organization that may have applicability to other organizations.

Chevron views best practices at four levels:
Good Idea—Unproved ideas not yet substantiated by data but which make a lot of sense intuitively and could have a positive impact on business performance. They need further review/analysis. If substantiated by data, these ideas could be candidates for implementation in one or more locations/sites.
Good Practice—A technique, methodology, procedure, or process that has been implemented and has improved which business results for an organization. This is substantiated by data collected at the location. A limited amount of comparative data from other organizations exists. It is a candidate for application in one or more locations.
Local Best Practice—A good practice that has been determined to be the best approach for all or a large part of an organization based on an analysis of process performance data. The analysis includes some review of similar practices outside Chevron.
Industry Best Practice—A practice that has been determined to be the best approach for all or large parts of an organization. This is based on both internal and external benchmarking work, including the analysis of performance data. External benchmarking is not confined to the organization's industry.

Research reveals that companies use different ways to share best practices.
Bumble bee approach
High-level managers can visit different plants/locations/sites/offices to understand what is going on. These executives make personal judgments about what they are hearing and pass along the relevant information to other offices. This approach can create peer pressure, holding up one unit as better than another. But it does not provide enough information or motivation to the weaker unit to adopt the practice. Moreover, this approach may facilitate sharing of 'explicit' knowledge—but not tacit knowledge. There is no direct interaction between the two groups. This approach does help identify people who have set the standard. Transferring such people to another location is probably a more effective way of transferring the best practice.

Benchmarking Teams
Benchmarking teams can be formed to assess the current state of the organization on a particular process, identify gaps and problems, and then search for best practices outside the company. Teams often start their benchmarking efforts by trying to compare measures and results in order to identify best practices. A comparison of financial and operating performance alone is not enough. Other factors can affect performance outcomes. Teams should spend less time arguing about "who is really good" and more on looking for breakthroughs in practices.

Best Practice Teams
Unlike benchmarking teams which tend to have a short life span, best-practice teams tend to be more enduring. These teams usually consist of managers or professionals with similar responsibilities in different divisions or plants in the company. The teams are usually led by functional experts who act as internal consultants assisting with transfer. Best Practice teams also often provide guidelines on what constitutes a "best-practice" in their function. Teams meet from time to time to share practices and issues and also remain in touch through various forms of electronic communication.  

Knowledge and Practice Networks
Unlike benchmarking and best-practice teams which are imposed from the top, knowledge and practice networks emerge from below. The right culture and necessary technological infrastructure, play a key role in the formation and functioning of these networks.

Internal Assessment and Audits
This fourth approach can range from formal technical assessments to internal audit programs. Assessment activities may also include the identification and transfer of best practices.

Transfer of best practices across an organization continues to be a major challenge. According to O’Deli and Jackson Grayson, the biggest barrier is ignorance. In most companies, particularly large ones, people do not know someone else has knowledge they require or would be interested in knowledge they have. Once they recognize that a better practice exists, the second biggest barrier to transfer is the absorptive capacity of the recipient. Potential recipients may have neither the resources nor enough practical details to implement it. The third barrier is the lack of a relationship between the source and the recipient. Personal ties must be strong enough and credible enough for both listening and helping to be effective. Finally, even in the best of firms, best practices take months to move from one part of the organization to another. This kind of a time lag is unacceptable in a fast changing business environment.

Technology can help in sharing best practices. But technology has its limitations. It should be remembered that all the important information about a process is too complex and too experiential to be captured electronically. Moreover, without the right organizational climate, technology will have little impact. But in many organizations, the instinctive reaction is to create a technical solution, usually an on-line database of best practices. Dozens of companies create internal electronic directories and databases and launch massive internal corporate PR campaigns to encourage the use of these databases. But few people enter information about their practices and few access it. There are several reasons for this.
The really important and useful information for improvement is too complex to put on-line.

There has to be a framework for classifying information.
The framework must provide a common vocabulary for people from different businesses and industries to identify similar or analogous processes. This framework must enable diverse units to talk to each other more effectively about their business problems.
Entering information into the system must be part of someone's job. Busy managers and professionals will rarely take the time to enter a practice into a database unless it is part of their job.
Culture and behaviors are the key drivers and inhibitors of internal sharing. Companies must address some fundamental questions: How do you get people to contribute to and use the system? Are people rewarded for taking the time to share or seek out best practices?

Satyam, one of India’s top IT services providers has recently launched an organization wide initiative to facilitate sharing of best practices. The initiative includes widespread email communication across the organization and interactive knowledge sharing sessions in which the best practices are explained by the people who have implemented them.

According to O’Deli and Jackson Grayson, there are seven lessons for firms about to embark on best-practice transfer.

  • Benchmarking must be used to create a sense of urgency or find a compelling reason to change.
  • Initial efforts must focus on critical business issues that have high payoff and are aligned with organizational values and strategy.


  • As resources are not infinite, an organization can only invest in and support a finite amount of change at any one time..
  • Measurements should not be taken too far as they can be distorted due to inconsistencies in data collection. They are also open to interpretation about local causes for the differences in performance. The debate should shift from "who's best" and why the measures are not fair, to identifying dramatic differences in performance. Such differences would establish beyond doubt  a real underlying process difference.


  • Realign the reward system to encourage sharing and transfer. Leadership can help by promoting, recognizing, and rewarding people who model sharing behavior, as well as those who adopt best practices. Rewards must be given for collective as well as individual contributions.
  • Use technology as a catalyst to support networks and the internal search for best practices, but don't rely on it as a solution. A combination of new information technology tools such as e-mail, "best practices databases," internal directories, and groupware can be used to support employees seeking knowledge and collaboration across the organization. But technology by itself will not create a vibrant market for sharing best practices.


  • Leaders must constantly spread the message of sharing and leveraging knowledge for the greater good. Leaders must encourage collaboration across boundaries of structure, time, and function. Some ways to do this are to promulgate success stories, provide infrastructure and support, and change the reward system to remove barriers.

According to O’Deli and Jackson Grayson, three themes seem to be evident in all successful internal benchmarking and transfer efforts. First, internal transfer is a people-to-people process. Relationships hold the key to meaningful sharing and transfer. Second, learning and transfer is an interactive, ongoing, and dynamic process that cannot rest on a static body of knowledge. Employees are inventing, improvising, and learning something new every day. New best practices keep emerging. Third, specific skills and capabilities are needed as a foundation. These capabilities include: a process improvement orientation, a common methodology for improvement and change, the ability to work effectively in teams, the ability to capture learning, and the technology to support cataloguing and collaboration.

Ultimately, the key to successful transfer of best practices lies in a personal and organizational willingness and desire to learn. A vibrant sense of curiosity and a deep respect and desire for learning from others are the prerequisites for success. (See Benchmarking, Benchlearning)

Blog: A user friendly website where individuals can express their thoughts, feelings, ideas and opinions often with hyperlinks to sources that have stimulated their thinking. While some dismiss blogging as a gimmick, others see it as grass-roots KM, somewhat similar to storytelling. Blogs can trigger the thinking of other people, especially when they have a contrarian or unconventional view that provokes other people to respond. This exchange of ideas facilitates knowledge sharing and in some cases even knowledge creation. Blogs can also be viewed as online personal diaries. Blogs provide a more personal way of showcasing a company’s products and eliciting feedback from customers. In the software industry, blogs provide a forum where new products can be introduced and developers educated on how to use the different features. Within an organization, blogs can be used to exchange project related and event related news.

Brand Knowledge: A brand, viewed from the right perspective, is a knowledge asset. It packs a lot of insights about what benefits customers are looking for – both functional and emotional. According to Satoshi Akutsu and Ikujiro Nonaka , brand knowledge includes brand meta knowledge, brand knowledge vision, brand experience and context creativity. Brand meta knowledge serves as a mental model for thinking about what can create valuable brand knowledge. It acts like a methodology for creating knowledge about the brand. Brand knowledge vision determines the sort of brand knowledge an organization should create to remain relevant and what governs the brand, including its promises to customers. Vision helps in creating a distinctive identity for the brand. Brand knowledge gets enhanced by capturing the experiences of employees, customers, associates, investors and the community.

The brand building process can be seen as a dynamic process of creating context. In some cases, differences in contexts need to be modified. On other occasions, the differences become opportunities to create something by making the best use of them. For example, the marketer may want to change the consumer brand image and bring it closer to the desired or ideal brand identity.    

Browser: Software that allows people to access documents on the Internet, typically using the HTTP protocol. Browsers read HTML and convert the code into web pages. Browsers serve as the primary front-end interface for knowledge management systems that rely on intranet technology. (See HTML)
Bulletin board: An electronic public forum created with software that supports multiple simultaneous callers. Participants can post their views and ideas. They can also comment on messages from other participants. A bulletin board facilitates exchange of ideas, announcement of events and collection of feedback from people.
Business Intelligence:  Organizations collect huge amounts of data in their information systems during the course of their day-to-day operations. Business Intelligence (BI) helps in converting data into information and then into knowledge. Our intelligence enables us to combine existing knowledge with new information and change our behavior in such a way that we succeed at our task or adapt to a new situation. Similarly, BI enables firms to collect information, develop knowledge about operations and change decision making behavior to achieve various business objectives. BI software can be used to gather, store, analyze and provide access to data and present that data in a simple, useful manner. Data warehousing is usually a part of this process. BI involves sifting through large amounts of data, extracting pertinent information and turning that information into knowledge, using which decisions can be taken. (See Data warehousing  and Data Mining)

Case Based Reasoning: Many business problems can be solved by identifying patterns. Case Based Reasoning (CBR) takes advantage of previous problems or cases handled by people and attempts to solve problems through analogies. Based on the attributes of the problem at hand, a search mechanism sifts through the cases available and retrieves the closest matches. 

The case-based approach is conceptual, not based on individual words. So the traditional Boolean rules do not work well. Through categorization, CBR connects similar cases. The search is on the basis of ideas and concepts, not key words.

The starting point in CBR is to input a series of “cases,” which represent knowledge about a particular domain expressed as a set of problem characteristics and solutions. When people are presented with a problem, its charac­teristics can be compared with the set of cases in the application, and the closest match can be selected.

According to Amrit Tiwana , CBR is one of the promising tools for any KM system. CBR is particularly useful when the choice is between deciding on the basis of some data and no data at all.  A branch of artificial intelligence, CBR is most commonly found in the customer service and support processes in firms. Take customer support or “help desk” applications, for example. The customer is on the telephone in real time. In this situation, the users can understand problems, but are not capable of solving some of them right away. CBR may be the best bet under these circumstances. CBR has also been successfully applied to planning, scheduling, design and legal deliberation. 

CBR systems need to be put in place after thorough initial planning. All possible attributes that may be needed in future, must be identified. If attributes are subsequently added, older cases that have those attributes will not show up in the search, unless more attributes are explicitly added to the old cases as well. (See Neural Networks)

Causal Knowledge: This kind of knowledge covers issues such as rationale for decisions, alternatives and eventual outcome of activities. Causal knowledge is much richer, deeper and consequently more valuable than factual or procedural knowledge. For example, when something goes wrong, managers can actually document the reasons and the circumstances underlying the failure. “Lessons learnt” databases contain some of the most valuable knowledge in organizations. Unfortunately, not many organizations invest sufficiently in storing and sharing causal knowledge. One of the best ways to encourage the development of casual knowledge is to encourage employees to ask why, when a problem is faced, something goes wrong, there is an unexpected success, etc.

Caves and Commons: Proper design of the work space can significantly enhance the productivity of knowledge workers. Caves and Commons denote two main types of physical working area. A Cave is a private area for concentrated thinking. Microsoft is famous for providing individual cabins to most of its knowledge workers. A Common is an open area for socialization, meeting rooms for team discussions and so on.  Both caves and commons are needed to improve the productivity of knowledge workers. (See Physical Environment, Work Ambience)
Channel Integration: In any business, there are several channels of communication that connect a company to its customers and partners. These include web browsers, voice, wireless hand held devices and computing devices and direct contact with customers and retailers. Channel integration refers to the integration of different channels to facilitate effective leveraging of knowledge. For example, customer knowledge can be integrated across all business processes including pre and post sale contacts, orders, delivery, after sales service, complaint resolution, etc. Such knowledge can be updated and made available in real time. The ultimate objective of channel integration is to exploit knowledge, lock in customers and increase switching costs. This approach is often called “getting a 3600 view of customers.” (See Customer Knowledge)
Chief Knowledge Officer: Many organizations these days have chief knowledge officers (CKOs) explicitly mandated to lead the KM function. Michael Earl and Ian Scott have given an excellent account of the work of CKOs. CKOs are usually appointed when the top management realizes that inadequate attention is being paid to management of knowledge in ongoing operations and the organizational knowledge is not being leveraged satisfactorily. Inability to learn from past failures and experiences in strategic decision making and difficulties in creating value or making money from knowledge embedded in products (or held by employees) are other reasons that prompt the appointment of CKOs. The role of CKOs is still evolving in most organizations. Different corporations are likely to have different expectations from the KM function. So CKOs often have to work out the agenda for themselves in consultation with key people in the organization.

In general, CKOs need to bring to the table multiple skills. CKOs must be passionate about learning. They must act entrepreneurially. They need to be self starters. They must be flexible and able to carry key people along with them as they implement projects. Typically, they should have been around in the organization for long. This not only gives them greater credibility but also a better understanding of cultural and organizational issues that makes implementation easier.

As evangelists, CKOs have to influence minds and behaviors. They have to get a buy in from senior managers about the importance of KM. They have to create a vision, spot opportunities and leverage existing initiatives.

As facilitators, CKOs act like consultants. They have to work with and through people. They have to enlist the support of champions, sponsors and partners. Champions are people who are excited about KM. They need no further selling. Sponsors are senior executives who fully support KM. Partners are typically people from Management Information Systems and Human Resources. They should be able to shape ideas, be good at interventions and work with line managers in pain areas.

As designers, CKOs must analyze situations, ask good questions and propose solutions. They may not actually deliver solutions but should know who can and work with them. They must understand quickly what is possible and what is not.

To kick start KM, CKOs can focus on specific themes such as knowledge directories, knowledge-repositories, knowledge-intensive business and management processes, knowledge exchange events and knowledge protection policies.

CKOs usually tend to have small budgets and small staff. They mobilize resources as the KM initiative picks up, need for investments in technology arises, and more line managers request advisory support. In general, CKOs are not “resource hungry” people.

Appointing a CKO is one way of giving momentum to a KM program. Over time, KM may get embedded into organizational routines, making the role of a CKO less critical. But initially, a leader is needed to set the agenda and spread awareness across the organization about knowledge sharing and learning. Chief Information Officers (CIOs) may not be able to take on the role of CKO. CIOs may have good technological and consulting capabilities. But they may not have the entrepreneurial mindset of CKOs. CIOs are used to managing a core function and controlling resources, not handling transitory teams. In contrast to CIOs, CKOs are more concerned with change and less with delivery. But CKOs often have to work closely with CIOs, while implementing KM projects.

Chris Argyris: Behavioral issues play a key role in organizational learning.  The work of Chris Argyris has influenced thinking in this area. People have mental maps with regard to how to act in situations.  It is these maps that guide people’s actions rather than the theories they explicitly espouse. What is more, few people are aware of the maps or theories they do use. Argyris and Schön suggest that two theories of action are involved. There are theories that are implicit in what we do as practitioners and managers, and those which we use to explain our actions to others. The former can be described as theories-in-use. They govern actual behavior and tend to be tacit. The words we use to convey what we, do or what we would like others to think we do, can be called espoused theory.
When people are asked how they would behave under certain circumstances, the answer they usually give is their espoused theory of action for that situation. However, the theory that actually governs their actions is the theory-in-use. For example, managers might mention that they rushed out of the office because an urgent meeting with a client had come up. Actually the managers may have become bored and tired by the paper work and viewed the customer meeting as a welcome change.
A key role of reflection is to reveal the theory-in-use and to explore the nature of the ‘fit’. Managers must identify the gulf between espoused theory and theory-in-use. This gulf is not bad by itself. Provided the two remain connected, the gap facilitates reflection and dialogue. But if it gets too wide, it can create problems.
A key aspect of learning is detecting and correcting errors. Where something goes wrong, many people look for another strategy that will work within the governing variables. In other words, the given goals, values, plans and rules are operationalized rather than questioned. According to Argyris, this is single-loop learning. An alternative response is to subject the governing variables themselves, to critical scrutiny. This double-loop learning, may then lead to an alteration in the governing variables and, thus, a shift in the way in which strategies are framed. (See Defensive Reasoning, Organizational Learning, Single Loop Learning, Double Loop Learning)
Clusters: An important concept in inter organizational knowledge creation. Michael Porter has coined the term “clusters” to describe geographical concentrations of interconnected companies and institutions in a particular business. Clusters include suppliers of components, machinery and services. Institutions which provide specialized infrastructure and demanding customers also form part of a cluster. Other members of a cluster include the local government, universities, research centers and think tanks who facilitate learning. Clusters are important drivers of global competitiveness because they facilitate inter organizational learning and knowledge sharing. Silicon valley in California, USA is probably the world’s most well known industrial cluster.
Clustering: The tendency to group objects, words, pictures or ideas into groups in some subjective ways. Data clustering is a technique for data analysis by partitioning a data set into subsets whose elements share common traits. Thus, a data mining tool can discover different groupings within data. For example, it can divide investors into groups based on their liquidity preferences. (See Search Strategy).
Codification: Codification aims at putting knowledge that people have, into a form that makes it easily accessible across the organization. It attempts to make knowledge as organized, explicit and portable, as possible. Codification allows knowledge to be shared, stored, combined and manipu­lated in various ways across the organization.
Some forms of knowledge, like patents are already codified and explicit. Similarly, manuals and other structured documents are examples of codified knowledge.  In other cases, reports can be generated. But not all kinds of knowledge are amenable to codification. The rich, tacit, intuitive knowledge of a seasoned expert, developed and internalized over a long period of time, is almost impossible to reproduce in a structured document or database. 
The challenge for organizations is to codify knowledge and still leave its distinctive attributes intact. The process of codification should not severely dilute the richness and context. One way to deal with this problem is that instead of trying to turn knowledge into a "code” or cram it into a template, companies can often encodethe stories themselves. That way, the context can be preserved and meaning conveyed without losing much of its value. For example, managers can prepare a video that can narrate how a key sale was made.
Cognition: Refers to activities such as thinking and reasoning. For the cognitive psychologist, behavior requires explanations at the level of mental events, mental representations, beliefs, intentions, etc. Cognitive science is the name given to the disciplines that study the human mind. Cognitive differences among people arise because of the different ways in which they perceive and assimilate data, make decisions, solve problems and relate to other people. Some people, for example, may use a lot of intuition while solving problems; others prefer a more analytical approach. People who use an analytical, logical, sequential approach to solving problems are left brained while those who use an intuitive, value-based and non-linear approach are right-brained. Some people like to collaborate while solving problems, while others like to be on their own.  Cognitive unconscious is a general term that describes a variety of mental processes and functions that take place largely independent of consciousness or awareness. Cognitive therapy is based on the assumption that the way in which individuals structure and interpret their experiences determine their subsequent behavior.   

Collaborative Filtering: This technology automatically compares attributes of one set of customers with other sets and facilitates personalization of websites by anticipating customer needs. It relies on an extensive base of similar customers. The software makes recommendations to users based on their presumed interests. Collaborate filtering requires scaleable personalization capabilities that can cope with increasing customer data volume. The Amazon website is a good example. The site recommends books to a site visitor, based on purchases by other customers with similar interests.

Collaborative Platform: Refers to the network, hardware and software that allow knowledge workers to perform tasks and work on projects together. Workers sitting at geographically dispersed locations can collaborate using such a platform. The ideal collaborative platform is characterized by portability, scalability, integration, customizability, security, flexibility, low implementation and training costs, minimum deployment time and open architecture.

Collaboration Work: A term coined by Tom Davenport. Such work involves a high degree of improvisation that in turn demands deep cross-functional expertise. Individual expertise and degree of interdependence among workers are both high in such kind of work. Investment banking is a good example. In case of an M&A deal, different experts in functions like legal, human resources, valuation and accounting may have to come together and collaborate. It is difficult to automate or create a process flow for such work. So, knowledge can be made available in repositories which people can access as and when needed.

Combination: A term coined by Hirotaka Takeuchi and Ikujiro Nonaka in their book, “The knowledge creating company,” as part of their SECI (Socialization, Externalization, Combination, Internalization) model. This mode of knowledge conversion involves combining different bodies of explicit knowledge. Combination is the process of creating new explicit knowledge by sorting, adding, categorizing and combining existing explicit knowledge. Many software services companies store valuable documents in repositories, for easy access by employees. People refer to these documents, offer comments and also contribute new documents. This way, new knowledge is generated.

Community of Interest (CoI): A group of people who share knowledge and experience around a common interest. These people are driven more by learning and less by outcomes, compared to a Community of Practice. A good example may be Business School professors having a common interest in a particular topic of research. Peer reviews, seminars and collaborative paper writing are some of the ways in which communities of interest are sustained.

Communities of Practice: A Community of Practice (COP) is a group of people who share and develop their knowledge and expertise. These people may not necessarily work in the same department or organization.

In many disciplines, knowledge is generated not by individuals but by a community of like minded peers. So formation and nurturing of communities of practice is becoming a key challenge for many companies. Etienne Wenger has given an excellent account of how COPs function. 

Most KM initiatives lay emphasis on making codified knowledge available in databases/portals. But important knowledge is often difficult to codify.  Only a small fraction of the knowledge in an organization is ever captured in content management systems, knowledge repositories and portals. Moreover, context is missing in such knowledge. It is context which gives a knowledge asset its richness. Context includes background information, alternatives that were tried but discarded, experiments that did not work the thinking behind a solution and reasons for the success or failure of an approach. Communities provide this context, by facilitating connections between knowledge seekers and the knowledge source. Within a community, members are likely to have common interests. They've developed relationships and built trust, and are used to helping and sharing knowledge with each other.

The common elements of COP are sense of joint enterprise, shared identification, relationships of mutual engagement that promote bonding and shared repertoire of resources that members develop over time through engagement. Communities can be formed within business units, across business units and across organizations.

A COP is not entirely homogeneous. Indeed COPs often have different categories of members:

  • Core group: There are the passionate and actively engaged people.
  • Full membership: These are the practitioners who make up the community.
  • Peripheral membership: They belong to the COP but have less involvement and authority.
  • Transactional participation: These are outsiders who interact with the COP occasionally to receive or provide service.
  • Passive access: There may be many other people who have access to artifacts produced by the community such as publications, website or tools.


A COP is different from other forms of organizational structure. It does not involve reporting relationships. It is based on collegiality. The power of the members comes from knowledge, not formal authority. Unlike a team which is defined by a task, a COP is defined by knowledge. A COP is held together, not by a project but by the passion of its members. Unlike a cross functional team, a COP does not form when a project starts or disappears when a project gets over. A COP provides a stable form of membership that enables people to move from one task to the next without any loss of continuity in terms of professional identity and development of expertise. A COP provides a context for the relevant exchange and local interpretation of information.

COPs usually start as loose networks with latent needs and opportunities. As the community matures and grows, members gradually establish a shared practice, a learning agenda and a group identity. COPs evolve over time. Some COPs are short lived. Others last for centuries.

Communities have to be nurtured carefully. They need activities to remain vibrant and get people involved. Meetings play a key role in many communities. A face-to-face meeting is often desirable early on, to socialize, build relationships and trust. Members can get to know each other -- what their strengths and interests are, what they're passionate about, the knowledge they hold, their experience, etc. 

At each stage in the life cycle of a COP, there are specific challenges or questions. In the early days, there is a need for an inspiring vision or a difficult task to advance the state of a practice or to achieve a challenging organizational objective. The challenge at the next stage, where more people want to participate, is scaling up, so that the community can handle larger numbers. When it reaches maturity, the problems faced by a COP include complacency and loss of vitality. People participate less and less. The key challenge here is to reinvigorate the community.

Collaborative and communication tools can support communities. In their early days, communities need tools that help develop relationships while enhancing divergent thinking. Collaborative environments like chat rooms, brainstorming tools and mechanisms to facilitate the sharing of member biographies and pictures, and simple portals with various features for collaboration may be ideally suited for young communities.

During the growth stage, a community needs tools that enable convergent thinking to help it agree on a course of action, a best practice, a recommended solution, or a decision about which product idea to pursue. It needs technologies that help it to find relevant knowledge assets quickly, and engage internal and external customers in dialogue. It needs the capability to vote on alternatives, and features that help surface and resolve inter-community conflicts. It also needs to integrate new members quickly.

During the maturity stage, the community may need tools that balance convergent and divergent thinking. Finally, when it is in decline, a community needs tools that archive and preserve knowledge.

Communities on the decline need to be re-energized. Movies, images and motivating stories can be used to revitalize the community. Face-to-face meetings, as well as skilled facilitation, may once again become essential.

The return on time invested in community activities can be evaluated using various metrics:

  • business problems solved in the community
  • new knowledge created in the community
  • joint learning, occurring in the community
  • existing knowledge reused by the community
  • innovations
  • the community's role in recruiting and retaining talent

In his book, “The Knowledge Management Toolkit”.

O'Dell, Carla; Grayson, C. Jackson. “If Only We Knew What We Know: Identification and Transfer of Internal Best Practices” California Management Review, spring 1998, pp154-174.

“Hitotsubhashi on Knowledge Management” John Wiley & Sons, 2004.

In his book, “Knowledge Management Toolkit”.

In their article, “What do we know about CKOs?” Knowledge Horizons, 2001.

6. Argyris, Chris. “On Organizational Learning” Blackwell Publishers, 1999.

Porter, Michael E. “Clusters and the New Economics of Competition” Harvard Business Review, November-December 1998, pp 77-90.

In her article, “Communities of practice: The structure of Knowledge Stewarding” “Knowledge Horizons - The present and the promise of knowledge management”, 2001.


Comprehension: Information overload is increasingly a problem today. The quality of decision making deteriorates when decision makers spend time going through more information than what is needed. That time might be better spent on comprehending, reflecting, analyzing and making decisions. Comprehension can be improved by eliminating duplicate or overlapping messages. Messages can also be filtered or prioritized. Visualizing techniques can be applied to help the user understand the available information more easily. Various KM tools are available to facilitate comprehension.

Concept Mapping: A visual representation of core concepts showing the relationships between them. A typical concept map has nodes (the concepts) with arrowed links between them (the causal relationships).
Concept mapping helps in visualizing the relationships between different concepts. These relationships are articulated in linking phrases, e.g., "gives rise to", "results in", "is required by," or "contributes to". Concept mapping helps to represent the mental models, i.e., the cognitive map of individuals, teams and organizations and also the structure of knowledge extracted from written documents. The addition of knowledge resources, e.g., diagrams, reports, other concept maps, spreadsheets, etc., to the concept nodes can further facilitate meaningful learning.
Concept maps are used to stimulate idea generation and to communicate complex ideas. Teachers can use them in the classroom to make learning more interesting and to reinforce key concepts. Formalized concept maps are used in software design.  
In short, concept maps are used for:

  • Taking notes and summarizing
  • Communicating complex ideas and arguments
  • Detailing the entire structure of an idea, train of thought, or line of argument for the scrutiny of others.
  • Capturing key concepts, their relationships and hierarchy from documents
  • Transforming tacit knowledge into an organizational resource.
  • Enabling knowledge retention by eliciting and mapping expert knowledge of employees prior to retirement
  • Facilitating the creation of shared vision and shared understanding within a team or organization

Condensation: The summarizing of data into a more manageable, concise form. For example, a series of data can be summarized into a table. Condensation is one of the ways by which we can convert data into information. (See Data, information)

Content Analysis: Analysis of a body of content (text) into its key concepts to identify trends, to generate keywords and thesaurus terms to improve subsequent text search and retrieval. (See Content Management System)

Content Management System (CMS): A Content Management System (CMS) makes it easier to develop enterprise portals and websites, by separating the management of content from its presentation (display). CMS facilitates collaborative creation of documents and other content. Blocks of content are tagged with metadata and other attributes and held in a database.
There are various kinds of CMS:

  • Web content management systems can automate various aspects of web publishing
  • Transactional CMS assist in managing e-commerce transactions
  • Integrated CMS help in managing enterprise documents and content
  • Digital asset management systems help in managing the lifecycle of digital media.
  • Similarly there are publications management, learning management and document imaging systems.

(See Document Management Systems)

Context Sensitivity: Rich knowledge tends to be highly contextual. Separating the context from the knowledge tends to take away much of its value. So it is important to understand for what purpose the data has been collected or the report has been prepared. This ensures that the right interpretation is made of a document available in the knowledge repository. Equally important, this knowledge must be applied carefully with necessary modification and customization in a different context. To take an example, the challenges involved in implementing an Enterprise Resources Planning (ERP) system for an oil company may be quite different from those for a pharmaceutical company.  (See Codification)

Constraint Based Systems: Constraint-based systems are suited for situations where data is available, but normally less quantitative than that required by neural networks. Like expert systems, they are suited for relatively narrow problem domains, such as product configuration or pricing. Constraint-based systems capture and model the constraints that govern complex decision making. These systems are usually object-oriented , not rule-based. So they are easier to modify than expert systems.  There are no complex interactions to understand and modify. (See Expert System, Neural Networks, Object Oriented Databases).

Cookies: A general mechanism in which server side connections can be used to store and retrieve information on the client side of the connection. The main purpose of cookies is to identify users, prepare customized web pages and make the site more personalized and user friendly. For example, the client is freed from retyping a user ID, every time. Sites can also store user preferences on the client. Every time connection is established with the site, those preferences can be supplied by the client. To facilitate this process, customers entering a website are usually asked to fill out a form. This information is packaged into a cookie and sent to the web browser which stores it for later use. The next time the customer visits the site, it will be customized. For example, the welcome page may have the user’s name on it. Cookies can be of different types. A session cookie, also called a transient cookie is erased when the user closes the web browser. Session cookies do not collect information from the person’s computer. They are based on session identification, not personal identification. A permanent cookie or a stored cookie is stored on a user’s hard drive until it expires or until the user deletes the cookie. Permanent cookies are used to collect unique information about the user such as web surfing behavior.  
Core Capabilities: They form the basis for the competitive advantage of a firm. Also called core competencies, they constitute a bundle of skills that together represent valuable knowledge that cannot be easily replicated. A firm’s knowledge strategy must be built around its core capabilities. The firm must develop knowledge in such a way that the core capabilities are strengthened. However, when there is a radical change in the industry or a new paradigm emerges, new kinds of knowledge not linked to the existing core capabilities may have to be developed. Otherwise, they may become core rigidities. ( See Core Rigidities)
Core Knowledge: Core knowledge is that minimum scope and level of knowledge required to compete. Core knowledge may act as a basic barrier to entry. But such knowledge is held by all players and therefore does not provide a sustainable competitive advantage. (See Advanced Knowledge, Innovative Knowledge)
Core Rigidities: An organization’s strengths can also be its weaknesses. Over time, as organizations develop these strengths, they tend to focus on one kind of knowledge at the expense of others.  If the existing strengths are not able to deliver value to customers, they may turn out to be a handicap. For example, Motorola’s strengths in analog technology became a core rigidity when digital technology took off. So core capabilities must be examined on an ongoing basis, to see how useful they are, in relation to current market needs. When the existing core capabilities have outlived their relevance, the focus must shift to building new core capabilities. (See Core Capabilities)
Corporate Amnesia: Is the loss of collective experience, embedded tacit knowledge and accumulated skills, when many people leave the firm during down-sizing and layoffs. In India, this has happened in some public sector units because of voluntary retirement schemes.
Corporate Culture: Culture plays a key role in KM. In a positive knowledge sharing culture, problems, errors, omissions, and failures are shared; not penalized or hidden. Debate and healthy conflicts are encouraged as legitimate means of solving problems. Consequently, people are open to learning and applying new ways of solving problems. In dysfunctional cultures, people have a closed mindset. They tend to become defensive when mistakes are pointed out or the scope for improvement is identified. As a result, people in such organizations find it difficult to learn and change their behavior. The Gartner group has identified three types of cultures in the context of knowledge sharing. The first category includes balkanized organizations where people compete against each other in an atmosphere of mutual suspicion and information hoarding. The potential for knowledge sharing is low here. The second category consists of “monarchies” with top-down authoritarian rule. The potential for knowledge sharing is higher here. The third category consists of federations with local autonomy and democratic means of dispute resolution. Cooperation is based on enlightened self-interest. The potential for knowledge sharing is high here.

According to William Ives, Ben Torrey and Cindy Gordon , various steps are involved in shaping a right culture for knowledge sharing. The first step is to identify knowledge sharing as a priority and then provide strong leadership and investment support. Leaders must display a strong sense of trust and integrity. Once trust is established, knowledge sharing must be embedded into the way of working. All project reviews should cover knowledge sharing and reuse of knowledge. Performance appraisals must take into account knowledge sharing. All newsletters and communications should provide links, where appropriate, to the KM system. Training courses should leverage the KM system. The company must also encourage collective inquiry into everyday experiences and sensitivity to the environment and willingness to change. Communities of Practice must be actively encouraged and nurtured. (See Defensive Reasoning, Learning Organization)      

Creative Abrasion: A term coined by Gerald Hirshberg, director of Nissan Design International. The concept has been further developed by Dorothy Leonard in her well known book, “Wellsprings of Knowledge”. Creative abrasion focuses on knowledge building at the work-group level within an enterprise as a result of arguments that occur when people with diverse backgrounds, experiences and skill sets come together to work on real business problems. A similar idea has been covered by Richard T Pascale in his book, “Managing on the Edge: How the smartest companies use conflict to stay ahead”.

Innovation, as Dorothy Leonard and Susan Straus mention, takes place when different ideas, perceptions and ways of processing and judging information collide. That is possible only when people who see the world in inherently different ways come together. But often, the constructive conflicts that should take place, do not happen. Some managers avoid clashes by keeping in their team people who think similarly like them. So nothing radically different or new emerges. In the rare cases, where managers are bold enough to bring diversity into the team, not much is done to encourage constructive conflicts. Only a few managers know how to promote creative abrasion. They do so by actively considering various approaches and taking different perspectives and by encouraging people to respect the thinking styles of other team members. These managers lay down necessary ground rules to discipline the creative process.  (See Productive Friction) 

Customer Capital: The value of an organization’s relationships with its customers. Often, it is these relationships that fetch business, not just the quality of the products/services offered by the vendor. That is why there is so much emphasis on Customer Relationship Management.
(See Customer Knowledge)

Customer Knowledge: Customer knowledge consists of the insights collected while dealing with customers. Customer knowledge is useful in understanding customer needs,  including those which are unmet and unarticulated. Various sources of customer knowledge can be integrated and analyzed both to serve customers better and to generate ideas for new products/services. Customer knowledge facilitates customer relationship management (CRM). Many IT services companies offer CRM solutions that help their clients in getting 360 degree views of customers. Information Technology (IT) can support ongoing efforts to improve customer identification, conversion, acquisition and retention and to deliver personalized services. IT facilitates high levels of personalization and decision support in a cost effective manner. But customer knowledge initiatives should not be driven by IT alone. Close personal interaction with customers is needed to get deep insights about what customers are really looking for. This is because customers sometimes find it difficult to articulate their needs.

Customer knowledge should lead to the following :

  • Customer Satisfaction: This can be measured as the percentage of customers completely satisfied with existing products/services.
  • Customer Retention: The metric here can be the percentage of customers still with the company compared with the previous year.
  • Product/Service Quality: This can be tracked by computing the percentage of customers complaining about product quality.
  • Average duration of customer relationship: This can be measured as the number of months for which an average relationship with customers continues.
  • Repeat Orders: The metric here can be the ratio of volume of business generated by repeat orders to the total business.
  • Growth in sales of key accounts: Both sales and profit growth can be tracked.


Data:  A set of particular and objective facts about an event or a transaction. For example, the number of customers arriving at a restaurant every hour. Or the total amount of purchases made at a departmental store during the day. We often have a very simplistic notion that the more the data we have, the better we are equipped to take the right decision. But data collection is the easier part. Indeed, too much data may be collected and distract our attention. And data by itself does not have any meaning. Moreover data can be cumbersome and voluminous to handle. Unless data is processed into information and subsequently converted into knowledge, it adds little value to the business.

Data Marts:  Scaled down version of a data warehouse that is tailored to contain information for use by a department.

Data Marts are also known as Local Data Warehouses. A data mart has the same characteristics as a data warehouse, but is usually smaller and is focused on the data for one division or one workgroup within an enterprise. Whereas a data warehouse combines databases across an entire enterprise, data marts focus on a particular subject or department.  For example, marketing data marts may be constructed to capture customer related information.
There are three different ways of building data marts:

  • The data warehouse can be first created, combining the information from the various legacy systems. Specialized data marts can then created not only to serve the unique needs of different departments but also to allow the querying load to be spread among several different computers. This can smoothen network traffic.
  • The data mart can be viewed as the prototype of a data warehouse. The division or group that would most benefit from data-based knowledge is first selected. A data mart is built with that group's data. Other information is added to the data mart over time till it becomes a data warehouse.
  • Data marts can be built independent of a data warehouse. It is usually quicker and cheaper to build a separate data mart instead of building an enterprise-wide data warehouse and then data marts from it. The problem here is that the company's data will not be integrated. There will quite likely be some duplication and inconsistency of data.

If there are too many data marts, complexity and costs will increase. (See Data Warehousing)
Data Mining: The process of identifying commercially useful patterns or relationships in databases through the use of information technology. Analyzing data involves the recognition of significant patterns. Human analysts can see patterns in small data sets. But large amounts of data need specialized mining tools. These tools can perform high level analyses of patterns and trends but also drill down to provide more detail when needed.
Data mining can be used to identify the attributes that characterize the customers who account for the bulk of the business. Thus, a consumer goods company may track hundreds of variables about each consumer segment, with scores of possible relationships among the variables. Similarly, data mining software can help retail companies find customers with common interests.
The term is commonly misused to describe software that presents data in new ways. The focus of data mining is not to change the presentation of the data, but discover previously unknown relationships among the data. (See Data Warehousing)
Data Slam: Refers to meaningless pieces of data which can clog corporate intranet sites and databases. They make systems slow, unwieldy and difficult to navigate. In the process, they slow down decision making.

Data Warehousing: A Data Warehouse facilitates integrated access to a company’s information. A Data warehouse stores both current and historical data that are of interest to managers across the organization. The data may originate in different operational systems and external sources. They may be in different forms – legacy systems, object oriented databases, HTML documents or XML documents. These data are standardized into a common data model and consolidated so that they are accessible to users through simple commands. A data warehouse provides data to decision makers without interfering with the transaction processing operations. Selected items are regularly pulled from transaction data files and stored in a central location. This may be done on an hourly, daily, weekly or monthly basis.

What makes a data warehouse different from other databases is its purpose. Most data are collected to manage day-to-day business activities.  The systems used to collect such operational data are referred to as OLTP (On-Line Transaction Processing). On the other hand, the distinguishing feature of a data warehouse is analysis. A data warehouse makes data available for the purpose of analysis. ". The systems used to work with such “informational data” are referred to as OLAP (On-Line Analytical Processing).
The main aim of a data warehouse is to hold in one place all the data needed for managerial decision making. So the starting point is determining the data needs. Indeed, the success of a data warehouse largely depends on how well the needs of managers have been identified.  The next step is to establish the sources of data. Then the data must be transformed and integrated so that it can be searched and analyzed efficiently by decision makers. Often, the data warehouse is created as a static copy of the original data. Instead of building a link to the original data files, it is easier to copy the data into new files. Once the data warehouse has been defined, programs are written to transfer the data from the legacy systems into the data warehouse.
One problem with a data warehouse is that managers will not always have the most current data. Often data is stored not in relational database management systems (RDBMS) but as collections of files and data items. So, the system is relatively easy to use but is less flexible compared to RDBMS.
(See Data Mining, Data Marts)

David J. Skyrme: A leading authority on knowledge management, Skyrme’s book Creating the Knowledge-based Business is described by many practitioners as “the Bible of Knowledge Management”. His book Measuring the Value of Knowledge is considered an outstanding contribution to the field of intellectual capital measurements. His other books include: Knowledge Networking: Creating the Collaborative Enterprise and Capitalizing on Knowledge: From e-business to k-business. Skyrme’s website  provides valuable information for KM practitioners.  
Decision Support Systems: Decision Support Systems (DSS) support managers in data collection, analysis and presentation of output. Such systems help managers in retrieving, summarizing and analyzing data for the purpose of decision making.  DSS may support a large group of managers in a networked environment with a data warehouse or a single user, desktop application.  A computer program churns through data and with human interpretation, reveals previously hidden trends and patterns, allowing managers to make smarter and faster decisions. The data collection is typically performed by a transaction processing system. This data is transferred to a model for analysis using the appropriate software. Finally, the DSS presents the output in a format that is easy to understand. Graphs are often, a useful way of presenting the output. Often the reports generated by the DSS are used to build a business case or to persuade other people. So the reports must be concise, accurate and visually appealing.

DSS must be designed carefully based on customer requirement. Even the best DSS will not eliminate bad decisions. It goes without saying that if managers ask the wrong questions or draw the wrong conclusion, DSS will be ineffective. So DSS must be used carefully.

DSS have not taken off as rapidly as expected. This is because of the difficulties involved in extracting decision rules or algorithms from human experts. Moreover, many managers, have a mental block about the ability of a computer to take decisions on their behalf.

Decision Diary: A diary which gives an account of decisions taken, along with the assumptions and reasoning behind them. This kind of knowledge facilitates experiential learning and future decision-making. (See Learning History, Causal Knowledge)

Decision Making: According to Herbert Simon, the Nobel prize winner, decision making takes place in four stages. “Intelligence” involves discovering, identifying and understanding the problem. “Design” includes identifying and exploring solutions to the problem. “Choice” consists of choosing among solution alternatives. “Implementation” means making the chosen alternative work. These stages explain how decision making should take place logically. In practice, the influence of various behavioral issues cannot be overlooked. Moreover, the four steps may not happen sequentially. They may overlap to some extent. And in many cases, decision making takes place in iterative fashion, accepting things that work and rejecting those that do not. Three key factors that are an impediment to good decisions are information quality, human filters and resistance to change. Information may not be accurate, complete, consistent or available on a timely basis. Managers have selective attention, various biases and focus on some dimensions of the problem while ignoring others. Last, but not the least, people are resistant to change. So, decisions often tend to be a balancing of the firm’s various interest groups rather than the most optimal solution. A KM system should take into account all these factors if it is to become an effective aid to managerial decision taking.

Declarative Knowledge: Declarative knowledge consists of meaningful concepts, categories, definitions and assumptions.

Deep Smarts: Some people can see the whole picture and zoom in on a problem that others have not identified. Almost intuitively, they make the right decision.  They combine expertise in individual areas with a system view. According to Dorothy Leonard and Walter Swap , these are people with deep smarts. Their judgment and knowledge are stored in their heads and hands. They bring very important knowledge to the table, so much so that, organizations cannot do without them. Those people know the business, customers and product lines overall and in depth. But their insight is neither documented nor evaluated. When these people leave their jobs or move on to a new role, their absence is really felt. Experience is the obvious reason that these deeply knowledgeable people make swift, smart decisions. Having encountered a wide range of situations over the years, they become a storehouse of knowledge and can reason swiftly and without a lot of conscious effort. They can identify patterns, trends and anomalies effortlessly.

Defensive Reasoning: A concept introduced by Chris Argyris, a former professor of Harvard Business School.  As Argyris mentions , defensive reasoning encourages individuals to keep private the assumptions, inferences and conclusions that shape their behavior and to avoid testing them in a truly, independent, objective fashion. When asked to examine their own role in an organization’s problems, most people become defensive. They put the blame on someone else. Defensive reasoning keeps people from identifying and admitting openly what has gone wrong. Companies need to help managers understand, analyze and reason about their behavior in more effective ways. Only then the defenses that block organizational learning can be broken. (See Chris Argyris, Organizational Learning)

Desktop Conferencing: Videoconferencing using a desktop PC. A small camera (webcam) is usually mounted on top of the user's display screen. As communication technology improves, greater bandwidth becomes available and costs come down, desktop conferencing can be expected to take off, especially as it is a more effective way of transferring knowledge than simply using email or searching through a repository. Where bandwidth availability is an impediment to transmitting video documents, audio can be used.

Dialectics: A form of thought emphasizing change and managing opposites, that goes back to ancient Greece. It is a method of discovering the truth of ideas by discussion and logical argument and by considering ideas that are opposed to each other. The starting point of the dialectical movement is a thesis. In the next stage, comes anti thesis, when the thesis is shown to be inadequate and inconsistent. In the third stage, synthesis, the previous thesis and antithesis are reconciled and transcended. The new thesis becomes the basis for another dialectical movement. According to Takeuchi and Nonaka, knowledge is created by synthesizing what appears to be opposites and contradictions. It goes through seemingly opposing concepts such as tacit and explicit, chaos and order, micro and macro, self and other, mind and body, part and whole, deduction and induction, creativity and control, top - down and bottom – up, etc. Dialectical thinking can facilitate knowledge creation by transcending and synthesizing such opposites. For example, tacit and explicit knowledge are portrayed as polar ends. But they are complimentary to each other and also inter dependent. The exercise of one form of knowledge requires the presence and utilization of the other form. There is some tacit knowledge in every piece of explicit knowledge and some explicit knowledge in every piece of tacit knowledge. Takeuchi and Nonaka point out that organizations do not merely use information to solve problems. Organizations create and define problems, develop and apply knowledge to solve the problems, and then further develop new knowledge through problem solving. In short, an organization is far more than an information processing machine. It is an entity that creates knowledge through action and interaction. Dialectic knowledge creation occurs as people in the organization synthesize tacit and explicit knowledge through interactions with others and the environment.

Dialogue: The role of conversations in creating knowledge is often underestimated. Through dialogue, differences in perspectives can function as a “thinking device,” creating new meaning. According to Nonaka and Takeuchi, the tacit knowledge of an individual or group can be articulated into explicit knowledge through dialogue. Healthy dialogues share some common attributes. They allow room for revision or negation. Participants can express their views freely and candidly. Disagreement for the sake of disagreement is not allowed. There is some degree of information redundancy. Dialogues play a key role in organizational knowledge creation. Yet their role in knowledge creation and sharing is often underestimated.

Digital Rights: The rights and conditions of use for a piece of digital content. These rights may be part of the product's wrapper, or may be embedded in the product . Digital rights are used to prevent illegal copying.

DIKAR Model: Data, information, knowledge, action and results make up the knowledge value chain. The conventional approach starts with data, which through a series of value adding steps, becomes knowledge. But as Peter Murray mentions, in a more dynamic environment, it may be better to work backwards. Given the desired results, what actions are needed? What knowledge is needed to perform these actions?  What information is needed to create this knowledge? What is the data to be collected for generating the necessary information? The role of KM is to marshal knowledge and experience and to integrate them and develop new capabilities that the market will value.

Discussion List: Sharing information and knowledge among a group of people, using a single email address. Thus all messages, generated during one day can be grouped together and sent as a single email in a 'digest'. More commonly, it saves the time of having to send the email to each person in the selected group, individually.

Document Management systems: Hundreds of documents get generated each day in any organization. Document management systems help ensure that these documents are stored properly for easy retrieval. These systems make vast amounts of documents easily accessible and adaptable through the web. Often, such systems incorporate workflow functionality that allows documents to be intelligently routed to select, relevant people.  

A useful document management tool is Microsoft SharePoint. SharePoint allows people to share Microsoft office documents with others through web pages. SharePoint sites are highly dynamic, unlike usual websites. Uploading of documents is a simple process. SharePoint also facilitates meetings, making public announcements, sending alerts and tracking work items. Instead of routing documents by email, people can set up a workspace on a SharePoint site. Email alerts notify reviewers when files are uploaded or modified. Reviewers can discuss changes online. Comments can be tracked and all the changes can be recorded in version history. Document workspaces are provided to store work-in-progress. A workspace often contains only one document that a team is working on. A document library is typically used to store multiple documents within a site.

Dorothy Leonard: A well-known scholar in the area of KM, Dorothy Leonard has done considerable research on managing knowledge for innovation and stimulating creativity in group settings. Her articles have appeared in academic journals (e.g., “Core Capabilities and Core Rigidities in New Product Development” awarded Best Paper by Strategic Management Journal for sustained impact on the profession), practitioner journals (e.g., “Deep Smarts” in Harvard Business Review) and books on technology management (e.g., “Guiding Visions” in The Perpetual Enterprise Machine). Her book, Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, published in 1995, has been widely acclaimed and translated into several languages. Her book When Sparks Fly: Igniting Group Creativity, (co-authored with Walter Swap) published in 1999 has also been widely translated and awarded the Best Book on Creativity by the European Association for Creativity and Innovation. Her latest book (with Walter Swap) is: Deep Smarts: How to Cultivate and Transfer Enduring Business Wisdom, published in January, 2005. (See Creative Abrasion, deep Smarts)
Double-loop Learning: Single-loop learning involves using knowledge to solve specific problems based on existing assumptions and is often based on what has worked in the past. But double-loop learning also called generative learning, goes a step further and questions existing assumptions in order to create new insights. (See Single Loop Learning, Learning Organization)
Dynamic Capability Building: John Hagel III and John Seely Brown, in their book, “The Only Sustainable Edge” define capability as the recurring mobilization of tangible and intangible resources for the delivery of distinctive value in excess of cost. Companies must take a more dynamic view of capabilities. Otherwise, they will find themselves outflanked by more aggressive competitors. Sustainable competitive advantage will ultimately come from a firm’s institutional capacity to rapidly strengthen its distinctive capabilities and to accelerate learning across enterprise boundaries. As Hagel and Brown mention, “….the primary role of the firm should be to accelerate the knowledge and capability building of its members so that all can create even more value. This perspective broadens managerial attention from the tasks of allocating existing resources to the tasks of deepening knowledge and capability in an increasingly uncertain environment. “Hagel and Brown suggest three mechanisms to accelerate capability building. Processes can be outsourced and in combination with offshoring can give the firm access to specialized capabilities. Distributed networks of specialized companies can also help in mobilizing resources. By bringing together people with diverse backgrounds and skills to solve business problems, capability building can again be accelerated. 

E-learning: With the availability of various technologies, learning in organizations is undergoing a sea change. E-learning is leading to a fundamental rethinking of the learning process in business environments. Unlike in the past, when people were brought together to one place for training, E-learning allows learning material and faculty expertise to be distributed to desktops. E-learning is moving training away from a push-model to a pull-model. Employees determine what is useful to them. They can learn as per their convenience and customize the training according to their specific needs and circumstances.

According to John Hagel III and John Seely Brown , e-learning not only imparts training inputs but also helps shape common points of view and vocabularies across a distributed and diverse work force. E-learning can facilitate innovation by enabling people from very different backgrounds to collaborate effectively, using common frameworks and vocabularies. Cisco is one company, which has deployed learning portals to serve the learning needs of its direct sales force, its system engineers and its channel partners. People can easily locate learning modules that are of the greatest relevance to them. Cisco has also been attempting to make the whole process more proactive by recommending to employees what kind of learning they must engage in, to be more effective in the work place. Thus, before a sales person meets a customer in a financial services company, the e-learning system might send a trigger suggesting that he may go through a new learning module that covers features of special interest to financial services companies.
Enterprise Information Systems: An Enterprise Information System (EIS) attempts to use the existing transaction data and display it in a form that is easy for top level executives to access. An EIS models the entire company. The landing page of an EIS is typically a graphical representation of the company. A CEO can drill down into required areas and ascertain relevant particulars. If there is a specific problem area, the CEO can do a more focused investigation and pinpoint the responsibilities. The primary aim of an EIS is to provide easy access to data for senior executives. Instead of waiting for the information, they can retrieve it as soon as it is available. An EIS is expensive to create and maintain. Integrating the data and formatting it to make it user friendly requires programmers and analysts to anticipate management needs and keep the system up-to-date. Another issue is that senior managers often find it more convenient to ask lower level managers for reports instead of trying to retrieve the information themselves.

Epistemology: Framework for categorizing knowledge. There are two kinds of knowledge – tacit and explicit. Tacit knowledge is personal, context-specific and difficult to formalize, document and articulate. Explicit knowledge can be transmitted in formal, systematic language. (See Tacit Knowledge, Explicit Knowledge)

Experiential Learning: Experience is considered life’s greatest teacher. In any company, people learn through experience. Experiential learning can be facilitated in various ways. One way is to institutionalize the “after action review” throughout the organization. Essentially, this is a structured approach to reviewing the learning from an initiative immediately after it is concluded. Another useful technique is the Learning History, a detailed account of what happened during an important event, with accompanying analysis.  Mentoring can also encourage experiential learning. Behavioral issues play a major role in experiential learning. Allowing learning from failure must be an integral part of the company’s culture. Otherwise, people will be reluctant to admit mistakes and share with their colleagues what went wrong. (See Learning History, Mentoring)

Expertise Directory: A database of people and their skills to help users to locate experts easily. An expertise directory is often referred to as 'Yellow Pages'. When combined with a search engine, it becomes an expert locator. The effective functioning of expertise location systems depends on the quality of expert profiles uploaded on the database. Expert profiles are often up to date. Moreover, they may be incomplete and sometimes may not tell the full story. Often, people do not articulate clearly what they know. So in many cases, expertise may have to be identified in other indirect ways. Expertise can sometimes be inferred from the contents of the documents with which a person’s name is associated. Authorship of a document indicates some familiarity with the subjects it discusses. Activities such as reading indicate some interest in the subject matter. The emails a person sends out can also be analyzed to write a profile of the person’s experience. Expertise can also be gauged by asking people whom they consult on specific issues. (Also see Expertise Profiling).

Expert Systems: In case of straightforward business problems, we can create a set of rules or procedures to follow. A computer can be programmed to follow these rules/procedures. But the situation becomes more complex when the problems are less structured and the data is not well defined. Experts are needed to solve problems involving non numeric data and complex inter relationships among the various factors. Special software programs called Expert Systems are an attempt to simulate these experts.

Expert systems can analyze symptoms and identify the cause. Even when, the decisions are less complex, expert systems can speed up the decision making process and thereby improve customer satisfaction. Expert systems can also facilitate consistent decision making, i.e. reaching the same conclusion for the same basic inputs.

There are three types of Expert systems. A rule based Expert system has a set of logical rules. The difficulty of course lies in establishing these rules. Experts do not always find it easy to express their thoughts in the form of rules. A rule based Expert system essentially attempts to connect relatively small chunks of data based on numbers and key words. A frame based Expert system deals with entire frames of data at one time. A frame consists of related sets of information that people group together. Case based reasoning is similar to frames. The only difference is that entire cases are described in one frame. As workers face problems and develop solutions, they write a small case. These cases come in handy while solving future problems. When a problem is encountered, the expert system searches the cases for similar situations and then retries the solution.

There are some important drawbacks with expert systems. They can be created only for specific and narrowly defined problems. When the problem is too complex with too many interactions and too many rules, it becomes difficult to express the interrelationships. It is also not very easy to modify the knowledge base in an expert system. As the environment changes, the system has to be updated. If there are many rules in the system with various interrelationships, the system may have to be designed from scratch, resulting in heavy expenditure. Last but not the least, determining the rules can itself be a complicated process. To set up an expert system, we need people who understand the process and can express the rules in a form that can be used by the system. Such people may not be all that easy to locate. (See Case Based Reasoning)

Expert Work: A term coined by Tom Davenport while categorizing different kinds of knowledge work. Expert work refers to knowledge work that is largely done by experts individually. It is highly judgment oriented and dependent on individual expertise. Such work is difficult to structure. It is also difficult to get experts to use the knowledge of others. Yet, over time, it has been found that there is scope to use information technology to inject relevant knowledge into the work process as and when needed by the expert.  For example, a medical diagnostics system can provide relevant information, just before the physician is going to write the prescription.

Explicit Knowledge: Knowledge that is documented in books, binders, databases, manuals and repositories. This type of knowledge can be articulated, codified and transmitted formally, in a systematic way. Explicit knowledge can be expressed in numbers, words or sound and shared in the form of data, scientific formulas, visuals, audio tapes, product specifications or manuals. For example, an SEI CMM V software company can lay down clearly how software development processes must be carried out. Similarly, a quality manual can indicate how food must be prepared and served in a fast food restaurant. New employees can visit the company’s intranet and familiarize themselves with the organization chart, performance appraisal system, profiles of different business units and their activities. Explicit knowledge is amenable to the use of Information technology. (See Codification)

 It is a database in which the operations carried out on information items (data objects) are considered part of their definition. 
When database capabilities are integrated with object programming language capabilities, the result is an object-oriented database management system or ODBMS. 
An ODBMS makes database objects appear as programming language objects in one or more existing programming languages. ODBMSs extend the object programming 
language with transparently persistent data, concurrency control, data recovery, associative queries, and other database capabilities.

“Knowledge Sharing is a “Human Behavior”, Knowledge Management – Classic & Contemporary Works, Edited by Daryl Morey, Mark May bury and Bhavani Thuraisingham, University Press, 2001.

Harvard Business Review, July-August 1997.    

Laurie J Bassi; Mark E Van Buren. “New Measures for a New Era” Knowledge Management – Classic and Contemporary Works Edited by Daryl Morey, Mark Maybury and Bhavani Thuraisingham, University Press, 2001.

Harvard Business Review, September 2004.

In his article, “Teaching smart people how to learn,” Harvard Business Review, May-June, 1991.

In his article, “Designing for Business Benefits form Knowledge Management,” Knowledge Horizon, 2001.

John Hagel III, John Seely Brown “The Only Sustainable Edge: Why Business Strategy Depends on Productive Friction and Dynamic Specialization” Harvard Business School Press, 2005.


Externalization: A term coined by Takeuchi and Nonaka, as part of their SECI (Socialization, Externalization, combination, Internalization) model of knowledge creation. This is the process of converting tacit knowledge into explicit concepts through metaphors, analogies, hypothesis or models. Metaphor can be viewed as a way of intuitively understanding one thing by imaging another thing symbolically. Metaphors help us to see one thing in terms of something else. Metaphors help in relating concepts that are far apart in our mind or even relate abstract concepts to concrete ones. As Takeuchi and Nonaka put it, “This creative, cognitive process continues as we think of the similarities among concepts and feel an imbalance, inconsistency or contradiction in their associations, thus often leading to the discovery of new meaning or even to the formation of a new paradigm.” Contradictions inherent in a metaphor can be harmonized by analogy. Association through metaphor is driven mostly by intuition and imagery and does not aim to find the differences between them. On the other hand, analogy works by rational thinking and focuses on structural/functional similarities between two things, along with their differences. (See SECI Model)

Extranet: A portion of an organization's intranet that is opened up for external Internet access on a selective basis e.g. for customers or suppliers to access certain information. Extranets can help in tapping knowledge that lies outside the organization.

Fuzzy Logic: Fuzzy logic provides solutions to problems requiring expertise that is difficult to represent in the form of crisp if-then rules. Fuzzy logic recognizes more than simple true and false values. With fuzzy logic, propositions can be represented as partially true or partially false. For example, the statement, today is sunny, might be 100% true if there are no clouds, 80% true if there are a few clouds, 50% true if it is hazy and 0% true if it rains all day. The same logic applies to a dirty cloth. Fuzzy logic systems cope with uncertainty to some extent, the way people manage uncertainty in their day-to-day life. One way people do this is to use subjective, incomplete descriptions. When people say it is hot outside, it is understood even though the term is subjective.  Fuzzy logic systems need to be trained by experts. Such experts may not be available. And even if they are available, these experts might not articulate their knowledge effectively. Fuzzy logic is used in applications such as washing machine settings, elevator control and some spell checkers (to suggest a list of probable words to replace a misspelled one). (See Artificial Intelligence)

Garbage In Garbage Out (GIGO): Information Technology is only as good as the quality of data and information fed into the system.

Genetic Algorithm Tools: These tools help arrive at an optimal solution by examining a very large number of possible solutions for that problem. The underlying principle is similar to the way living organisms adapt to their environments. Genetic algorithms facilitate the evolution of solutions to particular problems, controlling the generation, variation, adaptation and selection of possible solutions, using genetically based processes. As solutions alter and combine, the worst ones are discarded, while the best ones survive.

Genetic Algorithms are useful when decision makers do not know how to solve the problem but are likely to know the solution when they see it. Genetic algorithms can considerably simplify the amount of work required to solve a complex, decision related problem. They are useful while making decisions where standard rules of thumb are difficult or impossible to use.   These tools tend to be heavily dependent on software and the nature of the problem. As a result, their usability in other problem domains is somewhat limited.

GE has used genetic algorithms to optimize the design for jet turbine aircraft engines where each design change may involve changes in up to 100 variables. Genetic algorithms can also be used to optimize production scheduling models.

George Roth: A member of the research staff at the MIT 21st Century Initiative, Roth directs the learning history project. Roth has done extensive research on organizational learning and change. Roth’s current research interests include the following:

  • Learning Histories: The use of documentation to capture, assess, facilitate, diffuse and sustain organizational improvement initiatives.
  • Large Scale System Change: Issues involved in moving from team-based change efforts to organizational or system level change.
  • Technological Change: Creating mechanisms for effective change while implementing new technology.
  • Educational Interventions in Organizational Learning: Improving individual learning processes taking place in organizational settings.
  • Business Process Learning: Building better business processes by enabling people to think differently about their work and business processes.

Gestalt: Gestalt theory maintains that a psychological phenomenon can only be understood if it is viewed as organized, structured wholes. Learning is regarded by Gestaltists not as associations between stimuli and responses but as a restructuring or reorganizing of the whole situation. In short, Gestalt emphasizes unity and wholeness.  

Group Decision Support Systems: A Group Decision Support System (GDSS) enables a group of people to work on unstructured problems. It is unlike Groupware and video conferencing which focus primarily on communication. GDSS provides tools and technologies that facilitate group decision making. GDSS helps make meetings more effective. GDSS tools facilitate planning, generating, organizing and evaluating ideas, establishing priorities and documentation of meeting proceedings. Some of the commonly used GDSS tools are electronic questionnaires, electronic brainstorming tools, tools for voting or setting priorities and policy formulation tools. In traditional decision making meetings, having more than 4-5 people may make the process ineffective and indeed disruptive in some cases. When GDSS is used, the number of people taking part in the meeting can increase while productivity also goes up. Since people can contribute simultaneously, the meeting time can be used efficiently. Of course, GDSS will not be effective if the composition of the group is not right, the problem is not properly presented or the facilitation is not effective.
Groupware: The process of creating and sharing knowledge in any organization involves collaboration. People come together for complaint resolution, problem solving, brainstorming, idea generation, etc. These interactions may occur among people from different departments, spread across geographical locations. Groupware supports such collaboration. The software enables a group of users on a network to collaborate on a particular project in groups or teams. Groupware provides a virtual space, within which people can share experiences, conduct meetings, listen to presentations, hold discussions and share documents. Some applications support real time online meetings including video and text based conferencing, synchronous communication and chat. Other applications enable location of persons with common interests who are candidates to join a community.

There are three key components in groupware: communication, compound documents and databases. To share data effectively, people should be connected to each other through a network, which must be able to handle large data transfer efficiently. E-mail and scheduling are common applications. Groupware helps to extend email in various ways. For example, it helps in sorting and organizing and retrieving emails more effectively. Compound documents are a key focus area for groupware tools. These documents can contain text, images, graphs, sound and video clips. Each document can be revised and shared with other members of the team. Databases which form another crucial component, enable workers to share access to the same documents simultaneously. Each member of the team can work on the same document. Contributions from individuals are immediately available to the rest of the team. Comments and changes can be added at any time by team members. These changes are automatically recorded and made immediately available to other team members. There are security features to decide who can make changes and who can see documents. Groupware tools are especially useful in automating the workflow in service-based organizations.

By storing observations, insights and comments by various members of the team, workers are better equipped to deal with problems in the future. Groupware minimizes the impact when a knowledgeable worker leaves the organization. By standardizing on hardware, software and communication protocols, groupware tools make it possible to create ad-hoc problem-solving teams consisting of workers from different departments.

HTML: Short for Hyper Text Markup Language, HTML is the language used to format documents for viewing with a browser on the user’s machine or on a network. HTML tells browsers how to display type and images to the user and describes responses to user actions such as the activation of a link by a mouse click. HTML defines the structure and layout of a Web document by using a variety of tags and attributes. There are hundreds of other tags used to format and lay out the information in a Web page. Tags are also used to specify hypertext links. These allow Web developers to direct users to other Web pages with the click of the mouse.  

Hirotaka Takeuchi: Takeuchi is dean of the Graduate School of International Corporate Strategy at Hitotsubashi University in Tokyo and a visiting professor at Harvard Business School, Takeuchi has done extensive research on: the knowledge creation process within organizations, competitiveness of Japanese firms in global industries, new product development, and international corporate strategy. An April 1996 Fortune article introduced him as “among the intellectual leaders of the younger, globally-minded generation that is coming to power in Japan.” His book, The Knowledge-Creating Company, coauthored with Ikujiro Nonaka, is probably the most acclaimed book on KM.

Human capital: Knowledge, skills and experiences possessed by individual employees. Human capital includes both explicit conceptual knowledge such as how to create a budget or how to use an email system as well as more tacit knowledge like how to negotiate a sale or interpret a market trend. A company’s stock of human capital tells us about the current level of individual skills. By comparing the skill level with that of competition and what customers demand, gaps can be identified and necessary corrective steps can be taken.

Ikujiro Nonaka: One of the leading KM gurus in the world, Ikujiro Nonaka is a Professor at the Haas School of Business at the University of California, Berkeley and the Founding Dean of the Graduate School of Knowledge Science at the Japan Advanced Institute of Science and Technology (JAIST). He has authored or coauthored several books, including the widely acclaimed, “The Knowledge-Creating Company” and written several articles in various international academic and managerial journals. He has also been the editor of several international journals and conducted international KM seminars for managers.

Information: Information is processed data. Data becomes information when it is summarized, tabulated, processed and checked for errors. It is easier to make sense out of information than of data. Thus the heights of students in a class may represent data. But if we can tabulate, summarize and categorize this data, it becomes information. For example, we can consolidate this data into a frequency table consisting of two columns. The first column can indicate the range of heights (150-160cm. etc.) while the second may indicate the number of students falling in the range.  Alternatively, a histogram can be plotted that geographically depicts the frequency distribution. Information is something the human mind finds much easier to handle, than raw data. Information is less cluttered, better arranged and easier to grasp, than data. (See Data)

Innovative Knowledge: Innovative knowledge is needed for a firm to lead its industry and competitors and to significantly differentiate itself from its competitors. Innovative knowledge often enables a firm to change the rules of the game itself. In the automobile industry, Toyota has leapfrogged competitors with its knowledge of just-in-time and lean production. In the PC industry, Dell stands apart with its knowledge of the supply chain and in particular the order fulfillment process. (See Core Knowledge, Advanced Knowledge)

Insight: Can be viewed as an act of sensing intuitively the inner nature of something. It can be described as a novel, clear, compelling, understanding of something occurring without direct recourse to memories of past experiences. In Gestalt psychology, insight characterizes a sudden reorganization or restructuring of the pattern or significance of events allowing one to grasp relationships relevant to the solution. In more simple terms, insight is the ability to see and understand the truth about people or situations. Developing insight involves going below the surface and arriving at a well thought out explanation for a phenomenon. This involves careful observation and reflection. For example, the insight that customer demand should pull inventory, has been the guiding principle of Toyota’s Just-in-Time production system. (See Knowledge, Wisdom)

Instant Messaging: An increasingly popular way of communication in many organizations. While commonly associated with informal social groups, the tool is a useful complement to synchronous communication, for example to interact with peers during a virtual seminar. Unlike emails, instant messaging can help in resolving issues and closing action items faster. At the same time, an instant message is less intrusive than a phone call. One can keep responding to a message at an acceptable pace, with time lags, unlike a phone which interrupts the current work.

Integration Work:  A term coined by Tom Davenport. A kind of knowledge work which is systematic, repeatable and depends on integration across functional boundaries. In such work, there is scope for reuse of knowledge. For example, software companies keep libraries of reusable code. Similarly, automobile companies keep reusable component designs. 

Intellectual Capital: A hot topic of the day. According to Patricia Seemann, David De Long, Susan Stucky and Edward Guthrie , Intellectual Capital (IC) has three elements - Human Capital, Structural Capital and Social Capital. Human Capital refers to the knowledge, skills and experiences possessed by individual employees. Without human capital, no company can compete effectively in the market place. Structural capital refers to the explicit, rule based knowledge embedded in the company’s work processes, systems, policies, training documentation or best practices repository. Structural capital also includes patents and copyrights. Social capital refers to the ability of groups of employees to collaborate and work together. Effective networks of relationships constitute an extremely valuable, intangible asset that is often overlooked. Seemann, De long, Stucky and Guthrie have explained the relationship between intellectual capital and knowledge management (KM). KM is all about ensuring that intellectual capital is constantly enhanced, shared, sold or used to generate value. KM can be viewed as the deliberate design of processes, tools and structures to increase and improve the use of knowledge contained in the three kinds of IC. Many companies make the mistake of equating KM with structural capital, i.e. implementing shared databases or document repositories. Effective KM is all about managing, human, structural and social capital in an integrated way. According to Laurie J Bassi and Mark E Van Buren, managing IC involves :

  • Identifying IC types, needs and requirements
  • Creating new IC and uncovering existing IC
  • Compiling, gathering, representing, codifying and reorganizing IC
  • Disseminating, distributing and transferring IC
  • Applying, incorporating, reusing, exploiting and leveraging IC 


IC is not the same as intellectual property (IP). IP is that part of Intellectual Capital that is protected by law. IP includes patents, copyrights and trademarks. IP must be unique and not too obvious. Otherwise, it would be difficult to get a patent or copyright. As Carl Davidson and Philip Voss put it so well , the distinction between IC and IP is important. Knowledge does not have to be invented to be useful to an organization. “Originality is much less important than usefulness.”

Intelligent Routing: Responding to queries is an integral part of any business. Information technology facilitates intelligent routing of incoming queries. Filtering can be done on the basis of customer profile, customer requirements, past history and skills of the customer service agent.

Intention: A concept coined by Nonaka and Takeuchi. Intention is an important enabler of knowledge creation. There should be a clear intention on the part of the organization about what knowledge is important and the commitment of resources to developing that knowledge. Without a clear vision of where it is heading and what kind of knowledge needs to be developed, an organization will find it difficult to implement KM. As Nonaka and Takeuchi put it, intention provides the most important criterion for judging the “truthfulness of knowledge”. Without intention, it would be difficult to judge the value of information or knowledge perceived and created.

Internalization: A term coined by Takeuchi and Nonaka, as part of their SECI model. Internalization is the process of converting explicit knowledge into tacit knowledge. In this stage, knowledge is applied and used in practical situations and becomes the basis for new routines. Action, practice and reflection are the building blocks of the internalization process. Internalization essentially converts externalized explicit knowledge back into an individual’s tacit knowledge. Thus a Business School professor after reading a book may reflect on the various concepts covered in the book. He may then attempt to understand whether the examples given in the book will work in a different context. He may also examine whether the principles mentioned are universally applicable. In the process of reading the book and reflecting on its contents, knowledge gets internalized.  Here, internalization is taking place not by re-experiencing other people’s experiences but by relating to those experiences. (See SECI Model)      

Intranet:  A network designed to organize and share information and carry out digital business transactions within a company, using web pages, browsers, e-mail, news groups and mailing lists. An intranet is accessible only to those within the organization. Human resource policies, code of conduct, address book, travel rules, reimbursement of expenses, payroll, leave applications, etc. are usually available for easy access to employees on an intranet.

Just-in-case Knowledge Management:  Making knowledge available to users just in case it is needed.  This saves users the hassle of having to search for knowledge. But users may not perceive much value if the knowledge is not immediately relevant to the task at hand.  (See Just-in-time Knowledge Management)

Just-in-time Knowledge Management: Knowledge is often more valuable when it is delivered at the moment it is needed, rather than being available at all times. It is the dream of all KM practitioners to make knowledge flow into work processes as and when it is needed to solve business problems or facilitate decision making. It is under these circumstances that the full value of knowledge can be leveraged.

K-Spots: The knowledge areas on which a company can concentrate its KM efforts. These are promising areas which stand to gain the most through KM.

Karl Wiig: A leading expert in KM, Wiig has authored four books and over 40 articles on knowledge management. A co-founder of the International Knowledge Management Network, he is associated with various organizations in helping them build their internal knowledge management capabilities. Wiig’s website is full of useful resources for KM practitioners.

Knowledge: The understanding clarity and insights that we gain through education, practical experience, reflection and observing others. Knowledge goes far beyond data and information. According to Davenport and Prusak, it is the fluid mix of experiences, values, contextual information, insights and intuition. It originates in individual minds but over time, gets embedded in organizational routines, processes, practices, systems, software and norms.

Information becomes knowledge through:

  • Comparison: How does information about this situation compare with other situations?
  • Consequences: What implications does the information have for decisions and actions?
  • Connections: How does this bit of knowledge relate to others?
  • Conversation: What do other people think about this information?

Though data, information and knowledge may appear to lie on a continuum, there are major discontinuities. Knowledge is fundamentally different from information. The discontinuity is caused by how new knowledge is created from received information. To become knowledge, new insights are internalized by establishing links with already existing knowledge. Prior knowledge helps us make sense of received information. Once accepted for inclusion, people will internalize new insights by linking with prior knowledge. Hence, the new knowledge is as much a function of prior knowledge as it is of received inputs. (See Data, Information)     

Knowledge helps us to understand phenomena, make predictions and deal with situations we may not have encountered before.  Knowledge is actionable information. It facilitates decision making, problem solving and developing new concepts or processes. Much of valuable knowledge is difficult to document or capture in databases. It remains in the minds of the people. This is called tacit knowledge. Such knowledge is best transferred through human interaction. Knowledge which can be codified is called explicit knowledge. Technology is a major enabler in the dissemination of explicit knowledge. (See Explicit Knowledge & Tacit Knowledge). Knowledge can also be categorized in other ways: Technological Business/Environmental, Operational/strategic, Low perishability, high perishability. Intuition, ground truth (whether it works or not), judgments, experience, values, assumptions, beliefs and intelligence are the various components of knowledge.

Unlike information, knowledge has a component of judgment attached to it. We use knowledge to make decisions. When we make decisions, we use our judgment. Knowledge is largely derived from experience. Experience helps people develop rules of thumb and respond to new problems more effectively. Usually, business processes are based on deeply ingrained, unarticulated assumptions and values. These beliefs, values and assumptions are integral components of knowledge. Knowledge also contributes to corporate intelligence. As Amrit Tiwana mentions , “When knowledge can be applied, acted on when and where needed, and brought to bear on present decisions and when these lead to better performance or results, knowledge qualifies as intelligence. When it flows freely throughout a company, is exchanged, grows and is validated, it transforms an informated company into an intelligent enterprise.”
Knowledge Acquisition: Eliciting and formally coding tacit knowledge into facts and rules and entering them in a knowledge base. It is the process of developing insights and skills. Intelligent databases, electronic whiteboards, artificial intelligence tools and data warehousing are some of the technologies that can support knowledge acquisition.
Knowledge Activities: Refers to the various kinds of tasks done by knowledge workers: finding existing knowledge, creating new knowledge, packaging knowledge, distributing knowledge and applying knowledge. The common thread running through these activities is that they involve primarily thinking and information processing as opposed to physical work.

Knowledge Archaeology: The process of retrieving an organization's historical knowledge that has become lost or inaccessible.

Knowledge Asset: A piece of knowledge that has some intrinsic or extrinsic value. A proprietary methodology, a patent or a copy right would fall into this category.

Knowledge Audit: A knowledge audit aims at finding out what knowledge an organization has, who has it and how it flows through the enterprise.  A knowledge audit can show what changes are needed in organizational and personal behavior, business processes and enabling technologies so that knowledge can be applied to strengthen the competitive position of the firm. A successful audit can identify intellectual assets of value to the company. It can point out improvements to existing processes for knowledge creation and sharing. An audit can also identify people who have been acting as barriers to knowledge proliferation, whether inadvertently or on purpose. Thus a knowledge audit not only helps to determine where knowledge exists within organizations, but may also be seen as a type of roadmap for process improvement. A knowledge audit can cover various aspects of KM: Acquisition and Learning, Storage and Maintenance, Application and Exploitation, Dissemination and Transfer, Knowledge Creation, and Performance Measurement.

In general, a knowledge audit would proceed systematically along the following lines:

  • the identification of knowledge needs through the use of questionnaires,    interviews and
  •              focus groups;
  • the development of a knowledge inventory mainly focusing on the types of           knowledge available; where this knowledge is located; how it is maintained and stored, 
  •               what it is used for and how relevant it is;
  • analysis of knowledge flows in terms of people, processes and systems;
  • the creation of a knowledge map

Knowledge audit, if done properly, can facilitate the following: 

  • identifying the knowledge needed to support overall organizational goals and                      individual and team activities;
  • understanding the extent to which knowledge is being effectively managed and                     where improvements are needed;
  • understanding the knowledge that exists in the organization and how that knowledge moves around, across the organization;
  • understanding knowledge  gaps and duplication;
  • identifying  pockets of knowledge that are not currently being used effectively;
  • identifying best practices and barriers to knowledge sharing;
  • preparing  an inventory of knowledge assets, making them more     visible and more measurable and accountable and giving a clearer understanding of the contribution of knowledge to organizational performance;
  • providing vital information for the development of effective KM programmes and                    initiatives that are directly relevant to the organization’s specific knowledge needs                    and current situation.


Usually, organizations are unaware that they require an audit at all. Wiig (1993) has identified several signs that an organization requires a knowledge audit:

  • Information overload or lack of information. 
  • No awareness of knowledge or information available in the organization.
  • Knowledge duplication through different departments; reinventing the wheel.
  • Common use of out of date knowledge or knowledge with no quality or value.
  • Not knowing where to find appropriate knowledge or expertise.


Know-bot (Knowledge robot): An intelligent agent that gathers or exchanges knowledge from other agents or computer systems based on the user’s criteria. A Know bot is a kind of Bot. A Bot interacts with other network services intended for people, just like a real person. A typical use of Bots is in gathering information. It can also interact dynamically with a site. Some Bots can respond to questions asked in English and report the weather, sports score, etc. Bots can also be used maliciously, for example, to attack a website.

Know-how: The ability to go beyond factual information and leverage knowledge to deal with unexpected situations that ordinary people would find difficult to deal with. In cricket, for example, a great fast bowler knows when to fool the batsman by bowling a slower delivery.  An experienced driver knows when not to overtake a vehicle ahead. An expert negotiator knows when to maintain silence and let the other party talk. A good teacher can understand a question which a student is finding it difficult to articulate. Know-how is gained through learning by doing. Know-how is context dependent and difficult to codify. Know-how is usually embedded as organizational routines in the organization’s structure, communication channels, problem-solving methods and planning and management systems. Know-how is so innately routinized that it tends to be difficult to transfer across companies. (See know what, know why).


Knowing-Doing Gap: Knowledge is of little use unless we do something with it. According to Stanford professors, Jeffrey Pfeffer and Robert Sutton, the gap between knowing and doing is more important than the gap between ignorance and knowing. Today, knowledge is easily available. There are knowledge brokers like consulting firms who specialize in collecting knowledge about management practices, storing it and then transferring the information to those who need it. Better ways of doing things cannot remain secret for long. In most cases, however, the knowledge that is successfully transferred through seminars, training programs and consulting, is not implemented. Talking dominates action in many companies. It is the companies which can bridge the knowing-doing gap that emerge winners in the market place.

Knowledge Base: A knowledge base consists of basic data and a set of rules. In most situations, an inference engine applies new observations to the knowledge base and analyses the rules to reach a conclusion. A knowledge base consists of data along with the rules, logic and links among data elements. Usually, it contains less structured and more descriptive data. For example, in medicine, a knowledge base might include terms like “severe headache” or “severe abdominal pain”.

Knowledge Building: An innovative way of looking at learning, knowledge building refers to the creation of new cognitive artifacts as a result of collective discussion and synthesis of ideas. These artifacts should advance the current understanding of the individuals within a group to a point beyond their initial level of knowledge, and should be directed towards advancing the understanding of what is known of a topic or idea outside the group.

Knowledge building involves making a collective inquiry into some subject and coming to a deeper understanding through interactive questioning, dialogue and continuous improvement. The teacher becomes a guide rather than an instructor and allows students to assume most of the responsibility for their own learning including planning, execution and evaluation.
One of the hallmarks of knowledge building is a sense of “we” as opposed to “I,” a feeling that the group is operating collectively and not just as an assemblage of individuals.
Knowledge Business: A knowledge business leverages knowledge to create value for customers. All work involves some amount of knowledge. But in truly knowledge businesses, the core activity is processing data into information and knowledge that in turn creates value for customers. According to Michael Zack, knowledge based organizations have four characteristics. Such organizations spend substantial time on application of existing knowledge and creation of new knowledge. The boundaries of knowledge based organizations are blurred. They seek knowledge from customers, vendors, alliance partners and even competitors. Knowledge based organizations view knowledge as a key resource and keep asking what knowledge is needed to execute the company’s strategy. These companies make conscious attempts to bridge knowledge gaps. Last but not the least, knowledge based organizations take a different perspective compared to other equivalent organizations. They take into account knowledge in every aspect of their operations and treat every activity as a potentially knowledge enhancing act.

Knowledge Centre: A central function created by a company for managing knowledge resources. A typical knowledge centre will manage various knowledge resources - documents, databases, intranet content, expertise directories, etc. McKinsey, the consulting company, has a large knowledge center in Gurgaon. This centre supports McKinsey consultants all over the world by providing them industry and company related information.
Knowledge Champions: People in different business units, divisions and functions, who support the central KM team in implementing various KM initiatives. 
Knowledge Enablers: Knowledge creation and sharing are enabled under certain conditions. A high level of trust prevails in the company. Team based collaborative work is encouraged.  Individuals enjoy considerable autonomy. Accountability exists at the group, not individual level. Co-operation is rewarded. There is a strong focus on customer satisfaction. Culture is clearly one of the most important conditions for the success of a KM project. It is the hardest factor to build from scratch. An enabling culture has several different com­ponents. Employees must be bright and intel­lectually curious. They must be willing and free to explore. Knowl­edge-creating activities should be encouraged by the top management. Failure during experimentation should not be penalized heavily.
Knowledge Engineers: Play a key role in converting the tacit knowledge of experts into explicit knowledge. Knowledge engineers are trained to deal with experts to derive the rules needed to create an expert system. These engineers also convert the data and rules into the format needed by the expert system. In some systems, there are if-then rules, others use decision trees, yet others link frames. Knowledge engineers are recommended when several experts are involved and it is expected that a lot of time will be taken to develop the system.
Knowledge Growth Framework: Bohn has identified eight stages of knowledge growth.

  • Knowledge does not exist.
  • Knowledge is primarily tacit.
  • Knowledge is mostly written.
  • Knowledge is contained in methodologies. Records of processes and outcomes are maintained.
  • Knowledge is embodied in operating manuals.
  • Knowledge is found in empirical equations.
  • Procedures and algorithms exist. There is codification in computer software and process manuals.
  • KM becomes a natural part of work processes. This stage represents the ideal.

Knowledge-harvesting: The process of making tacit knowledge more explicit, by  capturing people's knowledge in documents.  
Knowledge Integration: Combining separate knowledge management programs into a more complete whole. This is a challenge that most organizations face. KM programs are more often than not, piecemeal and fragmented.
Knowledge Interrogators: Persons responsible for managing the content of organizational knowledge as well as its technology. Knowledge interrogators maintain the database, remove obsolete documents and connect the users with the information they seek.
Knowledge Mapping: The process of identifying where knowledge lies in the organization. A map may be portrayed in many visual formats, such as a hierarchical tree or a node and link diagram. Knowledge mapping is usually carried out as part of a knowledge audit.

A knowledge map plays a crucial role in identifying where knowledge resides in the organization. Developing a knowledge map involves locating important knowledge in the organization and then publishing a list or picture that shows where to find it, both people as well as documents and databases.


The main benefit of a knowledge map is to indicate whom to contact when some expertise is needed. Rather than managing with imperfect answers by contacting people who are the most accessible, the employee with a good knowledge map has relatively easy and quick access to the most appropriate knowledge sources in the organization. Without a knowledge map, it would be difficult or impossible to find such persons.

A firm’s organizational chart cannot substitute a knowledge map. Most organizational charts are hierarchical, describing formal reporting structures and usually with far more detail at the top than at the bottom. But key knowledge may exist anywhere in the company. Indeed, cutting edge technical knowledge is more likely to be found at the lower levels. Also the most avid knowledge seekers almost always need to cross departmen­tal boundaries and ignore reporting structures to get what they need. 

Technology can play a major role in constructing knowledge maps. On-line Yellow Pages can allow users to search by topic or key word, making it easy to locate and compare potential knowledge sources across the organization. Moreover, an electronic map can be revised frequently unlike a printed one. This is a huge advantage in a rapidly growing organization. Since successful knowledge transactions depend so heavily on trust and compatibility, personalizing the entries can make the map more effective. In many companies, Knowledge Yellow Pages show a photograph of the person listed. A few organizations include a brief video clip.

Organizational knowledge maps are political documents too. If knowledge is genuinely important to an organi­zation and those who have it are recognized and rewarded, then the knowledge map will be a picture of status and success as well as a knowledge locator.  So, political issues cannot be skirted. Indeed, if politics plays no part in a knowledge mapping exercise, it is an indication that people are not taking the exercise seriously. (See alsoKnowledge audit, Social Networks).
Knowledge Markets: Knowledge is exchanged, bought and bartered like any other commodity. Like markets for goods and services, the knowl­edge market has buyers and sellers who negotiate to reach a mutually satisfactory price for the goods exchanged. There are also brokers who bring buyers and sellers together. Knowledge market transac­tions will occur when the participants believe that they will benefit in some way.  Tom Davenport and Larry Prusak have given an excellent account of how knowledge markets function in their book, “Working Knowledge.”
Knowledge buyers are usually people trying to solve unusual or complex problems. They seek knowledge to make a sale, do a task more efficiently; improve their skills or make better decisions. In short, they want knowledge to do their work more effectively.
Knowledge sellers are people with a reputation for having substantial knowledge about a process or subject. Although virtually everyone is a knowledge buyer at one time or another, not everyone may be a seller. Some people are skilled but unable to articulate their tacit knowledge. Others have knowledge that is too specialized, personal, or limited to be of much value to others. Some people may possess valuable knowledge, but may be unwilling to share their knowledge. A knowledge seller is typically motivated by one or more of three factors: reciprocity, repute, and altruism.
Knowledge sellers will share knowledge enthusiastically if they expect the buyers to be willing sellers at a future point of time. Knowledge sellers usually want recognition from others. Having a reputation for knowledge sharing makes achieving reciprocity more likely. Having a reputation as a valuable knowledge source can also lead to job security, career advancement, visibility within the organization and all the rewards and trappings of an internal guru.
Altruism may also motivate knowledge sharing. After a certain age, some people have an urge to pass on what they have learned to others. Firms can encourage this tendency by formally recognizing mentoring relationships and giving managers time to pass on their knowledge. 
Knowledge markets are shaped by the social and political realities prevailing in the organization. If the political reality of an organization allows knowl­edge hoarders to thrive, there is no incentive for people to share their expertise. If it is considered a sign of weakness or incompetence within the culture of an organization to admit one cannot solve a problem, then the social cost of "buying" knowledge will be too high. Once again, the knowledge market won't operate well. The not-invented-here mentality, i.e. the willingness to accept an idea or innovation from another department is another barrier to knowledge sharing. A variation is the class barrier, an unwillingness to give knowledge to or accept it from people in the organization who have relatively low status.
Three factors in particular can cause knowledge markets to operate inefficiently in organizations:
Incompleteness. People may not know where to find their company’s own existing knowledge.
Asymmetry. Abundant knowledge on a sub­ject in one department of an organization, may coexist with a shortage somewhere else. This makes reciprocity based knowledge sharing difficult.
Localness of Knowledge. People usually get knowledge from their neighbors, as they know and trust them more. Face-to-face meetings are often the best way to get knowledge. Reliable information about more distant knowledge sources is usually not avail­able. Also, mechanisms for getting access to distant knowledge tend to be weak or nonexistent. People will be happy with whatever knowledge the person in the adjacent cubicle may have, rather than try to discover who in the company may know more.
Trust is particularly important in knowledge exchange. Top management must consciously promote trust in various ways:
1. Trust must be visible. The members of the organization must actually see people get credit for knowledge sharing.
2. Trust must be ubiquitous. Trust should pervade the organization. If part of the internal knowledge market is untrustworthy, the market becomes asymmetric and less efficient.
3. Trustworthiness must start at the top. Trust tends to flow downward through organizations. Only if top managers are trust­worthy, will trust permeate the whole firm.
Informal markets play an important role in the buying and selling of knowledge. Probably the best knowledge market signals flow through the informal communities of practice that develop in organizations. Within these webs, people ask each other who knows what and who has previously provided knowledge that turned out to be reliable and useful. If the person they approach doesn't know an appropriate seller, it is quite likely that she might know someone else who does know.
Informal networks engender trust because they function through personal contact and word of mouth. A recommendation that comes from someone we know and respect within the firm is more likely to lead us to a trustworthy seller with appropriate knowledge than would a cold call based on the organizational chart or corporate phone directory. Such informal net­works are also dynamic. Since people in the network communicate regularly with one another, they tend to update themselves as conditions change. People share information about who has left the company or moved to new projects, who has recently become a useful source of knowledge, and who has become reticent or less accessible. Of course, informal networks are not readily available to all those who need them. Their viability depends on chance conversations and local connections that sometimes work well but not so well on other occasions. So formal markets also have a role to play in knowledge exchange. Which is why the intranet, forums and seminars will continue to play an important role in facilitating knowledge creation and sharing.
Knowledge Metrics: Like any initiative, KM will make an impact only if its benefits can be quantified.
What constitutes success in KM? The impact of KM on financial performance is often indirect, rather than direct. Economic returns from knowledge may also not be easy to quantify. So we must rely on more general indicators of success. Yet, there should be some metrics to ensure that KM efforts are properly channelized. Some of the attributes that can be used to define success in KM are:

  • Comfort throughout the organization with the concept of KM.
  • Growth in the resources attached to the project, including staffing and budgets.
  • Growth in the volume of knowledge content and usage (for exam­ple, the number of documents in repositories and the number of downloads and number of partici­pants in discussion forums).
  • The likelihood that the project will be sustaining beyond a particu­lar individual or two, that is, the project is an organizational initia­tive.
  • Some evidence of financial return, either for the KM activity itself or for the larger organization. This linkage need not be rigorously specified and may be only perceptual.

Knowledge Networking: The process of sharing and developing knowledge through technology and human interaction. Exchange of emails, group discussions, seminars, online forums, wikis and even blogging facilitate knowledge networking.  The philosophy here is that KM is facilitated by the interaction of ideas and people, instead of depending totally on passive forms of knowledge sharing such as downloading documents from a repository.

Knowledge Object: A piece of knowledge held in a well-defined and structured format, such that it is easy to replicate and disseminate. Usually in the form of explicit knowledge, it may also contain some tacit knowledge.

Knowledge Packaging: Filtering, editing, searching and organizing pieces of knowledge. Journalists and research analysts do this kind of work. The task involved in knowledge packaging must not be underestimated. It usually involves careful understanding of what has been already documented and representing it in a user friendly format. 

Knowledge Product: A product which consists almost entirely of information or knowledge. Imaginative thinking can make even commodities knowledge intensive, if not knowledge products. By wrapping information around commodities, companies can create “intelligent products”. Thus Cemex has converted cement into an information business while Fedex has done this in case of document movement.
KM Projects: It is often difficult to launch a full blown KM initiative across an organization. A better way might be to introduce a series of short burst KM projects that can yield quick results. KM projects must be planned and executed carefully. Managing KM projects is quite different from managing other projects such as software development. Knowledge is naturally fluid and closely linked to the people who hold it. This means knowledge projects cannot be structured as tightly as other projects.
Success in the initial projects taken up is important to build the required momentum for KM. KM projects are more likely to succeed if they start with a recognized business prob­lem that relates to knowledge. That is what industry people call the “pain areas”. Customer defections, poorly designed products, loss of key personnel, or a lower "win rate" for service engagements are all business problems that might be traced to poor knowledge management.  Attacking these problems and using the business value of solving them as justification for KM initiatives are all good ways to build momentum.
It is often non core or feeder processes that benefit from KM most according to a survey done by the Cranfield School of Management. These feeder processes do not generate income but provide significant inputs to the main processes. Such processes often involve a wide range of knowledge and expertise that must be mobilized in a short time span. In these processes, documents and workflow are usually important.  
The following factors can contribute to the success of a KM project.

  • A knowledge-oriented culture
  • Technical and organizational infrastructure
  • Senior management support
  • Clarity of vision and language
  • Suitable metrics

Knowledge projects need the requisite technology and organiza­tion infrastructure.  Technological infrastructure is easier to put in place. Building an organizational infrastructure means establishing a set of roles and structures from which individual projects can benefit. Many companies find this difficult to do. Some firms have been able to establish multiple levels of new roles, from chief knowledge officers to knowledge project managers to knowledge reporters, editors, and knowledge network facilitators. (See Chief Knowledge Officer)
Knowledge Recipe: The process of using  existing knowledge assets as inputs and combining  them in distinctive ways to create useful outputs and outcomes.

Knowledge Refining: The process of filtering, aggregating and summarizing knowledge drawn from various sources.

Knowledge Repository: A store of knowledge documents and artifacts. The term typically refers to explicit forms of knowledge, such as documents and databases. The attributes of a good repository are comprehensiveness, taxonomy (classification), structure and an efficient search facility.

Once tacit knowledge is conceptualized and articulated, it can be converted into document form. These documents can be kept in a repository. The quality of documents can be assessed by the number of downloads, the number of times the document has been cited and judgments by experts. Besides written documents, audio and video recordings are also possible.

According to Michael Zack repositories can support Integrative and Interactive applications. Integrative applications mean explicit knowledge flows into and out of a repository. The repository is the prime medium for knowledge exchange. Interactive applications mean producers and users come together. The repository is a byproduct of interaction and collaboration rather than the primary focus of the application.

At one extreme, users and producers do not belong to the same practice community. This can be called electronic publishing. At the other extreme, users and producers belong to the same community and together work to integrate and build on their collective knowledge. This can be called an integrated knowledge base. A good example is a best practices database.

Electronic publishing can be highly cost effective. But an integrated knowledge base provides better support for solving problems, innovating and leveraging opportunities. The greatest impact may come from combining the two.
Knowledge Representation:  Knowledge Representation (KR) is a term commonly used to refer to representations intended for processing by modern computers. In the 1980s, work began on the development of formal KR languages and systems. The “Cye” project worked on encoding the information a reader needed in order to understand an encyclopedia. Prolog and KL-One programming languages facilitated KR. Then came XML. Now the semantic web is growing in size. In semantic networks, each node represents a concept and arcs are used to define the relationships among the concepts. (See Semantic Networks)
Efforts are on to represent knowledge in the same way that it is represented in the human mind and to represent knowledge in the form of human language. But we still do not know how knowledge is represented in the human mind. We also do not know how to manipulate human language in the same way the human mind does it.
According to Randal Davis, Howard Shrobe and Peter Szolovits of MIT , KR must be understood in terms of the five distinct roles it plays:

  • KR acts as a surrogate. Reasoning goes on internally but the things we wish to reason about lie externally. The representation is of things that exist in the external world. The correspondence between the surrogate and the intended referent is the semantics for the representation. The surrogate must be close to the real thing.
  • KR is an approximation of reality. Each representation attends to some things and ignores others. Essentially we decide how and what to see in the world. This helps us to bring some parts of the world into sharp focus while blurring others.
  • KR is a fragmentary theory of intelligent reasoning. The representation typically incorporates only part of the insight or belief that motivated it. The insight or belief is in turn only a part of the complex and multi faceted phenomenon of intelligent reasoning.
  • KR is a medium for efficient computation. Reasoning in machines is a computational process. In other words, to use a representation, we must compute with it.
  • KR is the means by which we express things about the world, the medium of expression and communication in which we tell the machine about the world. So the questions to be raised here are: How well does the representation function as a medium of expression? How general is it? How precise? How easy is it for us to talk or think in that language? What kinds of things can be easily communicated in the language? What things are difficult to communicate?

All the roles mentioned above are important. Ignoring any one of them may lead to serious inadequacies.

Knowledge Sharing: The process of disseminating and making available what is already known. This is a major challenge in large organizations which often do not know what they know. Knowledge sharing is largely a cultural issue. The organization must encourage people to part with their knowledge and reward them for doing so. Of course, efficient knowledge sharing also needs the appropriate IT and communications infrastructure including email, groupware and video conferencing. Without such infrastructure, knowledge sharing cannot be scaled up effectively in large, geographically dispersed organizations.  

Knowledge Utilization: Using accumulated knowledge to tackle problems, develop new products and deal with unfamiliar situations. Knowledge is of no use unless it is applied to solve business problems. Thus the effectiveness of a knowledge repository must be assessed less by the number of documents available and more by the number of downloads.  

Knowledge Value Chain: A sequence of knowledge processes including creation, organizing, dissemination and use that create value from knowledge stocks.

Know-what: This level of learning represents cognitive knowledge. It is basic knowledge that does not give a competitive edge. A good example would be reading a book on negotiation. Unless the principles mentioned in the book are actually applied in practice, the knowledge may have little value.  One cannot become a good negotiator merely by reading a book. 

Building Intangible Assets: A Strategic Framework for investing in Intellectual Capital, Knowledge Management – Classic & Contemporary Works, edited by Daryl Morey, Mark Maybury and Bhavani Thuraisingham

Laurie J Bassi; Mark E Van Buren. “New Measures for a New Era” Knowledge Management – Classic and Contemporary Works Edited by Daryl Morey, Mark Maybury and Bhavani Thuraisingham, University Press, 2001.

“Knowledge Management – An introduction to Creating Competitive Advantage from Intellectual capital” Vision Books, 2003.

“Knowledge Management Toolkit”.

Zack, Michael H. “Managing Codified Knowledge” Sloan Management Review, Summer 99, pp45-58.

Davis, Randall; Shrobe, Howard; Szolovits, Peter.  “What is a knowledge representation?” Artificial Intelligence, spring 1993.


Know-why: A system of knowledge about a causal relationship formulated using a certain number of variables, developing a good understanding of how they work and what impact they have. Know-why is shaped through learning-by-studying, with repeated experiments and simulations controlling various sources of influence. (See Causal Knowledge)

Knowledge Work Management: In the knowledge economy, managing knowledge work is becoming a huge challenge. Managing knowledge workers demands a change in paradigm. According to Tom Davenport , the specific changes required, include moving from organizing hierarchies to organizing communities, from evaluating visible job performance to assessing invisible knowledge achievements, from supporting the bureaucracy to fending it off and from relying on internal personnel to considering a variety of sources. In many ways, managing knowledge work is more challenging than doing knowledge work. Knowledge work management must strike a fine balance between leaving knowledge workers free to do their work and monitoring them to understand how they spend their time and how they can be made more productive, by imposing some amount of discipline.

Knowledge Workers: People who have a high degree of expertise, education or experience. The primary purpose of their jobs involves the creation, distribution or application of knowledge. All jobs involve some amount of knowledge. But this may not make everybody a knowledge worker. According to Tom Davenport, people can be called knowledge workers when the role of knowledge is central to their job. That means they must be spending considerable amount of their time on thinking and information processing.

Knowledge Wrapper: A term coined by David Skyrme. A knowledge wrapper accurately describes the contents within. It holds metadata in a standard format and may hold encrypted digital rights information. Wrappers typically include factual information, such as formats and size and subjective information such as reviews and quality rating, plus some elements of promotion. A good wrapper must be attractive to entice buyers, but it must also be informative and accurate. Unlike physical goods, knowledge cannot be returned after the wrapper has been opened. So a knowledge provider may offer a free trial period or a money back guarantee if the buyer is not satisfied. 

Laurence Prusak: A widely respected authority in KM, Prusak has written, published and consulted extensively.  His book, Managing Information Strategically (John Wiley & Sons, 1994), co-authored with James McGee, explains the role of information in generating competitive advantage. He has co-authored with Tom Davenport two popular books; Information Ecology (Oxford University Press, 1997), and Working Knowledge (Harvard Business School Press, 1997), and has edited an anthology Knowledge in Organizations (Butterworth-Heinemann, 1997). Some of his important articles include "Myth of InformationOverload" (International Journal of Information Management, 1995), "InformationPolitics" ( Sloan Management Review, Fall 1993), "Blow up the Corporate Library" (International Journal of Information Management, 1993), "Knowledge and Risk Management" (California Management Review, Spring 1996), and "Eleven Sins of Knowledge Management" (California Management Review, Spring 1998).

Learning History: Sometimes knowledge is communicated more effectively through a convincing narrative that is delivered with elegance and passion. Then it may be better to capture the knowledge in the form of a story, instead of trying to codify it in a rigidly defined structure/template.

A learning history is a written narrative of a company’s recent set of critical episodes such as a major change initiative, a radical process innovation, or a successful product launch. The document is presented in two columns. In the right hand column, relevant events are described by people who took part in them, were affected by them or observed them from close quarters. The left hand column contains analysis and commentary by learning historians, consisting of consultants, academics and knowledgeable insiders.

The learning history can be used as the basis for group discussions, which provide opportunities for collective reflection. They raise issues that people would like to talk about but have not had the courage to discuss openly. These discussions facilitate knowledge sharing, helping build a body of generalizable knowledge about what works and what does not.

Learning Management System (LMS): A Learning Management System (LMS) provides tools for managing, delivering, tracking and assessing various types of employee learning and training. LMS consolidates mixed-media training, automates the selection and administration of courses, assembles and delivers learning content and measures learning effectiveness. Sophisticated systems can correlate performance-on-the-job data with training data. An LMS is indispensable for a large, geographically dispersed, knowledge organization.

Learning Organization: An organization that realizes its success depends on continuous learning and modifying its behavior on an ongoing basis. According to David A Garvin , a learning organization works deliberately to become good at creating, acquiring, interpreting and retaining knowledge and then modifying its behavior to reflect new knowledge and insights. The concept has originated from Peter Senge’s 1990 book “The Fifth Discipline: The Art and Practice of the Learning Organization”. A learning organization has in place systems, mechanisms and processes that are used to continually enhance its capabilities by picking up new knowledge and adapting to the environment.

All organizations learn but the effectiveness of learning varies from one company to another. The key to effective learning lies in aligning individual and collective learning with the strategic intent of the firm. Effective organizational learning happens when explicit management efforts are made to build knowledge assets that support the firm’s strategy.

According to Garvin, learning organizations are skilled at:

  • Systematic problem solving
  • Experimentation with new approaches
  • Learning from own experience and past history
  • Learning from the experiences and best practices of others.
  • Transferring knowledge quickly and efficiently throughout the organization

As Garvin explains, organizational learning takes place in three overlapping stages:

  • The first step is cognitive. As they get exposed to new ideas, people expand their knowledge and begin to think differently.


  • The second step is behavioral. Employees begin to internalize new insights and alter their behavior.
  • The third step is performance improvement with changes in behavior leading to measurable improvements in results: superior quality, better delivery, increased market share and other tangible gains.


What Nonaka and Takeuchi call, “The Knowledge creating company,” seem to be for all practical purposes, the learning organization. As Nonaka has mentioned, in such a company, inventing new knowledge is not a specialized activity. It is a way of behaving, a way of being. Everyone in such a company is a knowledge worker and contributes to the learning process.

Rudiger Reinhardt has identified different levels of organizational learning. (See Table)


Learning levels

  • Individual learning
  • Team learning
  • Organizational learning
  • Inter organizational learning

Learning types

  • Single loop learning
  • Double loop learning
  • Deutero learning

Learning modes

  • Cognitive perspectives
  • Cultural perspectives
  • Action perspectives

Learning process

  • Identification/creation
  • Diffusion
  • Integration
  • Modification
  • Action


Argyris and Schon describe three types of organizational learning:

Single-loop learning: Errors may be detected and corrected but firms carry on with their present policies and goals. Single-loop learning is essentially lower level learning which does not challenge conventional wisdom or alter the fundamental nature of the organization's activities.

Double-loop learning: Besides detecting and correcting errors, the organization may question and modify existing norms, procedures, policies and objectives. So Double-loop learning is also called higher-level learning, generative learning (or learning to expand an organization's capabilities), and strategic learning. Strategic learning is “the process by which an organization makes sense of its environment and exploits the opportunities unfolding.  
Deutero-learning: Deutero learning or secondary learning is learning which results incidentally as a result of learning something else rather than as the result of a conscious effort. In particular, this is one of the aspects of enculturation, when values, norms and styles of learning are absorbed without being taught and may be crucial in determining a person's future behavior and learning patterns. Such learning includes learning that results from the reflection of learning processes and usually is a prerequisite for changing norms, values and assumptions.
Lessons Learned: Lessons learned are concise descriptions of knowledge derived from experiences. These lessons often reflect on what went right, what went wrong and what can be done to make the products and processes of the organization more appealing/effective in the future. These lessons can be communicated through mechanisms such as storytelling, debriefing etc, or summarized in databases. (See Learning History)
Management Information Systems: Management Information Systems (MIS) help managers monitor and control the operations of a business. MIS produce reports on a regular basis, based on data extracted and summarized from the company’s underlying transaction processing systems. For example, a report may show sales by region. Sometimes MIS reports are exception reports. Unlike decision support systems (DSS) which support semi structured and unstructured problems, MIS primarily deal with structured problems. (See Decision Support Systems)

Market-to-Book Ratio: A common method of valuing knowledge intensive companies. It is the ratio of the market value of outstanding shares to their book value. The ratio tends to be high for knowledge businesses, where intangibles account for much of the valuation.

Maturity of Knowledge Management: The level of adoption of KM within an organization. A KM maturity model  looks at stages of maturity starting from ad-hoc ways of managing knowledge to a stage when knowledge is fully embedded and integrated into the organization's core activities and business processes. However, rigid application of process maturity models like the ones used for software development by IT services companies is not advisable. Some important knowledge will always be shared directly through face-to-face informal, unstructured interactions by people coming together. It is difficult to impose a rigidly defined framework on such interactions. (See Agile Methodology, Process, Practice)

Memory:  The mental function of retaining information about stimuli, events, images and ideas after the original stimuli are no longer present. Memorial processes are extremely complex. Different memory tasks are handled differently. Yet, what memory can do is incredible. Memory helps us to deal with a problem with relative ease. But memory can also create difficulties while dealing with new problems that demand a new approach. (See Organizational Memory)
Mental Models: A mental model is how the thought process of human beings, visualizes something works in the real world. It is an internal representation of external reality. Mental models are deeply ingrained assumptions, generalizations, or images that influence how individuals understand the world and take action. Mental models have a significant impact on the pace and effectiveness of individual and organizational learning.
Mentoring: Mentoring is a one-to-one learning relationship in which a senior member of an organization is assigned to support the development of a newer or more junior member by sharing knowledge, experience, insights and wisdom with them. Mentoring relationships can be formal and informal. Well designed mentoring programs are guided by program goals, schedules, training and ongoing evaluation. New recruits and high potential managers identified as having high potential are typical candidates for mentoring programs.
Meta Information: Information about information. Meta information assists in defining, categorizing, and locating knowledge sources and resources.
Michael Earl: Previously professor of Information Management at London Business School, Michael Earl works at the intersection of business strategy and IT. Earl has published widely in reputed journals like Harvard Business Review, Sloan Management Review, MIS Quarterly, and the Journal of Management Information Systems. His book Management Strategies for Information Technology became a best-seller.
Michael Zack: Another reputed scholar in the area of KM. Zack’s research and publications have focused on the use of information and knowledge to improve organizational performance effectiveness.  His publications have appeared in a number of leading journals including Organization Science, Sloan Management Review, California Management Review, Information Systems Research, and Information & Management. Some of his important publications include: “Managing codified knowledge”, Sloan Management Review, Summer, 1999, “Developing a knowledge strategy”, California Management Review, Spring, 1999 and “The Design and Development of Information Products”, Sloan Management Review, Spring 1996.  
Middleware: Businesses are often tied down by legacy applications. These investments are heavy and often irreversible. So there is need for seamless integration across legacy systems. Middleware facilitates this integration. Middleware helps connect islands of data, facilitating better information utilization, adaptability and extensibility. Some middleware is simple, essentially meant to transport information from one system to another to complete a business transaction. Other middleware is more complicated. (See Service Oriented Architecture)
Migratory Knowledge: Knowledge that is independent of its owner or creator. The more the codification, the more the possibility of moving knowledge. In case of migratory knowledge, it is possible to transfer knowledge across people and organizations without losing context or meaning.
Mind: Can be seen as a totality of hypothesized mental processes and acts that may serve as explanatory devices for psychological data. It can also be seen as the sum of the conscious and unconscious mental experiences of an individual. These processes largely consist of two categories: perception and cognition.
Mind Map: Mind map is a diagram used for linking words and ideas to a central key word or idea. It is used to visualize, classify, structure, generate ideas and facilitate problem solving and decision making. Mind maps are useful for organizing individual or collective thought and representing it visually.
A mind map can present complex information in an organized easy-to-understand visual format. A mind map enables us to get the big picture through cascading connections between related topics and sub topics. It helps us to grasp the obstacles and paths so that we can quickly choose the best course of action and assign and manage tasks, resources, timelines and deliverables.
A mind map is similar to a semantic network or cognitive map but there are no formal restrictions on the kinds of links used. Most often, the map involves images, words and lines. The elements are arranged intuitively according to the importance of the concepts and organized into groups, branches, or areas. The uniform graphic formulation of the semantic structure of information on the method of gathering knowledge, may aid recall of existing memories.
Morten Hansen: A former professor of Harvard Business School, Morten T. Hansen is currently Professor of Entrepreneurship at INSEAD. Hansen has done extensive research on knowledge-based competition, corporate transformation, and building great companies. He has published articles in leading international academic journals including Harvard Business Review. His research work has been featured in the New York Times, Business Week, The Wall Street Journal, The Economist and Financial Times, among others. His article “How to Build Collaborative Advantage” received the Sloan Management Review/Pricewaterhouse Coopers Award for the article that has contributed most significantly to the enhancement of management practice. Hansen is the co-author of an influential article, “What is your strategy for managing knowledge?” (Harvard Business Review, April, 1999).

Multimedia: Technology that combines information available in various formats like text, audio and video. Multimedia facilitates seamless sharing of knowledge through audio files, pictures and video clips that can be combined with other knowledge objects, records, transactions and discussions. Mind maps and visual thinking tools make extensive use of multimedia features to capture and organize independent or collaborative thought processes.

Multivocality: The degree to which communication carries multiple perspectives. Multivocal communication generates divergent views. These views, in turn, can create new meaning and consequently more knowledge.
Neural Networks:  One of the key issues in Artificial Intelligence has been understanding how the human brain works and how to make computers function like the human brain. The human brain is good at recognizing patterns. Human beings can relate current problems to past problems. If computers can detect patterns, they would be extremely useful in solving business problems. Thus a manager in an insurance company would find it useful to identify fraudulent patterns while his counterpart in a mutual fund might be interested in patterns that help him understand how the financial markets will move.
Neural networks are used for modeling complex, poorly understood problems for which large amounts of data have been collected. They are especially useful in finding patterns and relationships in massive amounts of data that would be too complicated and difficult for a human being to analyze. Neural networks develop this knowledge by emulating the processing patterns of the biological brain.
The brain is a collection of cells called neurons that have many connections to each other. A neuron can be at rest or send a message. A neuron receives input from some cells and sends the output to other cells. A neural network is nothing but a collection of such cells.
Units of Neural networks can be described by a single number, their "activation" values. Each unit generates an output signal based on its activation. Units are connected to each other such that each connection has an individual "weight". Each unit sends its output value to all other units to which they have an outgoing connection. Using these connections, the output of one unit can influence the activations of other units. The unit receiving the connections calculates its activation by taking a weighted sum of the input signals. The output is determined by the activation function based on this activation. Networks learn by changing the weights of the connections.
Neural networks can identify patterns within data. Indeed, a well designed neural network can identify patterns, even if some data is missing. A neural network has three layers. The input layer receives data from external sources. The processing layer, which has already learned from solving earlier problems, tries to apply more lessons to the new data sets that are fed into the neural network. The output layer transmits the outputs or guesses to the user.  Unlike expert systems, which may have to be redesigned, when there is a change in the business/domain, neural networks have some capability to learn on their own, as they deal with newer and newer problems. Indeed, what is most exciting about neural networks is the possibility of learning.
Neural networks are useful in classifying cases into one category or another-say, whether a loan customer is likely to default or pay back the loan. As they deal with more cases and learn, the classification becomes more accurate.
In medicine, neural network applications are used for screening patients for coronary artery disease, for diagnosing patients with epilepsy and for performing pattern recognition of pathology images. Neural networks can also be used to predict the performance of equities, corporate bond ratings or corporate bankruptcies.  In the field of artificial intelligence, neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents or autonomous robots.
Neural networks require a lot of data and a high-powered computer. Considerable amount of time has to be spent in training the neural network, cleaning up the data and preprocessing for better comparison of the data being fed in. Doing the analysis and interpreting results can be very tricky. So these systems require a very knowledgeable user, at least to set up the initial model. Subsequent data may be analyzed with the same model.
Neural networks are also something of a “black box”. A particular case will be classified in a particular fashion according to nodes and variable weightings, and is therefore difficult to interpret. Some new neural networking tools, hide the complexity from the user and are able to explain to some degree why the system behaves the way it does. Still many managers do not like them because of difficulties in interpretation.

Nitin Nohria: A well known professor at the Harvard Business School, Nohria’s research centers on leadership, corporate accountability, and organizational change. His book Building the Information Age Organization, examines the role of information technology in transforming organizations. In Networks and Organizations: Structure, Form, and Action, an edited volume of original articles, he explores the emergence of network-like organizations. He is the author of over 75 journal articles, book chapters, cases, working papers, and notes. His article   “What’s Your Strategy for Managing Knowledge?” Harvard Business Review 77, no. 2 (March-April 1999): 106-116. co-written with  Morten Hansen and Thomas Tierney is a highly influential piece that explains how companies can strike a balance between IT and human intervention while managing knowledge.

Not-Invented-Here (NIH): Individuals/departments/organizations often have a mental block about using an idea/technology developed by an outsider. NIH is a major barrier to organizational learning. Companies have tried to deal with this syndrome in various ways such as by introducing “Steal Shamelessly” awards. 
Object Oriented Databases:  The type of database application should dictate the choice of database management technology, namely Relational databases and Object Oriented Databases. In general, database applications can be categorized into data collection and information analysis:

  • Data collection applications focus on entering data into a database and providing queries to obtain information about the data. These applications contain relatively simple data relationships and schema design. So, relational database management systems (RDBMs) are better suited for these applications. Examples are accounts payable, accounts receivable, order processing, and inventory control.
  • Information analysis applications involve navigation through and analysis of large volumes of data. Object-oriented databases (OODBs) are better suited for such applications. OODBs are also used in applications handling BLOBs (binary large objects) such as images, sound, video, and unformatted text. OODBs support diverse data types rather than only the simple tables, columns and rows of relational databases. Examples of these applications are CAD/CAM/CAE, production planning, network planning, and financial engineering.

OODBS facilitate the unification of the application and database development into a seamless data model and language environment. As a result, applications require less code and use more natural data modeling. So code bases are easier to maintain. Object developers can write complete database applications with a modest amount of additional effort.
In contrast to a relational DBMS where a complex data structure must be flattened out to fit into tables or joined together from those tables to form the in-memory structure, OODBs do not store or retrieve a web or hierarchy of interrelated objects. The one-to-one mapping of object programming language objects to database objects provides higher performance management of objects. It also enables better management of the complex interrelationships between objects. So OODBs are better suited for applications such as financial portfolio risk analysis systems, telecommunications service applications, world wide web document structures, design and manufacturing systems, and hospital patient record systems, which have complex relationships between data.
Online Analytical Processing (OLAP):  OLAP is part of the broader category of software applications which go by the name of business intelligence. The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business performance management, budgeting and forecasting, financial reporting and similar areas. OLAP is a slight modification of the traditional OLTP (On Line Transaction Processing). OLAP databases are capable of handling queries which are more complex than those handled by standard relational data bases through the ability to view data by different criteria, advanced calculation capability and specialized indexing techniques.

Ontology: Refers to the levels of knowledge creation. At the lowest level, we have the individual, then we have the organization and finally we have more than one organization. In a strict sense, knowledge is created only by individuals. The organization can provide the context and the necessary support but it is individuals who create knowledge. KM is all about amplifying this knowledge and crystallizing it as part of the knowledge network of the organization. From the individual level, the process moves to intra organizational and inter organizational levels.

Organizational Knowledge Awareness: Awareness of existing knowledge and the gaps which exist, is the starting point in KM. Knowledge awareness can be analyzed in various ways.  Elias Carayannis has identified four states of knowledge awareness as indicated in the matrix below:


of awareness


of awareness


of ignorance


of ignorance

Michael Earl has also developed a 2 x 2 Matrix as indicated below:

What you don’t know


What you know








Not Knowing





State of Knowledge

Organizational Knowledge Creation: According to the well known Japanese scholars, Tekeuchi and Nonaka, organizational knowledge creation takes place in five phases:

  • Sharing tacit knowledge:Rich untapped knowledge is shared by employees through socialization.
  • Creating concepts:Tacit knowledge is converted into explicit knowledge. A new concept is created.
  • Justifying concepts:The organization must determine if the concept is worthy of perusal.
  • Building an archetype: Concepts are converted into prototype/operating mechanism/a new system/an innovative organizational structure.
  • Cross Leveling knowledge:The knowledge created in one division is extended to other divisions and even to external stakeholders like customers and dealers.

Organizational Memory: The core knowledge of an organization's past, like project histories, important decisions and their rationale, key documents and customer relationships. It is the knowledge and understanding embedded in an organization’s people, processes and products or services, along with the company’s traditions and values. Organizational memory can either assist or inhibit the organization’s progress. Organizational memory helps  avoid 'reinventing the wheel' and repeating past mistakes. It also facilitates decision making. At the same time, in a fast changing environment, organizational memory can stand in the way of unlearning, a critical success factor. (See Memory)
Parsing: An algorithm that translates syntax into meaningful machine instructions. Parsing determines the meaning of statements issued in the data manipulation language. Parsing analyzes an input sequence in order to determine its grammatical structure with respect to a given formal grammar. The term parseable is generally applied to text or data which can be parsed. Parsing transforms input text into a data structure, usually a tree, which is suitable for later processing and which captures the implied hierarchy of the input. Generally, parsers operate in two stages, first identifying the meaningful tokens in the input, and then building a parse tree from those tokens.
Peer Assist: A peer assist is simply a process where a team of people who are working on a project or activity call a meeting or workshop to seek knowledge and insights from people in other teams. Seeking help from peers is not new. But the formal use of this process as a knowledge management tool and the coining of the term ‘peer assist’ were pioneered by British Petroleum. Peer assists facilitate ‘learning before doing’, i.e. gathering knowledge before embarking on a project or piece of work, or when facing a specific problem or challenge within a piece of work. The benefits of peer assists are quickly realized. Learning is directly focused on a specific task or problem, and so it can be applied immediately. A peer assist allows the team involved to gain input and insights from people outside the team, and to identify possible new lines of enquiry or approach. Peer assists facilitate the reuse of existing knowledge and experience, promote sharing of learning between teams and strengthen social networks. Peer assists are relatively simple and inexpensive to do. They do not require any special resources or any new, unfamiliar processes. They are particularly useful when a team is facing a challenge, where the knowledge and experience of others will really help, and when the potential benefits outweigh the costs of bringing people together.  
Personal Mastery: A term coined by Peter Senge. It is the discipline of continually clarifying and deepening personal vision, focusing energies, developing patience, and trying to see reality objectively as individuals strive to fulfill their highest aspirations.
Peter Senge: Founding Chair of the Society for Organizational Learning, a global community of corporations, researchers, and consultants committed “to increase our capacity to collectively realize our highest aspirations and productively resolve our differences” through the mutual development of people and institutions. The Journal of Business Strategy named him a “Strategist of the Century,” one of twenty-four men and women who have “had the greatest impact on the way we conduct business today” (September/October 1999). Senge believes that vision, purpose, reflectiveness, and systems thinking are essential for organizations to realize their potential. Senge is the author of several books, including the widely acclaimed, The Fifth Discipline: The Art and Practice of the Learning Organization (1990). Since its publication, more than a million copies have been sold. In 1997, Harvard Business Review identified it as one of the seminal management books of the past 75 years. His most recent book, Presence: Human Purpose and the Field of the Future, co-authored with C. Otto Scharmer, Joseph Jaworski and Betty Sue Flowers, outlines a new theory about change and learning.
Physical Environment: The way the office spaces are designed can influence the effectiveness of knowledge sharing. Many employees gain work related knowledge, not from manuals/formal training but from informal conversations on the corridor, near the water cooler, at the coffee vending machine and in the cafeteria. Indeed, realizing the importance of physical communication, some companies are creating physical spaces to promote this. If network connections are provided in these spaces, knowledge sharing can be further enhanced. The famous journalist, Tom Stewart once mentioned that the best hardware device for transferring knowledge is a coffeepot. But he added that coffee pots do not scale. By this he meant that while face-to-face informal conversation is the best way to share knowledge, leverage comes only with technology. Only if a large number of people have access to knowledge, will it make a good impact. And that kind of sharing on a large scale is possible only with technology. So workplaces should be designed both to increase human interaction and leverage technology. (See Spatial School under Schools of Knowledge Management and Work Ambience)
Practice: This refers to how knowledge workers actually accomplish their tasks.  Understanding work practice requires detailed observation and a philosophical acceptance that there must be some good reason for work being done in a particular way. Practice differs from process. A process orientation means laying down norms on how work should be done. Some jobs are very difficult for outsiders to understand and require a high proportion of practice orientation. Attempts to impose a process, may backfire. (See Process).

Procedural Knowledge:  Includes processes, sequences of events and activities and actions. Thus a company can maintain a database of methodologies pertaining to key areas such as project management and six sigma. But without the contextual understanding of how such knowledge can be applied, it may remain largely theoretical.

Process: Process is essentially a description of how work must be done. A process design is essentially an abstraction of how work should be done in the future.  A process orientation can impose discipline. But it may also stifle creativity. Often the design is done by people who have only a superficial understanding of how work is actually being done today. That is why a process orientation must be balanced by a certain degree of practice orientation while dealing with knowledge workers. Practice refers to how work actually gets done in an organization. (See Practice).

Process Networks: Process networks are a useful mechanism for facilitating inter organizational knowledge creation. According to John Hagel III and John Seely Brown, in their book, “The Only Sustainable Edge”, a specific form of network is evolving among world class companies in their endeavor to gain more flexible access to specialized capabilities on a global scale. Process networks typically extend beyond the first tier of business partners, and seek to coordinate activities across multiple tiers of enterprises within a business process. Process networks adopt a pull model where resources are flexibly provided in response to specific market demand. Process networks require formal orchestrators to function effectively. These networks are characterized by loose coupling. Relatively independent modules of activity are designated, with clear ownership and accountability for each module. The performance levels that each module must meet at the interfaces connecting it with other modules are defined. Module owners can improvise as long as they comply with the performance requirements. This kind of approach is not only more scalable but is also more effective in tapping the knowledge of a large number of specialized participants. In short, process networks are a way to multiply the value of a company’s capabilities. Relevant complimentary knowledge is tapped in a flexible way to provide more value to the customer.

Productive Friction: A concept coined by John Hagel III and John Seely Brown in their book, “The Only Sustainable Edge”. When people with diverse backgrounds, experiences and skill sets engage with each other on real problems, there is usually friction in the form of misunderstandings and arguments. Such friction can get dysfunctional. But if properly harnessed, this kind of friction can become very productive, accelerate learning, encourage innovation and foster trust across diverse participants. Productive friction can generate opportunities for capability building across specialized players within process networks. (See Ba, Creative Abrasion, Process Networks)

Professional Intellect: A term coined by James Brian Quinn, Philips Anderson and Sydney Finkelstein . The professional intellect of an organization operates at four levels. Cognitive knowledge is the basic mastery of a discipline achieved through training and certification. Advanced skills translate theoretical knowledge into effective execution. Systems understanding represents the deep knowledge of cause and effect relationships underlying a discipline. This is the understanding needed to solve large, complex problems, and anticipate and deal with unexpected scenarios. Systems understanding is reflected in highly trained intuition – whether the candidate who has come for the interview must be selected, whether the new project must be approved, etc. Finally, self-motivated creativity consists of will, motivation and adaptability for success. Self motivated creativity is needed in abundant measure to respond aggressively to changing external conditions.

Developing professional intellect calls for a systematic approach. The best people must be recruited and trained effectively by being exposed to real problems. Stretch goals must be set to make employees work hard and to exploit their full potential. Internal competition, peer pressure and timely performance appraisal and feedback are also important factors in shaping the professional intellect.

Pull System: This approach to KM believes that people should “pull” knowledge and use it as and when their work demands. Users get exactly what they need to know. They are not distracted by unwanted information or updates. The system depends on users taking the initiative to seek information.

Push Systems: Deliver knowledge to users after putting it through highly customized filters. Push systems deliver information to desktops or email accounts and are more likely to get noticed. Users do not have the hassle of going and looking for information. The danger is that people may be swamped with information which they may not need at that point of time.

Davenport, Thomas H. “Thinking For a Living” Harvard Business School Press, 2005.

In his article, “Building a Learning Organization,” Harvard Business Review, July-August 1993, pp 78-93.

Rudiger Reinhardt. “Knowledge Management: Linking Theory With Practice” in the book, Knowledge Management – Classic & Contemporary Works, Edited by Daryl Morey, Mark May bury and Bhavani Thuraisingham, University Press, 2001.

in their article “Managing Professional Intellect, Harvard Business  Review, March-April, 1996.


Radio Frequency Identification: Radio Frequency Identification (RFID) systems enable tracking the movement of goods throughout the supply chain. These systems use tiny tags with embedded microchips containing data about an item and its location to transmit radio signals over a short distance to special RFID readers. These readers pass data over a network to a computer for processing. RFID tags do not need line of sight contact to be read. The RFID tag is electronically programmed with information that can uniquely identify an item along with other information like location, place of manufacture, etc. The real savings from RFID come from the way it can improve an entire business process. RFID systems give suppliers, manufacturers, distributors and retailers much more detailed and real time data that facilitate improved inventory capital, shipping, etc. RFID is also likely to change the way invoices are settled by triggering an electronic payment to the shipper once a tagged pallet enters a retailer’s warehouse. Major enterprise vendors including SAP and Oracle now offer RFID ready versions of their supply chain management applications.

Reciprocity: A key concept in knowledge sharing. Reciprocity is what makes knowledge markets work. People will typically share knowledge when they know that others will reciprocate. People will contribute to a knowledge repository only when they feel they are gaining something valued by them in return.  Reciprocity may, however, stand in the way of some people enjoying access to a social network, if they have nothing to contribute for the moment. So many communities of practice allow legitimate “peripheral participation”.  Employees can “lurk” in electronic mailing lists and discussion groups and get a feel of what is happening within a group.

Redundancy: According to Nonaka and Takeuchi, redundancy is the conscious overlapping of company information, business activities and managerial responsibilities. Knowledge creation is facilitated when a company makes available information that goes beyond the immediate operational requirements of organizational members. Effectively, redundancy means concepts created by an individual or group are shared with other individuals who may not need the concepts immediately. Redundancy encourages dialogue and helps generate new ideas and consequently knowledge creation. When members of the organization share overlapping information, they can sense what others are struggling to articulate. Japanese companies like Canon organize product development teams into competing groups that develop different approaches to the same project and argue their case. This enables the project to be examined from multiple perspectives. Ultimately, the best approach is chosen. Redundancy can be facilitated through cross functional job rotation of employees.
Report Generator: A system that generates responses to queries, provides automated status reports, or reports on the contents of a database.
Rules of Thumb: Shortcuts to solutions to new problems that resemble problems previously solved by experienced workers. This kind of knowledge facilitates quick decision making. But one must be on the guard, when there is a paradigm shift and the existing thumb rules may no longer apply.

Scalability:  Refers to the ability of the KM system to support an increasing number of users and a growing volume of transactions. A system that performs well within a work group of limited size might not perform well, when it is extended across the enterprise. Scalability is an important issue in rapidly growing organizations. Indian IT services companies clearly fall in this category. Scalability depends on a number of factors, including the flexibility of the architecture and the capacity of the hardware.

Schools of Knowledge Management:  
Michael Earl, one of the well known KM gurus, has identified seven different schools of KM:

The systems school is perhaps the longest established, formal approach to knowledge management. The school believes in capturing specialist knowledge in databases and making it available across the organization.  The content is validated, through peer and superior review. Without information technology (IT), the systems school is not feasible. Computer systems which capture, store, organize, and display knowledge are the key drivers in this school.
The cartographic school is concerned with mapping organizational knowledge. By building knowledge directories, the aim is to make sure knowledgeable people in the organization are accessible to others for advice, consultation, or knowledge exchange. Knowledge directories are gateways to knowledge. People are expected to provide accurate and comprehensive profiles of their competencies and experience in the directories. The key driver of the mapping school is  people connectivity. Consequently, the principal contribution of IT is to connect people via intranets, extranets and the Internet.
The process school believes the performance of business processes can be enhanced by providing operating personnel with knowledge relevant to their tasks.  The process school focuses on enhancing the firm’s core capabilities with knowledge flows. IT can be used to provide shared databases across tasks, levels, entities, and geographies to all knowledge workers throughout a process.
The commercial school lays emphasis on both protecting and exploiting a firm’s knowledge or intellectual assets such as patents, trademarks, copyrights, and know-how to produce revenue streams. A specialist team or function is used to aggressively manage knowledge and intellectual property. Techniques and procedures are put in place to manage intellectual assets as a matter of routine. Many companies spend too much time trying to measure intellectual capital rather than actually developing and exploiting it.  The philosophy of the commercial school is commercialization of intellectual property.
The organizational school describes the use of organizational structures, or networks, for sharing or pooling knowledge. It believes in promoting “knowledge communities” or groups of people with a common interest, problem, or experience, within and across organizations. These communities can be intra- or inter-organizational.  Communities exchange and share knowledge interactively, often in non-routine personal and unstructured ways, as an interdependent network. The emphasis is on increasing connectivity among knowledge workers. IT, in the form of intranets and groupware, can help connect members and pool their knowledge, both explicit and tacit. 
Most people prefer conversation to documents or IT systems. Tacit knowledge is most likely to be discovered and exchanged through discussion. The spatial school centers on the use of spatial design—to facilitate knowledge exchange. Typical examples are the water cooler as a meeting place, the open-style coffee bar or kitchen as a “knowledge cafe” and the open-plan office as a “knowledge building.”  This school could also be called the social school, because the intent is to encourage socialization as a means of knowledge exchange. This school believes in nurturing and utilization of social capital that develops from people interacting, formally or informally, repeatedly over time. However, the label “spatial” is preferred because of the use of space to stimulate conversations and exchange.
The strategic school sees knowledge management as a dimension of competitive strategy. Indeed, it may be seen as the essence of a firm’s strategy. The aim is to build, nurture, and fully exploit knowledge assets through systems, processes, and people and convert them into value as knowledge-based products and services.  The strategic school provides an umbrella for the pursuit of all the other schools. This school views knowledge/intellectual capital as thekey resource. The firm consciously chooses to compete on knowledge. With the firm viewing itself as a knowledge business, knowledge creation and sharing, drive rather than just support competitive strategy. The strategic school is essentially concerned with raising awareness of how value can be created by treating knowledge as a strategic resource. Corporate mission and purpose statements are used to send out clear signals about the importance of KM.

Scripting: A popular technique to improve the productivity of people involved in low end knowledge work. An expert lays down a script that tells lower-level knowledge workers what to do under different circumstances. Scripting can bring the lowest performers to a certain level of proficiency. But it is unlikely to create a high-performing knowledge work force. Moreover, for jobs involving high levels of knowledge, scripting is unlikely to be effective.  The trick may lie in identifying the parts of the job that can be scripted. Thus, the power point slides for a B School course can be standardized. But it could be quite difficult to script the actual delivery of instruction in the class room. That depends critically on the skills of the instructor.  

Search Engine:The most important technology for the manipulation of explicit knowledge. Without effective search facility, a repository will be meaningless. Search engines contain software that looks for web pages containing one or more of the search terms. Then, they display matches ranked by a method that usually involves the location and frequency of search terms. Search engines create indexes of the web pages they visit. The search engine software then locates web pages of interest by searching through these indexes. The program used to perform the indexing function is called spider or crawler. Search queries are often ineffective because they retrieve many irrelevant documents. Improvements are possible through better understanding of the context of information needs and more knowledge of the domain being searched. An efficient taxonomy can help by arranging documents more systematically.

Search strategies can be of various types:

  • Metasearching: Based on meta categories and dependent on keywords and attribute tags. It minimizes the time spent in locating the right category. This approach emphasizes clarifying the context intended by the user through refinement and rejection.
  • Hierarchical: Knowledge is organized in a fixed hierarchy. Links can be used to locate the knowledge needed, efficiently. Hyperlinks are provided to dig deeper.
  • Tagged attribute: This approach matches user input attributes against attributes/tags associated with documents and pointers. Ranking of results is based on relevance.
  • Content: Search term, keyword or text string are matched to return results with relevant scores based on the frequency of matches. This strategy is slow and inefficient.
  • Combinatorial: It combines two or more of the approaches mentioned above and executes them in parallel.

Various automated mechanisms are available for enhancing knowledge search and retrieval capabilities. Clustering automatically finds groups of related documents such as technical reports. Categorization assigns new knowledge elements to one or more categories from a user-defined taxonomy. There are tools available to generate taxonomy as well.   Then there are translation capabilities which recognize and translate key concepts from one language to another. A thesaurus can be a useful tool for connecting inconsistently defined concepts in search queries.

SECI Model: Developed by Takeuchi and Nonaka, SECI (Socialization, Externalization, Combination and Internalization) is probably the most well known and comprehensive theory of organizational knowledge creation. The model views the process of knowledge creation as taking place in four phases. Socialization is the process of converting tacit knowledge into tacit knowledge by sharing experiences. Externalization is the process of converting tacit knowledge into explicit concepts. Combination is the process of combining and systematizing explicit concepts into a knowledge system. Internalization is the process of converting explicit knowledge into tacit knowledge through learning by doing or by relating to the experiences of others. The movement through the four modes of knowledge conversion is represented not by a circle but by a spiral. Knowledge gets amplified as it moves through the four stages of knowledge conversion. The SECI model views knowledge creation and knowledge sharing, and tacit & explicit knowledge holistically, rather than as watertight compartments. (See Socialization, Externalization, Combination, Internalization)

Semantics: Refers to the formal rules and procedures for representing meaning. Semantic feature is any defining characteristic of the meaning of a word which serves to distinguish it from the meaning of other words. (See Semantic Network, Semantic Web).

Semantic Network: A method of representing structured knowledge using nodes and links. The nodes are concepts or entities while  the links represent relationships and associations among the concepts.  A semantic network assumes information is stored in the form of words, concepts or propositions as independent units which are interconnected by links or relations.
Important semantic relations include:

  • Meronymy (A is part of B)
  • Holonymy (B has A as a part of itself)
  • Hyponymy (A is subordinate of B; A is a kind of B)
  • Hypernymy (A is superordinate of B)
  • Synonymy (A denotes the same as B)
  • Antonymy (A denotes the opposite of B)

There are various types of semantic networks like the Semantic Network Processing System (SNePS) of Stuart C. Shapiro or the MultiNet paradigm of Hermann Helbig (MultiNet is an acronym for "Multilayered Extended Semantic Network"). MultiNet is well suited for the semantic representation of natural language expressions.
A mind map can be considered a very free form variant of a semantic network. By using colors and pictures, the emphasis is on generating a semantic net which evokes human creativity. (See Mind Mapping).
Semantic Web:  Seen by some as the next evolution of the World Wide Web, the Semantic Web links up information in such a way as to be easily processable by machines, on a global scale. Much of the data on the Web is difficult to use on a large scale, because there is no global system for publishing data. The Semantic Web, thought up by Tim Berners-Lee, is still very much in its infancy. Although the future of the project appears to be bright, there is little consensus about the likely direction and characteristics of the early Semantic Web. However, it is expected that as more and more people want to publish data, semantic webs may take off. A large number of Semantic Web applications may be used for a variety of different tasks, increasing the modularity of applications on the Web.

Service Oriented Architecture: Service Oriented Architecture (SOA) attempts to increase the flexibility of available software resources. Existing application platforms make top executives feel their hands are tied. The cost of switching to a platform can be exceedingly high. SOA is an attempt to minimize these costs through the use of a more modular approach. Software is typically designed to support a business context. Services are designed without advanced knowledge of the tasks and uses they will be called upon to support. SOA helps to establish loosely coupled connections across existing applications and databases quickly and cost effectively. IT services can be accessed when needed from wherever they reside. Where the software is located becomes irrelevant as far as users are concerned. As John Hagel III and John Seely Brown mention in their book, “The only sustainable edge”, software will become increasingly commoditized and will be able to switch from one module to another. Loosely coupled connections can consume a lot of computing and network resources. So coordination of distributed processing power is exceedingly important. For SOAs to become economically applicable, computing power must be made flexibly available.

SOAs can leverage vast resources already available and make them accessible as services. This is unlike previous generations of architectures that demanded removal of existing technology platforms and heavy investments in new ones. SOA can enhance the potential of social software by making it easier to connect social software tools with existing software resources like databases, electronic documents and analytic tools. This can greatly enhance problem solving among the people mobilized by the social software. However, many more obstacles must be overcome before SOA becomes more popular. Businesses must develop a shared meaning regarding the content of business tasks. Currently, such architecture is used more to publish and distribute business information, not to automate business processes.
Single-loop Learning: Single-loop learning involves using knowledge to solve specific problems based on existing assumptions, and based on what has worked in the past. If a room is becoming too cool, one would adjust the thermostat setting, not question whether the air-conditioning system is over designed. In short, single-loop learning is limited in scope and does not lead to challenging the accepted wisdom. So, this kind of learning is lower level learning that takes many things as given.
Socialization:  One of the four components of Takeuchi and Nonaka’s SECI model.  This is the process of sharing experiences and thereby creating tacit knowledge such as shared mental models and technical skills. In socialization, knowledge sharing takes place through observation, imitation and practice. A good example is how the organizational culture is shared across employees in a company. Similarly, apprentices learn from their masters through observation, imitation and practice. On-the-job training and mentoring can also be viewed as forms of socialization. The key to socialization is shared experience. As Takeuchi and Nonaka put it , “Without some form of shared experience, it is extremely difficult for one person to project herself into another individual’s thinking process. The mere transfer of information will often make little sense if it is abstracted from embedded emotions and nuanced contexts that are associated with shared experiences.” Brainstorming can facilitate socialization. So can interactions between product development engineers and customers. (See SECI Model)

Social Capital: A kind of intellectual capital, that can be a valuable intangible asset if carefully nurtured. Refers to the ability of groups to collaborate and work together. Well functioning human networks facilitate exchange of ideas, problem solving and creation of new knowledge. Social capital is a function of trust.  Trust enables decisions to be taken more quickly and implemented more smoothly. So, social capital reduces transaction costs. The quality of knowledge is also high because when there is trust, ideas can be debated in a transparent way, with less defensive reasoning and without hidden agendas dominating. Social capital is an intangible asset that is highly contextual and strongly shaped by the organizational culture. So it cannot be easily imitated by competitors.

Social Networks: In most organizations, work gets done less through standardized processes and formal structures and more through informal networks of relationships. These networks must be actively encouraged and nurtured carefully. Yet the power of these invisible groups is often underestimated by many organizations.

Improving the functioning of social networks is not merely about increasing connectivity. While expanding network connectivity, companies need to determine exactly what they want to accomplish through informal networks and, the kind of connectivity needed to help them achieve their goals. Indiscriminate expansion of the network can take a toll on employees. Connectivity must be promoted only where it benefits an organization or individual. Connectivity that is not needed should be decreased.

According to Rob Cross, Jeanne Liedtka and Leigh Weiss , all informal networks help organizations do two things -- recognize opportunities or challenges and coordinate appropriate responses. Based on their unique value propositions, three different archetypes can be identified:

Customized Response: Sometimes both problems and solutions are ambiguous. New product-development teams, high-end investment banks, early-stage drug-development teams, and strategy consulting firms require networks that can rapidly define a problem or an opportunity and bring together relevant expertise. Here, people must quickly frame and solve a problem.

Modular Response: In other situations, the components of a problem and solution are known but the combination or sequence of those components is not yet known. Surgical teams, law firms, business-to-business sales, and mid-stage drug development teams require networks to identify problem components and address them with modularized expertise.  Such jobs involve delivering a unique response depending on the various elements of expertise required by the problem.

Routine Response: In many other situations, work is standardized. Problems and their solutions are well defined and predictable. In call centers and insurance claims-processing departments, efficient and consistent response to a set of established problems holds the key to success.

The essence of networking is building trust, strengthening human relationships and improving the richness of knowledge transferred. Much can be done by organizations to nurture social networks. Collaborative behavior should be an important criterion during recruitment. Helping employees develop an awareness of who knows what in the organization allows them to know whom to turn for help. Skill profiling systems, expertise locators and communities of practice can all go a long way in strengthening relationships and improving collaboration.  Performance appraisal systems can also promote collaborative behaviour. Leadership and culture have a profound influence on social networks. Leaders must send out clear signals that they support a collaborative culture. Experiential learning must be encouraged through mechanisms such as “after action review.” Mentoring, coaching and learning from failure should be actively encouraged across the organization.

High performing knowledge workers actively manage their networks. They know they receive a lot of information through network contacts. So they are careful to reciprocate with information and nourish network relationships. A variety of social networking software is also now available to form and nurture social networks. But software can only complement, not replace people-to-people connections. (See Social Software)

Social Networking Analysis: This is a useful technique for understanding the informal networks which exist outside the formal structure of an organization or for diagnosing the limitations of the existing formal structure. Information is collected on who communicates with whom. Specialized software is then used for analysis. Typically, the interactions are plotted graphically. The graph will indicate clearly whether the network is excessively dependent on some people. In that case, the structure needs to be corrected to ‘democratise’ the information flows.

Social Software: Though it is not completely new, it is only now that social software is developing the robust capabilities needed. Today, social software can help connect up people, provide them collaboration tools and create various records of interactions. Social software includes traditional tools like email and bulletin boards as well more recent innovations like instant messaging, blogging, wikis and social network analysis tools. One area where social software looks likely to play a crucial role is exception handling. Massive enterprise applications generate various exceptions that must be handled by people. The right people to handle the exception must be identified and brought together. Then these people have to be provided the relevant information and analytical tools to come up with an effective resolution. This also demands a good understanding of the context. More often than not, exceptions are handled in ad hoc fashion. Once the transaction is completed, records are not properly maintained. So the next time the same exception arises, the entire resolution must be repeated from scratch. As John Hagel III and John Seely Brown mention in their book, “The Only Sustainable Edge”, exceptions can actually be a major source of business innovation. Employees are forced to address unexpected challenges. Consequently, they often make significant refinements in the business processes involved. Social software can provide the tools that help reduce the cost of exception handling. It can also create a repository that documents the exceptions, the people involved in resolving the exceptions and the resolutions themselves. The repository can play a key role in disseminating business innovations across the organization.

One company which has made good use of social software to improve its business processes is Xerox. Social software has helped service engineers to tackle unexpected repair needs for printers and copiers. Till a few years back, the company’s standardized procedures only explained what happened when a single fault in the equipment occurred. But many malfunctions involved two or more faults simultaneously. So Xerox introduced Eureka a social network that mobilized tips contributed by the service engineers as they reported on their experiences while handling unexpected problems. Engineers who began to contribute tips became highly respected among peers. Within years, Xerox captured 30,000 tips resulting in savings of $100 million per year. Eureka has rapidly emerged as an important learning tool. Service engineers can use Eureka to sharpen their trouble shooting skills. Product designers can consult Eureka while trying to improve product performance. Experiences of engineers have been transformed into knowledge that can be shared across a geographically distributed work force.
Spider’s Web: A term coined by James Brian Quinn, Philip Anderson and Sydney Finkelstein . When a company encounters complex, poorly defined problems, no one person may know how to solve them. A self organizing network or spider’s web comes in handy in such cases. Such a web brings people quickly together to solve a problem and then disbands just as quickly once the job is done.  Research reveals that even with 8 – 10 collaborating independent professionals, a spider’s web can leverage knowledge capabilities by hundreds of times. Spiders' webs are particularly appropriate when knowledge is dispersed among many specialists who must come together to solve a different problem. Consulting firms, investment banks, research consortia and medical diagnostic teams have been known to use spider’s webs. (See Collaboration Work)
Storytelling: Stories represent an useful way of sharing knowledge and helping learning. Stories can be very powerful communication tools, and may be used to describe complex issues, explain events, communicate lessons learned, or bring about cultural change. Stories preserve the rich context that gets lost if attempts are made to cram information into rigidly defined templates. Unfortunately, many organizations do not pay adequate attention to this important method of knowledge sharing.
Structural Capital: A form of intellectual capital that remains with the firm, not individual employees. It includes the explicit rule-based knowledge embedded in the organization’s work processes and systems, or encoded in written policies. It also includes training documentation or best practices databases.

Summarization:Long documents are cumbersome and unwieldy. We all want to avoid them! Fortunately, today, technology is available for summarizing documents. Typically, all the key points in a large document can be summarized in less than twenty per cent of its original size using such technology. If not anything else, a summary enables users to avoid reading irrelevant documents. Commercially available summarizers use the sentence selection method, preparing a summary from what are judged to be the key sentences in a document.

Systems Thinking: A philosophy that emphasizes the importance of looking at a problem holistically. It is a conceptual framework, a body of knowledge and tools that have been developed over the past 50 years, to make the full patterns clearer, and to make it possible to bring about effective change with the least amount of effort by finding the leverage points in a system. (See also Learning organization).

Tacit Knowledge: The knowledge or know-how that people carry in their heads including subjective intuitions and hunches.  Such knowledge is not easily visible and expressible. As it is highly personal and hard to formalize, tacit knowledge is difficult to communicate or share with others. There are two dimensions of tacit knowledge. The technical dimension refers to the skills developed over time. The second dimension is cognitive, consisting of beliefs, perceptions, ideas, values, emotions and mental models so ingrained in us that we take them for granted.

Personal, context specific knowledge is difficult to formalize, articulate or record. It is developed through trial and error and best transferred through doing and observing. Observation, mentoring, story telling, discussions, dialogues and project based learning are some of the tools available to transfer tacit knowledge. Such knowledge is difficult to pass on through the use of information technology. Because tacit knowledge is difficult to document and replicate, it is often the most valuable form of knowledge.
Some authors draw a distinction between tacit and implicit knowledge, defining tacit knowledge as that which cannot be written down, and implicit knowledge as that which can be written down but has not been written down yet. In this context, explicit knowledge refers to knowledge which has already been written down. (See Socialization, SECI Model)
Tag : A tag is a keyword which acts like a subject or category, to organize webpages and objects on the Internet. Tags are used to find or organize objects with similar properties. Each user "tags" a webpage or image using his/her own unique tag.
Tags can be used to specify properties of an object that are not obvious to the object itself. Attribute tag searching works by using tags that define concepts not inherently captured in the content of the document. A tag can have a brief description of the business activity, the domain, the formal/physical representation of the knowledge, type of document, product/service to which it relates, time of creation of the document and the location of the knowledge element.
An image or webpage may have multiple tags that identify it. Webpages and images with identical tags are then linked together. Users may use the tag to search for similar webpages and images. Tags are used in markup languages (HTML and XML). Tagging content is an integral part of Content Management Systems.
Taxonomy: Taxonomy is effectively a classification system, which serves as the table of contents for an organization’s knowledge. Taxonomy allows an understanding of how knowledge can be broken down into parts, and how its various parts relate to each other. Taxonomies are used to organize information and help users to find it easily. Taxonomy provides the structure governing the way information, documents and libraries are constructed. This structure helps people in navigating, storing and retrieving needed information. Taxonomy can also provide pointers to human expertise or knowledge. Taxonomy is useful in breaking down silos and building a shared language across the organization. Taxonomy serves as a defacto communication tool that connects people together on a common platform so that they can contribute and share knowledge easily.  (See Search Engine)
Team learning: Teams, not individuals, are the fundamental unit of work in modern organizations. Unless teams learn, the organization cannot learn. Teams play a central role in knowledge creation. They provide a shared context where individuals can interact with each other and engage in meaningful conversations. Team learning depends on the ability to engage in dialogue' and the capacity of the members to suspend assumptions and enter into a genuine ‘thinking together' mode. Constructive dialogues lead to new points of view. Defensive reasoning is a major impediment to team learning. When there is defensive reasoning and people are not open, it is difficult for new perspectives to emerge. (See Defensive Reasoning)
Technology: Information technology has a key role to play in knowledge management. Technologies used in KM are different from those used for handling data. Technologies designed for managing data are structured, numerically oriented, and address large volumes of observations, and do processing with­out substantial human intervention.  On the other hand, technologies used in KM must deal frequently with text rather than numbers. These technologies are also more likely to be employed in an interactive and iterative manner by their users.

There are various types of KM technologies. Some involve participation by large groups of people; others involve only a few individuals. In case of some technologies, the user must be something of an expert. Others assume that the user plays a more passive role. Some knowledge-work environments allow time for search, synthesis, and reflection. A good example is an academic researcher. Others require real-time or near real-time performance. A good example is a doctor or call center worker.

According to Tom Davenport , technology can support knowledge work in different ways depending on the nature of the work – Transaction, Integration, Collaboration, and Expert. Transaction work involves low amounts of collaboration and judgment. Here, technology can automate structured transactions. Integration work involves a low level of judgment but a high level of interdependence. In this case, technology can structure the process and the flow of work and also facilitate the reuse of knowledge. Expert work calls for a high level of judgment but a low level of collaboration. Technology must embed knowledge into the flow of the work process. In Collaboration work, there are high levels of judgment and collaboration. Work is usually iterative and unstructured. Repositories can be useful here.

Repositories, Groupware technologies, Decision Support Systems, Expert Systems, Social Software and the Internet are some of the commonly used tools in KM. Groupware, probably the most commonly used technology in KM, supports collaboration. Groupware provides a virtual space in which people can share experiences, conduct meetings, listen to presentations, hold discussions and share documents.

The Web is ideal for publishing information across different computer platforms. The Web is good at displaying knowledge that is linked to other knowledge through hypertext links. The Web deals easily with audio, graphic, and video representations of knowledge. The hy­pertext structure of the Web facilitates easy navigation. Intranet Webs are often the easiest way to get KM started in an organization. Hypertext Markup Language (HTML) publishing tools for producing Web documents, a relational database system for storing them, text search-and-retrieval engines, and some approach to managing the “metaknowledge” that describes and facilitates access to the knowledge available, are some of the tools which can be used.

Early on in the life of KM initiatives, a “let a thousand flowers bloom” technology strategy may be helpful. Later on, however, the sharing of knowledge across organ­izational boundaries will be easier with a single, broadly deployed platform.

A good deal of new technology attends primarily to individuals and the explicit information that passes between them. But the social dimension must not be ignored. Indeed, technology will be effective only when it can build a community around it. When we go back in time, we notice that information sharing devices such as the telephone and the fax, like the book and newspaper before them, became popular not simply because they carried information to individuals, but because they were easily embedded in communities. 

In the early days, the Internet was designed primarily so that computers could exchange information electronically and computer users could exchange files. But some insightful programmers decided to introduce e-mail for transferring files. Email which helped transform a scientific network into a social network, still accounts for the bulk of Internet traffic. Similarly, Tim Berners-Lee realized that the World Wide Web would be much more interesting if it was used not simply for exchanging information between individuals, but to support collaborators. That is what has driven the Web’s extraordinary evolution.

IT facilitates capturing knowledge; defining, storing, categorizing, indexing and linking digital objects, searching for and subscribing to relevant content. Yet, many people are reluctant to use IT or they use it only when they are forced to. So IT strategy must begin by thinking about how people use information.

One important issue in technology involves the way the local informality found within communities is protected. Technologies vary in terms of formality and trust. At one end are systems that prevent people from behaving in ways other than those clearly defined and constrained by the technology. For high-security demands, such technologies will be increasingly important and indeed may appeal to people. A good example is ATM machines. But if new technologies ask people to negotiate all their social interrelations this way, the informal, the tacit, and the socially embedded dimensions will be completely ignored. The demands for formality demanded by technologies can disrupt informal relations. For instance, in many situations, asking for explicit permission changes social dynamics quite dramatically—and receiving a direct rejection can change them even further. Consequently, people negotiate many permissions tacitly. A great deal of trust grows up around the ability to work with this sort of implicit negotiation. Direct requests and insistence of rights and duties only serve to lower trust and heighten tension.

The limitations of technology should not be overlooked. Many important jobs in organizations get done through social networks. Informal water cooler and coffee vending machine conversation and impromptu unstructured meetings will continue to have a role to play in encouraging informal knowledge sharing.

Technology is not ideally suited for handling tacit knowledge. Also, technology cannot create new knowledge. Technology by itself cannot also be a change agent. Changing a company’s knowledge culture requires altering basic behaviors, attitudes, values, management expectations and incentives. But technology can expand access and ease the problem of getting the right knowledge to the right person at the right time. Technology can also raise the motivation to share knowledge. When people see their company investing time and money on its Web site or intranet for example, they may take KM more seriously.

Text Mining: Text mining refers generally to the process of extracting interesting and important information and knowledge from large amounts of unstructured text. Text mining combines information retrieval, data mining, machine learning, statistics and computational linguistics. Several research groups around the world, as well as R&D departments of big companies, are doing research on text mining. One of the largest text mining applications that exist is the classified ECHELON surveillance system. Until recently, websites mostly used text based lexical searches. Text mining will allow more "semantic" searches. For example, searching for a 'car company' may yield the home page of an automobile manufacturer even if the page does not contain the words 'car company' explicitly. (See Summarization)
Tom Davenport: One of the leading KM gurus in the world, Davenport has been associated with Ernst & Young, McKinsey & Company, and Accenture. He has written, co-authored or edited several books on business process reengineering, knowledge management, and the business use of enterprise systems. “Working Knowledge: How Organizations Manage What they Know”, coauthored with, Laurence Prusak (2000) is one of the most popular books ever written on KM. His book, “What’s the Big Idea: Creating and Capitalizing on the Best Management Thinking”, was named one of the three best books of the Spring 2003 season by Fortune magazine. His most recent book, “Thinking for a living,” has also received highly favorable reviews. Davenport has also written hundreds of articles and columns for such publications as Harvard Business Review, Sloan Management Review, California Management Review, Financial Times, Information Week, CIO and many others. His other books include: The Attention Economy: Understanding the New Currency of Business coauthored with, John C. Beck (2002);; Mastering Information Management coauthored with, Donald A. Marchand, (2000); Mission Critical: Realizing the Promise of Enterprise Systems ( 2000); Information Ecology: Mastering the Information & Knowledge Environment coauthored with, Laurence Prusak (1997) and Process Innovation: Reengineering Work Through Information Technology, (1992).
Transaction Work: A term coined by Tom Davenport. Transaction work is essentially routine work involving low discretion. Formal rules, procedures and training can be used to structure this kind of work.  Technology can facilitate automation in a big way. Thus, call center workers can be asked to do their jobs according to a clearly laid down script.

Univocality: The extent to which communication is dominated by one perspective. Univocal communication functions as an information-transmission device. Utterances made by religious leaders, political leaders, moral authorities and teachers are examples of univocality.  Such utterances are not challenged. They are accepted as gospel truth. By itself, univocality is not bad. Indeed, univocality is desirable in some situations where multiple perspectives are not desirable. Thus an organization’s shared values or corporate identity must be communicated without any ambiguity. But in many other situations, divergent thinking and multiple perspectives must be encouraged. New product development is a good example.

Virtual Private Network (VPN): A technology to create a secure private network using the Internet, without actually having to build a network. Effectively, a private pipeline is created for exchanging data using the Internet infrastructure. VPNs are designed in such a way that the security is as strong as in leased, private lines.

Visualizing Tools: When ideas and concepts are depicted pictorially, they are easy to understand. Powerful visualization tools are available to investigate the structure of knowledge domains and knowledge within domains. (See Concept Map & Mind Map)

Voiceover IP: An Internet protocol that facilitates real-time voice communications over the Internet. Voice is converted into information packets that are sent as streamed data and reconverted into voice at the receiving end. In some cases, the customers can talk through the browser itself. The integration of the browser and voice allows support staff to pick up from where a customer left rather than have the customer describe the problem again.   

Webinar (Web seminar): A presentation delivered over the Web using videoconferencing. A Webinar is a useful knowledge sharing tool in the sense that people can learn from an expert without leaving their desk. A webinar also facilitates peer group learning. In combination with facilities such as chat, spontaneous discussions can also take place, leading to a rich exchange of ideas among people attending a seminar.

Web Server: Software for locating and managing stored web pages. It locates the web pages requested by a user client on the computer where they are stored and delivers the web pages to the user’s computer. Web servers can also work with application servers to access information from a company’s internal information systems applications and their associated databases.
Web Services: These are loosely coupled software components that exchange information with each other using standard web communication standards and languages. They can exchange information between two different platforms regardless of the operating systems or programming languages on which the platforms are based. Different applications can use web services to communicate with each other in a standard way without custom coding which is time consuming. Web services can be used to link systems of two different organizations or to link disparate systems within a single company. The collection of web services used to build a firm’s software systems constitutes what is known as service-oriented architecture. (See Services Oriented Architecture)
Wiki: A collaboration tool that allows multiple authors to join hands in creating and updating documents. A wiki allows users to easily add, remove, or otherwise edit all content, very quickly and easily. The ease of interaction and operation makes a wiki an effective tool for collaborative writing. A wiki records each individual change that occurs over time, so that at any time, a page can be reverted to any of its previous states. A wiki may also include various tools, designed to provide users with an easy way to monitor the constantly changing state of the wiki as well as a place to discuss and resolve the various disputes that can arise over the content.

Willpower: Knowledge is actionable information. Unless managers get into action mode, knowledge is of little use. Heike Bruch and Sumantra Ghoshal, mention in their book, “A Bias for Action”, that despite all their knowledge and competence, their influence and resources at their disposal, managers do not grab the opportunities to achieve something significant. Purposeful action requires energy and focus. More than motivation is needed to spur people to purposeful action. What is needed is willpower. Willpower is what enables managers to take action even when they are not inclined to do something. Managers with willpower overcome barriers, deal with setbacks and persevere to the end. Just as defensive reasoning can block learning, lack of will power can block action.

Wisdom: Wisdom goes beyond knowledge. The essence of wisdom is understanding clearly which knowledge to use for what purpose. In many ways, wisdom is the ability to make correct judgments and decisions. Many people think it is an intangible quality gained through experience. According to Encarta, wisdom is the accumulated knowledge of life or of a sphere of activity that has been gained through experience. Wisdom is often determined in a pragmatic sense by popularity, longevity and ability to predict future events. Wisdom is also accepted from cultural, philosophical and religious sources. Some think of wisdom as foreseeing consequences and acting to maximize beneficial results. For many, wisdom connotes an enlightened perspective, something used for the long-term common good. Recall King Solomon in the Bible. According to Andrew Hargaddon, if knowledge is the grasp we have over a subject, wisdom is the grip. A wise person is not only knowledgeable but is also prepared to give up existing knowledge for new knowledge when the situation demands. 

Work Ambience: The physical work environment has an impact on knowledge work productivity. Knowledge workers prefer to work in closed offices but seem to communicate better in open ones. Since knowledge workers like to collaborate, there must be meeting spaces and conference rooms. But when concentration is necessary, knowledge workers require quiet settings with few distractions. Knowledge workers like flexibility and they like to work at home occasionally. But they don’t want their homes to be their only offices. They want to come together from time to time and exchange notes about their work.

Knowledge workers vary in their tasks and needs. So the most optimal physical work environment may well vary across workers. Transaction workers need work environments in which they can concentrate on their transactions, while sitting at their desk. Expert workers also want to concentrate while doing their work, but they may need more space to keep books, journals, etc which they may refer from time to time. Integration workers need an environment in which they can easily communicate with coworkers.

The right approach to workplace design depends on various factors:

  • How homogeneous is the organization?
  • How important is it for the organization to align knowledge workers’ needs and their work settings?
  • How much freedom does the management want to give knowledge workers in designing their work space?
  • How much is the company willing to invest?


Firms predominantly engaged in one type of work can provide one standard work setting for all employees. Those with a moderate degree of segmentation can group their employees into a limited number of categories and assign pre defined work settings to each. Yet other firms need to have different work settings for different groups of employees. (See Caves & Commons)

Workflow Management Tools: Essentially an offshoot of traditional flowcharting tools, they facilitate process management in information intensive organizations. Workflow tools help specify the movement of documents and facilitate a better understanding of information processes. Workflow software can be used to remake and streamline business processes. It focuses on the steps that make up processes and redesigns these steps. Work is routed automatically from employee to employee. Workflow software effectively helps in eliminating paperwork and bureaucracy. Such software also makes the management of projects/activities more transparent.  

XML:  A programming language that allows for the creation of customized tags for individual information fields. XML is essentially a Web-based markup language that allows a wide range of user-defined tags. XML is an updated version of HTML. XML not only describes the way to lay out content on a web page for display or printing, but also describes the nature of the content. XML provides a simple way to handle data exchange over the internet. Whereas HTML is limited to describing how data should be presented in the form of web pages, XML can present, communicate and store data. An XML file can contain the data too, as in a database.
HTML has an inflexible, single-purpose vocabulary of elements and attributes. XML makes it easier to write software that accesses the document's information, since the data structures are expressed in a formal, relatively simple way.
The easy availability of word processors facilitates rapid XML document authoring and maintenance. Before the arrival of XML, there were very few data description languages that were general-purpose, Internet protocol-friendly, and easy to learn. In fact, most data interchange formats were proprietary, special-purpose, "binary" formats that could not be easily shared by different software applications or computing platforms.
XML makes it possible for computers to manipulate and interpret their data automatically and perform operations on the data, without any human intervention. Programmed rules can be used for applying and displaying data. XML provides a standard format for data exchange, enabling web services to pass data from one process to another.  XML database management systems are commonly used in B2B e-commerce. Because they use documents and not tables, they can perform much faster than conventional database systems. It is much easer for people to exchange data without getting involved in the underlying database design.
Yellow Pages: A colloquial term for an expertise directory. It provides a list of experts, a brief account of their expertise and their contact details. (See Expertise Directory). 




Nonaka, Ikujiro; Takeuchi, Hirotaka, “The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation”Oxford University Press, 1995.

Harvard Business Review, March  2005.

In the article “Managing Professional Intellect: Making the Most of the Best” Harvard Business Review, March-April 1996, pp 71-80.

Davenport, Thomas H. “Thinking For a Living” Harvard Business School Press, 2005.




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