Relationship amongst data information and knowledge

relationship amongst data information and knowledge

In Information Management, we been thought about the relation between data, information, knowledge and wisdom, most of us, maybe who doesn&#. Information refers to data that has been given some meaning by way of relational connection. In computing terms it is data that has been. perspective, establish a w orking relationship between these two important . define data, information, and knowledge and their inter-relationships, often in.

Garvin,Avery Publishing Group Information is a flow of messages Knowledge is created by the very flow of information, anchored in the beliefs and commitment of its holder. Davenport and Laurence Prusak, This subject is not an easy one; it involves extensive conceptual thinking dealing with many abstract concepts and semantics.

Nevertheless, a thorough understanding of this topic is the quintessential foundation of information and knowledge management.

Difference Between Knowledge and Information

Good definitions include several essential characteristics: Thereafter, we can look into the inter-relationships between the defined subjects. Definitions Data are recorded captured and stored symbols and signal readings.

Theory 1 Topic 4.1.1 Data Information and Knowledge

The main purpose of data is to record activities or situations, to attempt to capture the true picture or real event. Therefore, all data are historical, unless used for illustration purposes, such as forecasting. These symbols have complex structures and rules. Information therefore comes in a variety of forms such as writings, statements, statistics, diagrams or charts. Some information theorists insist on the concept of form as the differentiating factor and the essence of information.

Where does knowledge fit in this scenario? Information becomes individual knowledge when it is accepted and retained by an individual as being a proper understanding of what is true Lehrer, and a valid interpretation of the reality. Conversely, organizational or social knowledge exists when it is accepted by a consensus of a group of people.

Common knowledge does not require necessarily to be shared by all members to exist, the fact that it is accepted amongst a group of informed persons can be considered a sufficient condition. The fact that it is readily available in writing or published material does not entail that everybody should be knowledgeable about it to meet the condition of being "common knowledge".

Godbout presents these definitions in an article discussing roles of computers in the field called "knowledge management. Educational Implications It appears that one of the issues in defining the terms data, information, knowledge, and wisdom is the role of understanding and meaning making. One can memorize data, and parrot it back. One processes data organizes it into meaningful chunks? Parroting such chunks sounds more like being educated--but this can be done with little understanding or ability to make use of the information.

Knowledge is a step further on the scale. It involves understanding and ability to make use of the data and information to answer questions, solve problems, make decisions, and so on. Wisdom has to do with using one's knowledge in a responsible wise manner. In recent years, Robert Sternberg has taken the position that wisdom can and should be taught in schools, even at the elementary school level.

A summary of his ideas and definitions is available in the following reference. November 13, When schools teach for wisdom, they teach students that it is important not just what you know, but how you use what you know--whether you use it for good ends or bad.

relationship amongst data information and knowledge

They are teaching for what the Bush administration referred to recently, in a White House conference, as the "fourth R": Smart but foolish and irresponsible people, including, apparently, some who run or have run major businesses in our country, exhibit four characteristic fallacies in their thinking. I define wisdom as the application of intelligence and experience toward the attainment of a common good.

This attainment involves a balance among a intrapersonal one's ownb interpersonal other people'sand c extrapersonal more than personal, such as institutional interests, over the short and long terms. Thus, wise people look out not just for themselves, but for all toward whom they have any responsibility. The ISTE National Educational Technology Standards for students, teachers, and school administrators all stress the responsible use of computer systems. Or, here is a slightly different twist on the situation.

We want students, teachers, and school administrators to be responsible, wise use of computer systems. As students learn to be responsible and wise, we want transfer of learning to occur among many different application areas, including IT. In carrying out this 1. Hence this distinction is novice designer with appropriate knowledge to provide him or discussed later in the paper. In terms of the knowledge used, a design produced with the support of such a system would not be 1.

The system would contain the and novices has focused on problems where constraints and appropriate knowledge currently possessed by experienced context are well defined, and a limited number of rules apply. This knowledge needs to be identified, captured, Examples are chess and domain problems such as mechanics stored and made available to novice designers.

This research is problems. Chase and Simon found the ability to recall investigating how far this scenario can be realised. The capacity of short-term memory is limited by the Cross et al describe the limitations in drawing too many number of chunks.

Miller states short-term memory capacity is defined, e. More experienced such as computer programming which deals with ill-defined chess players can hold a complete chess configuration as one problems.

Design problems are described as ill-defined. One of chunk of information. Less experienced chess players hold the reasons for this is that many possible solutions exist much smaller amounts of information in each chunk, requiring [Goldschmidt,Cross, ].

In addition, not all of the too many chunks to hold in memory [Newell, ]. Similar characteristics of the situation can be found in the problem findings were found in solving domain problems [see Blessing, statement [Blessing, ].

The study described in this paper for an overview]. Waldron et al observed the abilities of found some of the differences between novices and experts in experts, semi-experts graduate designers and naive designers relatively well-defined problem areas were also applicable in to reproduce mechanical engineering drawings [Waldron, the ill-defined area of design. This confirms the suggestions ].

Journal of Knowledge Management Practice,

They observed that experts made fewer errors; required made by Blessing [Blessing ]. The ability to draw for longer before 2. To achieve this, the differences between memory.

relationship amongst data information and knowledge

Experts were also found to be more successful at experienced and novice designers need to be better understood. De Groot observed experienced chess players to plan several moves ahead, 2. The carrying out design tasks. The experienced designers had difference in the ability to find and selectively explore the most between 8 and 32 years of experience and the novice designers promising moves suggests a difference in the internal between 8 and 30 months.

They were from two different representation of knowledge [Ericsson, ]. In the domains groups within the aerospace company.

relationship amongst data information and knowledge

An backwards, unlike experts who reasoned forwards. This example of the tasks observed was the design of a second-stage suggests experts access their knowledge systematically [De high-pressure compressor blade.

A 15 - 20 minute interview Jong,Ericsson, and Zeitz, ]. When solving followed each observation. The problem space is used to environments. The designers were observed individually and achieve the goal state of the problem and consists of were asked to think aloud. Problem space is thought to cognitive processing if subjects were asked to simply verbalise increase with expertise [Christiaans, ]. They were only prompted to speak during periods of silence. The implications of this are: However, the use of a single design task for all subjects might have resulted in a task that was too easy for no occurrences observed.

Categories related to the design process are discussed in expertise; or too difficult for a novice designer. The All the observations and interviews were audio recorded designers would generate a decision and then immediately and transcribed, resulting in 51 pages of transcripts.

No implement this decision. The results of the implementation categorisation of data was determined prior to the observations. If rejected, the process was repeated. This summarise the designers thoughts and actions. Categories were process of trial and error was observed many times with novice created to summarise all these thoughts and actions. A few designers and only once with experienced designers. Smith and additional categories were required in the analysis of later Leong found a similar difference in their observations of transcripts to accommodate all the data.

The final transcripts students novices and professional engineers [Smith, ]. This may explain the need to Twenty-one categories of thoughts and actions were generated implement a decision before evaluating to determine if this from the analysis of the observations.

They were also observed undertaking tasks to gain a better understanding of the problem. Occurrences of Thoughts and Actions number of occurrences observed: Experienced designers were aware of the experienced of the novice designers with two and a half years reasons behind a particular component or a specific experience. He was occasionally observed considering issues, manufacturing process that was used in a particular design.

Data, Information, Knowledge, and Wisdom

This They assessed the reasons and their applicability to the typical behaviour of experienced designers. If the component or process was not necessary and leaving it out would help solve the problem at hand, the component or process would be removed or modified.

This illustrates how the boundaries of the Generate Implement Evaluate problem solution space could be expanded. A specific example was the use of a cold expansion process, which caused difficulty in manufacturing.

The designer was aware of the stress conditions that required this additional manufacturing process, and was confident that Reject these conditions would not occur. He therefore decided that this process was no longer required.