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DIKW pyramid

The DIKW pyramid, also known variously as the DIKW hierarchy, wisdom hierarchy, knowledge hierarchy, information hierarchy, information pyramid, and the data pyramid,[1] refers loosely to a class of models[2] for representing purported structural and/or functional relationships between data, information, knowledge, and wisdom. "Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge".The DIKW acronym has worked into the rotation from knowledge management. It demonstrates how the deep understanding of the subject emerges, passing through four qualitative stages: D – data, I – information, K – knowledge and W – wisdom[1]

Not all versions of the DIKW model reference all four components (earlier versions not including data, later versions omitting or downplaying wisdom), and some include additional components.[3] In addition to a hierarchy and a pyramid, the DIKW model has also been characterized as a chain,[4][5] as a framework,[6] and as a continuum.[7]

Description[edit]

The DIKW model "is often quoted, or used implicitly, in definitions of data, information and knowledge in the information management, information systems and knowledge management literatures, but there has been limited direct discussion of the hierarchy".[1] Reviews of textbooks[1] and a survey of scholars in relevant fields[2] indicate that there is not a consensus as to definitions used in the model, and even less "in the description of the processes that transform elements lower in the hierarchy into those above them".[1][27]


This has led Zins to suggest that the data–information–knowledge components of DIKW refer to a class of no less than five models, as a function of whether data, information, and knowledge are each conceived of as subjective, objective (what Zins terms, "universal" or "collective") or both. In Zins' usage, subjective and objective "are not related to arbitrariness and truthfulness, which are usually attached to the concepts of subjective knowledge and objective knowledge". Information science, Zins argues, studies data and information, but not knowledge, as knowledge is an internal (subjective) rather than an external (universal–collective) phenomenon.[2]

American information scientist Anthony Debons's characterization of information as representing "a state of awareness (consciousness) and the physical manifestations they form", such that "[i]nformation, as a phenomenon, represents both a process and a product; a cognitive/affective state, and the physical counterpart (product of) the cognitive/affective state."

[32]

Danish information scientist Hanne Albrechtsen's description of information as "related to meaning or human intention", either as "the contents of databases, the web, etc." (italics added) or "the meaning of statements as they are intended by the speaker/writer and understood/misunderstood by the listener/reader."

[33]

"synthesis of multiple sources of information over time"

"organization and processing to convey understanding, experience [and] accumulated learning"

"a mix of contextual information, values, experience and rules"

[19]

Wisdom[edit]

Although commonly included as a level in DIKW, "there is limited reference to wisdom"[1] in discussions of the model. Boiko appears to have dismissed wisdom, characterizing it as "non-material".[8][29]


Ackoff refers to understanding as an "appreciation of 'why'", and wisdom as "evaluated understanding", where understanding is posited as a discrete layer between knowledge and wisdom.[8][18][31] Adler had previously also included an understanding tier,[8][20][21] while other authors have depicted understanding as a dimension in relation to which DIKW is plotted.[6][31]


Cleveland described wisdom simply as "integrated knowledge—information made super-useful".[8][14] Other authors have characterized wisdom as "knowing the right things to do"[6] and "the ability to make sound judgments and decisions apparently without thought".[8][28] Wisdom involves using knowledge for the greater good. Because of this, wisdom is deeper and more uniquely human. It requires a sense of good and bad, right and wrong, ethical and unethical.


Zeleny described wisdom as "know-why",[17] but later refined his definitions, so as to differentiate "why do" (wisdom) from "why is" (information), and expanding his definition to include a form of know-what ("what to do, act or carry out").[4] According to Nikhil Sharma, Zeleny has argued for a tier to the model beyond wisdom, termed "enlightenment".[15]

The value chain starts with data quality describing the information within the underlying command and control systems.

Information quality tracks the completeness, correctness, currency, consistency and precision of the data items and information statements available.

Knowledge quality deals with procedural knowledge and information embedded in the command and control system such as templates for adversary forces, assumptions about entities such as ranges and weapons, and doctrinal assumptions, often coded as rules.

Awareness quality measures the degree of using the information and knowledge embedded within the command and control system. Awareness is explicitly placed in the cognitive domain.

 – Classification system in education

Bloom's taxonomy

 – Concept in education and education reform

Higher-order thinking

 – Stages of intelligence information processing

Intelligence cycle

 – Metaphorical model of cognition and action by Chris Argyris

Ladder of inference

 – Framework for scoring how complex a behavior is

Model of hierarchical complexity

 – Theory of developmental psychology, a similar graphic in the field of psychology

Maslow's hierarchy of needs

a metaphor used by journalists and writers to prioritise and structure the most newsworthy info and important details over general info

Inverted pyramid (journalism)

(December 2004). "The Data, Information, Knowledge, Wisdom chain: the metaphorical link" (PDF). Archived from the original (PDF) on 2 December 2007. Retrieved 2 October 2016.

Hey, Jonathan