What is data coding and data integrity? Rules of data coding? What are the objectives of data coding?

Data Coding

 Code is an abbreviation of a full name. A code is an abbreviation for the name of something. Data coding is the process of abbreviating something using numbers, shapes, or symbols to create a unique signal. Data coding increases work efficiency and accuracy of data processing results manifold.

Purpose or Benefits of Data Coding: The need for data coding is very important in processing data with the help of computer. As a result of data coding, there are many benefits in program writing and processing. Following are the objectives or benefits of data coding --

 (i) Data can be presented easily and concisely.

 (ii) Data coding takes less space in computer memory.

(iii) Less chance of error in data typing and saves typing time.

(iv) Data coding also speeds up processing.

(v) It can easily categorize information.

(vi) It helps to keep information confidential.

(vii) It enables disclosure of confidential information.

Rules of Data Coding: The importance of data coding in computer programming is immense. In the case of computers, certain rules have to be followed during data coding. The rules of data coding are given below--

(i) Code must make sense.

(ii) Code must be unique.

(iii) The code should be uniform.

(iv) Code should be permanent.

(v) Code should be such that it is easy to understand.

(vi) Code should not be misleading.

(vii) Codes need to be concise.

Data Integrity: Presenting data as reliable information is called data integrity. Data quality has to be ensured to maintain data integrity. Data integrity depends on the following factors:

Accuracy: The accuracy of the data is checked before use in the system and corrected as necessary. Incorrect data weakens the foundation of the system, as correct output cannot be expected from incorrect input. For example, overwriting a bank check to pay a bill can cause serious damage.

Completeness: Data must be checked for completeness before using it in the system. If the data is incomplete then it is corrected properly. Incomplete data like wrong data also weakens the foundation of the system. For example, in a bank check, the amount of money is written in numbers but not in words. As a result, the money will not be withdrawn through the check.

Timeliness: Consistency between data and time is very important. The system becomes ineffective if the data used in the system is not timely. For example - in the case of online stock exchange its performance is completely dependent on timely data entry.

Relevance: There should be no inconsistency in the input data. Inconsistency in homogeneous data affects the output of the system. For example - Date of birth and age data of a student should be such that birth date minus current date equals age.

Security: Data security is an important issue for any organization or individual. It is ensured who can input any data and who can view or update it. For example, while withdrawing money by credit card, a secret number or password given by a certain person is used as input, which no one but the user can know.

Conciseness: Data should be concise so that it can be easily input into the system. Data is coded to summarize the data.

Relevant: The data must be directly or indirectly related to the purpose of the information system. Irrelevant data increases system complexity.

Ensuring data quality:

Data is given as input to a data processing system and this data is processed to make sense in a context that is used as information. The accuracy of the information received in the information system depends on the data provided as input and the accuracy/truthfulness and validity. Accurate information can never be obtained if wrong data is given as input. This is called Garbage in Garbage Out GIGO.

Incorrect data will generate incorrect information, which will defeat the purpose of using the information system. Misinformation will create various complications. So the accuracy and validity of the data used in the information system is very important.

The input data may be incorrect for the following reasons, viz

• Mistakes in data collection,

• Errors in arranging data in the format required by the program,

• Errors in data transfer,

• Error in reading data by system input device

• Typing mistakes etc. during data entry.

Accurate data is required in the system to maintain data integrity or correctness by ensuring data quality. To ensure data quality, the following three tasks must be performed smoothly:

(i) Effective data coding

(i) Effective and efficient data entry and

(iii) Verification of authenticity and validity of data.