DIKW pyramid

[5] In the latter years of that decade, interest in the models grew after explicit presentations and discussions, including from Milan Zeleny, Russell Ackoff, and Robert W.

[10][needs update] Zins work, published in 2007—from studies in 2003-2005 that documented "130 definitions of data, information, and knowledge formulated by 45 scholars", published in 2007—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.

Finally, using intelligent software agents to continually observe the battle sphere, apply models and simulations to analyze 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.

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.

[14]Many authors think that the idea of the DIKW relationship originated from two lines in the poem "Choruses", by T. S. Eliot, that appeared in the pageant play The Rock, in 1934:[8] Where is the wisdom we have lost in knowledge?

[20] In 1955, English-American economist and educator Kenneth Boulding presented a variation on the hierarchy consisting of "signals, messages, information, and knowledge".

[14][21] 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",[14] in a 1974 journal article.

[22] Other early versions (prior to 1982) of the hierarchy that refer to a data tier include those of Chinese-American geographer Yi-Fu Tuan[23][verification needed][24] and sociologist-historian Daniel Bell.

[24] In 1980, Irish-born engineer Mike Cooley invoked the same hierarchy in his critique of automation and computerization, in his book Architect or Bee?

[25][verification needed][24] Thereafter, in 1987, Czechoslovakia-born educator Milan Zeleny mapped the components of the hierarchy to knowledge forms: know-nothing, know-what, know-how, and know-why.

[6][verification needed] 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.

"[14] 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.

[14][7] 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.

[8] 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.

[26] Meanwhile, Chaim Zins' extensive primary research analysis conceptualizing data, information, and knowledge in that same year makes no explicit comment regarding wisdom, although citations included by Zins do make mention of the term (e.g., Dodig-Crnković, Ess, and Wormell cited therein),[9]: 482f, 486 In 2013, Baskarada and Koronios attempted a relatively thorough review of the definitions of individual components, to that date.

[2] In the context of DIKW-type models, data is conceived, per Zins' 2007 formulation, as being composed of symbols or signs, representing stimuli or signals,[9] that, in Rowley words (in 2007), are "of no use until ... in a usable (that is, relevant) form".

[14] Rowley, following her 2007 study of DIKW definitions given in textbooks,[1] separately 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.

[14][23] 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,[according to whom?]

[12] 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" (e.g., Harmon, as cited by Zins)[9]: 483

[9] 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.

[26] Data, in this sense, as described by Liew, likewise in 2007, are "recorded (captured or stored) symbols", including "words (text and/or verbal), numbers, diagrams, and images (still and/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.

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".

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".

[3] American philosophers John Dewey and Arthur Bentley, in their 1949 book Knowing and the Known, argued that "knowledge" is "a vague word", and presented a view, distinct but foreshadowing DIKW-type models, that outlined nineteen "terminological guide-posts".

[citation needed] As such, the knowledge component of DIKW-type models is generally understood to be a concept elusive and difficult to define.

"[3] Ackoff, likewise, described knowledge as the "application of data and information", which "answers 'how' questions",[7][verification needed][32] that is, in Rowley's view, "know-how".

[14][23] Other authors have characterized wisdom as "knowing the right things to do"[11] and "the ability to make sound judgments and decisions apparently without thought".

[9][page needed]Alternatively, in Zins' 2007 analysis referencing Roberto Poli, information and knowledge might be seen as synonyms.

"[36] He points out there is a discontinuity between Data and Information (which are stored in computers), versus Knowledge and Wisdom (which are human endeavours).