The document explores the continuum of data, information, knowledge, and wisdom, emphasizing that data is raw and without context while information is processed data that has meaning. It discusses the importance of context in transforming data into useful information and the characteristics of good quality information. The text also highlights the roles of knowledge workers, the value of information in decision-making, and the advantages and disadvantages of coding information.

1. Data, Information &
Knowledge

2. The old adage / saying goes along the lines that
knowledge can be defined as knowing a tomato
is a fruit…
And that wisdom is therefore knowing that you
don't add a tomato to a fruit salad...
There are a number of models and frameworks
that investigate the data-information-knowledge-
wisdom continuum
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5. Data
 Data are raw facts and
figures that on their
own have no meaning
 These can be any
alphanumeric
characters i.e. text,
numbers, symbols
Note the “are” bit above? What does this mean?
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6. Data Examples
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 Yes, Yes, No, Yes, No, Yes, No, Yes
 42, 63, 96, 74, 56, 86
 111192, 111234
 None of the above data sets have any
meaning until they are given a CONTEXT
and PROCESSED into a useable form

7. Data Into Information
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 To achieve its aims the organisation will
need to process data into information.
 Data needs to be turned into meaningful
information and presented in its most
useful format
 Data must be processed in a context in
order to give it meaning

8. Information
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 Data that has been processed within a
context to give it meaning
OR
 Data that has been processed into a
form that gives it meaning

9. Examples
 In the next 3 examples
explain how the data
could be processed to
give it meaning
 What information can
then be derived from
the data?
Suggested answers are given at the end of this presentation
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10. Example 1
Yes, Yes, No, Yes, No, Yes,
No, Yes, No, Yes, Yes
Raw Data
Context
Responses to the market
research question – “Would
you buy brand x at price y?”
Information ???
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Processing

11. Example 2
Raw Data
Context
Information
42, 63, 96, 74, 56, 86
Jayne’s scores in the six
AS/A2 ICT modules
???
Processing
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12. Example 3
Raw Data
Context
Information
111192, 111234
The previous and current
readings of a customer’s
gas meter
???
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Processing

13. Encoding Information
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• Processing turns data into information
• Sometimes you might want to turn information
into data – i.e. to store it – this is called
encoding
• How do you code information to make it easy
to re-process, without losing it’s meaning?

14. Sources of Data
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Internal or External?
• Internal communication is communication with people inside the
same organisation or company
• External communication is with people outside the company, such
as suppliers or customers.
Direct or Indirect?
• Direct data are collected for the purpose of the processing being
undertaken – e.g. time cards for pay
• Indirect data are originally collected for another purpose, but is now
being processed to provide extra information - e.g. spending patterns
from credit cards

15. Information Channels
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Formal or Informal?
• Formal channels are the official (or reliable!) ones,
such as memos, letters, the company noticeboard,
etc.
• Informal channels are the unofficial ones, such as
office gossip, informal meetings and rumours –
these can often be unreliable.

16. The Value of Information
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• It is often said that we are in the information age, and that information
is a valuable commodity.
• Why is information valuable? Because:
•It allows us to plan how to run our business more effectively – e.g.
shops can stock what customers want, when they want it, and
manufacturers can anticipate demand
• Marketing materials can be targeted at people and customersthat
you know could be interested in your products and services
• This can lead to increased customer satisfaction andtherefore
profit

17. Good Quality Information
• The characteristics of good quality information – it should be:
•Accurate
•Up-to-date
•Relevant
•Complete
•On-time
• Appropriatelypresented

18. Collecting Information
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How is information about people collected?
1. Obviously you can ask people questions about their spending
habits, etc. (but they might not like it!)
2. Or you can use a more indirect approach:
•
•
• Supermarket loyalty cards
- e.g. easily identify vegetarians!
Credit card transactions
- amounts and locations
- can help prevent fraud, too!
ATMs, CCTV, till transactions, etc.

19. Coding Information
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•
•
•
Information stored in a computer is often coded
Coding categorises information and can replace
long, description strings with a few letters or
numbers (or both!)
You are probably familiar with examples such as F
for female and M for male

20. Coding - Advantages
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Information is often coded because:
•
•
•
•
•
It is quicker to enter into the computer
It require less disc space to store, and less memory to process
It can make processing easier – or possible – as there will be
fewer responses
It improves the consistency of the data as spelling mistakes are
less likely
Validation is easier to apply

21. Coding - Disadvantages
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Coding also has some negative effects :
•
• Information is coarsened by forcing it all into
categories – there might not be a category that
matches what you want to record – e.g. hair colour
The same can be true of rounding numbers – the
intervals(An Interval is all the numbers between two given numbers. Showing if the beginning and end number are
included is important. There are three main ways to show intervals:) or numbers of
categories, this needs to be chosen carefully to
maintain the quality of the information

22. Exam Tip
 You’ll nearly always be asked to give
examples of data processed into
information
 Don’t use:
• Traffic lights
• Dates of birth
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23. Knowledge
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 Knowledge is the understanding of rules
needed to interpret information
“…the capability of understanding the
relationship between pieces of
information and what to actually do
with the information”

24. Knowledge
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•
•
•
•
•
Data and information deal with facts and figures
Knowing what to do with them requires knowledge
Knowledge = information + rules
Rules tell us the likely effect of something
For example: you are more likely to pass your A
level IF you do your coursework and revise for your
exam!

25. Knowledge Examples
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 Using the 3 previousexamples:
• A Marketing Manager could use this information to
decide whether or not to raise or lower price y
• teacher could analyse the results to determine
whether it would be worth her re-sitting a module
• Looking at the pattern of the customer’s previous gas
bills may identify that the figure is abnormally low and
they are fiddling the gas meter!!!

26. Knowledge Workers
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 Knowledge workers have specialist
knowledge that makes them “experts”
• Based on formal and informal rules they have
learned through training and experience
 Examples include doctors, managers,
librarians, scientists…

27. Expert Systems
 Because many rules are based
on probabilities computers can
be programmed with “subject
knowledge” to mimic the role
of experts

One of the most common uses of
expert systems is in medicine
• The ONCOLOG system shown here
analyses patient data to provide a
reference for doctors, and help for
the choice, prescription and follow-
up of chemotherapy
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28. Summary
Information Data Context Meaning
= +
+
Processing
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Data – raw facts and figures
Information – data that has been processed (in a context) to give it meaning