In knowledge management literature, there is a misconception about the use of the terms; data, information and knowledge (Corner et al., 1997), which makes the understanding of knowledge management difficult to comprehend. It has often been highlighted that the connection between knowledge, information and data is often misinterpreted and this confusion arises from mistaking data to mean either information or knowledge (Harmaakorpi and Melkas, 2008). Table 3.1 provides the different definitions of data, information and knowledge by various authors in literature; some authors take a hierarchical view of data, information and knowledge. According to Alavi and Leidner, (2001) data becomes information when meaning and understanding are added into the data. They further suggest that information transforms into knowledge when an individual‘s personal experience, beliefs and values are included.
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Table 3 1: Some Definitions of Data, Information and Knowledge
Author(s) Data Information Knowledge
Wiig(1993) - Facts organised to
describe a situation or condition
Truths and beliefs, perspectives and concepts, judgements and expectations, methodologies and know-how Nonaka and Takeuchi (1995) - A Flow of meaningful messages Commitments and beliefs created from these messages Spek and S Pijkervet (1997) Not yet interpreted symbol
Data With Meaning Commitments and beliefs created from these messages Davenport (1977) Simple
observation
Data With relevance and purpose
Valuable information from the human mind Davenport and Prusak(1998) A set of discrete facts A message meant to change the receiver‘s perception Experience, values insights and contextual information Quigley and Detlor(1999)
Text that does not answer questions to a particular problem
Text that answers the questions who, when, what or where
Text that answers the questions why or how
Choo, Detlor and Turnbull ( 2000)
Facts and messages
Data vested with meaning
Justified, true beliefs Source: (Stenmark, 2002)
Many scholars (Nonaka and Takeuchi, 1995; Johannesen et al., 2002; Shaari, 2009) assert that data, information and knowledge are part of a sequential order; data is viewed as the raw material for information and information is the raw material for knowledge. However, Davenport and Prusak, (2000) point out that ―knowledge is neither data nor information, though it is related to both, and the differences between these terms are often a matter of degree and confusion about what data, information, and knowledge are - how they differ‖. Figure 3.1 shows the different levels of knowledge hierarchy; data is at the lowest point and it is regarded as a collection of facts and figures; followed by information which is seen as structured data and finally knowledge at the top of the hierarchy is
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regarded as information about information. Some literature includes wisdom in the hierarchy; others refer to it as the knowledge pyramid (Frické, 2009) or wisdom hierarchy (Rowley, 2007). However for this study the knowledge hierarchy will be sufficient as the study‘s aim is to explore knowledge sharing. According to Galup and Hicks (2003), ―knowledge hierarchy depicts the conventional concept of knowledge transformations where data is transformed into information and information is transformed into knowledge‖.
Figure 3 1: The Knowledge Pyramid Adapted from (Qui et al., 2006)
There are a number of variations to this widely adopted idea. Data has generally been seen as simple facts that can be structured to become information. Information on the other hand, becomes knowledge when meaning is added to it, that is, when it is interpreted and put into context. The widely held view is that data is less than information and information is less than knowledge. Therefore, for data to become functional and applicable in understanding actions the prior knowledge of a representative is a very fundamental factor. This is because data is converted to information as soon as there is a clear understanding of the message being put across. Corner et al., (1997) point out that the concept of
Increasing Cognitive Content Data Information Knowledge Structure and interpretation Action and Application
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data, information and knowledge are closely related. Although different, the three concepts are often confused. The confusion is due mainly to the meaning that is assigned to each concept in terms of the message that is being communicated and the fact that the features of one concept aid the formation of another.
However, according to (Davenport and Prusak, 2000; Roberts, 2001), data is a raw material, dry facts to create information, whilst information changes to data as it gives data a meaningful pattern of value. In this regard, knowledge is a component of information and human minds, experience, and skills gained from that experience. Nonaka and Takeuchi (1995) discerned that knowledge is related to beliefs, while information is not; knowledge is allied to action, whereas information is not. Knowledge is a collection of information analyses and knowledge, like information, is connected to meaning (Table 3.1). Consequently, information and what exists in human minds is not always the same. As a result, data are distinct raw facts and figures, information is processed data and knowledge is validated information. Information relates to facts, interpretations, ideas, concepts and judgments, and will be processed in the minds of individuals to form knowledge (Alavi and Leidner, 2001).
Knowledge is converted into information when expressed in the form of text, graphics and words. Knowledge is different from information as it is restricted to context and is connected to behaviour (Shaari, 2009). On the other hand, ―information becomes knowledge when it is interpreted by individuals and given a context in the beliefs and commitments of individuals‖ (Nonaka et al., 2000). It is a general view that knowledge is broader and richer than data and information. Some authors (Nonaka, 1995; Wiig, 2004) argue that knowledge exists only in the human mind and it is the mind which has the power to act and make decisions. According to Davenport et al., (1998) knowledge becomes meaningful when it is seen in the larger context, through the interpretation and reflection of one‘s culture, which evolves out of one‘s beliefs and philosophy.
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Knowledge is a high-value form of information that is ready to be applied to decisions and actions. Many researchers are yet to agree on the dissimilarities between knowledge and information. Nonaka (1994) views information to be just ―a flow of messages‖ whereas knowledge is based on ―information and justified by one's belief‖. Nonaka and Takeuchi (1995) further state that ―information is a flow of messages, while knowledge is created by that very flow of information, anchored in the beliefs and commitments of its holder‖. Other researchers (Machlup, 1980; Zander and Kogut, 1995) consider all information to be knowledge rather than knowledge being more than just information, i.e. know- how. According to Nonaka and Takeuchi (1995) the difference between knowledge and information is that ―information is a flow of messages, while knowledge is created by that very flow of information, anchored by the beliefs and commitments of its holder”. Consequently, knowledge is an idea that is turned into information to create knowledge; in other words, the same unit of knowledge becomes information when it is stored, but then becomes knowledge again when it is transferred to another human.
Some researchers (Bartol and Srivastava, 2002; Makhija and Ganesh, 1997) use the terms knowledge and information interchangeably, emphasising that there is no much practical value in distinguishing knowledge from information in knowledge sharing research. Thus, in distinguishing these three concepts it is important to state that information is a step away from data and knowledge is the human application of information. Data is unprocessed fact while information is refined fact and knowledge usable fact. Data in its raw form is, in most cases, rarely mistaken information and knowledge. As noted in literature, it is evident that data on its own does not provide any meaning unless an explanation of the representation of the data is given. Information requires some form of clarification and explanation. While knowledge requires actual human contribution in order for it to be used for actions.
For the purpose of this research, it is important to distinguish these concepts from the start so that information is not taken entirely to mean knowledge, but seen as
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a very fundamental component of knowledge. This research does not lose sight of the reality that when knowledge is mentioned to providers of floating support services what comes to mind is information. As a result of this, information is presented alongside knowledge especially at the data collection (interview and survey questionnaire) stage. The rationale for this is that information is very close in meaning to knowledge and this enables the floating support workers and adult social services workers to understand the meaning of knowledge sharing within the context of the provision of floating support services in sheltered housing. In view of the above the clarification of these three concepts (data, information and knowledge) was undertaken early in this research in order that knowledge sharing, which is the bases of this study, can have the necessary momentum when being evaluated. In view of the foregoing clarification, knowledge as a concept can be viewed from different viewpoints. Clear boundaries between data, information and knowledge have been established. It is possible to go a step further and look at the forms in which knowledge exists and the different ways that it can be accessed, shared, stored and distributed. The next section highlights the different types of knowledge.