If we are concerned about knowledge management, we need to be clear about what we mean by the word knowledge. Taking off from the difference between, data, information, and knowledge illustrated above, a working definition of knowledge as suggested by Thomas Davenport and Laurence Prusak, is given below:
“Knowledge is a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied m the minds of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices, and norms ” (Davenport & Prusak1)
To put it more simply: Knowledge is simply actionable information. Actionable refers to the notion of relevant, and nothing but the relevant information being available in the right place at the right time, in the right context, and in the right way so anyone can bring it to bear on decision-making all the time. Knowledge is the key resource in intelligent decision-making, forecasting, design, planning, diagnosis, analysis, evaluation, and intuitive judgment-making. It is formed in and shared between individual and collective minds. It does not grow out of databases but evolves with experience, successes, failures, and learning over time.
From a management perspective, there are clear distinctions between the two types of knowledge. Common practice now refers to them as explicit and tacit knowledge. They can be described as follows:
- Explicit knowledge is precisely and clearly expressed, with nothing left to implication. Generally, in business situations, it is fully stated and openly expressed without reservation.
- Tacit knowledge is understood but not clearly expressed. It is often personal knowledge embedded in individual experience and involves intangible factors, such as personal belief, perspective, and values.
We need to develop the characteristics of these categories of knowledge to understand how they can be managed.
Companies hold substantial documented knowledge of patents, technical specifications, and procedures. Additionally, information is routinely collected, stored, and distributed as management information. Financial, marketing, production, and customer service/ support information is usually codified and is ready for different distribution channels. This information makes up the majority of explicit knowledge.
All of this information has value in its own right and in most organisations could be used more effectively. There is also a need to seek even more explicit knowledge in the daily conduct of business. Explicit knowledge is normally available readily in all communications with customers, suppliers, distributors, competitors, government agencies, and the community at large.
By definition, tacit knowledge is more difficult to recognise and collect; let alone codify, store, and distribute. Yet this is the key component of knowledge management. Releasing the true potential of this asset on a continuous basis poses the sheer challenge for consulting companies and forms an important component for an effective performance measure for knowledge management.
The most valuable asset of every organisation – particularly a consulting firm — is the hidden or tacit knowledge buried in the minds of employees and other people in regular contact with the organisation. This includes experience, learning from doing as well as study, observation, and informal information or even gossip.
Components of Knowledge
Apart from the explicit knowledge available in various company documents and codified in computers, KM strategy and a KM system in any company must support the following key components of knowledge:
Very unlike data and information based on data association, knowledge has a component of judgement attached to it. A colourful and precise stock ticker and a real-time graph on the website can be excellent information for a share broker, however in real value it means nothing if he can’t act upon it or make a decision based on the data they provide. Unactionable information is not knowledge. However if the share broker recognises that he needs to sell the shares when the trend chart looks like a particular pattern or need to hold when it looks like another pattern, he is making judgement based on it. Judgement allows knowledge to rise above and beyond an opinion when it reexamines itself and refines every time it is applied and acted upon.
Knowledge is largely derived from experience. Being able to transfer knowledge implies that a part of experiential knowledge also gets transferred to the recipient. The benefit of experience lies in the fact that it provides a historical perspective that helps better understand present situations. Experienced people are usually valued in a company and are often paid more because they possess this historical perspective from which they can view current situations – something that a typical newcomer will almost never have. This perspective allows them to make connections with what is happening now with what might have happened earlier, and evaluate decisions in that light.
As people’s experience in their jobs increases, they begin to figure out shortcut solutions to problems they have seen before. When they see a new situation, they match it to compare patterns that they are aware of. An experienced car driver, for example, recognises that excessive rattling in the car could mean a flat tire. Similarly, a computer hardware technician can diagnose the fault for a computer that fails to boot up, with help from his earlier experience of having diagnosed a failed power supply or a bad hard drive for computers with similar fault symptoms. With experience, these scripts guide our thinking and help avoid useless decision paths that we might have followed earlier. Such rules of thumb or heuristics provide a single option out of a limited set of specific, often approximate approaches to solving a problem or analysing a situation accurately, quickly, and efficiently.
Not only in simplistic situations like the above but even in the complex business environment, it is the subconscious repertoire of scripts and rules of thumb that make experienced managers more valuable than experienced new hires. Many such rules of thumb are in people’s heads as tacit knowledge, providing the power that decades of machine learning research have been unable to give to businesses.
Values, Assumptions, and Beliefs
Business processes are, very often, based on a set of assumptions. These are so natural and so deeply ingrained within the minds of people who hold them that they find their way into most of the decisions that people make, but they are never expressed. For example, engineers, by their training, assume that anything that is behaving strangely has to have an underlying rationale. Managers often assume that their ordinate goal is to maximise their profit center’s financial profits. One level above this, people might assume that companies are rational and neutral. And for a good reason, after the widespread influence of Herbert Simon’s research on the concept of bounded rationality.
Companies are often shaped by the beliefs of a few key people working there. In some companies – particularly the visual media and dot-com companies – the culture of having fun is ingrained in their work environments while creating innovative and aesthetically great products (like iMac or iBook) is done as a matter of conviction and belief in other companies like Apple. The belief in profits and market dominance by Microsoft’s founder Bill Gates has been brought into the very character of the firm.
Such values, beliefs, and assumptions are integral and key components of knowledge. These values and beliefs explain the varying reactions of different companies to the same development and often differentiate a risk-taking competitor from a risk-averse one. And knowing, capturing, and sharing this component of knowledge can make all the difference between complete knowledge and incomplete, unactionable information. It is mentioned here that not all beliefs can be captured or codified explicitly and this is still a separate area of ongoing research in the field of KM.
Intelligence When knowledge can be applied, acted upon when and where needed, and brought to bear on present decisions, and when these lead to better performance and results, that knowledge often qualifies as intelligence. When it flows freely throughout the company, is exchanged, and is developed further, it transforms the company into an intelligent enterprise.
Apart from the above key components, knowledge comes into the KM system of a company from various other sources. A roundup of the sources, which feed a KM system of a company, is given in the Table below:
Table: Sources of Knowledge that feed a KM System2
|Source||Explicit/ Codifiable||Tacit/ Needs Explication|
|Employee knowledge, skills, and competencies||Yes||Yes|
|Experiential knowledge (individual/group level)||Yes||Yes|
|Team-based collaborative skills||Yes|
|Informal shared knowledge||Yes||Yes|
|Knowledge embedded in physical systems||Yes||Yes|
|Knowledge embedded in internal structures||Yes|
|Knowledge embedded in external structures||Yes||Yes|
|Experience of employees||Yes||Yes|
Although the list given above is not exhaustive, it is clear that much of the knowledge can be explicated, put into KM systems, and reused. However, some critical pieces of tacit knowledge are extremely difficult, if not impossible, to externalise in such a maimer.
For citing this article use:
- Arora, C. S. (2004). Emerging knowledge management performance measures for consulting firms.
- Working Knowledge How Organizations Manage What They Know, Harvard Business School Press, Boston (1998), 5
- Source- Tivwana, Amrit The Knowledge Management Toolkit, Prentice-Hall (2000), 71