6. Why data is 'encoded'

Keeping it short

Data is often coded before storage.Coding means to change the original data into a shortened version by assigning a code.

For example consider these data codes:

Gender: Instead of Male, Female it could be shortened to M, F

 

 

Dates: The full date of 18th January 2000AD could be shortened to JAN

 

 

 

The colours of the houses could said to be 'lilac, 'light blue', 'black', 'sage' and so on - but the data coding could change these to Pink, Blue, Black, Blue.

(Do you see the problem with coding? - see below)

 

Advantages of coding

• Less storage space required
• Comparisons are shortened and can therefore be made quicker, thus speeding up searches
• A limited number of codes makes data input faster and simplifies validation.

Problems of Coding

Precision of the data is 'coarsened', e.g emerald green and light green both coded as Green - see the house example above. This coarsening may not be apparent to the user if the code is turned back into a colour name on output.

Original picture:

The colours go into the database as: Pink, Blue, Black, Blue

And so the information comes out as:

The subtle details are lost.

 

Coding obscures the meaning of data

A reader seeing the 'gender' data as M/ F is pretty likely to know that it means Male/ Female. But with more obscure codes such as Switzerland being coded as CHE means the reader must be given the complete list of possibilities to understand the meaning of the data.

 

Coding of Value Judgements

For example, was that curry too spicy? As it is to be coded as a judgement of 1-4. This will be coded differently by different people and makes comparison difficult.

 

The problem with a value-judgement is that there is no single correct value. The value depends on someone's opinion.

Coding of value judgements will inevitably lead to coarsening of the data since there will be a wide range of opinions that could be held and only a limited number of codes available.