DIKW pyramid: Difference between revisions

Content deleted Content added

Inline

Revision as of 13:22, 13 March 2007

DIKW is a proposal of the structuring of data, information, knowledge and wisdom in an information hierarchy where each layer adds certain attributes over and above the previous one. Data is the most basic level; Information adds context; Knowledge adds how to use it; and Wisdom adds when to use it^{[citation needed]}. As such, DIKW is a model that can be useful to understanding analysis and the importance and limits of conceptual works. DIKW is meant to apply to the fields of Information science and Knowledge Management.

Description

The DIKW model is based on assuming the following chain of action:

Data comes in the form of raw observations and measurements.
Information is created by analyzing relationships and connections between the data. It is capable of answering simple "who/what/where/when/why" style questions. Information is a message, there is an (implied) audience and a purpose.
Knowledge is created by using the information for action. Knowledge answers the question "how". Knowledge is a local practice or relationship that works.
Wisdom is created through use of knowledge, through the communication of knowledge users, and through reflection. Wisdom answers the questions "why" and "when" as they relate to actions. Wisdom takes care of the future, it takes implications and lagged effects into account^{[citation needed]}.

Data has commonly been seen as simple facts that can be structured to become information. Information, in turn, becomes knowledge when it is interpreted, put into context, or when meaning is added to it. There are several variations of this widely adopted theme. The common idea is that data is something less than information, and information is less than knowledge. Moreover, it is assumed that we first need to have data before information can be created, and only when we have information, knowledge can emerge. Data are assumed to be simple isolated facts. When such facts are put into a context, and combined within a structure, information emerges. When information is given meaning by interpreting it, information becomes knowledge. At this point, facts exist within a mental structure that consciousness can process, for example, to predict future consequences, or to make inferences. As the human mind uses this knowledge to choose between alternatives, behavior becomes intelligent. Finally, when values and commitment guide intelligent behavior, behavior may be said to be based on wisdom.

Data

Specific local properties^{[citation needed]}:

1: factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation <the data is plentiful and easily available.>

2: information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful.

Information

Specific local properties ^{[citation needed]}:

(1): knowledge obtained from investigation, study, or instruction

(2) : intelligence, news

(3) : facts, data.

Knowledge

Specific local properties ^{[citation needed]}:

(1) the range of one's information.

Wisdom

Specific local definition ^{[citation needed]}:

(1) accumulated philosophic or scientific learning: Knowledge. (2) wise attitude or course of action.

According to these definitions, “data” is the basic unit of “information,” which in turn is the basic unit of “knowledge,” which itself is the basic unit of “wisdom.” So, we have four levels in our understanding and decision-making hierarchy. The whole purpose in collecting data, information, and knowledge is to be able to make wise decisions. However, if the data sources are flawed, then in most cases the decisions will also be flawed.

DIKW In Scientific Organizations

The ‘Data’ to ‘Information’ to ‘Knowledge’ to ‘Wisdom’ (DIKW) learning pathway is an integral component of the day-to-day operations of effective scientific organizations.

The word ‘science’ derives from the latin term ‘scientia’, meaning ‘having Knowledge’. Thus, by definition, the term ‘Knowledge’ is vital to the operation of scientific organizations, as are the processes by which scientific Knowledge is generated. ( [1] )

Scientific Knowledge is generally viewed as ‘the ability to predict response-patterns, based upon evidence collected by the scientific method.' All scientific Knowledge is derived from scientific ‘Information’: At the same time, once new scientific knowledge is established, there is an easy-to-demonstrate dynamic interplay between scientific knowledge and its relevant scientific information. Some experts define scientific ‘Knowledge’ as scientific ‘Information in context’ ( [2] ).

Scientific Information is a specific configuration of scientific Data, collected by the scientific method, that reveals new significant relationships ( [3] ). Functionally, scientific ‘Information’ tends to be experienced as ‘a demand upon the observer’s attention’. Some experts define scientific ‘Information’ as scientific ‘Data in context’.

Scientific Data, in turn, is viewed by scientists as the ‘infinite facts of the universe’ ( [4] ), , and thus scientific Data forms the root basis of all scientific observation and discovery.

Beyond Scientific Knowledge in the DIKW scientific learning pathway lies Scientific Wisdom, which is more than just scientific 'Knowledge in context': For a complete review of this advanced scientific knowledge management topic, please see Wisdom (scientific perspective).

Please note that the DIKW scientific learning pathway is not capable of creating any form of 'truth', at least not in any traditional sense. In this fashion, the scientific DIKW landscape is free of dogma, and instead is populated by reproducible, evidence-based predictive insights about the operation of our universe -- that can readily be replaced by more-powerful, more-reproducible, more-predictive evidence-based insights.

It is likely, that for the foreseeable future, scientists will be facing increased pressure to refine their understandings of the DIKW scientific learning pathway, especially as Western economies transition out of their former Industrial/Manufacturing traditions into the new Science & Technology Information Age.

It is reasonable to expect further refinements at this level of scientific understanding of the scientific learning process itself, as the 21st Century progresses.

References

Russell L. Ackoff, "From Data to Wisdom," Journal of Applied Systems Analysis 16 (1989): 3-9.
Milan Zeleny, "Management Support Systems: Towards Integrated Knowledge Management," Human Systems Management 7, No 1 (1987): 59-70.

External links

Data, Information, Knowledge, and Wisdom by Gene Bellinger, Durval Castro, Anthony Mills
on Wisdom by Douglas Reay
The Data, Information, Knowledge, Wisdom Chain: The Metaphorical link by Jonathan Hey

@@ Line 47: / Line 47: @@
 According to these definitions, “data” is the basic unit of “information,” which in turn is the basic unit of “knowledge,” which itself is the basic unit of “wisdom.” So, we have four levels in our understanding and [[decision-making]] hierarchy. The whole purpose in collecting data, information, and knowledge is to be able to make wise decisions. However, if the data sources are flawed, then in most cases the decisions will also be flawed.
+== DIKW In Scientific Organizations ==
+The ‘'''D'''ata’ to ‘'''I'''nformation’ to ‘'''K'''nowledge’ to ‘'''W'''isdom’ ('''DIKW''') learning pathway is an integral component of the day-to-day operations of effective scientific organizations.
+The word ‘[[science]]’ derives from the latin term ‘scientia’, meaning ‘having '''K'''nowledge’.
+Thus, by definition, the term ‘'''K'''nowledge’ is vital to the operation of scientific organizations, as are the processes by which scientific '''K'''nowledge is generated.  ( [http://plato.stanford.edu/entries/scientific-knowledge-social/] )
+Scientific '''K'''nowledge is generally viewed as ‘the ability to predict response-patterns, based upon evidence collected by the [[scientific method]].'  All scientific [[Knowledge]] is derived from scientific ‘'''I'''nformation’: At the same time, once new scientific knowledge is established, there is an easy-to-demonstrate dynamic interplay between scientific knowledge and its relevant scientific information.  Some experts define scientific ‘'''K'''nowledge’ as scientific ‘'''I'''nformation in context’ ( [http://www.systems-thinking.org/dikw/dikw.htm]  ).
+Scientific '''I'''nformation is a specific configuration of scientific '''D'''ata, collected by the scientific method, that reveals new significant relationships ( [http://www.ils.unc.edu/~losee/b5/book5.html] ). Functionally, scientific ‘'''I'''nformation’ tends to be experienced as ‘a demand upon the observer’s attention’. Some experts define scientific ‘'''I'''nformation’ as scientific ‘'''D'''ata in context’.
+Scientific '''D'''ata, in turn, is viewed by scientists as the ‘infinite facts of the universe’ ( [http://www.google.com/search?hl=en&rls=GGLD,GGLD:2004-26,GGLD:en&defl=en&q=define:data&sa=X&oi=glossary_definition&ct=title]   ), , and thus scientific '''D'''ata forms the root basis of all scientific observation and discovery.
+Beyond Scientific '''K'''nowledge in the '''DIKW''' scientific learning pathway lies ''Scientific '''W'''isdom'', which is more than just scientific ''''K'''nowledge in context':  For a complete review of this advanced scientific knowledge management topic, please see [[Wisdom]] (scientific perspective).
+Please note that the DIKW scientific [[learning]] pathway is not capable of creating any form of '[[truth]]', at least not in any traditional sense.  In this fashion, the scientific DIKW landscape is free of [[dogma]], and instead is populated by [[reproducible]], [[evidence]]-based [[predictive]] insights about the operation of our [[universe]] -- that can readily be replaced by more-powerful, more-reproducible, more-predictive evidence-based insights.
+It is likely, that for the foreseeable future, scientists will be facing increased pressure to refine their understandings of the '''DIKW scientific learning pathway''', especially as Western economies transition out of their former Industrial/Manufacturing traditions into the new  Science & Technology Information Age.
+It is reasonable to expect further refinements at this level of scientific understanding of the scientific learning process itself, as the 21st Century progresses.
 ==References==