The DIKW hierarchy represents the chain from ‘Know-Nothing’ (data) to ‘Know-What’ (information) to ‘Know-How’ (knowledge) to ‘Know-Best’ (wisdom). This model was offered by Ackoff (1989) and has been the most cited model in the literature of information science.
31 | P a g e He also includes another concept between ‘knowledge’ and ‘wisdom’ which is called ‘Know- Why’ (understanding). As a whole scholars have little consensus on describing the process of transforming the lower elements of DIKW hierarchy into those above them. Rowley (2007) represents a model (Figure 2.2) that describes the transitions from the lowest element ‘data’ to the highest element ‘wisdom’. This model is called ‘The understanding hierarchy model of DIKW’.
Figure 2.2 – The Understanding Model of DIKW, (Rowley, 2007)
This model illustrates a better understanding of transforming data to wisdom. Data is formed by doing research and gathering parts. Information is formed by both connecting the different parts of data and understanding relations between them. Then the gathering of appropriate information and understanding the patterns between them will lead to form knowledge. Wisdom, know-best, is formed by joining integrated knowledge and understanding the principles which will result in increasing the ability of making decisions and using judgement. The model also describes data, information, and knowledge as based on experience and past- oriented; whilst wisdom is future-oriented and used to create ideas. Furthermore, this model represents a way of understanding that is started by researching data, absorbing data and information, followed by acquiring information and knowledge, interacting knowledge and finished by reflecting wisdom.
32 | P a g e The DIKW model was developed by many researchers like Nonaka (1991) and Rowley (2007). Fricke (2009) criticised by arguing that the hierarchy model (DIKW) is methodologically undesirable and unsound and has a theoretical and intellectual gap between the interrelationship and nature of its components. He adds value to information science by representing positive theories about the nature of the components of the DIKW model. He introduces data as “anything recordable in a semantically and pragmatically sound way”, both information and knowledge as “weak knowledge”, and wisdom as “the possession and use, if required, of wide practical knowledge, by an agent who appreciates the fallible nature of that knowledge”.
2.3.2 E2E Model
Faucher et al. (2008) adopt a complex-based perspective to analyse and extend the DIKW model. They proposed a new model by adding two new components, ‘Existence’ and ‘Enlightenment’, called the E2E model. Furthermore, they argue that the relationship is non- pyramidal and non-linear among six components (Existence, Data, Information, Knowledge, Wisdom and Enlightenment), which means that each component can occur without any specific order. Faucher et al. (2008) sit an example of a new receptionist having the wisdom for managing customer relationships without having any data about customers. The required wisdom could be achieved during his/her formative years. The DIKW model lies in between existence and enlightenment that provide the boundaries for the cognitive system of knowledge. In other words, the DIKW is the abstraction of the existence and the enlightenment is the highest level of abstraction which leads to understanding.
According to Faucher et al. (2008), the elements of the DIKW model are based upon the abstraction of existence and could be either tacit or explicit. The level of understanding of these elements is the basis of both differentiating them and the conversion process among them. Figure 2.3 illustrates the E2E model based on Faucher et al. (2008).
33 | P a g e Figure 2.3 – Knowledge System (Faucher et al., 2008)
Based on foregoing the discussion on knowledge and its related concepts, Faucher et al. (2008) also reviewed the literature on the components of the knowledge system. It should be mentioned that few authors have defined wisdom. Their definitions and findings are presented in Table 2.3.
Table 2.3 – Definition of Data, Information, Knowledge and Wisdom (Faucher et al., 2008)
Data Information Knowledge Wisdom Information
Scientist
is “symbols” is “data that are processed to be useful” is “ability to answer “How” questions” defined “as an evaluated understanding” Ackoff (1989) is “static, unorganised and unprocessed facts. Set of discrete facts about events” “facts based on reformatted or processed data. Aggregation of data that makes decision making easier and has
a meaning, purpose and relevance” “higher level of abstraction that resides in people’s minds. Includes perception, skills, training, common sense, ad experiences”
“as the highest level of abstraction, with
vision, foresight, and the ability to see beyond the
horizon” Awad &Ghaziri (2004) “is a basic interpretation of existence” “is viewed as a meaningful interpretation of existence, one that
has a purpose” “is a meaningful and procedural abstraction of existence” “is understood as a meaningful, procedural, and justified abstraction of existence based on experience” Faucher et al., (2008)
defined “as the critical ability to use knowledge in a
constructive way and to discern ways in which new
ideas can be created”
Matthews (1998)
“structured data useful for analysis and
decision making”
“obtained from experts based on
experience”
defined “as the ability to judge soundly over time”
Thierauf & Hoctor (2006)
Level of Understanding Classical linear hierarchy Linear extension
34 | P a g e After providing a range of definitions, it is concluded that there is no universal accepted definition. Eysenck (1979) suggests that when there is no consensus on an accepted definition, it is better to share the personal understanding applicable in the context. Regarding this issue, similarities and differences of presented definitions are discussed in the following section. This will lead to the generation of the operational definitions.