Purpose of the Literature Review
2.3 Data, Information, and Knowledge
Liew (2007) reviews a number of definitions of the terms data, information and knowledge and stresses that these definitions are still short of providing a clear and comprehensive account of the terms nor their relationships to each other.
Defining these concepts is important for knowledge management and other related fields. He argues that a good definition should have boundaries, purpose, and characteristics, and proposes definition that he believes fit these criteria. Data, he states, are recorded representations that consist of texts, numbers, diagrams, etc., and signals, e.g., sensory feelings. Information is a message that carries meaning for and is useful for decision and/or action; and knowledge is the ability to recognize and comprehend which resides in the mind which takes place within a context or situation. He offers the following framework to replicate these ideas as it shows in Figure 3.
Figure 3: Formation of Information and Data
32 Current
Activities
Situations Information Decisions
Generate Cognitive Processing
Captured &
Stored
Source: Liew, (2007:6)
In the same vein, Mutongi (2016) reviews the data, information and knowledge concepts, stating that they are critical for communication studies and ICT, as well as knowledge management fields. He then introduces a fourth concept: wisdom, thus constituting what is conventionally known as the DIKW model. However, he contends that the model does not adequately address the entire facets of knowledge management, and proposes that a person’s approach to the model involves six purposes and processes. Sat the data stage, a person is collecting and organizing; and at the information stage, he is summarizing and analyzing; and in the knowledge stage, he is synthesizing and making decisions.
Figure 4 explain the Data, Information and Knowledge model.
Figure 4: Data, Information and Knowledge (DIK) Model Data
Processed or Analyzed (Basically, a reconstructed picture of past activities or situations)
Source: Mutongi (2016:66)
As per Rowley (2007), the DIKW plays a central role in KM; however, there remains much debate about the definition of knowledge and about the DIKW hierarchy itself and the concept of wisdom and its relationship to organizational processes. From his part, Mutongi (2016) claims that the DIKW model does not satisfy all the requirements for knowledge management; and in support if this position, he develops a suggested knowledge model to address the limitations of the DIWK. His position is that knowledge cannot be part of the DIWK hierarchy because each of the other three concepts requires knowledge. He argues that in order to collect data, a person should have knowledge about what data he needs and how to organize them. Similarly, devising, obtaining, arranging, assessing and sharing and using information necessitates knowledge in information handling.
Likewise, a person needs to know how to apply gained wisdom. His suggested model is shown in Figure 5.
Collecting Organizing
Summarizing Analyzing
Synthesizing Decision Making
Knowl edge
Information
Data
Figure 5: Knowledge Model
Source: Mutongi (2016:68)
Nonaka and Toyama (2003) state that knowledge is a context-specific reality perceived from a particular viewpoint and, contrary to what was perceived historically, organizations are not information-processing entities because they are consciously creating knowledge. According to Al-Alawi, Al-Marzooqi, and Mohammed (2007), knowledge is a mix of contextualized previous observations and practices, values, factual data, and insights that facilitate the evaluation and adoption of new information and practices. They believe knowledge is accumulated and stored in people’s minds and is manifested in activities and behaviors. Knowledge is characterized by two features: It is not imitable (i.e., unique), and it cannot be emulated (i.e., it is original).
Nonaka (2007) argues that knowledge is spiral and revolves in a cycle that translates tacit knowledge to tacit knowledge; explicit knowledge to explicit knowledge; tacit knowledge to explicit knowledge, and explicit knowledge to tacit knowledge, and starts all over again. Zack (1999) believes that knowledge has two
Knowledge
Knowledge about Data Data
nformation Knowledge
Information
Wisdom on Certain Knowledge
Wisdom
basic constituents: a repository (for knowledge possession) and a refinery (for knowledge conversion and application). Ipe (2003) perceives knowledge as an interactive human process that explains an individual’s belief towards truth, as it is a mix of experience, values, and expert insights framed for evaluating and accommodating new experiences and information.
Knowledge is linked to the person’s social context, personal views of the world, and previous experience to form meaning (Lindner & Wald, 2011).
Knowledge plays an integral role in accomplishing organizational goals on time and thereby provides a competitive advantage (Barney, 1991). The influencers of knowledge in organizations are the organizational culture, organizational processes, IT technologies, and other informal aspects like leadership, structure, and communication (Lindner & Wald, 2011).
Bidmeshgipour, Ismail, and Omar (2012) argue that research on KM practices in the Middle East is very limited, hence there is a need to develop initiatives that align strategies with the desired KM outcomes. Effective KM programs and systems need to adopt practices that embrace supportive policies and strategies, committed leadership, the best reward systems, adequate process for knowledge acquisition and retention, employee supportive developmental programs, and an effective flow of communication.
It is concluded from this review that knowledge has been defined in the literature in various forms and perceived though different lenses. To most of these
authors, knowledge is context-specific and is found to be meaningful when viewed within its operational environment. It is also concluded from the preceding review that there are clear distinctions between knowledge, information, and data, which are all links in a chain. Knowledge is the highest form in the chain, and it is defined as the interpretation of information as perceived by the user from synthesized data. In short, knowledge is the meaning inferred by the user from information perceived by him or her. The terms
“knowledge,” “information,” and “data” are all parts of a pattern and denote the element of processing meaning between stages, and each has its own place in the knowledge process, as will be discussed later.
The above review makes it possible to summarize the Data, Information, and Knowledge (DIK) hierarchy in the following conceptual representation (the researcher). Figure 6 explains the hierarchy of data, information, and knowledge.
Figure 6: Data, Information and Knowledge (DIK) hierarchy
Data are:
- events - facts - figures - statistics
Information is
data given meaning by:
- processing - interpreting - organizing - structuring
Knowledge is
- information received, synthesized, inferred and interpreted by the receiver within a specific context
Source: (The researcher)