TOPIC 1

TOPIC 1

1. Evaluating data for fitness for purpose, accuracy and bias

   • GIGO: Abbreviation for garbage in garbage out. It means if you out rubbish into the computer then you get rubbish out.
   • DATA: Raw facts and figures, e.g. readings from sensors and survey facts, etc. Data can be numbers, words, images, sound.
   • Knowledge: Derived from information by applying rules to it. Example would be we can see an increase in the athlete’s wages.
   • Processing: Performing calculations or arranging the data into a meaningful order. Example would be working out an athlete’s average age over 3 years
   • Information: Data that has been processed by the computer. Data + Meaning + Context = Information
   • Transaction: A piece of business, e.g. an order, purchase, return, delivery, transfer of money etc. An example would be buying clothes online.

2. Encoding and the storage of data

   • Encoding: Assigning codes to the data reducing its size.
     • Two examples would be: 1. Size of clothes- S= Small, M= Medium, L= Large 2. Airport codes- LHR= London Heathrow, MAN= Manchester
   • Advantages of encoding: takes up less storage space, easily transferable, easier to back.
   • Disadvantages of encoding: Easily stolen, training needed, security problem, viruses etc.

3. Data validation

   • Transcription error
     • : Error made when typing in data using a document as the source of the data. Example mishearing what a person says over the telephone and entering it into the computer.
   • Transposition error
     • Error made when characters are swapped around so they are in the wrong order. Example would be putting faceboko instead of facebook.
   • Check digit
     • A decimal number (or alphanumeric character) added to a number for the purpose of detecting the sorts of errors humans normally make on data entry. Example account numbers will have check digits, to check that the numbers inputted are correct.
   • Range check
     • Data validation technique which checks that the data input to a computer is within a certain range. Example would be checking the data of birth of a yr 9 pupil. If there date of birth is lower than 1998, the system wouldn’t allow that person to enter that date as they are too old for yr 9.
   • Validation check
     • Are checks a developer of a solution sets/ creates, using the software, in order to restrict the data that a user can enter, so as to reduce errors. Presence and range checks are validation check examples.
   • Presence check
     • Is used to check that data is entered into the correct fields. For example entering your username and password on facebook.

4. Data verification

   • Batch total
     • A meaningful total that is used to check that the computer has input all the data. For example adding up all the totals of a batch of sold ebay items.
   • Hash total
     • Meaningless total of numbers, such as order numbers, used to check that all the data has been entered.
   • Parity Check
     • Check to make sure that the data sent is the same as that received when data is transmitted from one computer to another. An example would be an email sent from one person to another.
   • Verification
     • Checking that the data being entered into the ICT system perfectly matches the source of the data. An example would be checking the text/ data in an email and making sure when it arrives to the recipient that no error have been introduced.