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Understanding Data, Information, Knowledge And Their Inter-Relationships

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Knowledge, Information, and Data are key words and also fundamental concepts in knowledge management, intellectual capital, and organizational learning. This paper includes the reasons for vagueness and confusion commonly associated with those key terms, proposed definitions of the key terms, and two models of their transformations and interactions.
Journal of Knowledge Management Practice, Vol. 7, No. 2, June 2007
Understanding Data, Information, Knowledge And Their Inter-
Anthony Liew, Walden University
Knowledge, Information, and Data are key words and also fundamental concepts in
knowledge management, intellectual capital, and organizational learning. This paper
includes the reasons for vagueness and confusion commonly associated with those
key terms, proposed definitions of the key terms, and two models of their
transformations and interactions.
Keywords: Knowledge management, intellectual capital, organizational learning,
knowledge, data, information
1. Introduction
Despite many attempts at the definition of ‘Data’, ‘Information’, and ‘Knowledge’,
there still seems to be a lack of a clear and complete picture of what they are and the
relationships between them. Although many definitions are relevant, they are far from
being complete. It is not the intention of this paper to criticize those whom have paved
the way to better understanding of the topic. Rather, the goal is to provide a different
or new perspective in the context of business and knowledge management. Below is a
table of various definitions of Data, Information, and Knowledge from different
authors. The table also includes definitions from Webster’s Collegiate Dictionary.
Most if not all of the definitions shared a common anomaly; they are defined with
each other, i.e. data in terms of information, information is defined in terms of data
&/or knowledge, and knowledge is defined in terms of information. If we are just
describing the inter-relationships, that is all very well. However, with regard to
definitions, this is a logical fallacy i.e. circular definitions or argumentations. (It is in
Philosophy 101 – Critical Thinking and Reasoning).
Table 1: Definition
Data Information Knowledge Source
Data is comprised of the
basic, unrefined, and
generally unfiltered
Information… is much
more refined data… that
has evolved to the point
of being useful for some
form of analysis
Knowledge resides in the
user…happens only
when human experience
and insight is applied to
data and information
Knowledge Nirvana –
Achieving The
Competitive Advantage
Through Enterprise
Content Management
and Optimizing Team
Collaboration; by Juris
Kelley, 2002, Xulon
Davenport and Prusak
have come up with this
definition of knowledge:
it is a mixture of
organized experiences,
values, information and
insights offering a
framework to evaluate
new experiences and
An Intelligent
Organization –
Integrating Performance,
Competence and
Management; by Pentti
Sydanmaanlakka, 2002,
Capstone Publishing
Information: Processed
data… formalized,
capture and explicated;
can easily be packaged
into reusable form
Knowledge: Actionable
information… often
emerges in minds of
people through their
The Essential Guide to
Knowledge Management
– E - Business and CRM
Applications; by Amrit
Tiwana, 2001, Prentice –
Information is data put in
context; it is related to
other pieces of data.
Information is about
meaning, and it forms
the basis for knowledge
encompasses the belief s
of groups or individuals,
and it is intimately tied
to action
Enabling Knowledge
Creation – How to
Unlock the Mystery of
Tacit Knowledge and
Release the Power of
Innovation; by Georg
Von Krogh, Ichijo, and
Nonaka, 2000, Oxford
University Press
Information has been
defined as data that is “
in formation” – that is,
data that has been stored,
analyzed, and displayed,
and is communicated
through spoken
language, graphic
displays, or numeric
Knowledge… is defined
as the meaningful links
people make in their
minds between
information and its
application in action in a
specific setting
Common Knowledge –
How Companies Thrive
by Sharing What They
Know; by Nancy M.
Dixon, 2000, Harvard
Business School Press
Knowledge is a body of
information, technique,
and experience that
coalesces around a
particular subject
Managing Knowledge
Workers – New Skills
and Attitudes to Unlock
the Intellectual Capital in
Your Organization; by
Frances Horibe, 1999,
John Wiley & Sons
Data are elements of
Information is data with
Knowledge is
information with
Innovation Strategy for
the Knowledge
Economy: The Ken
Awakening; by Debra M.
Amidon, 1997,
Data must be organized
to become information
Information must be put
into context to become
The Art of Being Well
Informed – What You
Need To Know To Gain
The Winning Edge In
Business; by Andrew P.
Garvin, 1996, Avery
Publishing Group
Information is a flow of
Knowledge is created by
the very flow of
information, anchored in
the beliefs and
commitment of its
The Knowledge -
Creating Company –
How Japanese
Companies create the
Dynamics of Innovation,
by Ikujiro Nonaka and
Hirotaka Takeuchi, 1995,
Oxford University Press
Data is a set of discrete,
objective facts about
events… as structured
records of transactions
Information… as
message… in the
(various) form of
communication… to
have an impact on
judgment and behavior
Knowledge is a fluid mix
of framed experience,
values, contextual
information, and expert
insights that provides a
framework for
evaluating and
incorporating new
experiences and
Working Knowledge:
How Organizations
Manage What They
Know. By Thomas H.
Davenport and Laurence
Prusak, 2000. Harvard
Business School Press.
Data: 1. factual
information used as a
basis for reasoning,
discussion, or
calculation; 2.
information output by a
sensing device or organ
that includes both useful
and irrelevant or
redundant information
and must be processed to
be meaningful; 3.
information in numerical
form that can be digitally
transmitted or processed.
Information: 1. the
communication or
reception of knowledge
or intelligence; 2.
knowledge obtained
from investigation,
study, or instruction; 3.
Facts, Data; 4.
quantitative measure of
the content of
Knowledge: 1.
Cognizance; 2. the fact
or condition of knowing
something with
familiarity gained
through experience or
association; 3. the range
of one’s information or
understanding; 4. the
sum of what is known:
the body of truth,
information, and
principles acquired by
Merriam Webster’s
Collegiate Dictionary
10th ed.
For all intents and purposes, we need definitions that are concise, definitive, and
distinct in attributes or characteristics, exhibit probable purpose, and/or offer inter-
relationships. This subject is not an easy one; it involves extensive conceptual
thinking dealing with many abstract concepts and semantics. Nevertheless, a thorough
understanding of this topic is the quintessential foundation of information and
knowledge management.
Personal experience leads me to conclude that ‘definitions’ can never be overstated in
terms of their importance. Good definitions include several essential characteristics:
(1) boundaries (i.e. exclusive, nothing left out); (2) purpose (i.e. what it does), and (3)
attributes or characteristics (i.e. what it is). My proposed definitions of ‘Data’,
‘Information’, and ‘Knowledge’ fall within the parameter of a good definition.
Thereafter, we can look into the inter-relationships between the defined subjects.
2. Definitions
Data are recorded (captured and stored) symbols and signal readings.
Symbols include words (text and/or verbal), numbers, diagrams, and images
(still &/or video), which are the building blocks of communication.
Signals include sensor and/or sensory readings of light, sound, smell, taste,
and touch.
As symbols, ‘Data’ is the storage of intrinsic meaning, a mere representation. The
main purpose of data is to record activities or situations, to attempt to capture the true
picture or real event. Therefore, all data are historical, unless used for illustration
purposes, such as forecasting. [Note: However, Rehauser and Kremar (1996, p.6;
cited by Probst et al., 2000) made a distinction between symbol and data with syntax.]
Information is a message that contains relevant meaning, implication, or input for
decision and/or action. Information comes from both current (communication) and
historical (processed data or ‘reconstructed picture’) sources. In essence, the purpose
of information is to aid in making decisions and/or solving problems or realizing an
Knowledge is the (1) cognition or recognition (know-what), (2) capacity to act
(know-how), and (3) understanding (know-why) that resides or is contained within the
mind or in the brain. The purpose of knowledge is to better our lives. In the context of
business, the purpose of knowledge is to create or increase value for the enterprise
and all its stakeholders. In short, the ultimate purpose of knowledge is for value
Given the definitions for data, information, and knowledge, the relationships between
data and information, information and knowledge, why they are most often regarded
as interchangeable and when they are not, the processes and their relevance to our
intended application can be explored. The key to understanding the intricate
relationship between data, information, and knowledge lies at the source of data and
information. The source of both is twofold: (1) activities, and (2) situations. Both
activities and situations generate information (i.e. ‘relevant meaning’ to someone) that
either is captured thus becoming Data, or becomes oblivious (lost).
Examples of activities where information is generated and data can be collected
include business activities like production, sales transactions, or advertising
campaigns. Situations pertain to changes in the environment that may or may not be
related to human activities, such as changes in the climate. Changes in the climate
would affect such human activities as agriculture, or other economic activities such as
cargo shipping. A situation is a context that affects decisions. For example, the
deterioration of a factory building may impact production. In short, activities and
situations generate information that feed into the decision-making process. The
following diagram illustrates the relationships between data and information.
Figure 1: Formation of Information and Data
Once they are captured and stored, data can be processed back into information
through compilation and analysis. The picture of past activities and situations can thus
be reconstructed. There are two fundamental aspects of data processing, compilation,
and/or analysis:
Data to data
Data to context
Captured &
News, Communication, and Monitoring Systems’ warning
For example, ‘Anthony’ represents a person, and ‘555-2345’ represents a phone
number. Both pieces of data may have a relationship, such as ownership, that means
‘555-2345 is Anthony’s phone number’, which in turn implies a message or decision
where there is a likelihood of reaching Anthony via phone call. Further compilation of
names of customers and their contact numbers may lead to information of how many
customers one can reach and possible times needed to complete the task, i.e. 100
customers vs. 10,000 customers. An example of data to context data processing is
‘Anthony’ located in a current phone book vs. ‘Anthony’ located on a tombstone.
Both the same data in different context would yield different meaning, implications or
information that may necessitate a different decision or consequence.
Diagram 2: Relationships Amongst Knowledge, Information, And Data
The key to understanding the relationship between information and knowledge is to
know where the information resides. Recall that information is at its essence a
message that is generated from activities and situations. However, information resides
in storage media (database, print, video tapes, etc.) in the form of data, or in the
human mind as knowledge (in its simplest form of know-what or the higher forms of
Processed & Analyzed:
A reconstructed picture
of historical events
&/or projection of
possible future
Captured & Stored
Internalized: Absorbed &
understood by the
human mind
Verbalized &/or
know-how and know-why). If this is the case, then the overlap between data and
information vis-à-vis information and knowledge becomes obvious, i.e. they occupy
different space at the same time. This also explains why many perceive data and
information, as well as information and knowledge as interchangeable. “…one man’s
data can be another man’s knowledge, and vice versa, depending on context”
(Stewart, 2002, p.6 footnote). However, they are not interchangeable in terms of their
accepted distinct definitions. So, what is a book: knowledge, information or data? It is
all the above in various context. A book is knowledge from the author’s perspective,
information for the potential reader, and data as well which is contained in a storage
media (called ‘book’).
These distinctions can help us crystallize our understanding in terms of managing
data, information, and knowledge within the business model or organization. The
importance or usefulness of definitions cannot be overstated when it comes to
execution of management activities and business programs that involve millions upon
millions of dollars.
Data management is the capture, storage, structure, compilation, retrieval, and
analysis of records. It is the reconstruction of recent or historical events as inputs for
decision-making and/or problem solving.
Information management includes reconstructing a picture of historical events,
collecting current or recent market intelligence, as well as projecting possible future
events (forecasting and scenario planning), and of course analysis for decision making
and/or problem solving. Thereafter, action can be taken and then reviewed.
Knowledge management, on the other hand, is, in essence, the management of
human capital (tacit knowledge that resides in the human mind) relationship capital
such as customer, supplier, strategic alliance, social capital (tacit and explicit), and
structural capital (explicit knowledge a.k.a. data and information), the source and
stock of knowledge; and the flow of knowledge as in knowledge creation, sharing,
and application to create and/or sustain organizational value and competitive
3. Conclusion
Knowledge management is not an isolated concept. Topics such as individual and
organizational learning, creativity and innovation, leadership and teamwork,
community networking, technology, corporate culture, and strategy contribute to the
process of creating, capturing, and applying knowledge for value creation. Knowledge
management is neither a fleeting concept nor a fad. It is just elusive because of its
multi-disciplinary characteristics. In time, as more research and understanding is
applied it will be better understood.
Final words on the definition of data, information, and knowledge may not and should
not come from this document. Nevertheless, this paper has hopefully clarified certain
issues for future applications.
4. References
Amidon, D.M. (1997) Innovation Strategy for the Knowledge Economy: The Ken
Awakening; Butterworth-Heinemann, Newton, MA, USA.
Davenport, T.H. and Prusak, L. (2000) Working Knowledge: How Organizations
Manage What They Know; Harvard Business School Press, Boston, MA, USA.
Dixon, N.M. (2000) Common Knowledge: How Companies Thrive by Sharing What
They Know; Harvard Business School Press, Boston, MA, USA.
Garvin, A.P. (1996) The Art of Being Well Informed – What You Need To Know To
Gain The Winning Edge In Business; Avery Publishing Group, New York, NY, USA.
Horibe, F. (1999) Managing Knowledge Workers – New Skills and Attitudes to
Unlock the Intellectual Capital in Your Organization; John Wiley & Sons, New York,
Kelley, J. (2002) Knowledge Nirvana: Achieving The Competitive Advantage Through
Enterprise Content Management and Optimizing Team Collaboration; Xulon Press,
Fairfax, VA, USA.
Merriam Webster’s Collegiate Dictionary, Springfield, MA, USA, 10th ed.
Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company – How
Japanese Companies create the Dynamics of Innovation; Oxford University Press,
New York, NY, USA.
Probst, G., Raub, S., and Romhardt, K. (2000) Managing Knowledge: Building
Blocks for Success; John Wiley & Sons, Chichester, England, UK.
Rehauser, J., and Kremar, H. (1996) Wissensmanagement im Unternehman, in:
Schreyogg, G./Conrad, P. (eds) Managementforshung 6: Wissenmanagement, 1-140,
Berlin/NewYork: de Gruyter.
Stewart, T.A. (2002) The Wealth of Knowledge: Intellectual Capital and the Twenty-
First Century Organization; Nicholas Brealey Publishing, London, UK.
Sydanmaanlakka, P. (2002) An Intelligent Organization – Integrating Performance,
Competence and Knowledge Management; Capstone Publishing, Knoxville, TN,
Tiwana, A. (2001) The Essential Guide to Knowledge Management – E - Business
and CRM Applications; Prentice – Hall, Upper Saddle River, NJ, USA.
Von Krogh, G., Ichijo, K., and Nonaka, I. (2000) Enabling Knowledge Creation: How
to Unlock the Mystery of Tacit Knowledge and Release the Power of Innovation;
Oxford University Press, New York, NY, USA.
Contact the Author:
Anthony Liew, 53 Nan Kang Road, Section 3, Taipei, Taiwan, R.O.C. 115; Tel: (886)
988-062228; Email:
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The problem of designing didactic processes has not been solved to this day, although some specific issues are considered. The article presents a generalized approach model based on the reference ADDIE model including the organisational level to the design and implementation of the didactic process. The model uses Bloom’s taxonomy and Gardner’s Theory of Multiple Intelligences. The model has been modified towards a dynamic design oriented on quality, efficiency and adaptive learning. A competency-based decomposition is used. The lowest level of decomposition is the activities level where the information flows as well as the learning and forgetting are taken into account. The competences-objectives-activities-data linking is discussed. The level of activities is represented in the form of an electrical-like network. The network represents differential equations describing dynamic learning and forgetting as well as the structure and information flows of the didactic process. The element models can be easily extended. The networks can be simulated and optimized. The results can be used during the design process. The simulation result as well as the structure of network equations enables inference. The article presents the results of simulations of some aspects of didactic processes including scheduling and sequencing, gap effect, workload, predicting achievements and effectiveness.
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В роботі розглянуто літературу з акцентом на навчальні застосування візуалізації. На потребу в них вказують скарги студентів на труднощі зі сприйняттям концепцій, існуючих на мікрорівні (ДНК, гени), або таких, що описують невидимі об’єкти та властивості (розподіли фізичних величин). Ефективність сприйняття користувачами результатів візуалізації є ключовим чинником успіху її методу. Якісна візуалізація сприяє інтуїтивному розумінню представленої інформації та отриманню й аналізу необхідних знань. В роботі, за можливістю, цитований матеріал із текстової форми перетворено на візуальну. Перспективним методом дослідження візуалізації вважається окулографія, оскільки сприяє розумінню витоків знань, які ми отримуємо за посередництва візуалізації. Найважче студентами сприймається візуалізація знань, в тому числі самостійна. Тому вони мають навчитись виконувати правильний вибір відповідного способу на основі класифікації типів знань. Далі вони мають переходити від розуміння наявного матеріалу (даних, інформації, знань) через постановку когнітивної задачі до пошуків та надбання нового знання. Таким чином, мета даної роботи – виявити підґрунтя та розробити приклади для створення розділу навчального курсу з вивчення особливостей візуалізації знань. В роботі наведено схему сформованого навчального курсу з вивчення методів візуалізації. Більш детально розглянуто розділ, присвячений візуалізації знань. Наводиться демонстраційний методичний приклад такої візуалізації. Відмічається, що для навчання коректній побудові карт знань студенти повинні спочатку працювати з тими знаннями, якими вони добре володіють. Тому студентам надається відомий їм текст з елементарної математики за темою «Рівність трикутників». На першому етапі на основі аналізу тексту вони будують схему знань. Далі відбувається її аналіз, та робиться висновок, що ця схема не є повною. Вона відповідає не на всі питання (немає відповіді на питання «Чому?»). Але вона слугує за постановку когнітивної задачі для пошуку відповіді, що забезпечать повноту знань. Задача формулюється питанням «Чому немає четвертої ознаки рівності?». Відповідь на це питання вимагає від студентів переформулювання методів доведення (у вигляді задачі на побудову) для отримання відповіді, яка візуалізується на другому етапі у вигляді розширеної схеми знань. У доступній літературі, нажаль, дуже мало розглядаються процеси аналізу знань (у тому числі й їхнього текстового подання) з подальшим вибором засобів та способів візуалізації та подальшим формуванням відповідної карти. Там здебільшого наводяться результати експериментів чи наукових досліджень авторів. Нам бачиться, що особливу цінність могли б представляти публікації з елементами методики, які показували б, як відбувається процес не лише візуалізації знань, а й попереднього їх аналізу, постановки проміжних когнітивних задач тощо.
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The purpose of this study is to analyze the impact of e-procurement on supply chain performance by mediating the role of partnerships, information sharing, and supply chain integration. The survey was conducted on two large companies engaged in the energy sector. Data collection for this survey was carried out by distributing the survey to respondents who were determined by the sampling method of interest. The research design used in this study is hypothesis testing, and the data analysis method used to test the hypothesis is the SEM (Structural Equation Modeling) method using the AMOS program. The results show that of the three mediating variables, namely partner relationships, supply chain integration and information sharing, it shows that supply chain integration provides the greatest mediating role. While the other two variables are not sufficient to facilitate the impact of e-procurement on supply chain performance. Direct testing of the effect of e-procurement on the three mediating variables, also shows that e-procurement only has a direct significant impact on supply chain integration, while the effect on partnership relationships and information exchange is not significant. For testing the direct effect of the three mediating variables on supply chain performance, the results show that partnership relationships have not been sufficient to encourage supply chain performance. Meanwhile supply chain integration and information exchange show their role in driving supply chain performance.
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Definitions of Informatics, Digital and Information System is key to master the most powerfull lever of the new economy: information.
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