<< How do search engines work>>
**5 example search engines on the internet**
วันจันทร์ที่ 17 ตุลาคม พ.ศ. 2554
วันจันทร์ที่ 10 ตุลาคม พ.ศ. 2554
Chapter: 7 Flooding in Thailand!!!!
The flood water have affected 60 provinces in Thailand and this is the time for help each others. There are to many ways to help each others such as donate money through online channel to support and helping people, donate some dry food and neceesary stuff for helping people, packing stuff for helping them or donate boats for helping them.
วันจันทร์ที่ 3 ตุลาคม พ.ศ. 2554
Chapter:6 Report and Citation
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วันจันทร์ที่ 26 กันยายน พ.ศ. 2554
วันจันทร์ที่ 19 กันยายน พ.ศ. 2554
Chapter:4 Reference collection
The Reference Collection
-bibliographies
-atlases
-encyclopedias
-dictionaries
-almanacs
-directories
วันจันทร์ที่ 12 กันยายน พ.ศ. 2554
chapter:3 (9.1)
Identify the difference between Library of Congress Classification System<LC> and Deway Decimal Classification System <D.D.C>
The Library of Congress classification system organizes books and other library materials according to subject, making it easy for you to browse the shelves for materials about a specific topic. Library of Congress call numbers begin with letters of the English alphabet. The letters identify the subject of the work, for example, the letter P at the beginning of a call number indicates that the work is about language or literature. The call number also functions as a location code. You will find it taped to the lower spine of each item. It also appears in the library's online catalog (CONSULS) in the full catalog record representing the physical item.
Reference
: http://people.wcsu.edu/reitzj/res/lcclass.html
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The Dewey Decimal Classification is a system of library classification made up of ten classes, each divided into ten divisions, each having ten sections (although there are only 99 of 100 divisions and 908 of 999 sections in total, as some are no longer in use or have not been assigned).
Reference: http://en.wikipedia.org/wiki/List_of_Dewey_Decimal_classes
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LINK TO :
The Library of Congress website:
http://www.loc.gov/index.html
British Library:
http://www.bl.uk/
Thai National Library:
http://www.nlt.go.th/
Sripatum University Library:
http://library.spu.ac.th/e-library/index4.html
ASEAN Community website:
http://www.asean.org/
วันจันทร์ที่ 5 กันยายน พ.ศ. 2554
Chapter: 2
Objective Facts:
Example:
Of the 7.5 million kilowatt hours of electricity used annually, 580 thousand are used exclusively to illuminate the tower. The tower's annual operation also requires the use of 2 tons of paper for tickets, 4 tons of rag or paper wipes, 10,000 applications of detergents, 400 liters of metal cleansers and 25,000 garbage bags. (reference)
On the four facades of the tower, the 72 surnames of leading turn-of-the-century French scientists and engineers are engraved in recognition of their contributions to science. This engraving was over painted at the beginning of the 20th century and restored in 1986-1987 by the Société Nouvelle d' Exploitation de la Tour Eiffel, a company contracted to operate business related to the Tower.
Source:
http://corrosion-doctors.org/Landmarks/eiffel-history.htm
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Subjective Opinion:
Example:
Example:
Eiffel Tower History
The Eiffel Tower was built for the International Exhibition of Paris of 1889 commemorating the centenary of the French Revolution. The Prince of Wales, later King Edward VII of England, opened the tower. Of the 700 proposals submitted in a design competition, Gustave Eiffel's was unanimously chosen. However it was not accepted by all at first, and a petition of 300 names - including those of Maupassant, Emile Zola, Charles Garnier (architect of the Opéra Garnier), and Dumas the Younger - protested its construction.
At 300 meters (320.75 m including antenna), and 7,000 tons, it was the world's tallest building until 1930. Other statistics include:
In 1889, Gustave Eiffel began to fit the peak of the tower as an observation station to measure the speed of wind. He also encouraged several scientific experiments including Foucault's giant pendulum, a mercury barometer and the first experiment of radio transmission. In 1898, Eugene Ducretet at the Pantheon, received signals from the tower. |  |
According to NASA climate scientist Jay Zwally, the 'Arctic is the canary in the coal mine.' What do you think would happen to the Eiffel Tower due to global warming?Please send your thoughts to our Webmaster! ... and do not be surprised if these are published on our Web site.
After Gustave Eiffel experiments in the field of meterology, he begun to look at the effects of wind and air resistance, the science that would later be termed aerodynamics, which has become a large part of both military and commercial aviation as well as rocket technology. Gustave Eiffel imagined an automatic device sliding along a cable that was stretched between the ground and the second floor of the Eiffel Tower. (reference)
The tower was almost torn down in 1909, but was saved because of its antenna used both for military and other purposes, and the city let it stand after the permit expired. When the tower played an important role in capturing the infamous spy Mata Hariduring World War I, it gained such importance to the French people that there was no more thought of demolishing it.- used for telegraphy at that time. From 1910 and on the Eiffel Tower became part of the International Time Service. French radio (since 1918), and French television (since 1957) have also made use of its stature. During its lifetime, the Eiffel Tower has also witnessed a few strange scenes, including being scaled by a mountaineer in 1954, and parachuted off of in 1984 by two Englishmen. In 1923 a journalist rode a bicycle down from the first level. Some accounts say he rode down the stairs, other accounts suggest the exterior of one of the tower's four legs which slope outward. (reference) |  |
On the four facades of the tower, the 72 surnames of leading turn-of-the-century French scientists and engineers are engraved in recognition of their contributions to science. This engraving was over painted at the beginning of the 20th century and restored in 1986-1987 by the Société Nouvelle d' Exploitation de la Tour Eiffel, a company contracted to operate business related to the Tower.
Source:
http://corrosion-doctors.org/Landmarks/eiffel-history.htm
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Subjective Opinion:
Example:
Top 10 best movies queotes
1.
Bruce Willis
Actor, The Sixth Sense
Bruce Willis grew up mainly in Penns Grove, New Jersey, and graduated from high school there before going to New York to become an actor. He waited tables and tended bar for a living until he began to get roles in plays. While tending bar one night he was seen by a casting director who liked his personality and needed a bartender for a small movie role.
“ "Yippie kay-yay, motherf--ker."
Die Hard (1988) ” - catojune-1
Die Hard (1988) ” - catojune-1
2.
Al Pacino
Actor, The Godfather
One of the greatest actors in all of film history, Al Pacino established himself during one of film's greatest decades, the 1970s, and has become an enduring and iconic figure in the world of American movies. Born on April 25, 1940, in the Bronx, New York, Pacino's parents (Salvatore and Rose) divorced when he was young...
“ "Say 'hello' to my little friend!"
Scarface (1983) ” - catojune-1
Scarface (1983) ” - catojune-1
3.
Heath Ledger
Actor, The Dark Knight
When a young, hunky 20 year old heart-throb Heath Ledger first came to the attention of the public in 1999, it was all too easy to tag him as a "pretty boy" and an actor of not much depth. He has spent the past five years trying desperately to sway this image away, but this has indeed been a double-edged sword...
“ 'Why so serious?
The Dark Knight (2008) ” - catojune-1
The Dark Knight (2008) ” - catojune-1
4.
Hanna Hall
Actress, Forrest Gump
“ "Run, Forrest, run. Run, Forrest!"
Forrest Gump (1994) ” - catojune-1
Forrest Gump (1994) ” - catojune-1
5.
Kevin Spacey
Actor, American Beauty
As enigmatic as he is talented, Kevin Spacey has always kept the details of his private life closely guarded. As he explained in a 1998 interview with the London Evening Standard, "It's not that I want to create some bullshit mystique by maintaining a silence about my personal life, it is just that the less you know about me...
“ "The greatest trick the devil ever pulled was convincing the world he didn't exist. And like that - he's gone."
The Usual Suspects (1995) ” - catojune-1
The Usual Suspects (1995) ” - catojune-1
6.
Arnold Schwarzenegger
Actor, Terminator 2: Judgment Day
With an almost unpronounceable surname and a thick German accent, who would have ever believed that a brash, quick talking bodybuilder from a small European village would become one of Hollywood's biggest stars, marry into the prestigious Kennedy family, amass a fortune via shrewd investments and one day be the Governor of California...
“ "I'll be back"
The Terminator (1984) ” - catojune-1
The Terminator (1984) ” - catojune-1
7.
Cuba Gooding Jr.
Actor, Jerry Maguire
Cuba Gooding Jr. was born on January 2, 1968, in The Bronx, New York. His father, Cuba Gooding, was the lead vocalist for the R&B group The Main Ingredient, which had a hit with the song "Everybody Plays The Fool". His mother, Shirley, was a backup singer for The Sweethearts. His father moved the family to Los Angeles in 1972...
“ "Show me the money!"
Jerry Maguire (1996) ” - catojune-1
Jerry Maguire (1996) ” - catojune-1
8.
James Earl Jones
Actor, The Lion King
James Earl Jones was born in 1931 in Arkabutla, Mississippi, USA. At an early age he started to take dramatic lessons to calm himself down. It appeared to work as he has since starred in many films over a 40-year period, beginning with the Stanley Kubrick classic Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb. Probably best known for his role as Darth Vader (for the voice only...
“ "I am your father."
Star Wars: Episode V - The Empire Strikes Back (1980) ” - catojune-1
Star Wars: Episode V - The Empire Strikes Back (1980) ” - catojune-1
9.
Gerard Butler
Actor, 300
Born in Paisley, Scotland, to Margaret and Edward Butler, Gerard Butler was raised along with his older brother and sister in his hometown of Paisley, Scotland. He also spent some of his youth in Canada. His parents divorced when he was a child, and he and his siblings were raised primarily by their mother...
“ This is where we hold them! This is where we fight! This is where they die!
300 (2006) ” - catojune-1
300 (2006) ” - catojune-1
10.
William Fichtner
Actor, Black Hawk Down
A small-town guy with a big heart, William Fichtner has been captivating the hearts of Western New Yorkers for decades. Bill was born in 1956 on Long Island, New York, raised in Cheektowaga, and graduated from Maryvale High School in 1974. His first roles were in soap operas such as As the World Turns and sitcoms like Grace Under Fire...
Reference:
Chapter:1
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.
Reference:
http://www.trainmor-knowmore.eu/FBC5DDB3.en.aspxThe 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.
Reference:
Chapter:1
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.
Reference:
http://www.trainmor-knowmore.eu/FBC5DDB3.en.aspxThe 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.
Reference:
Chapter:1
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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