Basic Knowledge Concepts - Data, Information, Knowledge and Wisdom
The concept of knowledge has been discussed for centuries and in the works of the ancient Greek philosophers, knowledge originates with people. Plato, for instance, put forward the idea that correct belief can be turned into knowledge by fixing it through the means of reason or a cause. Aristotle thought that knowledge of a thing involved understanding it in terms of the reasons for it. In Western philosophy knowledge is seen as abstract, universal, impartial and rational. It is considered as a stand-alone artefact (a physical record) that could be captured in technology and which will be truthful in its essence [4]. This understanding of knowledge affected, to a great extent, the nature of the first KM tools developed during the 90s. Most tools and KM models during this period tried to manage knowledge as an artefact rather than as an element deeply rooted in human understanding, human behaviour and social interactions at work. According to research, the majority of the first generation of KM tools failed, or at least did not fulfil their initial aims, due to the lack of focus on human factors. Knowledge has a far more complex nature than simple data and information and requires the active contribution of people to manage knowledge systems. Therefore, for proper KM implementation it is essential to clarify at an early stage, the main differences between data, information and knowledge.
The academic community has spent years discussing and clarifying what constitutes data, information and knowledge. Variations emerge in the definitions and the basic terminology used depending on the background of the author and the specific aims he pursues.
The relationship between data, information, knowledge and wisdom form a pyramid. The pyramid has data as its base, followed in the hierarchy by information, then knowledge, with wisdom at the top. Figure 1.3 (1) below shows the relationships between data, information knowledge and wisdom.
Figure 1.3 (1) Relations between data, information, knowledge and wisdom. Source: Adapted from Liebowitz, (2003)
Data: a set of discrete objective facts about an event or a process which have little use by themselves unless converted into information. Data for example are numerical quantities or other attributes derived from observation, experiment, or calculation. Cost, speed, time and capacity are quantitative data.
Information: data endowed with relevance and purpose. It has meaning and it is organized for some purpose. Information for example, is a collection of data and associated explanations, interpretations, and other textual material concerning a particular object, event, or process.
Data could be converted into information using 5 main processes [3]:
Condensation – items of data are summarized into a more concise form and unnecessary depth is eliminated;
Contextualization –the purpose or reason for collecting the data in the first place is known or understood;
Calculation - data is processed and aggregated in order to provide useful information
Categorization – is a process for assigning a type or category to data;
Correction – is a process for removal of errors.
Knowledge: a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of people. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices, and norms [3].
Knowledge is based on information that is organized, synthesized, or summarized to enhance comprehension, awareness, or understanding. Knowledge represents a state or potential for action and decisions in a person, organization or a group. It could be changed in the process of learning which causes changes in understanding, decision or action. A visual definition relates knowledge with a bite from a red apple - ‘a bite (of information) should be taken, chewed, digested, and acted upon so that it becomes knowledge’ [6].
Typical questions in relation to data and information include who, what, where and when, while questions relating to knowledge include how and why.
Wisdom: the ability to identify truth and make correct judgments on the bases of previous knowledge, experience and insight. Within an organization, intellectual capital or organizational wisdom is the application of collective knowledge.
Data, Information, Knowledge and Wisdom - Practical Example
Business Situation:
The Quality Control function of a manufacturing process in a wine-making factory.
Data:
The data might concern numerical quantities of process elements that could include bottle weight, data about the wine colour as well as data about the percentage of wine ingredients. Only when these sets of data are put in the right order or in a more specific and more organized framework will they have a meaning.
Information:
In this example information could be an excel data sheet that describes several production elements of a specific red wine lot. For example, the title of the sheet could be: Weight of bottles for Red Chardonnay, Lot No 12445, produced on 14/6/2006. It is obvious that this sheet with organized information has a specific purpose (to control the bottle weight between acceptable limits) and it is associated to a particular production element or object (Red Chardonnay) and production event (bottles filled for lot No 12445 on 14/6/2006).
Knowledge:
When the particular knowledge associated with the above data and information is discussed it could be easily realized that:
1. Someone, who is expert in quality statistical control, must interpret the data sheet. This knowledge-based process apart from the expert insight requires a fluid mix of framed experience, values, and contextual information.
2. In addition, this person, in order to make his decision, needs a framework for evaluating this information. He could compare it with other lots of wine or with the acceptable weight limits of a wine bottle imposed by state regulations. The final decision of the quality manager could be to send the bottles back for refilling or to rank the lot as quality A or quality B and then decide to which markets the lot should be pushed to.
3. Only this expert was able to decide how the wine lot in question varied from the past lots and from the quality standards and why this lot should be pushed to market A (more strict customers) or to market B (not so strict customers).
Wisdom:
In this example the corresponding wisdom could be described as the ability of the quality expert or quality department to improve the whole quality control process by reviewing the quality standards again as well as by reviewing the required control process taking into consideration previous knowledge and experience. In any of the above-mentioned cases the company will improve the quality control process.
The academic community has spent years discussing and clarifying what constitutes data, information and knowledge. Variations emerge in the definitions and the basic terminology used depending on the background of the author and the specific aims he pursues.
The relationship between data, information, knowledge and wisdom form a pyramid. The pyramid has data as its base, followed in the hierarchy by information, then knowledge, with wisdom at the top. Figure 1.3 (1) below shows the relationships between data, information knowledge and wisdom.
Figure 1.3 (1) Relations between data, information, knowledge and wisdom. Source: Adapted from Liebowitz, (2003)
Data: a set of discrete objective facts about an event or a process which have little use by themselves unless converted into information. Data for example are numerical quantities or other attributes derived from observation, experiment, or calculation. Cost, speed, time and capacity are quantitative data.
Information: data endowed with relevance and purpose. It has meaning and it is organized for some purpose. Information for example, is a collection of data and associated explanations, interpretations, and other textual material concerning a particular object, event, or process.
Data could be converted into information using 5 main processes [3]:
Condensation – items of data are summarized into a more concise form and unnecessary depth is eliminated;
Contextualization –the purpose or reason for collecting the data in the first place is known or understood;
Calculation - data is processed and aggregated in order to provide useful information
Categorization – is a process for assigning a type or category to data;
Correction – is a process for removal of errors.
Knowledge: a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of people. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices, and norms [3].
Knowledge is based on information that is organized, synthesized, or summarized to enhance comprehension, awareness, or understanding. Knowledge represents a state or potential for action and decisions in a person, organization or a group. It could be changed in the process of learning which causes changes in understanding, decision or action. A visual definition relates knowledge with a bite from a red apple - ‘a bite (of information) should be taken, chewed, digested, and acted upon so that it becomes knowledge’ [6].
Typical questions in relation to data and information include who, what, where and when, while questions relating to knowledge include how and why.
Wisdom: the ability to identify truth and make correct judgments on the bases of previous knowledge, experience and insight. Within an organization, intellectual capital or organizational wisdom is the application of collective knowledge.
Data, Information, Knowledge and Wisdom - Practical Example
Business Situation:
The Quality Control function of a manufacturing process in a wine-making factory.
Data:
The data might concern numerical quantities of process elements that could include bottle weight, data about the wine colour as well as data about the percentage of wine ingredients. Only when these sets of data are put in the right order or in a more specific and more organized framework will they have a meaning.
Information:
In this example information could be an excel data sheet that describes several production elements of a specific red wine lot. For example, the title of the sheet could be: Weight of bottles for Red Chardonnay, Lot No 12445, produced on 14/6/2006. It is obvious that this sheet with organized information has a specific purpose (to control the bottle weight between acceptable limits) and it is associated to a particular production element or object (Red Chardonnay) and production event (bottles filled for lot No 12445 on 14/6/2006).
Knowledge:
When the particular knowledge associated with the above data and information is discussed it could be easily realized that:
1. Someone, who is expert in quality statistical control, must interpret the data sheet. This knowledge-based process apart from the expert insight requires a fluid mix of framed experience, values, and contextual information.
2. In addition, this person, in order to make his decision, needs a framework for evaluating this information. He could compare it with other lots of wine or with the acceptable weight limits of a wine bottle imposed by state regulations. The final decision of the quality manager could be to send the bottles back for refilling or to rank the lot as quality A or quality B and then decide to which markets the lot should be pushed to.
3. Only this expert was able to decide how the wine lot in question varied from the past lots and from the quality standards and why this lot should be pushed to market A (more strict customers) or to market B (not so strict customers).
Wisdom:
In this example the corresponding wisdom could be described as the ability of the quality expert or quality department to improve the whole quality control process by reviewing the quality standards again as well as by reviewing the required control process taking into consideration previous knowledge and experience. In any of the above-mentioned cases the company will improve the quality control process.
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