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UNIT 1: TOPIC 1: Data and Quality of Data
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Mind Map on UNIT 1: TOPIC 1: Data and Quality of Data, created by tangj024 on 04/15/2013.
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tangj024
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Resource summary
UNIT 1: TOPIC 1: Data and Quality of Data
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.
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
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.
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.
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