UNIT 1: TOPIC 1: Data and Quality of Data

UNIT 1: TOPIC 1: Data and Quality of Data

1. Keywords and Definitions

   • 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.
   • Processing: Performing calculations or arranging the data into a meaningful order. Example would be working out an athlete’s average age over 3 years.
   • Transaction: A piece of business, e.g. an order, purchase, return, delivery, transfer of money etc. An example would be buying clothes online.
   • Information: Data that has been processed by the computer. Data + Meaning + Context = Information
   • Knowledge: Derived from information by applying rules to it. Example would be we can see an increase in the athlete’s wages.

2. Encoding

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

3. Data Validation

   • Transcription error: Error made when typing in data using a document as the source of the data.
     • Examples:
       • 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.
     • Examples:
       • Example would be putting faceboko instead of facebook.
   • Check digit: A decimal or alphanumeric added to a number for the purpose of detecting the sorts of errors humans normally make on data entry.
     • Examples:
       • 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.
     • Examples:
       • Checking the data of birth of a yr 9 pupil, if DOB is lower than 1998, the system wouldn't allow that person to enter that date as they are too old.
   • Presence check: Is used to check that data is entered into the correct fields.
     • Examples:
       • For example entering your username and password on facebook.
   • Validation checks: Are checks a developer of a solution sets/ creates, using the software, in order to reduce errors.
     • Examples:
       • Presence and range checks are validation check examples.

4. Data Verification

   • Batch total: A meaningful total that is used to check that the computer has input all the data.
     • Examples:
       • 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.
     • Examples:
       • 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.
     • Examples:
       • 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.