Topic 1 - Data, Information and Knowledge


Topic 1 - Data, Information and Knowledge

Data - Raw facts and figures that have no meaning on their own

Information - Data that has been processed within a context to give it meaning

Knowledge - Is derived from information by applying rules to it

Example - Data = 53.7, 49.1, 45.6, 59.8, 59.2, 51.7

                 Information = Times of swimmers taking part in a race

                 Knowledge = The swimmer with the fastest time wins the race

Example -  Data = 120/60, 135/65, 140/70

                  Information = A patient's blood pressure readings at 9, 10 and 11 o clock

                  Knowledge = The patient's condition is worsening, and needs medical assisance

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Topic 1 - Encoding Data

Reasons for Encoding Data

  • Coded data takes less effort to type in
  • Takes up less storage space
  • Fewer transcription errors

Examples - GB = Great Britain, LHR = London Heathrow, M = Medium

Problems with Encoding Data

  • People may not understand the codes
  • It coarsens the precision of data

Example - Entering the details of a criminal into a database, eye colour is blue/green, categories are blue, green, brown, grey, which category does it fit into? Leads to inaccurate data    

Value Judgements

Encoding data involves person collecting or entering data making judgements about which code

Example - Heights of 10 people, decide what category heights go in (tall, medium, short)

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