Data and Quality of Data

Data and Quality of Data

1. Data

   • Data is raw facts and figures
   • Data has no meaning or relevance
   • Data can be alphanumeric, graphics or audio etc...
   • Not possible to understand data
   • Examples of data: #123098"£
   • Data has not been processed

2. Information

   • Information is data that has been processed
   • Data + Meaning + Context= Information
   • Processing gives data meaning
   • Processed data means having calculations performed on it, organizing it and converting it to give meaning

3. Knowledge

   • Knowledge is obtained by applying rules to the information
   • Knowledge is the application of information to a situation
   • Example of knowledge: The sell by date on a salad is 17th September 2014 tomorrow it will be out of date so I have to eat it today because if I eat it tomorrow I will be ill

4. Example of data, information and knowledge

   • Data: 0930
     • Knowledge: The supermarket closes at 9:30 so I have to go there before it closes or I won't get my bread
     • Information: 09:30 pm is the time the supermarket closes,

5. Good Quality Data

   • Good quality data has to be accurate
   • Has to be fit for purpose
   • Has to be unbiased
   • Has to be up to date
   • Has to be valid

6. GIGO

   • GIGO stands for garbage in, garbage out
   • It is when you put incorrect or inaccurate data in to a computer system you get inaccurate data processed and not produce information that is useless and unusable

7. Data Encoding

   • Encoding is putting or assigning codes to data and reducing it's size
   • Example of encoding: Size of clothes: S= Small, M= Medium, L= Large
   • Encoded data is quicker to input in the computer system
   • Encoded data takes less storage space on disk
   • Encoded data is easier to check validation methods
   • Encoded data is faster to access
   • Encoded data is quicker to send over a network

8. Types of Errors

   • Transcription errors
     • Transfer data from a form to a computer system
     • Example: Mishearing someone on the phone
   • Transposition errors
     • Typing quickly and mixing the numbers/letters
     • Example: 1243 instead of 1234
   • Input Errors
     • Example: Keyboard errors
   • Processing errors
     • Example: Mistakes in the formulas of a spreadsheet
   • Transmission errors
     • Example: Data becomes corrupted as it travels through cables/ wireless due to electrical storms

9. Validation

   • Validation is the process that ensures that data is entered is sensible and within acceptable boundaries( within a certain range)
   • Data type check: Making sure that the right type of data is entered
   • Presence Check: Ensuring that data has been entered into a field
   • Range Check: To make sure that a number lies within a certain range
   • Format Check: Checks to make sure that the data contains the correct combination of letters and numbers
   • Check digits: A number placed out the end of a string of numbers to check that they have been correctly input into the computer

10. Data Verification

    • Data verification means checking that the data being entered in to the ICT system perfectly matches the source of the data.
    • Visual checks: One user carefully reading what they have typed in and comparing it with what's on the data source for any errors that can be corrected
    • Double entry of data: Enter the same detail twice in the system. Only matching identical details will be accepted eg. creating a new password
    • Parity Checking: Involves checking data after it has been through a wire or wireless. Checks the data has not been corrupted.

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