Topic 1- Data and Quality of Data

Topic 1- Data and Quality of Data

1. SUB TOPIC: Evaluating data for fitness for purpose, accuracy and bias

   • GIGO: Abbreviation for garbage in garbage out. It means if you put 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 or sounds.
   • Processing- Performing calculations or arranging the data into a meaningful order. For example, 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. For example, 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. For example, we can see an increase in the athletes wages

2. SUB TOPIC: Encoding and the storage of data

   • Encoding: Assigning codes to the data, reducing its size.
     • Example: Size of Clothes. S=Small. M=Medium. L=Large
     • Example: Airport Codes. LHR- London Heathrow. MAN- Manchester
   • Advantages of Encoding
     • Takes up less storage
     • Easily Transferrable
     • Easier to back up
   • Disadvantages of Encoding
     • Easily Stolen
     • Training Needed
     • Security Problem (viruses etc.)

3. SUB TOPIC: Data Validation

   • Transcription Error: Error made when typing in data using a document as the source of the data.
     • For 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
     • For example, 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 the data input to a computer is within a certain range.
     • Example: Checking the date of birth of a Year 9 pupil . If their date of birth is lower than 1998, the system wouldn't allow that person to enter that date as they are too old for year 9
   • Presence Check: used to check that data is entered into the correct fields.
     • Example: Entering username and password on a website
   • Validation Checks: are checks a developer of a solution sets or creates, using the software, in order to restrict data that a user can enter, so as to reduce errors
     • Examples: Range checks and presence checks

4. SUB TOPIC: Data Verification

   • Batch Total: A Meaningful total that is used to check that the computer has input all the data
     • 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 send is the same as that received when data is transmitted from one computer to another
     • Example: An email sent from one person to another
   • Verification: Check that the data being entered into the ICT system perfectly matches the source of the data.
     • Example: Checking the text/data in an email and making sure when it arrives to the recipient that no errors have been introduced