2. Prof Elaine Ferneley
Data, Information, and Knowledge
Data: Unorganized and
unprocessed facts; static; a set
of discrete facts about events
Information: Aggregation of
data that makes decision
making easier
Knowledge is derived from
information in the same way
information is derived from
data; it is a person’s range of
information
3. Prof Elaine Ferneley
Some Examples
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.
5. Prof Elaine Ferneley
Definitions: Data, Information, Knowledge,
Understanding and Wisdom
Data is raw, it is a set of symbols, it has no meaning in
itself
Quantitatively measured by:
How much does it cost to capture and retrieve
How quickly can it be entered and called up
How much will the system hold
Qualitatively measured by timeliness, relevance, clarity:
Can we access it when we need it
Is it what we need
Can we make sense of it
In computing terms it can be structured as records of
transactions usually stored in some sort of technology
system
6. Prof Elaine Ferneley
Definitions: Data, Information, Knowledge,
Understanding and Wisdom
Information is data that is processed to be useful
Provides answers to the who, what, where and when type
questions
given a meaning through a relational connector, often regarded as
a message
Sender and receiver
Changes the way the receiver perceives something – it informs them
(data that makes a difference)
Receiver decides if it is information (e.g. Memo perceived as
information by sender but garbage by receiver)
Information moves through hard and soft networks
Transform data into information by adding value in various ways
7. Prof Elaine Ferneley
Definitions: Data, Information, Knowledge,
Understanding and Wisdom
Quantitative information management measures
e.g….
Connectivity (no. of email accounts, Lotus notes users)
Transactions (no. of messages in a given period)
Qualitative information management measures
Informativeness (did I learn something new)
Usefulness (did I benefit from the information)
In computing terms a relational database makes
information from the data stored within it
8. Prof Elaine Ferneley
Definitions: Data, Information, Knowledge,
Understanding and Wisdom
The application of data and information – answers the
how questions
Collection of the appropriate information with the intent of
making it useful
By memorising information you amass knowledge e.g. memorising
for an exam – this is useful knowledge to pass the exam (e.g.
2*2=4)
BUT the memorising itself does not allow you to infer new
knowledge (e.g.1267*342) – to solve this multiplication requires
cognitive and analytical ability the is achieved at the next level –
understanding
In computing terms many applications (e.g. modelling and
simulation software) exercise some type of stored
knowledge
9. Prof Elaine Ferneley
Definitions: Data, Information, Knowledge,
Understanding and Wisdom
The appreciation of why
The difference between learning and memorising
If you understand you can take existing
knowledge and creating new knowledge, build
upon currently held information and knowledge
and develop new information and knowledge
In computing terms AI systems possess
understanding in the sense that they are able to
infer new information and knowledge from
previously stored information and knowledge
10. Prof Elaine Ferneley
Definitions: Data, Information, Knowledge,
Understanding and Wisdom
Evaluated understanding
Essence of philosophical probing
Critically questions, particularly from a human
perspective of morals and ethics
discerning what is right or wrong, good or bad
A mix of experience, values, contextual
information, insight
In computing terms may be unachievable –
can a computer have a soul??
11. Prof Elaine Ferneley
A Sequential Process of Knowing
Understanding supports the transition from one stage to the next, it is not
a separate level in its own right
12. Prof Elaine Ferneley
Rate of Motion towards Knowledge
What is this (note the point when you realise what it is but
do not say)
I have a box.
The box is 3' wide, 3' deep, and 6' high.
The box is very heavy.
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.
The box has a door on the front of it.
When you open the door the light comes on.
You usually find the box in the kitchen.
It is colder inside the box than it is outside.
There is a smaller compartment inside the box with ice in it.
When I open the box it has food in it.
13. Prof Elaine Ferneley
Rate of Motion towards Knowledge
It was a refrigerator
At some point in the sequence you
connected with the pattern and understood
When the pattern connected the
information became knowledge to you
If presented in a different order you would
still have achieved knowledge but perhaps
at a different rate
14. Prof Elaine Ferneley
Learning
Learning by experience: a
function of time and talent
Learning by example: more
efficient than learning by
experience
Learning by sharing,
education.
Learning by discovery: explore
a problem area.
15. Prof Elaine Ferneley 15
From tacit to articulate knowledge
“We know more than we can tell.”
Michael Polanyi, 1966
Tacit
Articulated
High Low
MANUAL
How to
play
soccer
Codifiability
16. Prof Elaine Ferneley 16
Knowledge is experience,
everything else is just
information.
-Albert Einstein
“We know more than we can tell.”
17. Prof Elaine Ferneley
Explicit Knowledge
Make a cake
Service a boiler
Formal and systematic:
easily communicated &
shared in product
specifications, scientific
formula or as computer
programs;
Management of explicit
knowledge:
management of processes
and information
Are the activities to the
right information or
knowledge dependent ?
18. Prof Elaine Ferneley
Tacit Knowledge Examples
Co-ordinate colours
Arrange furniture
Highly personal:
hard to formalise;
difficult (but not
impossible)to articulate;
often in the form of know
how.
Management of tacit
knowledge is the
management of people:
how do you extract and
disseminate tacit
knowledge.
20. Prof Elaine Ferneley
Knowledge As An Attribute of Expertise
An expert in a specialized area
masters the requisite knowledge
The unique performance of a
knowledgeable expert is clearly
noticeable in decision-making
quality
Knowledgeable experts are
more selective in the
information they acquire
Experts are beneficiaries of the
knowledge that comes from
experience
21. Prof Elaine Ferneley
Expertise, Experience & Understanding
Experience – rules of thumb:
What e.g. gardener might have
Understanding – general knowledge:
What a biology graduate might have
Expertise – E + U in harmony
What an expert has
24. Prof Elaine Ferneley
Expert’s Reasoning Methods
Reasoning by analogy:
relating one concept to
another
Formal reasoning:
using deductive or
inductive methods (see
next slide)
Case-based
reasoning: reasoning
from relevant past cases
25. Prof Elaine Ferneley
Deductive and inductive reasoning
Deductive
reasoning: exact
reasoning. It deals
with exact facts and
exact conclusions
Inductive reasoning:
reasoning from a set of
facts or individual cases
to a general
conclusion
