Module 5'1'1

1
Module 5.1.1

• Data, Information, Knowledge and Processing

2
Definition of Data

• Data is raw facts and figures
• Any alphanumeric character
• Data consists of raw values that, on their own
  have no meaning

3
Examples of Data

• 301083
• FB6RT78
• 43AB34YT
• Each of the above pieces of data has no meaning

4
Definition of Information

• Information is processed data that is given
  meaning by its context
• It is data that has been processed into a form
  that is useful
• If a book company recorded each sale the facts
  recorded are data.
• If they combined them into a monthly sales
  figure
• That is information

5
Formula for Information
Information
Data
Context
Meaning



6
Example of Information

• Data 02221732
• This has no meaning or context
• Context It is a US Date
• This allows us to recognise it as 22nd February
  1732
• It still has no meaning and is therefore not
  information
• Meaning The Birthday of George Washington
• This gives us all the elements required for
  information

7
Range of Definitions

• Meaning extracted by humans
• Message
• Semantic
• Syntactic
• Representation methods

8
Representation Methods

• Graphically
• Numbers are easier to visualise in graphical
  format
• Symbols
• Language independent
• Universal recognition
• Some symbols may have different meanings so care
  is needed
• Some symbols are recognised but their meaning
• is not so well known.

9
Semantic

• Covers the Meaning for the data the way that
  meaning is attached to a statement.
• Take the following statement
• "Fruit flies like a banana"
• Do small insects prefer a banana
• or does fruit glide through the air in a way
  similar to a banana
• Semantic representation of data is of importance
  when attempting to code data

10
Syntactic

• Concerning the Rules of the Data
• Data 10/12/90
• Rules dd/mm/yy
• Gives the date of 10th December 90
• Rules mm/dd/yy
• Gives the date of 12th October 90

11
Semantic and Syntactic

• Remember
• Syntactic Rules
• Semantic Meaning
• Learn some examples

12
Definition of Knowledge

• Knowledge is the result of interpreting
  information
• We need more tins of spaghetti hoops might be
  the knowledge acquired from interpreting the
  information given in the stock report.
• We use knowledge to build up sets of rules
• E.g. It is hotter in July therefore we will sell
  more ice cream, so we need to increase the order
  for ice cream in June.

13
Difference Between Information and Knowledge

• Information is based on facts
• Knowledge is based on rules, and these rules are
  based on probabilities, not certainties
• Double clicking an icon in Windows will open an
  application
• This is not information as it is not a certainty.
• Icons are pictures
• This is information not knowledge

14
Concepts and Understanding

• Microsoft Windows 98 is an operating system
• This is information.
• To be knowledge you would need to have an
  understanding of what an operating system is.
• The concept of an operating system gives you an
  understanding of what is meant by the statement.

15
Data Types

• Boolean
• Can hold one of two values true/false, 1/0
• Integer
• Holds whole numbers only
• Real
• Holds decimal numbers
• Text/String
• Holds any alphanumeric character, can include
  numbers and symbols

16
Examples of Data Types

• Boolean
• Are you married?
• Integer
• For storing school years, e.g. 7,8,9,10,11,12,13
• Real
• For storing currency e.g. 10.56 (it cannot store
  the currency symbol)
• Text/String
• For storing any text, e.g. Name, address,
  telephone number, postcode

17
Sources of Data

• Gathered from original source
• From Indirect source
• Data passed on
• Data purchased
• By product of processing an original data set
• Archives

18
Gathered from an original source

• Collected as part of a transaction in a shop
• e.g Credit Card Number
• Collected in a survey
• e.g. Recorded on an OMR form
• Recorded in an interview
• Collected using sensors
• E.g. weather station
• From an original source is where there is no
  third party between the source of the data and
  the collection device/person

19
Indirect Source

• Data used for a purpose different to that for
  which it was originally collected
• E.g. a credit card firm uses data about each
  transaction to bill the customer. If it then
  used the data to find out about their spending
  habits to send them focused adverts, then this is
  using the data from an indirect data source.
• Data Passed on/Purchased
• These are methods of acquiring the data and the
  data will then be used in a method different to
  that originally intended.

20
By-Product of Processing

• Data produced by the processing of source data
• The source data from a supermarket might be the
  number of cans of spaghetti hoops at the
  beginning of the month and the number at the end.
  
• The result of processing is the number sold
  during the month

21
Archive

• Data which is not used frequently and has been
  placed in an archive
• E.g. Pupils who have left school are archived
• Any information that needs to be kept but is not
  used frequently
• Bills (utility gas, telephone)
• Past employees

22
Effect of Quality of Data Source on Information
Produced

• Unreliable Questionnaires
• If the wrong individual has been asked then the
  data will be accurate but cannot be relied upon
  e.g. asking a five year old their views on
  washing liquid.
• Incomplete Data
• Goods can leave a store by many different ways
  the main one is by sales which are recorded by
  bar code readers. If the management only relied
  on this data then their information produced
  would be inaccurate. Goods could also be stolen,
  or damaged for example.

23
Effect of Quality of Data Source on Information
Produced

• GIGO
• Garbage in Garbage Out
• If the data source is corrupt, then the resulting
  information produced will be corrupt

24
Effect of Quality of Data Source on Information
Produced

• Factors affecting the quality of the data source
  include
• Relevance
• If the information is not relevant
• Age
• If the information is out of date
• Completeness
• If some of the information is missing
• Presentation
• If the information cannot be found because of the
  way the it has been presented
• Level of Detail
• Too much detail or too little both have an
  effect

25
Coding of Data

• This is changing the original data into a
  shortened version in order to store it in the
  computer.
• Storing days of the week as Mo, Tu, We etc, or
  months of the Year as Jan, Feb, Mar

26
Problems of Coding Data

• Precision of data coarsened
• E.g. Light Blue coded as Blue
• The user needs to know the codes utilised
• If the user is not aware of the codes then they
  cannot interpret the data
• Coding of Value judgements
• E.g. Did you like the film? to be coded as a
  judgement of 1-4. This will be coded differently
  by different people and makes comparisons
  difficult.

27
Benefits of Coding Data

• Less storage space required
• If Tue is stored instead of Tuesday then not as
  much storage space is required
• Comparisons are shorted and can therefore be made
  quicker, thus speeding up searches
• As less data is being stored it is faster to
  search and to make comparisons between pieces of
  data
• A limited number of codes exists aiding in
  validation of input
• With a limited number of codes it is easier to
  match them against rules and make sure that only
  codes that exist are entered
• Codes can be easier to remember
• Short codes can be easier to remember than full
  names

28
Testing

• Every system must be reliable and the data it
  produces trusted
• This is done through testing
• Testing gives the users and management confidence
  that the system works

29
Purpose of Test Data

• Normal
• To test the system works under normal conditions
  with normal data
• Extreme
• This tests with accurate data but at the lower
  and upper extremes of the range of data required
• Erroneous
• This tests with incorrect data

30
Importance of Testing

• To test the system under all conditions
• To emulate users and their actions and ensure the
  system continues to work
• To cover all potential actions and entries into
  the system
• To give users confidence in the system
• To allow the system to be signed off and payment
  received

31
Importance of Test Plans

• To ensure all avenues are covered and none
  forgotten
• To document the data used
• To list the actions taken
• To list the start point of any testing
• To enable all tests to be reproduced
• To list expected results
• To be able to tie expected results to actual
  results

32
Factors affecting quality of information

• Accuracy
• Relevance
• Age
• Completeness
• Presentation
• Level of Detail

33
Verification

• Ensuring the source data is the same as the
  object data
• In other words, the contents of the piece of
  paper in your hand are the same as the contents
  entered into the computer.
• Three methods of verification
• Computer verification
• You enter the data in twice and the computer
  checks the entries.
• Manual verification
• You enter the data in and check manually from the
  screen against the source.
• Lookup verification
• Having part of the data and retrieving the
  rest/checking the rest by looking up the data on
  a list
• Postcode enter postcode to get street

34
Verification (cont.)

• Designed to trap transcription errors
• Problems
• Manual
• Blurred eyes
• Computer
• May have made the same error and therefore it is
  not picked up
• Lookup
• List may be incomplete or contain incorrect data
• May be multiple values returned in lookup
  postcode returns more than one address

35
Validation

• Making sure that the data value entered is
  sensible and reasonable
• Types of Validation
• Field Presence Check
• Field Length Check
• Range Check
• Format/Picture Check
• Check Digit

36
Types of Validation

• Field Presence Check
• Makes sure data has been entered into a field
• Called a required field in MSAccess
• Field Length Check
• Checks the number of characters entered (minimum
  and maximum)
• Range Check
• To check that the value entered is within a
  pre-determined range.

37
Types of Validation (cont.)

• Format/Picture Check
• Makes sure that the data entered follows a known
  pattern (e.g. Postcodes, National Insurance
  Numbers)
• Check Digit
• Allows a number to be self checking the
  computer applies a set of rules which determines
  of the numbers entered are valid. (ISBN)

38
Validation and Verification

• Can only ensure that the data is reasonable
• Cannot guarantee accuracy
• If the source data is wrong then the data entered
  into the system will also be incorrect
• Verification makes sure the source is the same as
  the object
• Validation makes sure the data is within
  acceptable boundaries
• Neither ensures accuracy of data

39
Costs of Producing Information

• Information costs money to produce.
• Hardware
• To collect, analyse and output the data
• Storage space to hold the data
• Purchasing of equipment and updating the
  equipment
• Software
• Required to analyse the data and to report on the
  results
• Software licences
• Maintenance agreements
• Manpower
• People employed to collect or enter the data
• Maintenance of hardware and software

40
Costs of Producing Information

• Additional Factors
• Training of staff
• User manuals
• Consumables
• Paper
• Toner cartridges

41
Information as a Commodity

• Information is used for a variety of purposes
• Decision Making
• Planning
• Control
• Recording Transactions
• Measuring Performance
• Intended use affects its value.
• Costs must be balanced against the benefits
• the greater the benefit the higher the cost you
  will be prepared to pay

42
Rule Based Systems

• Humans interpret information to gain knowledge
• This knowledge is used as the basis for making
  decisions
• An expert/rule based system is used to support
  the decision making process

43
Rule Based System - Definition

• A rule based system is a computer program that
  attempts to solve a problem in the same way as a
  human expert
• It has three components
• Knowledge base
• Inference engine
• User interface

44
Rule Based Systems (cont.)

• They appear intelligent but are not
• Some expert systems are heuristic
• This means that they can increase the rule base
  and knowledge base through experience, just as a
  human expert does.
• The knowledge base consists of If..Then rules
• E.g. IF it is raining THEN I need to take an
  umbrella with me
• Has a set of solutions
• Uses questions to narrow down the possible
  answers until only left with one

45
Reporting

• Business applications which produce standard
  reports would take the data and present it in a
  format which is readily understood
• Take the total number of beans left in stock at
  the end of a month and present a graph of stock
  levels for the whole year.
• A Rule base system would take the data and
  analyse it to make deductions
• Based on the data the system could recommend
  amounts to order to ensure there is not a surplus
  or a deficit of stock.

46
Reporting

• Looking at the difference between
• Standard report
• For example, queries created in a database and
  then a report is created based on the results of
  the queries
• Static
• Rule base system
• Can make recommendations based on data
  extrapolate and look at trends to give probable
  outcomes

47
ICT Terms

• Input
• Taking data external to the current system and
  entering it into the system.
• Processing
• Manipulating the data into information usually
  into a form understandable by the user doing
  something with the data
• Output
• Taking data within the system and presenting it
  to the user, or in a format specified by the user
  (e.g on disk, screen, paper, etc.)

48
ICT Terms (cont.)

• Storage
• Holding either the input or the results of
  processing for use at a later date.
• Feedback
• Where the output of the system influences the
  input.
• There is a continuous loop of input resulting in
  output which in turn affects the subsequent input.

49
ICT Structure Diagram
50
ICT Feedback Example

• Setting
• School registers via OMR sheets
• Input
• Taking of register in the morning is the pupil
  present or absent?
• Processing
• Input of register details into the system and
  collating present attendance with pupils record
  of attendance
• Output
• At end of the week an absence list of unaccounted
  absences for that pupil is produced, at the end
  of each month a record of the pupils attendance
  is produced.

51
ICT Feedback Example (cont.)

• Storage
• The storage of the attendance data on the pupil
  during their school career
• Feedback
• Filling in the absence list with reasons, which
  is re-input into the system
• The next weeks absence list should be shorter
  with fewer entries, with the absences with
  reasons removed.
• Negative feedback is where the system is stable.
• In the above example, stable is where none of the
  absences are unaccounted for and the feedback
  moves towards this state.