Data, Information and Knowledge

Problems of Encoding

1) - Coarsening data can lead to loss of precision if dark brown, mousy brown, light brown are all classed as brown. 2) - Value judgements can lead to inconsistency for example if asked "Was the meal 'excellent', very good', 'good', or 'poor'?" One person's excellent meal is only good for another.

Data, Information and Knowledge

Data consists of raw facts and figures. Information is data which has been processed by the computer or data in a context or with a meaning. Knowledge is derived from information by applying rules to it.

Encoding

Encoding is the process where data is shortened. Examples of encoding are: - Size of clothes: • S = Small • M = Medium • L = Large • XL = Extra large

Examples of Data, Information and Knowledge

Example 1: Data = 1, 63.6, 2, 59.3, 3, 59.7 Information = Race times: Swimmer 1 63.6s, Swimmer 2 59.3s, Swimmer 3 59.7s Knowledge: Swimmer 2 is the fastest and consequently wins. Example 2: Data = 11/05/94 Information = John's date of birth is 11th May 1994 Knowledge = John is over 18 and so now he can vote in the next election.

Advantages of Encoding Data/Why data is encoded

- fewer transcription errors - greater data consistency - easier to validate - less hard disk space required - takes up less room on the hard drive - less memory needed - Processing is faster (because less RAM required) - faster to search (pattern matching)