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Wisdom is the oldest term, and data the newest, in English. As shown by Hicks et al. Among the multiple ways in which information has been defined are: B data that makes a difference King, ; B data with special relevance and purpose Drucker, ; B data in context Gallup et al. Definitions of knowledge show even greater disparity: B information whose validity has been established through test of proofs Liebeskind, ; B social actions Stacey, ; B a human, highly personal asset representing the pooled expertise and efforts of networks and alliances Smith, ; B the capacity to act Argyris, ; and B a set of insights, experiences, and procedures considered true and appropriate Bourdreau and Couillard, ; Liebowitz and Wilcow, Laihonen regarded knowledge as containing an interpretation of a knower, while Williams characterized knowledge as is dynamic, strategic, political, and subject to change.
Table II provides a compilation of alternative ways of defining data, information, and knowledge. This table demonstrates that there is no consensus within the literature of knowledge management, but it also shows interesting similarities. Most of the authors defined knowledge, fewer defined information, fewer still defined data, and almost none defined wisdom. Consequently, wisdom has been omitted from Table II although the concept does form part of the discussion presented here.
Definitions of wisdom appear to be somewhat more consistent than those for knowledge or information. Ackoff defined wisdom as an evaluated understanding. Matthews described wisdom as the critical ability to use knowledge in a constructive way and to discern ways in which new ideas can be created.
Awad and Ghaziri defined it as the highest level of abstraction, with vision, foresight, and the ability to see beyond the horizon. Most recently, Thierauf and Hoctor defined wisdom as the ability to judge soundly over time. Would academics consider data to be the most basic unit of knowledge management?
However, as will be shown, this is open to discussion. Table II does show one area of agreement: There is a hierarchy among the concepts of data, information, and knowledge. The knowledge hierarchy is usually seen as a pyramid ascending from data to wisdom.
However, Tuomi suggested reversing that hierarchy on the basis that data were more important than knowledge, also pointing out that knowledge had to come first in order to create data. A few authors e. From the seeker point of view, data is put into context to create information, and information that is actionable becomes knowledge. From the creator perspective, knowledge is needed to create information, which is in turn needed to create data.
Therefore, it seems sensible that a general hierarchy of data, information, knowledge, and wisdom should permit transition in both directions — a notion supported by Williams The following definitions of data, information, knowledge, and wisdom attempt to capture the common essence of the various definitions presented in the knowledge management literature: B Data are considered to be unprocessed raw representations of reality.
B Information is considered to be data that has been processed in some meaningful ways. B Knowledge is considered to be information that has been processed in some meaningful ways. B Wisdom is considered to be knowledge that has been processed in some meaningful ways. Understanding how to create new meanings out of isolated information Awad and Ghaziri, Static, unorganized and Facts based on reformatted or Higher level of abstraction that unprocessed facts.
Includes of discrete facts about that makes decision making easier perception, skills, training, common events and has a meaning, purpose and sense, ad experiences relevance Gallup et al. Information is Justified true belief. Knowledge is tied about meaning to action. New data, information, knowledge, and wisdom are respectively added to their established base. Figure 1 illustrates the traditional knowledge pyramid in which data, information, knowledge, and wisdom are perceived as distinct categories. One area of potential controversy regarding the definitions and nature of the various knowledge related constructs relates to the distinction between tacit and explicit aspects.
According to Nonaka et al.
Explicit knowledge can be expressed in formal and systemic language, and can easily be shared by codifying it through many sorts of data, which can be stored. Tacit knowledge is rooted into actions, procedures, routines, commitments, ideals, values, and emotions Nonaka et al. Understanding the form of knowledge and knowledge creation implies recognizing this dualistic view of knowledge. This perspective has been commonly distorted to hold that data and information are explicit, and knowledge and wisdom are tacit e.
Heskett, , Zeleny, It is also important to note that all tacit knowledge cannot be made explicit Tsoukas, As suggested by Polanyi in The Tacit Dimension p. As suggested by Plato in Meno, if all knowledge is explicit, then neither a problem can be known nor can its solution be looked for as it would be impossible to know that the problem exists. This is why Polanyi suggested that things that cannot be told can still be known. Therefore, knowledge management has to find a way to cope with tacit knowledge.
The distinction between tacit and explicit could exist all along the continuum between data and wisdom. Indeed, the authors postulate that data is not purely an explicit construct, and wisdom is not purely a tacit one. Information and knowledge display different levels of tacitness and explicitness as well.
Information can be carried by human brains without being transformed into knowledge, as suggested by Alavi and Leidner The same applies to data, which may be carried in a tacit state; there is no universal requirement that it be transformed into explicit data. An example of this is provided by Monroe and Lee , whose research suggests that buyers are influenced by data and information which are stored in their implicit memory. In the same way, one can discern explicit knowledge and wisdom. Folk sayings, or proverbs, are just one example of explicit wisdom e. Some authors, including Ackoff , have identified that explicit component of wisdom.
Of course, knowledge is usually more tacit than data, but this implies neither that data is always explicit nor that knowledge is always tacit. Figure 2 summarizes this idea. The model uses an interesting framework: Understanding is therefore identified as the transformational relationship among data, information, knowledge, and wisdom to create an outcome at a higher level.
Although this model does not show whether or how one can j j VOL. Redefining the scope of the hierarchy All the definitions and models reviewed have led to a linear hierarchy, where data is the basis for information, which is the basis for knowledge, which is itself the basis for wisdom.
The reverse of the ascent from data to wisdom is also possible, following the same reasoning.
In 'Key Issues in the New Knowledge Management,' Firestone and McElroy, the architects of the New Knowledge Management (TNKM) provide an in-depth. Key Issues in the New Knowledge Management. A volume in KMCI Press. Book • Authors: Joseph M. Firestone and Mark W. McElroy. Browse book.
Authors can describe it as a pyramid, a hierarchy, or a circle, but it remains linear as there are no feedback loops. The first step for improving these models is to realize that they have neither a starting point nor an ending point. In other words, these models need clearer boundaries. From Table II it is obvious that the literature focuses on defining the difference between information and knowledge, but little attention is paid to the definition of data.
Data is not found in nature; it does not grow on trees, and it does not fall from the sky for free. Data have to be made out of something. Data are usually described as observations of reality. Back in prehistoric times, Cro-Magnons used pictographic representation for data while counting animals; later in history, Sumerians applied symbolic representations of data to capture and record grain harvests and other economic data.
Hence, data are more that just observations; they are a level of understanding of existence. Existence describes the whole environment that humans can grasp and create data about. Data are a very basic processed outcome of human observation of existence. What is higher than wisdom? Buddhists refer to enlightenment as the awakening of beings. To awaken is to achieve a level of insight and understanding equal to that of the Buddhas Van Hien Study Group, However, they make a distinction between awakening and supreme enlightenment, as there are many levels of awakening.
It is not the intention of this paper to discuss metaphysics; however, it is useful in reaching the full scope of a hierarchy of knowledge. Enlightenment is the highest form of understanding. Therefore, it should be incorporated into a model that purports to represent a complete perspective on the hierarchy of knowledge. The result is illustrated in Figure 3. Indeed, it is suggested in this paper that not having the two constructs of enlightenment and existence means not taking into account the appropriate borders of the knowledge system.
Consequently, traditional models such as the knowledge pyramid are closed systems. Because knowledge management would profit from complexity theory McElroy, , a more coherent model of the knowledge system should be open. Existence and Enlightenment are two states of being which provide the boundaries of the knowledge system. Data, information, knowledge, and wisdom are cognitive constructs lying in between those two states. While this diagram summarizes useful extensions to the traditional hierarchy it still does not embrace all the improvements possible by using ideas from complex systems.
In particular, the diagram still shows a linear hierarchy and it does not show any feedback systems. For example, is it possible to create new knowledge by linking new data with previous wisdom? Can new information be created by linking previous knowledge and new knowledge?
How can the need of knowledge to create or use data be depicted? All the models presented previously do not help to show the relationships that exist among data, information, knowledge, and wisdom.
Linear thinking is holding back the creation of good metaphors to describe the concept of knowledge completely. Firestone and McElroy made an attempt at generating a non-linear model. However, they failed to see that their model was creating another kind of linear hierarchy. What is needed is a model without a linear hierarchy between data, information, knowledge, and wisdom, because - as shown later — they are all made up from the same basic unit.
They are all labels used to structure human understanding of the same construct: The real distinction among them is learning experience and understanding. Redefining the basis of knowledge management Simple mathematical notation can be employed to explain how data, information, knowledge, wisdom, and enlightenment relate to existence.
The following is a metaphor to demonstrate this point. Therefore, the system can be described in the following terms: One can also argue that data is made of symbols Ackoff, , but that does not change the result because symbols are still abstractions of existence. Regardless of the type of concepts applied — such as meaning, judgment, or anything else — they are still all based on the same thing.
What is important is the coefficient that differs among them. The distinction among these constructs is a level of abstraction and understanding. Therefore, a, b, c, d, and e all represent transformation through different level of understanding, the factor suggesting an exponential degree of thinking: Data, information, knowledge, wisdom, and enlightenment are transformations of existence. Therefore, the traditional hierarchy is obsolete, as it does not represent the totality of the possibilities.
These equations emphasize that point by showing how data, information, knowledge, and wisdom could be portrayed from a different perspective. However, this is still not sufficient. Social interactions are the basis for the existence of data, information, knowledge, and wisdom. Indeed, according to many authors, data, information, and knowledge are linked through social interactions e.
The fourth form, wisdom, should be added to this list, and the possibility of cognitive as well as social interaction as a linking mechanism should not be overlooked. These four forms can interact in non-linear ways as well as along the traditional linear paths. Hence, existence, data, information, knowledge, wisdom, and enlightenment form a feedback system with positive and negative feedback loops.
This is a non-linear appraisal consistent with complexity theory, which helps to reveal the nature of the links among data, information, knowledge, and wisdom and helps to understand why the classical hierarchy is not appropriate. The model shows the cognitive system of knowledge and how understanding permits conceptual linking of Existence to Enlightenment. The E2E model accommodates the classical linear hierarchy of data, information, knowledge, and wisdom, and also incorporates the extension on both ends of the hierarchy from Existence to Enlightenment previously discussed in this paper.
Figure 4 illustrates this. Cognition is the facilitation process through which the system functions; it is the process by which knowledge and understanding are developed. One implication of complexity theory is that a cognitive system of knowledge will emphasize what a system does, not what it is composed of. Note also that existence and enlightenment are two states of being.
Therefore, cognition is involved at the transitional states between existence and enlightenment, but not at the two ends themselves. Indeed, data, information, knowledge, and wisdom are different cognitive constructions intermediate between these two states. The book is in two parts. The first presents a very concise and in-depth overview of knowledge management KM , organizational learning, organizational memory , organizational culture, and so on.
The second part offers one of the most in-depth looks at knowledge management systems that I have ever seen in a KM book. A short book focused on knowledge retention. It offers many interesting case studies, taking a more practical oriented approach than other texts. There are many noted authors that I did not list above e.
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Managing knowledge for sustained competitive advantage: Strategies for Interweaving Groupware and Organizational Structure.