DIKW pyramid

In 1927, Clarence W. Barron addressed his employees at Dow Jones & Company on the hierarchy: "Knowledge, Intelligence and Wisdom."[12]

In 1955, English-American economist and educator Kenneth Boulding presented a variation on the hierarchy consisting of "signals, messages, information, and knowledge".[9][13] However, "[t]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",[9] in a 1974 journal article.[14]

Other early versions (prior to 1982) of the hierarchy that refer to a data tier include those of Chinese-American geographer Yi-Fu Tuan[15][16] and sociologist-historian Daniel Bell.[15].[16] 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.[17][16]

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.[18] Zeleny "has frequently been credited with proposing the [representation of DIKW as a pyramid ]... although he actually made no reference to any such graphical model."[9]

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.[19] Subsequent authors and textbooks cite Ackoff's as the "original articulation"[1] of the hierarchy or otherwise credit Ackoff with its proposal.[20] Ackoff's version of the model includes an understanding tier (as Adler had, before him[9][21][22]), interposed between knowledge and wisdom. Although Ackoff did not present the hierarchy graphically, he has also been credited with its representation as a pyramid.[9][19]

In 1989, Bell Labs veteran Robert W. Lucky wrote about the four-tier "information hierarchy" in the form of a pyramid in his book Silicon Dreams.[10]

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.[9][23]

In 1994 Nathan Shedroff presented the DIKW hierarchy in an information design context which later appeared as a book chapter.[24]

Jennifer Rowley noted in 2007 that there was "little reference to wisdom" in discussion of the DIKW in recently published college textbooks,[1] and does not include wisdom in her own definitions following that research.[20] Meanwhile, Zins' extensive analysis of the conceptualizations of data, information, and knowledge, in his 2007 research study, makes no explicit commentary on wisdom,[2] although some of the citations included by Zins do make mention of the term.[25][26][27]

Rowley, following her study of DIKW definitions given in textbooks,[1] 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."[20] In Henry's early formulation of the hierarchy, data was simply defined as "merely raw facts",[14] while two recent texts define data as "chunks of facts about the state of the world"[29] and "material facts",[30] respectively.[9] Cleveland does not include an explicit data tier, but defines information as "the sum total of ... facts and ideas".[9][15]

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.

In the subjective domain, data are conceived of as "sensory stimuli, which we perceive through our senses",[2] or "signal readings", including "sensor and/or sensory readings of light, sound, smell, taste, and touch".[28] Others have argued that what Zins calls subjective data actually count as a "signal" tier (as had Boulding[9][13]), which precedes data in the DIKW chain.[8]

American information scientist Glynn Harmon defined data as "one or more kinds of energy waves or particles (light, heat, sound, force, electromagnetic) selected by a conscious organism or intelligent agent on the basis of a preexisting frame or inferential mechanism in the organism or agent."[31]

The meaning of sensory stimuli may also be thought of as subjective data:

Information is the meaning of these sensory stimuli (i.e., the empirical perception). For example, the noises that I hear are data. The meaning of these noises (e.g., a running car engine) 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 (i.e., the empirical perception). Accordingly, in the example above, the loud noises, as well as the perception of a running car engine, are data.[2] (Italics added. Bold in original.)

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".[2]

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.

Whether the DIKW definition of data is deemed to include Zins's subjective data (with or without meaning), data is consistently defined to include "symbols",[19][32] or "sets of signs that represent empirical stimuli or perceptions",[2] of "a property of an object, an event or of their environment".[20] Data, in this sense, are "recorded (captured or stored) symbols", including "words (text and/or verbal), numbers, diagrams, and images (still &/or video), 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."[28]

Boulding's version of DIKW explicitly named the level below the information tier message, distinguishing it from an underlying signal tier.[9][13] Debons and colleagues reverse this relationship, identifying an explicit symbol tier as one of several levels underlying data.[9][23]

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".[2]

Rowley, following her review of how DIKW is presented in textbooks,[1] 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 "[t]he difference between data and information is structural, not functional."[20]

In his formulation of the hierarchy, Henry defined information as "data that changes us",[9][14] 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".[9][15]

American educator Bob Boiko is more obscure, defining information only as "matter-of-fact".[9][30]

Information may be conceived of in DIKW models as: (i) universal, existing as symbols and signs; (ii) subjective, the meaning to which symbols attach; or (iii) both.[2] Examples of information as both symbol and meaning include:

• 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."[33]
• 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."[34]

Zeleny formerly described information as "know-what",[18] but has since refined this to differentiate between "what to have or to possess" (information) and "what to do, act or carry out" (wisdom). To this conceptualization of information, he also adds "why is", as distinct from "why do" (another aspect of wisdom). Zeleny further argues that there is no such thing as explicit knowledge, but rather that knowledge, once made explicit in symbolic form, becomes information.[4]

Mirroring the description of information as "organized or structured data", knowledge is sometimes described as:

• "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"[20]

One of Boulding's definitions for knowledge had been "a mental structure"[9][13] and Cleveland described knowledge as "the result of somebody applying the refiner's fire to [information], selecting and organizing what is useful to somebody".[9][15] A 2007 text describes knowledge as "information connected in relationships".[9][29]

Zeleny defines knowledge as "know-how"[4][18] (i.e., procedural knowledge), and also "know-who" and "know-when", each gained through "practical experience".[4] "Knowledge ... brings forth from the background of experience a coherent and self-consistent set of coordinated actions.".[9][18] Further, implicitly holding information as descriptive, Zeleny declares that "Knowledge is action, not a description of action."[4]

Ackoff, likewise, described knowledge as the "application of data and information", which "answers 'how' questions",[19][32] that is, "know-how".[20]

Meanwhile, textbooks discussing DIKW have been found to describe knowledge variously in terms of experience, skill, expertise or capability:

• "study and experience"
• "a mix of contextual information, expert opinion, skills and experience"
• "information combined with understanding and capability"
• "perception, skills, training, common sense and experience".[20]

Businessmen James Chisholm and Greg Warman characterize knowledge simply as "doing things right".[6]

Knowledge is sometimes described as "belief structuring" and "internalization with reference to cognitive frameworks".[20] One definition given by Boulding for knowledge was "the subjective 'perception of the world and one's place in it'",[9][13] while Zeleny's said that knowledge "should refer to an observer's distinction of 'objects' (wholes, unities)".[9][18]

Zins, likewise, found that knowledge is described in propositional terms, as justifiable beliefs (subjective domain, akin to tacit knowledge), and sometimes also as signs that represent such beliefs (universal/collective domain, akin to explicit knowledge). Zeleny has rejected the idea of explicit knowledge (as in Zins' universal knowledge), arguing that once made symbolic, knowledge becomes information.[4] Boiko appears to echo this sentiment, in his claim that "knowledge and wisdom can be information".[9][30]

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 (e.g., "every triangle has three sides"), religious knowledge (e.g., "God exists"), philosophical knowledge (e.g., "Cogito ergo sum"), 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: (1) the individual believe[s] that it is true, (2) S/he can justify it, and (3) It is true, or it [appears] to be true.[2] (Italics added. Bold in original.)

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".[9][30]

A flow diagram of the DIKW hierarchy

DIKW is a hierarchical model often depicted as a pyramid,[1][9] 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 (lower levels are focused on first), DIKW describes purported structural or functional relationships (lower levels comprise the material of higher levels). Both Zeleny and Ackoff have been credited with originating the pyramid representation,[9] although neither used a pyramid to present their ideas.[9][18][19]

DIKW has also been represented as a two-dimensional chart[6][36] or as one or more flow diagrams.[28] In such cases, the relationships between the elements may be presented as less hierarchical, with feedback loops and control relationships.

Debons and colleagues[23] may have been the first to "present the hierarchy graphically".[9]

Throughout the years many adaptations of the DIKW pyramid have been produced. One evolving adaptation, in use by knowledge managers in the United States Department of Defense, attempts to show the progression transforming data to information then knowledge and finally wisdom to enable effective decisions, as well as the activities involved to ultimately create shared understanding throughout the organization and managing decision risk.[37]

DoD DIKW Evolving Adaptation

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.[38]

Using advanced distributed simulation to support information, knowledge, and wisdom representation

The following example describes a military decision support system, but the architecture and underlying conceptual idea are transferable to other application domains:[38]

• 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.

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 (but not yet operationally used), 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.[38]