1.1 Introduction

1.1 Introduction

The term “knowledge” has always been a topic of great debate. Much has been written but researchers from different domain acknowledge that there are deep mysteries of the concept yet to be explored. Therefore, before entering into the details of knowledge management (KM), it would be imperative to define a working definition of term “knowledge". This is also important because, in his positioned paper Daniel G Andriessen (2008) pointed out that in KM literature at least 22 different metaphors for knowledge are used. Further research shows that these metaphors are primarily Western metaphors while in Eastern philosophy many other metaphors for knowledge are used. The choice of metaphors for knowledge has great influence about the way we think about KM. Our choices determine what we diagnose as KM problems in organizations and what we develop as KM solutions.
            Andriessen (2008) elaborated it through many examples. Like, many KM approaches advice organizations to make an ‘inventory’ of knowledge, check where “knowledge is located”, “store important or vulnerable knowledge in databases”, “use technology to improve access to knowledge”, etc. What is important to see is that knowledge is not literally located and stored. After all, we cannot see it and we cannot grab it and put it in a container. A knowledge inventory is not literally an inventory like the inventory of a warehouse. And access to knowledge is not literally access like we have access to objects in a warehouse. These are all metaphors and they make sense to us because we are very familiar with the “knowledge as a resource” metaphor. It makes sense as we use the “time as a resource” metaphor very often, for example, when we say ‘I got plenty of time’, ‘he wasted my time’ or ‘this will save time’.
            There is another aspect. The word "knowledge" often takes on a variety of meanings within everyday language, within specific fields, and even within the same disciplines. This complexity of term “knowledge” is well acknowledged in literature. For example, Nonaka and Takeuchi (1995) admit that knowledge is very complex and generated only in people's minds. It has to be, because human actions depend on a large number of parameters. It is the complexity that enables the adoption of different meanings. Others believe, like a computer program which can solve a certain number of problems by using different parameters, knowledge provides different reactions depending on the context (Wilson, 1984; Zack, 1999).
            We must realize the case of a computer program is much simpler compare to human mid as the parameters of knowledge in human minds are hardly countable and definable. Therefore, this makes it difficult to record or document knowledge in such a way that others can fully benefit from it. Though it is a difficult task, but not impossible. Knowledge in human minds, known as tacit knowledge, can be acquired, stored and transferred and be later turned into “explicit knowledge” (Polanyi, 1966). However, it has been argued that such explicit knowledge never describes the original tacit knowledge as a whole, but instead assumes a common basis of understanding within a specific context on which the transmission of explicit knowledge is based (Isaacs, 1999).
            Yet there is another problem how to locate knowledge and find out who has knowledge about what because human minds can have knowledge about many things. This may not always be a problem when dealing with a small number of people. But dealing with a group of people between 200 and 300, it becomes almost impossible for everyone to know who knows what (Brown and Duguid, 1998). In a distributed environment, as it becomes more and more common with organizations acting globally, it becomes even more difficult. For example, Anya, et.al. (2009) acknowledge that the problem of locating and matching appropriate knowledge resources from across organizational boundaries to various tasks within specific e-Work projects remains a huge challenge. Therefore, for an effective and efficient KM, besides storage, a major problem is how to make knowledge locatable.
            But even if knowledge is stored and can be located, the problem of determining who needs what knowledge, and when, stays. Many such issues raise the need of creating association between KM processes with other organizational processes. Such association can allow the identification of needs for knowledge and accurate determination of these needs based on which the stored knowledge can be then retrieved (Anya et.al., 2009).
            The use of information and communication technology is commonly advocated as a potential solution to address the above discussed issues as it offers new and extended possibilities, such as knowledge codification and transmissions across distances, communications via videoconferences etc. (Joshi & Joshi, 2011). However, many researchers have argued that knowledge is mainly about human and therefore the role of technology can only be of assisting nature (Davenport and Prusak 1998; López, et.al., 2009).
            Yet, there is another important point which needs attention. In real life we deal with terms like data, information, understanding, wisdom, etc. What is the relationship between data, information, knowledge, understanding and wisdom? A basic understanding of these concepts within the context of knowledge management can help us to understand complexities and mysteries associated with knowledge management. In following sections, we will try to cover these aspects and attempt to explain what constitutes knowledge and what falls under the category of information and data.