DIKW pyramid


The DIKW pyramid, also known variously as the DIKW hierarchy, wisdom hierarchy, knowledge hierarchy, information hierarchy, and the data pyramid, refers loosely to a class of models 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".
Not all versions of the DIKW model reference all four components, and some include additional components. In addition to a hierarchy and a pyramid, the DIKW model has also been characterized as a chain, as a framework, as a series of graphs, and as a continuum.

History

Danny P. Wallace, a professor of library and information science, explained that the origin of the DIKW pyramid is uncertain:
The presentation of the relationships among data, information, knowledge, and sometimes wisdom in a hierarchical arrangement has been part of the language of information science for many years. Although it is uncertain when and by whom those relationships were first presented, the ubiquity of the notion of a hierarchy is embedded in the use of the acronym DIKW as a shorthand representation for the data-to-information-to-knowledge-to-wisdom transformation.
while many authors agreed that DIKW, at least IKW, originated from the play The Rock by T. S. Eliot in 1934. The play contains wisdom-knowledge-information in the following lines:

Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?

Data, Information, Knowledge

In 1955, English-American economist and educator Kenneth Boulding presented a variation on the hierarchy consisting of "signals, messages, information, and knowledge". However, "he first author to distinguish among data, information, and knowledge and to also employ the term 'knowledge management' may have been American educator Nicholas L. Henry", in a 1974 journal article.

Data, Information, Knowledge, Wisdom

Other early versions of the hierarchy that refer to a data tier include those of Chinese-American geographer Yi-Fu Tuan and sociologist-historian Daniel Bell.. In 1980, Irish-born engineer Mike Cooley invoked the same hierarchy in his critique of automation and computerization, in his book Architect or Bee?: The Human / Technology Relationship.
Thereafter, in 1987, Czechoslovakia-born educator Milan Zeleny mapped the elements of the hierarchy to knowledge forms: know-nothing, know-what, know-how, and know-why. Zeleny "has frequently been credited with proposing the ...although he actually made no reference to any such graphical model."
The hierarchy appears again in a 1988 address to the International Society for General Systems Research, by American organizational theorist Russell Ackoff, published in 1989. Subsequent authors and textbooks cite Ackoff's as the "original articulation" of the hierarchy or otherwise credit Ackoff with its proposal. Ackoff's version of the model includes an understanding tier, interposed between knowledge and wisdom. Although Ackoff did not present the hierarchy graphically, he has also been credited with its representation as a pyramid.
In the same year as Ackoff presented his address, information scientist Anthony Debons and colleagues introduced an extended hierarchy, with "events", "symbols", and "rules and formulations" tiers ahead of data.
In 1994 Nathan Shedroff presented the DIKW hierarchy in an information design context which later appeared as a book chapter.
Jennifer Rowley noted in 2007 that there was "little reference to wisdom" in discussion of the DIKW in recently published college textbooks, and does not include wisdom in her own definitions following that research. Meanwhile, Zins' extensive analysis of the conceptualizations of data, information, and knowledge, in his recent research study, makes no explicit commentary on wisdom, although some of the citations included by Zins do make mention of the term.

Description

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". Reviews of textbooks and a survey of scholars in relevant fields 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".
This has led Israeli researcher Chaim 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 or both. In Zins's 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 rather than an external phenomenon.

Data

In the context of DIKW, data is conceived of as symbols or signs, representing stimuli or signals, that are "of no use until...in a usable form". Zeleny characterized this non-usable characteristic of data as "know-nothing".
In some cases, data is understood to refer not only to symbols, but also to signals or stimuli referred to by said symbols—what Zins terms subjective data. Where universal data, for Zins, are "the product of observation", subjective data are the observations. This distinction is often obscured in definitions of data in terms of "facts".

Data as fact

Rowley, following her study of DIKW definitions given in textbooks, characterizes data "as being discrete, objective facts or observations, which are unorganized and unprocessed and therefore have no meaning or value because of lack of context and interpretation." In Henry's early formulation of the hierarchy, data was simply defined as "merely raw facts"., while two recent texts define data as "chunks of facts about the state of the world" and "material facts", respectively. Cleveland does not include an explicit data tier, but defines information as "the sum total of...facts and ideas".
Insofar as facts have as a fundamental property that they are true, have objective reality, or otherwise can be verified, such definitions would preclude false, meaningless, and nonsensical data from the DIKW model, such that the principle of garbage in, garbage out would not be accounted for under DIKW.

Data as signal

In the subjective domain, data are conceived of as "sensory stimuli, which we perceive through our senses", or "signal readings", including "sensor and/or sensory readings of light, sound, smell, taste, and touch". Others have argued that what Zins calls subjective data actually count as a "signal" tier, which precedes data in the DIKW chain.
American information scientist Glynn Harmon defined data as "one or more kinds of energy waves or particles selected by a conscious organism or intelligent agent on the basis of a preexisting frame or inferential mechanism in the organism or agent."
The meaning of sensory stimuli may also be thought of as subjective data:
Information is the meaning of these sensory stimuli. For example, the noises that I hear are data. The meaning of these noises is information. Still, there is another alternative as to how to define these two concepts—which seems even better. Data are sense stimuli, or their meaning. Accordingly, in the example above, the loud noises, as well as the perception of a running car engine, are data.

Subjective data, if understood in this way, would be comparable to knowledge by acquaintance, in that it is based on direct experience of stimuli. However, unlike knowledge by acquaintance, as described by Bertrand Russell and others, the subjective domain is "not related to...truthfulness".
Whether Zins' alternate definition would hold would be a function of whether "the running of a car engine" is understood as an objective fact or as a contextual interpretation.

Data as symbol

Whether the DIKW definition of data is deemed to include Zins's subjective data, data is consistently defined to include "symbols", or "sets of signs that represent empirical stimuli or perceptions", of "a property of an object, an event or of their environment". Data, in this sense, are "recorded symbols", including "words, numbers, diagrams, and images, which are the building blocks of communication", the purpose of which "is to record activities or situations, to attempt to capture the true picture or real event," such that "all data are historical, unless used for illustrative purposes, such as forecasting."
Boulding's version of DIKW explicitly named the level below the information tier message, distinguishing it from an underlying signal tier. Debons and colleagues reverse this relationship, identifying an explicit symbol tier as one of several levels underlying data.
Zins determined that, for most of those surveyed, data "are characterized as phenomena in the universal domain". "Apparently," clarifies Zins, "it is more useful to relate to the data, information, and knowledge as sets of signs rather than as meaning and its building blocks".

Information

In the context of DIKW, information meets the definition for knowledge by description, and is differentiated from data in that it is "useful". "Information is inferred from data", in the process of answering interrogative questions, thereby making the data useful for "decisions and/or action". "Classically," states a recent text, "information is defined as data that are endowed with meaning and purpose."

Structural vs. functional

Rowley, following her review of how DIKW is presented in textbooks, describes information as "organized or structured data, which has been processed in such a way that the information now has relevance for a specific purpose or context, and is therefore meaningful, valuable, useful and relevant." Note that this definition contrasts with Rowley's characterization of Ackoff's definitions, wherein "he difference between data and information is structural, not functional."
In his formulation of the hierarchy, Henry defined information as "data that changes us", this being a functional, rather than structural, distinction between data and information. Meanwhile, Cleveland, who did not refer to a data level in his version of DIKW, described information as "the sum total of all the facts and ideas that are available to be known by somebody at a given moment in time".
American educator Bob Boiko is more obscure, defining information only as "matter-of-fact".

Symbolic vs. subjective

Information may be conceived of in DIKW models as: universal, existing as symbols and signs; subjective, the meaning to which symbols attach; or both. Examples of information as both symbol and meaning include:
Zeleny formerly described information as "know-what", but has since refined this to differentiate between "what to have or to possess" and "what to do, act or carry out". To this conceptualization of information, he also adds "why is", as distinct from "why do". Zeleny further argues that there is no such thing as explicit knowledge, but rather that knowledge, once made explicit in symbolic form, becomes information.

Knowledge

The knowledge component of DIKW is generally agreed to be an elusive concept which is difficult to define. The DIKW definition of knowledge differs from that used by epistemology. The DIKW view is that "knowledge is defined with reference to information." Definitions may refer to information having been processed, organized or structured in some way, or else as being applied or put into action.
Zins has suggested that knowledge, being subjective rather than universal, is not the subject of study in information science, and that it is often defined in propositional terms, while Zeleny has asserted that to capture knowledge in symbolic form is to make it into information, i.e., that "All knowledge is tacit".
"One of the most frequently quoted definitions" of knowledge captures some of the various ways in which it has been defined by others:
Knowledge is 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 knowers. In organizations it often becomes embedded not only in documents and repositories but also in organizational routines, processes, practices and norms.

Knowledge as processed

Mirroring the description of information as "organized or structured data", knowledge is sometimes described as:
One of Boulding's definitions for knowledge had been "a mental structure" and Cleveland described knowledge as "the result of somebody applying the refiner's fire to , selecting and organizing what is useful to somebody". A recent text describes knowledge as "information connected in relationships".

Knowledge as procedural

Zeleny defines knowledge as "know-how", and also "know-who" and "know-when", each gained through "practical experience". "Knowledge...brings forth from the background of experience a coherent and self-consistent set of coordinated actions.". Further, implicitly holding information as descriptive, Zeleny declares that "Knowledge is action, not a description of action."
Ackoff, likewise, described knowledge as the "application of data and information", which "answers 'how' questions", that is, "know-how".
Meanwhile, textbooks discussing DIKW have been found to describe knowledge variously in terms of experience, skill, expertise or capability:
Businessmen James Chisholm and Greg Warman characterize knowledge simply as "doing things right".

Knowledge as propositional

Knowledge is sometimes described as "belief structuring" and "internalization with reference to cognitive frameworks". One definition given by Boulding for knowledge was "the subjective 'perception of the world and one's place in it'", while Zeleny's said that knowledge "should refer to an observer's distinction of 'objects' ".
Zins, likewise, found that knowledge is described in propositional terms, as justifiable beliefs, and sometimes also as signs that represent such beliefs. Zeleny has rejected the idea of explicit knowledge, arguing that once made symbolic, knowledge becomes information. Boiko appears to echo this sentiment, in his claim that "knowledge and wisdom can be information".
In the subjective domain:
Knowledge is a thought in the individual's mind, which is characterized by the individual's justifiable belief that it is true. It can be empirical and non-empirical, as in the case of logical and mathematical knowledge, religious knowledge, philosophical knowledge, and the like. Note that knowledge is the content of a thought in the individual's mind, which is characterized by the individual's justifiable belief that it is true, while "knowing" is a state of mind which is characterized by the three conditions: the individual believe that it is true, S/he can justify it, and It is true, or it to be true.

The distinction here between subjective knowledge and subjective information is that subjective knowledge is characterized by justifiable belief, where subjective information is a type of knowledge concerning the meaning of data.
Boiko implied that knowledge was both open to rational discourse and justification, when he defined knowledge as "a matter of dispute".

Wisdom

Although commonly included as a level in DIKW, "there is limited reference to wisdom" in discussions of the model. Boiko appears to have dismissed wisdom, characterizing it as "non-material".
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. Adler had previously also included an understanding tier, while other authors have depicted understanding as a dimension in relation to which DIKW is plotted.
Cleveland described wisdom simply as "integrated knowledge—information made super-useful". Other authors have characterized wisdom as "knowing the right things to do" and "the ability to make sound judgments and decisions apparently without thought".
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", but later refined his definitions, so as to differentiate "why do" from "why is", and expanding his definition to include a form of know-what. According to Nikhil Sharma, Zeleny has argued for a tier to the model beyond wisdom, termed "enlightenment".

Representations

Graphical representation

DIKW is a hierarchical model often depicted as a pyramid, with data at its base and wisdom at its apex. In this regard it is similar to Maslow's hierarchy of needs, in that each level of the hierarchy is argued to be an essential precursor to the levels above. Unlike Maslow's hierarchy, which describes relationships of priority, DIKW describes purported structural or functional relationships. Both Zeleny and Ackoff have been credited with originating the pyramid representation, although neither used a pyramid to present their ideas.
DIKW has also been represented as a two-dimensional chart or as one or more flow diagrams. In such cases, the relationships between the elements may be presented as less hierarchical, with feedback loops and control relationships.
Debons and colleagues may have been the first to "present the hierarchy graphically".
Throughout the years many adaptations of the DIKW pyramid have been produced. One example, in use by knowledge managers in the United States Army, attempts to show the progression transforming data to information then knowledge and finally wisdom, as well as the activities involved to ultimately create shared understanding throughout the organization and manage decision risk.

Computational representation

Intelligent decision support systems are trying to improve decision making by introducing new technologies and methods from the domain of modeling and simulation in general, and in particular from the domain of intelligent software agents in the contexts of agent-based modeling.
The following example describes a military decision support system, but the architecture and underlying conceptual idea are transferable to other application domains:
By the introduction of a common operational picture, data are put into context, which leads to information instead of data. The next step, which is enabled by service-oriented web-based infrastructures, is the use of models and simulations for decision support. Simulation systems are the prototype for procedural knowledge, which is the basis for knowledge quality. Finally, using intelligent software agents to continually observe the battle sphere, apply models and simulations to analyse what is going on, to monitor the execution of a plan, and to do all the tasks necessary to make the decision maker aware of what is going on, command and control systems could even support situational awareness, the level in the value chain traditionally limited to pure cognitive methods.

Criticisms

, a philosopher based in Germany, argues that data is an abstraction, information refers to "the act of communicating meaning", and knowledge "is the event of meaning selection of a system
from its 'world' on the basis of communication". As such, any impression of a logical hierarchy between these concepts "is a fairytale".
One objection offered by Zins is that, while knowledge may be an exclusively cognitive phenomenon, the difficulty in pointing to a given fact as being distinctively information or knowledge, but not both, makes the DIKW model unworkable.
s Albert Einstein's famous equation "E = mc2" information or knowledge? Is "2 + 2 = 4" information or knowledge?

Alternatively, information and knowledge might be seen as synonyms. In answer to these criticisms, Zins argues that, subjectivist and empiricist philosophy aside, "the three fundamental concepts of data, information, and knowledge and the relations among them, as they are perceived by leading scholars in the information science academic community", have meanings open to distinct definitions. Rowley echoes this point in arguing that, where definitions of knowledge may disagree, "hese various perspectives all take as their point of departure the relationship between data, information and knowledge."
American philosophers John Dewey and Arthur Bentley, in their 1949 book Knowing and the Known, argued that "knowledge" was "a vague word", and presented a complex alternative to DIKW including some nineteen "terminological guide-posts".
Information processing theory argues that the physical world is made of information itself. Under this definition, data is either made up of or synonymous with physical information. It is unclear, however, whether information as it is conceived in the DIKW model would be considered derivative from physical-information/data or synonymous with physical information. In the former case, the DIKW model is open to the fallacy of equivocation. In the latter, the data tier of the DIKW model is preempted by an assertion of neutral monism.
Educator Martin Frické has published an article critiquing the DIKW hierarchy, in which he argues that the model is based on "dated and unsatisfactory philosophical positions of operationalism and inductivism", that information and knowledge are both weak knowledge, and that wisdom is the "possession and use of wide practical knowledge.
David Weinberger argues that although the DIKW pyramid appears to be a logical and straight-forward progression, this is incorrect. "What looks like a logical progression is actually a desperate cry for help." He points out there is a discontinuity between Data and Information, versus Knowledge and Wisdom. This suggests that the DIKW pyramid is too simplistic in representing how these concepts interact. "...Knowledge is not determined by information, for it is the knowing process that first decides which information is relevant, and how it is to be used."