Category: Critical thinking
There is probably no segment of activity in the world attracting as much attention at present as that of knowledge management. Yet as I entered this arena of activity I quickly found there didn't seem to be a wealth of sources that seemed to make sense in terms of defining what knowledge actually was, and how was it differentiated from data, information, and wisdom. What follows is the current level of understanding I have been able to piece together regarding data, information, knowledge, and wisdom. I figured to understand one of them I had to understand all of them.
According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories:
Ackoff indicates that the first four categories relate to the past; they deal with what has been or what is known. Only the fifth category, wisdom, deals with the future because it incorporates vision and design. With wisdom, people can create the future rather than just grasp the present and past. But achieving wisdom isn't easy; people must move successively through the other categories.
A further elaboration of Ackoff's definitions follows:
Data. data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.
Information. information is data that has been given meaning by way of relational connection. This "meaning" can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.
Knowledge. knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone "memorizes" information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the "times table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the times table). But when asked what is "1267 x 300", they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level. understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.
Understanding. understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning" and "memorizing". People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.
Wisdom. wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine).
Personally I contend that the sequence is a bit less involved than described by Ackoff. The following diagram represents the transitions from data, to information, to knowledge, and finally to wisdom, and it is understanding that support the transition from each stage to the next. Understanding is not a separate level of its own.
Data represents a fact or statement of event without relation to other things.
Ex: It is raining.
Information embodies the understanding of a relationship of some sort, possibly cause and effect.
Ex: The temperature dropped 15 degrees and then it started raining.
Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next.
Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains.
Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic.
Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.
Yet, there is still a question regarding when is a pattern knowledge and when is it noise. Consider the following:
It is quite likely this sequence represents 100% novelty, which means it's equivalent to noise. There is no foundation for you to connect with the pattern, yet to me the statements are quite meaningful as I understand the translation with reveals they are in fact Newton's 3 laws of motion. Is something knowledge if you can't understand it?
Now consider the following:
A refrigerator. You knew that, right? At some point in the sequence you connected with the pattern and understood it was a description of a refrigerator. From that point on each statement only added confirmation to your understanding.
If you lived in a society that had never seen a refrigerator you might still be scratching your head as to what the sequence of statements referred to.
Also, realize that I could have provided you with the above statements in any order and still at some point the pattern would have connected. When the pattern connected the sequence of statements represented knowledge to you. To me all the statements convey nothing as they are simply 100% confirmation of what I already knew as I knew what I was describing even before I started.References:
in my continuing quest to understand my own field, I've previously blogged about definitions of informatics. one such definition is that it is "the process of, or the study of the process of, transforming data into information." of course, this gets into tricky question about what constitutes "data" and "information," but for now I'll leave that to this rather old post .
turns out, some folks have argued that this data-to-information process is only one step in a larger trajectory. generally this goes somethings like the following (partially cribbed from the DIKW Wikipedia article. partially from an alternative hierarchy. and partially my synopsis/interpretation):
data - representations of direct observations of the world.
information - data assembled to answer specific "who," "what," "when," and "where" style questions. I might call this, "data made meaningful."
knowledge - the application of information for the accomplishment of a specific purpose, answers "how" questions. I might call this, "data and information made useful."
wisdom - understanding the significance and value of knowledge, answers "why" questions. I might call this, "data, information, and knowledge made valuable."
now, I don't think this is a particularly good categorization of how the world works, or how we work in the world. the Wikipedia article linked above has some pretty good discussion about how these various terms (data, information, knowledge, wisdom, etc.) might be so ambiguous, or polysemous, as to be not particularly useful. however, I think these sorts of hierarchies and typologies are interesting from a rhetorical/critical standpoint, as a way of understanding how different groups of people talk about and thinking about such topics as information and understanding.
this interlude was prompted by the ever-provocative Virtual Politik. now, back to work (in this case, grant writing).
musings on HCI, AI, social media, design, and other researchly things. also with occassional rants about the environment, organic food, nutrition, fitness, and whatever else happens to cross my keyboards.About Me
post-doc at the information science department at cornell. doing research on HCI, social media, computational linguistics, and technologies to foster critical thinking. a philosopher masquerading as a sociologist masquerading as a computer scientist.Previous
The "DIKW Hierarchy", also known variously as the "Wisdom Hierarchy", the "Knowledge Hierarchy", the "Information Hierarchy", and the "Knowledge Pyramid", [ 1 ] refers loosely to a class of models [ 2 ] for representing purported structural and/or functional relationships between d ata, i nformation, k nowledge, and w isdom. "Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge". [ 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. In addition to a hierarchy and a pyramid, the DIKW model has also been characterized as a chain, [ 3 ] [ 4 ] as a framework, [ 5 ] and as a continuum. [ 6 ]Contents History
"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." [ 7 ]Information, Knowledge, Wisdom
Educator Danny P. Wallace traces the earliest conception of a hierarchy involving knowledge and wisdom to 1927 and 1941, in early works of American philosopher Mortimer Adler. later to be formalized as "goods of the mind", [ 7 ] in 1970--"knowledge, understanding, prudence, and even a modicum of wisdom" [ 8 ] --and later revised, in 1986, as follows: "As health, strength, vigor and vitality are bodily goods, so information, knowledge, understanding and wisdom are goods of the mind - goods that acquired, perfect it." [ 9 ]
The earliest formalized distinction between wisdom, knowledge, and information may have been made by poet and playwright T.S. Eliot [ 10 ] [ 11 ] :
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?
Nearly half a century later, American composer Frank Zappa articulated an extended version of the information-knowledge-wisdom hierarchy [ 12 ] :
Information is not knowledge,
Knowledge is not wisdom,
Wisdom is not truth,
Truth is not beauty,
Beauty is not love,
Love is not music,
and Music is THE BEST.
-- from Frank Zappa, "Packard Goose"
Thereafter, American author and educator Harlan Cleveland cited Eliot in his 1982 article discussing the hierarchy. [ 7 ] [ 13 ]Data, Information, Knowledge, Wisdom
Other early versions (prior to 1982) of the hierarchy that refer to a data tier include those of Chinese-American geographer Yi-Fu Tuan [ 13 ] [ verification needed ] [ 14 ] and sociologist-historian Daniel Bell. [ 13 ] [ verification needed ]. [ 14 ] 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. [ 15 ] [ verification needed ] [ 14 ]
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. [ 16 ] [ 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." [ 7 ]
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. [ 17 ] Subsequent authors and textbooks cite Ackoff's as the "original articulation" [ 1 ] of the hierarchy or otherwise credit Ackoff with its proposal. [ 18 ] Ackoff's version of the model includes an understanding tier (as Adler had, before him [ 7 ] [ 8 ] [ 9 ] ), interposed between knowledge and wisdom. Although Ackoff did not present the hierarchy graphically, he has also been credited with its representation as a pyramid. [ 7 ] [ 17 ]
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. [ 7 ] [ 19 ]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". [ 7 ] [ 20 ] 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", [ 7 ] in a 1974 journal article. [ 21 ]
Jennifer Rowley notes that there is "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. [ 18 ] Meanwhile, Zins' extensive analysis of the conceptualizations of data, information, and knowledge, in his recent research study, makes no explicit commentary on wisdom, [ 2 ] although some of the citations included by Zins do make mention of the term. [ 22 ] [ 23 ] [ 24 ]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". [ 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 ] [ 25 ]
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 (what Zins terms, "universal" or "collective") 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 (subjective) rather than an external (universal–collective) phenomenon. [ 2 ]Data
In the context of DIKW, data is conceived of as symbols or signs. representing stimuli or signals, [ 2 ] that are "of no use until. in a usable (that is, relevant) form". [ 18 ] Zeleny characterized this non-usable characteristic of data as "know-nothing" [ 16 ] [ verification needed ]. [ 14 ]
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. [ 2 ] Where universal data. for Zins, are "the product of observation " [ 18 ] (italics in original), subjective data are the observations. This distinct is often obscured in definitions of data in terms of "facts ".Data as Fact
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." [ 18 ] In Henry's early formulation of the hierarchy, data was simply defined as "merely raw facts". [ 21 ] while two recent texts define data as "chunks of facts about the state of the world" [ 26 ] and "material facts", [ 27 ] respectively. [ 7 ] Cleveland does not include an explicit data tier, but defines information as "the sum total of. facts and ideas". [ 7 ] [ 13 ]
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", [ 2 ] or "signal readings", including "sensor and/or sensory readings of light, sound, smell, taste, and touch". [ 25 ] Others have argued that what Zins calls subjective data actually count as a "signal" tier (as had Boulding [ 7 ] [ 20 ] ), which precedes data in the DIKW chain. [ 6 ]
American information scientist Glynn Harmon defines 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." [ 28 ]
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.Data as Symbol
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", [ 17 ] [ 29 ] or "sets of signs that represent empirical stimuli or perceptions", [ 2 ] of "a property of an object, an event or of their environment". [ 18 ] 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 ." [ 25 ]
Boulding's version of DIKW explicitly named the level below the information tier message. distinguishing it from an underlying signal tier. [ 7 ] [ 20 ] Debons and colleagues reverse this relationship, identifying an explicit symbol tier as one of several levels underlying data. [ 7 ] [ 19 ]
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 ]Information
In the context of DIKW, information meets the definition for knowledge by description ("information is contained in descriptions [ 18 ] [Italics in original]), and is differentiated from data in that it is "useful". "Information is inferred from data", [ 18 ] in the process of answering interrogative questions (e.g.. "who", "what", "where", "how many", "when"), [ 17 ] [ 18 ] thereby making the data useful [ 29 ] for "decisions and/or action". [ 25 ] "Classically," states a recent text, "information is defined as data that are endowed with meaning and purpose." [ 7 ] [ 26 ]Structural vs. Functional
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." [ 18 ]
In his formulation of the hierarchy, Henry defined information as "data that changes us", [ 7 ] [ 21 ] 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". [ 7 ] [ 13 ]
American educator Bob Boiko is more obscure, defining information only as "matter-of-fact ". [ 7 ] [ 27 ]Symbolic vs. Subjective
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:
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." [ 30 ]
understood/misunderstood by the listener/reader." [ 31 ]
Zeleny formerly described information as "know-what" [ 16 ] [ citation needed ]. 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. [ 3 ]Knowledge
The knowledge component of DIKW "is generally agreed to be an elusive concept which is difficult to define. Knowledge is typically defined with reference to information." [ 18 ] 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, [ 2 ] while Zeleny has asserted that to capture knowledge in symbolic form is to make it into information, i.e.. that "All knowledge is tacit ". [ 3 ]
"One of the most frequently quoted definitions" [ 7 ] 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. [ 7 ] [ 32 ]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" [ 7 ] [ 20 ] and Cleveland described knowledge as "the result of somebody applying the refiner's fire to [information], selecting and organizing what is useful to somebody". [ 7 ] [ 13 ] A recent text describes knowledge as "information connected in relationships". [ 7 ] [ 26 ]Knowledge as Procedural
Zeleny defines knowledge as "know-how" [ 3 ] [ 16 ] (i.e.. procedural knowledge ), and also "know-who" and "know-when", each gained through "practical experience". [ 3 ] "Knowledge. brings forth from the background of experience a coherent and self-consistent set of coordinated actions.". [ 7 ] [ 16 ] Further, implicitly holding information as descriptive, Zeleny declares that "Knowledge is action, not a description of action." [ 3 ]
Ackoff, likewise, described knowledge as the "application of data and information", which "answers 'how' questions" [ 17 ] [ verification needed ]. [ 29 ] that is, "know-how". [ 18 ]
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". [ 5 ]Knowledge as Propositional
Knowledge is sometimes described as "belief structuring" and "internalization with reference to cognitive fameworks". [ 18 ] One definition given by Boulding for knowledge was "the subjective 'perception of the world and one's place in it'", [ 7 ] [ 20 ] while Zeleny's said that knowledge "should refer to an observer's distinction of 'objects ' (wholes, unities)". [ 7 ] [ 16 ]
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. [ 3 ] Boiko appears to echo this sentiment, in his claim that "knowledge and wisdom can be information". [ 7 ] [ 27 ]
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 discourse and justification, when he defined knowledge as "a matter of dispute". [ 7 ] [ 27 ]Wisdom
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". [ 7 ] [ 27 ]
Zeleny described wisdom as "know-why", [ 16 ] 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"). [ 3 ] According to University of Michigan Ph.D. candidate Nikhil Sharma. Zeleny has argued for a tier to the model beyond wisdom, termed "enlightenment". [ 14 ]
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. [ 7 ] [ 17 ] [ 29 ] Adler had previously also included an understanding tier, [ 7 ] [ 8 ] [ 9 ] while other authors have depicted understanding as a dimension in relation to which DIKW is plotted. [ 5 ] [ 29 ] Rowley attributes the following definition of wisdom to Ackoff:
Wisdom is the ability to increase effectiveness. Wisdom adds value, which requires the mental function that we call judgment. The ethical and aesthetic values that this implies are inherent to the actor and are unique and personal. [ 18 ]
Cleveland described wisdom simply as "integrated knowledge--information made super-useful". [ 7 ] [ 13 ] Other authors have characterized wisdom as "knowing the right things to do" [ 5 ] and "the ability to make sound judgments and decisions apparently without thought". [ 7 ] [ 26 ]Representations
A flow diagram of the DIKW hierarchy.
DIKW is a hierarchical model often depicted as a pyramid, [ 1 ] [ 7 ] 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, [ 7 ] although neither used a pyramid to present their ideas. [ 7 ] [ 16 ] [ 17 ]
DIKW has also been represented as a two-dimensional chart [ 5 ] [ 33 ] or as one or more flow diagrams. [ 25 ] In such cases, the relationships between the elements may be presented as less hierarchical, with feedback loops and control relationships.
Debons and colleagues [ 19 ] may have been the first to "present the hierarchy graphically". [ 7 ]Criticisms
Raphael Capurro, 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 (psychic/social) system from its ‘world’ on the basis of communication". As such, any impression of a logical hierarchy between these concepts "is a fairytale". [ 34 ]
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.
[I]s Albert Einstein’s famous equation “E = MC 2 ” (which is printed on my computer screen, and is definitely separated from any human mind) information or knowledge? Is “2 + 2 = 4” information or knowledge? [ 2 ]
Alternatively, information and knowledge might be seen as synonyms. [ 35 ] 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. [ 2 ] Rowley echoes this point in arguing that, where definitions of knowledge may disagree, "[t]hese various perspectives all take as their point of departure the relationship between data, information and knowledge." [ 18 ]
American philosophers John Dewey and Arthur Bentley, arguing that "knowledge" was "a vague word", presented a complex alternative to DIKW including some nineteen "terminological guide-posts". [ 7 ] [ 36 ]
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 submitted an article critiquing the DIKW hierarchy for publication, 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. [ 37 ]References External links
I know I have written about the data-information-knowledge hierarchy in the past. Many people still use it as a metaphor, but now it seems that it has outlived its usefulness. I came across it again in a new-to-me blog from Phil Green at Inmagic, Social media is challenging notions of the DIKW hierarchy. One of the biggest concerns in the hierarchical view of DIKW is that it reduces these human concepts to "objects" upon which computers can try to act. And it reduces human behavior down to computation, which isn't the case. This is also why it has been popular with the KM-as-technology lines of discussion.
The DIKW model is an easy way to describe the relationship between these concepts, but it breaks down when you put more critical thought into the question. The whole idea that one can be drawn from the other becomes difficult when you consider the connections of experience, domains, language, culture, social networks and the rest of human behavior. I left a comment on his blog that says as much too.
Dave Snowden - who has been rather vociferous in his condemnation of the DIKW model - wrote recently that It's information to date we need, not DIKW. And I like the simplified version that Eli Goldratt uses in The Haystack Syndrome. Information is the "answer to the question asked." If you have no question, there is no context or meaning, thus no information. And this handily avoids the question of knowledge, let alone wisdom.
As far as the proposed topic of Phil's post itself, there is one paragraph where he suggests the DIKW hierarchy metaphor is challenged by social media - and that he will be writing more in the future:
The DIKW model is a uniquely relevant topic as social technologies take hold and challenge not only the relationships between data, information, and knowledge within enterprise organizations, but also how information and knowledge is captured and transferred amongst your staff.
This is something that could lead down an interesting path. Maybe it will help develop a new metaphor for "what is knowledge" that doesn't give us such a simplistic, computational view of the world.
p.s. I note that the title of this post was also used by Steve Major in May, but I swear I've heard versions of this before.
[Photo: "Pirámide de Kefren - Khafre's pyramid" by Xavier Fargas ]2 Comment(s)
Mike Sivertsen said:
All good points Jack and areas I've considered and written about in depth while completing a Masters degree in knowledge management in 2009. My Capstone paper and a subsequent presentation at a national conference in April 2010 (see below) explored David Snowden's Cynefin framework and discussed the utter failings of models in two important areas: the U.S. financial system and 'man-made global warming.' In both of these areas the complete failure of the predictive capability of the models should convince decision-makers to abandon models when working in a complex adaptive system (CAS) where the only model is the system itself (as Snowden has said). Unfortunately, models continue to look 'sophisticated' and 'smart-looking' and can easily become a surrogate for real thought or an ideological agenda. Acknowledging the inherent messiness of economic, ecological, or human states is uncomfortable as it means sharing decision-making power. Misplaced ordered system approaches in which models are used in a vain attempt to understand or predict a CAS results in ecological damage and human suffering. Conversely, understanding the dangers of entrained thinking and embracing the value of a CAS with weak constraints can improve systems - as indicated by events surrounding the Longitude Prize, Netflix Prize and the traders vs. Marines competition - all covered in my presentation.
Abstract and bio from the Lean Software and Systems Conference, April 2010
Scroll down to: "Cognitive Kanban: Improving Decisions in a Complex World, " at this page for a richly annotated PDF in a "Beyond Bullet Points" format and an accompanying MP3 podcast.
Not that I was worried about modeling at this level, I wonder about the idea of small models of very specific elements of the larger system - rather than a massive model - could be more effective in these complex situations. I'm thinking of the basic model of flocking behavior: if you model one bird with some simple rules, the appearance of the V-shaped flying formation emerges from the models interacting together.