Hierarquia DIKW - Wikipédia, a enciclopédia livre

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DIKW é uma hierarquia informacional utilizada principalmente nos campos da Ciência da Informação e da Gestão do Conhecimento, onde cada camada acrescenta certos atributos sobre a anterior.

[editar] Características

Os seus componentes, em ordem crescente de importância e normalmente dispostos em um sistema de coordenadas cartesianas, são os seguintes:

* Dados (Data) é o nível mais básico;
* Informação (Information) acrescenta contexto e significado aos dados;
* Conhecimento (Knowledge) acrescenta a forma como usar adequadamente a informação;
* Sabedoria (Wisdow) acrescenta o entendimento de quando utilizá-los.

Desta forma, a hierarquia DIKW é um modelo teórico que se mostra útil na análise e no entendimento da importância e limites das atividades dos trabalhadores do conhecimento.

[editar] Ligações externas

* Data, Information, Knowledge, and Wisdom (em inglês)"

Artigos sobre DIKW

DIKW - Wikipedia, the free encyclopedia

DIKW - Wikipedia, the free encyclopedia: "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 data, information, knowledge, and wisdom. '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
[hide]

* 1 History
o 1.1 Information, Knowledge, Wisdom
o 1.2 Data, Information, Knowledge, Wisdom
o 1.3 Data, Information, Knowledge
* 2 Description
* 3 Data
o 3.1 Data as Fact
o 3.2 Data as Signal
o 3.3 Data as Symbol
* 4 Information
o 4.1 Structural vs. Functional
o 4.2 Symbolic vs. Subjective
* 5 Knowledge
o 5.1 Knowledge as Processed
o 5.2 Knowledge as Procedural
o 5.3 Knowledge as Propositional
* 6 Wisdom
* 7 Representations
* 8 Criticisms
* 9 References

[edit] 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 in embedded in the use of the acronym DIKW as a shorthand representation for the data-to-information-to-knowledge-to-wisdom transformation.'[7]

[edit] 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?

-- from T.S. Eliot, 'Choruses from 'The Rock''

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 to Eliot in his 1982 article discussing the hierarchy.[13][7]

[edit] 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, Checkoslovakian-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[8][9][7]), interposed between knowledge and wisdom. Although Ackoff did not present the hierarchy graphically, he has also been credited with its representation as a pyramid.[17][7].

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.[19][7].

[edit] 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'.[20][7]. 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's 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].

[edit] 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]

[edit] 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'.

[edit] 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'.[13][7]

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.

[edit] 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[20][7]), 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.

[edit] 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.[20][7] Debons and colleagues reverse this relationship, identifying an explicit symbol tier as one of several levels underlying data.[19][7].

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]

[edit] 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.'[26][7]

[edit] 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'[21][7], 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'.[13][7]

American educator Bob Boiko is more obscure, defining information only as 'matter-of-fact'.[27][7].

[edit] 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:

* 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 phenomena, represents both a process and a product; a cognitive/affective state, and the physical counterpart (product of) the cognitive/affective state.'[30]
* 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.'[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]

[edit] 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.[32][7]

[edit] Knowledge as Processed

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'[18]

One of Boulding's definitions for knowledge had been 'a mental structure'[20][7] and Cleveland described knowledge as 'the result of somebody applying the refiner's fire to [information], selecting and organizing what is useful to somebody'[13][7]. A recent text describes knowledge as 'information connected in relationships'.[26][7].

[edit] Knowledge as Procedural

Zeleny defines knowledge as 'know-how'[16][3] (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.'[16][7]. 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:

* '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'[18].

Businessmen James Chisholm and Greg Warman characterize knowledge simply as 'doing things right'.[5]

[edit] 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''[20][7], while Zeleny's said that knowledge 'should refer to an observer's distinction of 'objects' (wholes, unities)'[16][7].

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'.[27][7].

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'.[27][7].

[edit] 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'.[27][7]

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[17][7][29]. Adler had previously also included an understanding tier[9][8][7], 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'.[13][7] 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'[26][7].

[edit] Representations
A flow diagram of the DIKW hierarchy. Public domain.
It is requested that a diagram or diagrams be included in this article to improve its quality.
For more information, refer to discussion on this page and/or the listing at Wikipedia:Requested images. (January 2009)

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.[16][17][7].

DIKW has also been represented as a two-dimensional chart[33][5] 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].

[edit] 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 phenomena, 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 = MC2” (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'.[36][7].

Information processing theory argues that the physical world is made of information itself. Under this definition, data is either comprised 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]

[edit] References
Please add ISBNs for the books listed in this article or section.
Listing ISBNs makes it easier to conduct research. If the {{Cite book}} or {{citation}} templates are in use, you may add ISBNs automatically. Alternatively, you can add them manually or discuss this issue on the talk page. (January 2009)

1. ^ a b c d e f g h i j k Rowley, Jennifer (2007). 'The wisdom hierarchy: representations of the DIKW hierarchy'. Journal of Information Science 33 (2): 163-180. http://jis.sagepub.com/cgi/content/abstract/33/2/163.
2. ^ a b c d e f g h i j k l m n o p Zins, Chaim (22 January 2007). 'Conceptual Approaches for Defining Data, Information, and Knowledge'. Journal of the American Society for Information Science and Technology (Wiley Periodicals, Inc.) 58 (4): 479-493. doi:10.1002/asi. http://www.success.co.il/is/zins_definitions_dik.pdf. Retrieved on 7 January 2009.
3. ^ a b c d e f g h Zeleny, Milan (2005). Human Systems Management: Integrating Knowledge, Management and Systems. World Scientific. pp. 15-16. ISBN 9789810249137.
4. ^ Lievesley, Denise (September 2006). 'Data information knowledge chain'. Health Infomatics Now (Swindon: The British Computer Society) 1 (1): 14. http://www.bcs.org/server.php?show=ConWebDoc.7163. Retrieved on 8 January 2008.
5. ^ a b c d e Chisholm, James; Greg Warman (2007). 'Experiential Learning in Change Management'. in Silberman, Melvin L.. The Handbook of Experiential Learning. Jossey Bass. p. 321-40. ISBN 9780787982584.
6. ^ a b Choo, Chun Wei; Brian Detlor, Don Turnbull (September 2006). Web Work: Information Seeking and Knowledge Work on the World Wide Web. Kluwer Academic Publishers. p. 29-48. ISBN 978-0792364603.
7. ^ a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag ah ai aj ak al Wallace, Danny P. (2007). Knowledge Management: Historical and Cross-Disciplinary Themes. Libraries Unlimited. pp. 1-14. ISBN 9781591585022.
8. ^ a b c Adler, Mortimer Jerome (1970), The Time of Our Lives: The Ethics of Common Sense, Holt, Reinhart and Winston, p. 206, ISBN 9780030818363
9. ^ a b c Adler, Mortimer Jerome (1986), A Guidebook To Learning For The Lifelong Pursuit Of Wisdom, Collier Macmillan, p. 11, ISBN 9780025003408
10. ^ Eliot, T.S. (1934). ROCK: A Pageant Play Written For Performance at Sadler's Wells Theatre 28 May - 9 June 1934 On Behalf of the Forty-Five Churches Fund of the Diocese of London. London: Faber and Faber.
11. ^ Eliot, T.S. (c1964). Selected Poems. New York: Harcourt Brace Jovanovich.
12. ^ Zappa, Frank (1979). 'Packard Goose' (Song lyric). Joe's Garage: Act II & III (album).
13. ^ a b c d e f g Cleveland, Harlan (December 1982). 'Information as a Resource'. The Futurist: 34-39.
14. ^ a b c d e Sharma, Nikhil (4 February 2008). 'The Origin of the “Data Information Knowledge Wisdom” Hierarchy'. Retrieved on 7 January 2009.
15. ^ Cooley, Mike (1980). Architect or Bee?: The Human / Technology Relationship. Monroe: South End Press.
16. ^ a b c d e f g h Zeleny, Milan (1987). 'Management Support Systems: Towards Integrated Knowledge Management'. Human Systems Management 7 (1): 59–70.
17. ^ a b c d e f g Ackoff, Russell (1989), 'From Data to Wisdom', Journal of Applied Systems Analysis 16: 3-9
18. ^ a b c d e f g h i j k l m n o p q Rowley, Jennifer; Richard Hartley (2006). Organizing Knowledge: An Introduction to Managing Access to Information. Ashgate Publishing, Ltd.. pp. 5-6. ISBN 9780754644316.
19. ^ a b c Debons, Anthony; Ester Horne, Scott Cronenweth (1988). Information Science: An Integrated View. Boston: G.K. Hall. p. 5.
20. ^ a b c d e Boulding, Kenneth (1955). 'Notes on the Information Concept'. Exploration (Toronto) 6: 103-112. CP IV, pp. 21-32..
21. ^ a b c Henry, Nicholas L. (May/June 1974), 'Knowledge Management: A New Concern for Public Administration', Public Administration Review 34: 189
22. ^ Dodig-Crnkovi, Gordana, as cited in Zins, id., at pp. 482.
23. ^ Ess, Charles, as cited in Zins, id., at p. 482-83.
24. ^ Wormell, Irene, as cited in Zins, id., at p. 486.
25. ^ a b c d e Liew, Anthony (June 2007). 'Understanding Data, Information, Knowledge And Their Inter-Relationships'. Journal of Knowledge Management Practice 8 (2). http://www.tlainc.com/articl134.htm. Retrieved on 7 January 2009.
26. ^ a b c d Gamble, Paul R.; John Blackwell (educator) (2002). Knowledge Management: A State of the Art Guide. London: Kogan Page. p. 43.
27. ^ a b c d e Boiko, Bob (2005). Content Management Bible (2nd ed.). Indianapolis: Wiley. p. 57.
28. ^ Harmon, Glynn, as cited by Zins, id., at p. 483.
29. ^ a b c d e Bellinger, Gene; Durval Castro, Anthony Mills (2004). 'Data, Information, Knowledge, and Wisdom'. Retrieved on 7 January 2009.
30. ^ Debons, Anthony, as cited in Zins, id., at p. 482.
31. ^ Albrechtsen, Hanne, as cited in Zins, id., at p. 480.
32. ^ Davenport, Thomas H.; Laurence Prusack (1998). Working Knowledge: How Organizations Manage What They Know. Boston: Harvard Business School Press. pp. 5.
33. ^ Choo, Chun Wei (May 10, 2000). 'The Data-Information-Knowledge Continuum'. Web Work: Information Seeking and Knowledge Work on the World Wide Web. Retrieved on 9 January 2009.
34. ^ Capurro, Raphael, as cited in Zins, id., at p. 481
35. ^ Poli, Roberto, as cited in Zins, id., at p. 485.
36. ^ Dewey, John; Arthur F. Bentley (1949). Knowing and the Known. Boston: Beacon Press. pp. 58, 72-74.
37. ^ Frické, Martin (2008). 'The Knowledge Pyramid: A Critique of the DIKW Hierarchy' (preprint article). http://dlist.sir.arizona.edu/2327/01/The_Knowledge_Pyramid_DList.pdf"

Data, Information, Knowledge, & Wisdom

Data, Information, Knowledge, & Wisdom: "Data, Information, Knowledge, and Wisdom

by Gene Bellinger, Durval Castro, Anthony Mills

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:

1. Data: symbols

2. Information: data that are processed to be useful; provides answers to 'who', 'what', 'where', and 'when' questions

3. Knowledge: application of data and information; answers 'how' questions

4. Understanding: appreciation of 'why'

5. Wisdom: evaluated understanding.

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:

* Abugt dbesbt regtc uatn s uitrzt.
* ubtxte pstye ysote anet sser extess
* ibxtedstes bet3 ibtes otesb tapbesct ehracts

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:

* I have a box.
* The box is 3' wide, 3' deep, and 6' high.
* The box is very heavy.
* The box has a door on the front of it.
* When I open the box it has food in it.
* It is colder inside the box than it is outside.
* You usually find the box in the kitchen.
* There is a smaller compartment inside the box with ice in it.
* When you open the door the light comes on.
* When you move this box you usually find lots of dirt underneath it.
* Junk has a real habit of collecting on top of this box.

What is it?

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:

* Ackoff, R. L., 'From Data to Wisdom', Journal of Applies Systems Analysis, Volume 16, 1989 p 3-9.
* Gadomski, Adam Maria, Information, Preferences and Knowledge, An Interesting Evolution in Thought
* Sharma, Nikhil, The Origin of the Data Information Knowledge Wisdom Hierarchy

theWay of Systems * Feedback * Musings
Copyright © 2004 Gene Bellinger"

The origin of DIKW Hierarchy

The origin of DIKW Hierarchy:

The Origin of the “Data Information Knowledge Wisdom” Hierarchy
Nikhil Sharma
[Updated: February 4, 2008]

Image originally published in the December 1982 issue of THE FUTURIST. Used with permission from the World Future Society, 7910 Woodmont Avenue, Suite 450, Bethesda, Maryland 20814. Telephone: 301/656-8274; Fax: 301/951-0394; http://www.wfs.org

The Hierarchy

The Data Information Knowledge and Wisdom Hierarchy (DIKW) has been gaining popularity in many domains. In most Knowledge Management literature the hierarchy is often referred to as the "Knowledge Hierarchy" or the “Knowledge Pryamid”, while the “Information Science” domain refers to the same hierarchy as "Information Hierarchy" or “Information Pryamid” for obvious reasons. Often the choice between “Information” and “Knowledge” is based on what the particular profession believes to me manageable.

While there has been a lot of articulation of the hierarchy itself, the origins of this ubiquitous and frequently used hierarchy are largely unexplored. In this short piece we trace the trails of this hierarchy. Like an urban legend, it’s everywhere yet few know where it came from.

The Domains

While the domains of Information Science and Knowledge Management both refer to the DIKW hierarchy, they do not cross-reference. Thus there are two separate threads that lead to the origin of the hierarchy.

In Knowledge Management, Russell Ackoff is often cited as the initiator of the DIKW hierarchy. His 1988 Presidential Address to ISGSR is considered by many to be the earliest mention of the hierarchy. Ackoff’s presidential address was printed in a 1989 article "From Data to Wisdom" [1] and it does not cite any earlier sources of the hierarchy.

Searching for the orginis of the hierarchy in the Knowledge management domain, we find Milan Zeleny to be an earlier proponent of the hierarchy. In his article on “Management Support Systems” [2], Zeleny details out the DIKW hierarchy in 1987. Zeleny builds the knowledge hierarchy by equating Data, Information, Knowledge and Wisdom to various knowledge forms: “know-nothing”, “know-what”, “know-how” and “know-why” respectively. Yet, the trail stops again, Zeleny’s 1987 mention of the hierarchy is earlier than Ackoff’s 1989 address, but he also does not cite any earlier sources of the hierarchy. It can thus be argued that Zeleny was the first to mention the hierarchy in the field of Knowledge Management.

The domain of design has also drawn on and referred to the DIKW hierarchy. Almost at the same time as Milan Zeleny’s article, Michael Cooley’s book published in 1987: “Architecture or Bee?” [3], builds the DIKW hierarchy during his discussion of tacit knowledge and common sense. Once again no earlier work is cited or referred to by Cooley and trail of the origin has an ubrupt ending.

It is in Information Science domain that the trail can be picked up again. Here the hierarchy is mentioned as early as 1982, when Harlan Cleveland [4] wrote about it in a Futurist article. Cleveland’s article mentions the Information-Knowledge-Wisdom hierarchy in detail giving an example. What is different about this article from the ones mentioned above is that Cleveland points to the surprising origin of the hierarchy itself.

The Origin

Interestingly the first ever mention of the hierarchy is neither in the Knowledge Management field, nor the Information Science domain, but in an unexpected place: poetry. In his Futurist article, Cleveland cites T.S. Eliot as the person who suggested the hierarchy in the first place. Cleveland names it "the T.S. Eliot hierarchy". The poet T.S. Eliot was the first to mention the "DIKW hierarchy" without even calling it by that name. In 1934 Eliot wrote in "The Rock"[5]:

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?

This is the first vague mention of the hierarchy that was expanded by Cleveland. Though this is the first mention of the hierarchy in the arts, it is not the only one. Before management and information science caught on, Frank Zappa alluded to the hierarchy in 1979 [6]:

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.

Beyond Eliot’s hierarchy

In his Futurist article [4], Harlan Cleveland concedes that information scientists are “still struggling with the definitions of basic terms” of the hierarchy. He uses Elliot’s hierarchy as a starting point to explain the basic terms. Cleveland also agrees that there are many ways in which the elements of the hierarchy may be defined, yet universal agreement on them need not be a goal in itself. While Cleveland himself doesn’t add ‘Data’ to Eliot’s hierarchy he mentions Yi-Fu Tuan’s and Daniel Bell’s versions of the hierarchy in the article which includes “data” [4].

Russell Ackoff’s version of the DIKW hierarchy has another “layer” of “understanding” built in. Thus Ackoff’s hierarchy is Data-Information-Knowledge-Understanding & Wisdom. “Understanding” requires diagnosis and prescription, which Ackoff considers to be beyond “knowledge” but below “wisdom”. Discussing the temporal dimension of his version of the hierarchy, Ackoff points out that while information ages rapidly, knowledge has a longer life-span and understanding has only an aura of permanence. It is wisdom that he considers to be “permanent” in the true sense.

Zeleny also proposes additions to the DIKW hierarchy. According to him “enlightenment” should be on the top of the familiar DIKW framework [2]. Enlightenment, according to Zeleny (personal communication, October 29, 2004) “is not only answering or understanding why (wisdom), but attaining the sense of truth, the sense of right and wrong, and having it socially accepted, respected and sanctioned.”

Acknowledgements

George Furnas suggested this essay. Milan Zeleny, Adam Keen and Paul Link provided important feedback, pointers and references.

References:

1. Russell .L. Ackoff, "From Data to Wisdom," Journal of Applied Systems Analysis 16 (1989): 3-9.
2. Milan Zeleny, "Management Support Systems: Towards Integrated Knowledge Management," Human Systems Management 7, no 1 (1987): 59-70.
3. M. Cooley, Architecture or Bee? (London: The Hogarth Press, 1987).
4. Harland Cleveland, "Information as Resource," The Futurist, December 1982, 34-39.
   T.S. Eliot, The Rock (Faber & Faber 1934).
5. Frank Zappa, "Packard Goose" in album Joe's Garage: Act II & III (Tower Records, 1979).
   
   Contact me for comments & suggestions
   Nikhil Sharma, Doctoral Student, School Of Information, University of Michigan, Ann Arbor. (nsharma AT umich DOT edu)