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Complete a 1-2 synthesis of Rowley, 2006 and critique the DIKW frameworkBelow I have attached the following article you are going to review I will also attach a separate file on the specific instructions you must follow for reviewing this article please follow all instructions as they are very specific and critical.THE ARTICLE REVIEW IS 1-2 PAGES LONG, WRITTEN IN CHICAGO STYLE, 12 POINT FONT, DOUBLE SPACED WITH ONE INCH MARGINS ON ALL SIDES.THERE SHOULD ALSO BE PAGE NUMBERS THAT START ON THE FIRST PAGE AND ARE PUT IN THE UPPER RIGHT CORNER. PLEASE BE SURE TO READ ALL INSTRUCTIONS BEFORE WRITING THE ARTICLE REVIEW.
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The wisdom hierarchy:
representations of the
DIKW hierarchy
Jennifer Rowley
Bangor Business School, University of Wales, Bangor, UK
Received 30 January 2006
Revised 25 May 2006
Abstract.
This paper revisits the data–information–knowledge–wisdom (DIKW) hierarchy by examining the articulation
of the hierarchy in a number of widely read textbooks, and analysing their statements about the nature of data,
information, knowledge, and wisdom. The hierarchy referred to variously as the ‘Knowledge Hierarchy’, the
‘Information Hierarchy’ and the ‘Knowledge Pyramid’ is one of the fundamental, widely recognized and
‘taken-for-granted’ models in the information and knowledge literatures. It 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. After revisiting Ackoff’s original articulation of the hierarchy, definitions of data, information, knowledge and wisdom
as articulated in recent textbooks in information systems and knowledge management are reviewed and
assessed, in pursuit of a consensus on definitions and transformation processes. This process brings to the
surface the extent of agreement and dissent in relation to these definitions, and provides a basis for a discussion as to whether these articulations present an adequate distinction between data, information, and
knowledge. Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge, but there is less consensus in the description of the processes that transform elements lower in the hierarchy into those above them, leading to a lack of definitional clarity. In addition, there
is limited reference to wisdom in these texts.
Keywords: DIKW hierarchy; wisdom hierarchy; wisdom; knowledge management; wisdom management
1.
Introduction
The data–information–knowledge–wisdom hierarchy (DIKW), referred to variously as the
‘Knowledge Hierarchy’, the ‘Information Hierarchy’ and the ‘Knowledge Pyramid’ is one of the
fundamental, widely recognized and ‘taken-for-granted’ models in the information and knowl-
Correspondence to: Jennifer Rowley, Bangor Business School, University of Wales, Bangor, Gwynedd, LL57
2DG, UK. E-mail: [email protected]
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163
J. Rowley
edge literatures. It is often quoted, or used implicitly in definitions of data, information and
knowledge in textbooks in information management, information systems and knowledge management. The hierarchy is used to contextualize data, information, knowledge, and sometimes
wisdom, with respect to one another and to identify and describe the processes involved in the
transformation of an entity at a lower level in the hierarchy (e.g. data) to an entity at a higher level
in the hierarchy (e.g. information). The implicit assumption is that data can be used to create
information; information can be used to create knowledge, and knowledge can be used to create
wisdom. As Ackoff [1], whose paper is often cited when the DIKW hierarchy is quoted, explains,
each of the higher types in the hierarchy ‘includes the categories that fall below it’ (p.3).
The definitional role of the DIKW hierarchy positions it as a central model of information management, information systems and knowledge management. Yet, whilst there has over the years been
significant debate about related issues such as the nature and definition of information, both before
and since Ackoff’s paper, e.g. [2–11], and more recently considerable focus on the definition of
knowledge, e.g. [12–20], there has been:
• little direct discussion of the DIKW hierarchy itself, its meaning and contribution; and
• limited discussion of the nature of wisdom, and even less discussion of the organizational
processes that contribute to the cultivation of wisdom.
The objective of this paper then is to revisit the DIKW hierarchy, by examining the articulation of
the hierarchy in a number of widely read textbooks, and to analyse their statements about the nature
of data, information, knowledge, and wisdom. This paper is a theoretical paper designed to open
debate, promote reflection, and lift the discussion to wisdom from where it is languishing currently at
the level of knowledge. An improved appreciation of the relationships between knowledge and wisdom, as well as the ‘foundational concepts’ of data and information, may provide a context for achieving more convincing success in knowledge management, and more importantly organizational
achievement. This paper does not seek to make a broader theoretical contribution to the philosophical debates about the nature of information or knowledge advanced variously in the literatures of information philosophy and knowledge management. Rather its focus is on popular articulations of the
hierarchy to which students and professionals are exposed. Shenton [21] suggests that the research
subject’s and the professional’s notion of ‘information’ are a critical factor in the study of information
behaviour. The pragmatic approach adopted in this article therefore has equal, although different, relevance for information practice and research as do the more numerous philosophical debates.
The preoccupation with information and knowledge has led to the DIKW hierarchy being called
respectively the information hierarchy or the knowledge hierarchy. Here we refer to the DIKW hierarchy as the wisdom hierarchy, for two reasons:
Fig. 1.
The DIKW hierarchy.
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J. Rowley
• Wisdom is identified as the pinnacle of the hierarchy.
• One of our objectives in revisiting the DIKW hierarchy is to further illuminate the notion of ‘wisdom’.
This article, then, starts with a section that outlines the wider literature that explores the nature
of information and knowledge, and then revisits and summarizes Ackoff’s original articulation of
the hierarchy. Next, definitions of data, information, knowledge and wisdom as articulated in recent
textbooks in information systems and knowledge management are reviewed and assessed, in pursuit of a consensus on definitions and transformation processes. This process brings to the surface
the extent of agreement and dissent in relation to these definitions, and provides a basis for a discussion as to whether these articulations present an adequate distinction between data, information
and knowledge. In addition, there is limited reference to wisdom in these texts. The article concludes with suggestions for further theoretical development.
2.
Theoretical context
The original formulation of the DIKW hierarchy will have been informed by theoretical discussion on
the nature of information and knowledge. Whilst the purpose of this paper is to present an analysis
based on ‘popular’ articulation and definitions of data, information, knowledge and wisdom, and the
relationships between these, it is useful to outline briefly the range of the theoretical debates that
underlie and inform these more popular representations. The theoretical and philosophical discussion
has two major branches: information philosophy, focusing on the nature of information; and knowledge management, which contributes to notions of knowledge. Whilst these fields are distinct they do
share some common foundations, and since some authors argue either that information and knowledge are the same thing, or that they are used interchangeably [22–24], it may be difficult to justify any
discussion of information that does not also explore knowledge and vice versa. Choo suggests that ‘the
knowing organisation represents an information-based view of organisations’ [25, p. 1].
The essential nature of information, since it is fundamental to our existence, has been considered
by many disciplines, including communications theory, library and information science, information
systems, cognitive science, and organization science [5]. This has generated multiple perspectives on
the nature of information. Floridi suggests that ‘Of our mundane and technical concepts information
is currently one of the most important, most widely used and least understood.’ [6, p. 459] He identifies six approaches to the definition of information, respectively: the communication theory approach,
the probabilistic approach, the modal approach, the systemic approach, the inferential approach, and
the semantic approach. Recently, there has been a renewed interest in this area arising out of the formulation of the new discipline of the philosophy of information [6–10]. Information philosophy
focuses on ‘the critical investigation of the conceptual nature and basic principles of information,
including its dynamic (especially computation and information flow)’ [7, p. 555]. Contributors to this
debate agree that the word information has been given different meaning by different writers, and that
consensus on the meaning of the word ‘information’ has not been achieved [26].
Debates about the nature of knowledge are equally longstanding, and have also gathered momentum in recent years with the blossoming of the discipline of knowledge management. Plato [27] first
defined knowledge as ‘justified true belief’ and this concept has been debated over the centuries by
Aristotle [28], Descartes [29], Kant [30], Polanyi [31] and others. Kakabadse et al. [19], drawing on these
debates, suggest that knowledge ‘can be conceived as information put to productive use’. Knowledge
management, like information philosophy, has been influenced by a variety of disciplines, including:
philosophy, cognitive science, social science, management science, information science, knowledge
engineering, artificial intelligence, and economics. Kakabadse et al. [19] propose five different knowledge management perspectives each of which takes a different stance on the nature of knowledge and
knowledge processes: philosophy-based, cognitive, network, community, and quantum.
To conclude and lead into our more specific exploration of the literature on the DIKW hierarchy,
both the information philosophy and knowledge management literatures are longstanding, and offer
multiple perspectives on the definition of information and knowledge. Some contributions also
Journal of Information Science, 33 (2) 2007, pp. 163–180 © CILIP, DOI: 10.1177/0165551506070706
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J. Rowley
explore the nature of data and wisdom, but much of the discussion is focused on one of the elements
in the DIKW hierarchy rather than on all of the elements, and the relationship between them.
3.
The origins of the wisdom hierarchy
Many authors agree that the first appearance of the hierarchy was in T.S. Eliot’s poem The Rock in
1934 [32]. This poem contains the following lines:
Where is the wisdom that we have lost in knowledge?
Where is the knowledge that we have lost in information?
In more recent literature, authors often cite Ackoff’s 1989 paper as a source for the hierarchy.
Ackoff’s article, entitled From data to wisdom, proposed a hierarchy with the following levels: data,
information, knowledge, understanding and wisdom. Ackoff included understanding in his hierarchy, but more recent commentators have disputed that understanding is a separate level.
Ackoff defines data, information, knowledge, understanding, intelligence and wisdom and
explores the processes associated with the transformation between these elements. Most of these
definitions and processes are described from an information systems perspective, despite Ackoff’s
initial description of the types in the hierarchy as content of the human mind.
Wisdom is located at the top of a hierarchy of types […] Descending from wisdom there are understanding,
knowledge, information, and, at the bottom, data. Each of these includes the categories that fall below it – for
example, there can be no wisdom without understanding and no understanding without knowledge [1, p. 3].
Ackoff offers the following definitions of data, information, knowledge and wisdom, and their
associated transformation processes:
• Data are defined as symbols that represent properties of objects, events and their environment.
They are the products of observation. But are of no use until they are in a useable (i.e. relevant)
form. The difference between data and information is functional, not structural.
• Information is contained in descriptions, answers to questions that begin with such words as
who, what, when and how many. Information systems generate, store, retrieve and process data.
Information is inferred from data.
• Knowledge is know-how, and is what makes possible the transformation of information into
instructions. Knowledge can be obtained either by transmission from another who has it, by
instruction, or by extracting it from experience.
• Intelligence is the ability to increase efficiency.
• Wisdom is the ability to increase effectiveness. Wisdom adds value, which requires the mental
function that we call judgement. The ethical and aesthetic values that this implies are inherent
to the actor and are unique and personal.
Ackoff’s article is not the only early mention of the hierarchy. Cleveland [33] makes an early mention of the hierarchy which is to be found in the information science literature. At around the same
time as Ackoff, Zeleny [34] also discusses the DIKW hierarchy, and proposes an additional level,
enlightenment, at the top of the hierarchy. Zeleny’s model is compared with Ackoff’s in Table 1.
Enlightenment is not only answering or understanding why (which he defines as wisdom), but
going further and attaining the sense of truth, the sense of right and wrong, and having it socially
accepted, respected and sanctioned. Also Cooley [35] builds the DIKW hierarchy during his discussion of tacit knowledge and common sense.
More recently, Bellinger et al. [36] have elaborated further on Ackoff’s exposition, suggesting that
understanding is not a separate level, but rather that understanding supports the transition from
each stage to the next. They suggest that moving from data to information involves ‘understanding
relations’, moving from information to knowledge involves ‘understanding patterns’, and moving
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Table 1
Comparing Ackoff’s and Zeleny’s definitions of data, information, knowledge and wisdom
Zeleny [34]
Ackoff [1]
Data
Know nothing
Symbols
Information
Know what
Data that are processed to be useful; provides
answers to who, what, where and when questions
Knowledge
Know how
Application of data and information;
answers how questions
Understanding
Appreciation of why
Wisdom
Know why
Evaluated understanding
Enlightenment
Attaining the sense of truth, the sense
of right and wrong, and having it socially
accepted, respected and sanctioned
from knowledge to wisdom involves ‘understanding principles’. The label ‘DIKW hierarchy’, and
the omission of understanding as a separate level in re-iterations of the hierarchy in other sources
suggest that there is something of a consensus and that Bellinger et al. [36] are articulating a shared
view that understanding should not be considered as a separate level.
Amongst the recent information systems and knowledge management texts analysed below only
four actually draw the hierarchy. Chaffey and Wood [37] show the hierarchy in Figure 2, with the
additional axes of meaning and value. Pearlson and Saunders [38] suggest that human input goes
up in the higher levels of the hierarchy, whilst computer input goes down. Jashapara [39] shows a
hierarchy with the levels: data, information, knowledge, wisdom and truth. Choo [25] draws a rather
different diagram focusing on the transformation processes between signals, data, information and
knowledge.
Typically all of these formulations of the hierarchy share a common view that:
• the key elements are data, information, knowledge, and wisdom;
• these key elements are virtually always arranged in the same order, although some models offer
additional stages, such as understanding, or enlightenment;
High
High
Knowledge
Meaning
Information
Value
Data
Low
Fig. 2.
Low
Data, information and knowledge, according to Chaffey and Wood [37].
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J. Rowley
Non-algorithmic
Non-programmable
Wisdom
Knowledge
(actionable Information)
Information
(data “in formation”)
Data
Algorithmic
Fig. 3.
Programmable
Data, information and knowledge, according to Awad and Ghaziri [20].
High
Belief
structuring
Cognitive
structuring
INFORMATION
KNOWLEDGE
Beliefs
Justification
Order/
Structure
Physical
structuring
DATA
SIGNALS
Sensing
Selecting
Low
Fig. 4.
Meaning
significance
Human Agency
High
Data, information and knowledge, according to Choo [25].
• the higher elements in the hierarchy can be explained in terms of the lower elements by identifying an appropriate transformation process; and
• the implicit challenge is to understand and explain how data is transformed into information,
information is transformed into knowledge, and knowledge is transformed into wisdom.
4.
Aims and methodology
This research does not seek to review all writing that presents definitional debates in relation to
data, information, knowledge and wisdom. Rather we examine the popular explicit or implicit articulations of the wisdom hierarchy in a number of recent textbooks in those disciplines at the core of
the knowledge revolution, information systems and knowledge management. This analysis is
intended to ascertain how some of the key authors of recent books that are read by students and others define the terms, and to investigate:
Journal of Information Science, 33 (2) 2007, pp. 163–180 © CILIP, DOI: 10.1177/0165551506070706
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• which items in the hierarchy are typically defined by writers of textbooks in information systems
and knowledge management;
• the extent of any consensus on the definition of data, information, knowledge and wisdom;
• the essential nature of these elements, as defined and described in these sources; and
• the transformation processes associated with moving between levels in the hierarchy.
The textbooks were chosen using the following criteria:
• Recently published, preferably published in 2003 or later. This criterion was important to ensure
that all the books were written at a similar time, and had the opportunity to be influenced by current theoretical debates, particularly in the area of knowledge management.
• Published by a major publisher, and therefore having the potential to be widely read, and influential.
• Where possible, books in their second or subsequent edition were chosen, to ensure the selection
of books that were established as authoritative. This criterion proved more difficult to apply to
knowledge management books, since some of the most useful texts in this area have only been
published in their first edition quite recently.
• Availability and convenience.
• Textbooks were preferred to readers or collections of articles because these books were less likely
to offer any definition, and when they did those definitions might not be consistent with one
another; this made for more difficult analysis and might have unbalanced the perspectives in
favour of these sources offering multiple perspectives.
The books in information systems and those in knowledge management were analysed separately
in order to investigate any differences in definitions or emphases rooted possibly in their different
disciplinary perspectives. Textbooks included in the analysis were [20, 25, 37–50].
The textbooks were analysed through the use of their index and scanned for key phrases that
characterized their definition of data, information, knowledge and wisdom. In some instances, these
texts offer succinct and straightforward definitions, perhaps with examples. In others a rather more
sophisticated exposition is offered which embraces concepts from semiotics, pragmatics and semantics, e.g. [47]. In some texts the definitions are clearly labelled as such, whereas in others the definitions which form the core basis of the comments in the next section have been extracted to capture
‘the flavour’ of the definition articulated by the author. Typically, definitions of knowledge tend to
be more elaborate and discursive than those of data and information.
5.
Findings
This section first notes the extent of definition of data, information, knowledge and wisdom in the
textbooks that were studied. It then proceeds to summarize and discuss, in turn, the definitions of
data, information, knowledge and wisdom offered in this literature.
5.1.
Which items are discussed?
Table 2 summarizes the extent of mention of the elements in the wisdom hierarchy. Most books offer
a description of data, information and knowledge that can be regarded as a definition. Two of the
knowledge management textbooks did not define data or information, but did offer a definition of
knowledge. Such books may take the definition of data and information as a given, even though they
define knowledge in relation to information. One of the information systems textbooks did not
define knowledge. In general then most books recognized the importance of defining all three concepts, whether their primary focus was on ‘information’ within information systems, or ‘knowledge’
as in knowledge management. However, concepts above knowledge in the wisdom hierarchy
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J. Rowley
[20] E.M Awad and H.M. Ghaziri, Knowledge Management (Pearson Education International, Upper Saddle
River, NJ, 2004).
[25] C.W. Choo, The Knowing Organization: how Organizations use Information to Construct Meaning,
Create Knowledge, and make Decisions (OUP, Oxford, 2006).
[37] D. Chaffey and S. Wood, Business Information Management: Improving Performance using
Information Systems (FT Prentice Hall, Harlow, 2005).
[38] K.E. Pearlson and C.S Saunders, Managing and using Information Systems: a Strategic Approach
(Wiley, New York, 2004).
[39] A. Jashapara, Knowledge Management: an Integrated Approach (FT Prentice Hall, Harlow, 2005).
[40] L.M. Jessup and J.S. Valacich, Information Systems Today (Prentice Hall, Upper Saddle River, N J, 2003).
[41] P. Bocij, D. Chaffey, A. Greasley, and S. Hickie, Business Information Systems: Technology, Development
and Management for the e-Business 2nd edn (FT Prentice Hall, Harlow, 2003).
[42] T.R. Groff and T.P. Jones, Introduction to Knowledge Management: KM in Business (Butterworth
Heinemann, Amsterdam, 2003).
[43] K.C. Laudon and J.P. Laudon, Management Information Systems: Managing the Digital Firm 9th edn.
(Pearson Prentice Hall, Upper Saddle River, NJ, 2006).
[44] E. Turban, R.K. Rainer, and R.E. Potter, Introduction to Information Technology, 3rd edn (New York,
Wiley, 2005).
[45] D. Boddy, A. Boonstra, and G. Kennedy, Managing Information Systems: an Organizational Perspective,
2nd edn (FT Prentice Hall, Harlow, 2005).
[46] G. Curtis and D. Cobham Business Information Systems: Analysis, Design and Practice, 5th edn (FT
Prentice Hall, Harlow, 2005).
[47] P. Beynon-Davies, Information Systems: an Introduction to Informatics in Organizations (Palgrave,
Basingstoke, 2002).
[48] S. Newell, M. Robertson, H. Scarbrough, and J. Swan, Managing Knowledge Work (Palgrave
Macmillan, Basingstoke, 2002).
[49] S. Barnes, Knowledge Management Systems: Theory and Practice (Thomson Learning, London, 2002).
[50] C. Depres and D. Chauvel, Knowledge Horizons (Butterworth Heinemann, Boston, 2000).
Fig. 5.
Textbooks included in the analysis.
received very little attention. Wisdom was only defined by three books, and other higher levels were
mentioned by two authors.
5.2.
Defining data
Where definitions of data are offered these are typically clearly and succinctly stated, sometimes
with examples. In summary the definitions variously suggest that:
• Data has no meaning or value because it is without context and interpretation [27, 40–42].
• Data are discrete, objective facts or observations, which are unorganized and unprocessed, and
do not convey any specific meaning [20, 37, 38, 41].
• Data items are an elementary and recorded description of things, events, activities and transactions [43–45].
Choo [25] suggests that data are often elements of larger physical systems (such as books, or
instrument panels) which give clues about what data to notice and how they should be read.
Table 2
Extent of definition of data, information, knowledge and wisdom
Information systems textbooks (n = 8)
Knowledge management textbooks (n = 7)
Total (n = 15)
Data
Information
Knowledge
Wisdom
Other (e.g. Truth)
8
5
13
8
5
13
7
6
13
1
2
3
0
2
2
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J. Rowley
Jashapara [39] and Choo [25] also introduce the concept of signals. Jashapara [39] suggests that
we acquire data from the external world through our senses and try to make sense of these signals
through our experience. Choo [25] develops this further and specifically identifies signals as the origin of data, and proposes the processes of sensing and selecting, together described as physical
structuring, as transforming signals into data.
Interestingly, these definitions are largely in terms of what data lacks; data lacks meaning or value,
is unorganized and unprocessed. They lay the foundations for defining information in terms of data.
5.3.
Defining information
Information systems books tend to focus on the relationship between data and information, often
defining information in terms of data. The concepts of format, structure, organization, meaning and
value feature in the various definitions:
• ‘Information is formatted data […(and)] can be defined as a representation of reality’ [40, p. 7].
• ‘Information is data which adds value to the understanding of a subject’ [37, p. 223 based on the
European Framework for Knowledge Management].
• ‘Information is data that have been shaped into a form that is meaningful and useful to human
beings’ [43, p. 13].
• ‘Information is data that have been organized so that they have meaning and value to the recipient’
[44, 45].
• ‘Information is data processed for a purpose’ [46, p. 3].
Bocij et al. [41] concur with the findings that there are a number of definitions of information in
common use, which they suggest are:
• data that have been processed so that they are meaningful;
• data that have been processed for a purpose; and
• data that have been interpreted and understood by the recipient.
Bocij et al. [41] and Curtis and Cobham [46] identify the processes associated with converting
data into information. They agree that these are: classification, rearranging/sorting, aggregating, performing calculations, and selection. They do not discuss whether these processes are performed by
information systems, or people, or both.
Pearlson and Saunders [38] suggest that such processing of data requires a decision about the type
of analysis, and this, in turn, requires an interpretation of the content of the data. To be relevant and
have a purpose, information must be considered within the context where it is received and used.
Boddy et al. [45] point out that the notion of meaning is subjective, and that what one person sees
as valuable information another may see as data with no particular significance. Beynon-Davies
[47], recognizing that the meaning of information is both critical and open to many interpretations,
embarks on an explanation based on semiotics or semiology. He argues that information can be seen
as embodied in signs, and discusses how the elements of semiotics, pragmatics, semantics, syntactics and empirics inform thinking about communication and information.
Five of the knowledge management textbooks also define information, and these definitions also
define information in relation to data. For example:
• ‘Information is data that have been given meaning by way of context’ [42, p. 2].
• ‘Information is an aggregation of data that makes decision making easier’ [20, p. 36].
• ‘Information is data that is endowed with meaning, relevance and purpose’ [39, p. 14].
Jashapara also agrees with Boddy et al. [45] that the human receiver determines whether a message is data or information:
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It is the receiver of the data that determines whether a message is data or information […] Meaning in data
often occurs through some form of association with experience or relationships with other data [39, p. 16].
Choo [25] calls this process, which assigns meaning and significance to the perceived facts and
messages, ‘cognitive structuring’.
To conclude, in both the information systems textbooks and the knowledge management literature, information is defined in terms of data, and is seen to be organized or structured data. This
processing lends the data relevance for a specific purpose or context, and thereby makes it meaningful, valuable, useful and relevant.
5.4.
Defining knowledge
Definitional statements on knowledge are often much more complex than those for data or information. Indeed a number of the knowledge management texts offer extended definitional discussions on the nature of knowledge, its various representations and manifestations, and philosophical
debates on the nature of knowledge. These debates make it more difficult to distil the essence of the
statements on the nature of knowledge than it is to capture and represent the definitional statements
that relate to data and information. Indeed, as some texts opine:
• ‘Knowledge is an intrinsically ambiguous and equivocal term’ [49, p. 3].
• ‘There is still no consensus on the nature of knowledge, except that it is based on perception that
can provide a rational justification for it’ [39, pp. 16–17].
Six of the information systems books offer definitional statements in relation to knowledge, frequently defining knowledge in terms of data and information. For example:
• ‘Knowledge is the combination of data and information, to which is added expert opinion, skills,
and experience, to result in a valuable asset which can be used to aid decision making’ [37, p. 223,
quoting the European Framework for Knowledge Management].
• ‘Knowledge is data and/or information that have been organized and processed to convey understanding, experience, accumulated learning, and expertise as they apply to a current problem or
activity’ [44, p. 38].
• ‘Knowledge builds on information that is extracted from data […] While data is a property of things,
knowledge is a property of people that predisposes them to act in a particular way’ [45, p. 9].
Pearlson and Saunders concur that knowledge is information from the human mind and includes
reflection, synthesis, and context:
Knowledge consists of that mix of contextual information, values, experience, and rules […] Knowledge
involves the synthesis of multiple sources of information over time. The amount of human contribution
increases along the continuum from data to information to knowledge [38, pp. 13–14].
Bocij et al. [41] differentiate between explicit knowledge and tacit knowledge, suggesting that
explicit knowledge can be recorded in information systems, whereas tacit knowledge cannot be
recorded since it is part of the human mind.
Some of the knowledge management texts also agree that knowledge is based on information.
Barnes [49], for example, suggests that knowledge is information processed in the mind of an individual and that knowledge is justified personal belief that increases an individual’s capacity to take
effective action. Choo [25] concurs that information becomes knowledge through the process of belief
structuring or the formation of justified, true beliefs about the world. Jashapara [39] defines knowledge as ‘actionable information’, and proposes that actionable information allows us to make better
decisions and to provide an effective input to dialogue and creativity in organizations. Awad and
Ghaziri [20] suggest that knowledge is human understanding of a specialized field of interest that has
been acquired through study and experience, and knowledge may be viewed as an understanding of
information based on its perceived importance or relevance to a problem area. Despres and Chauvel
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