1. What is Knowledge? Prof. Elaine Ferneley E.Ferneley@salford.ac.uk

2. 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
Prof Elaine Ferneley

3. The DIKW Pyramid
Prof Elaine Ferneley

4. 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.
Prof Elaine Ferneley

5. 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
Prof Elaine Ferneley

6. 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
Prof Elaine Ferneley

7. Definitions: Data, Information, Knowledge, Understanding and Wisdom
Quantitative information management measures e.g….
Connectivity (no. of 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
Contextualisation – we know what the data was gathered for
Categorisation – we know the units of analysis or key components of the data
Calculated – the data may be analysed mathematically
Corrected - errors have been removed from the data
Condensed – the data may have been summarized into a more concise form
Prof Elaine Ferneley

8. 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
Prof Elaine Ferneley

9. 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
Prof Elaine Ferneley

10. 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??
Prof Elaine Ferneley

11. 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
Prof Elaine Ferneley

12. 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.
Prof Elaine Ferneley

13. 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
Prof Elaine Ferneley

14. From tacit to articulate knowledge
“We know more than we can tell.”
Michael Polanyi, 1966
MANUAL
How to play soccer
High
Low
Important dimension of knowledge is tacit vs articulate. Another well-known example of this is how do we recognize faces?
Knowledge that is written, spoken or expressed in documents, Knowledge that is intuitive and difficult to explain
Knowledge on other hand tends to be more intangible, tacit context, affects meaning, transfer requires learning, and are not easily reproducible.
To rephrase Polanyi, orgs know more than they can say (K&Z)
What we see is extent to which knowledge can be codified. Golf example, reading manual does not teach you how to play golf. Relating this to organizations can also possess tacit knowledge, eg to be innovative, try to describe how Sony or HP creates new process. But clearly not something that can be learned by another organization.
Knowledge or know-how has to do with process of learning, understanding, and applying information
Tacit group knowledge – team coordination, tacit organizational knowledge – corporate culture
Codifiability
Articulated
Tacit
Prof Elaine Ferneley 14 14

15. “We know more than we can tell.”
Knowledge is experience,
everything else is just information.
-Albert Einstein
Prof Elaine Ferneley 15

16. Explicit Knowledge Formal and systematic: Calculate Mend a tax
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 ?
Mend a
broken leg
Calculate
tax
Make a cake
Raise an
invoice
Build an
engine
Service a boiler
Prof Elaine Ferneley

17. Tacit Knowledge Examples
Work in
team
Get 100%
in an
assignment
Co-ordinate colours
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.
Ride a
bike
Design a
presentation
Arrange furniture
Prof Elaine Ferneley

18. Illustrations of the Different Types of Knowledge
‘that’
Know
‘how’
Prof Elaine Ferneley

19. 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
Prof Elaine Ferneley

20. 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
Prof Elaine Ferneley

21. Expertise, Experience & Understanding 2
Prof Elaine Ferneley

22. Reasoning and Thinking and Generating Knowledge
Prof Elaine Ferneley

23. 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
Prof Elaine Ferneley

24. 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
Prof Elaine Ferneley