Data Processing Techniques | ICT For AP TET 2022

7.1              D Definition of Data Processing

Data processing is be defined as the methods and procedures of converting data into information. That is, the manipulation of data, its retention and subsequent retrieval.

Data processing is the activities involved in the transformation of data into information.

• Methods of Data Processing

Various methods of data processing are discussed below:

Manual method: This involves the use of pen, pencil, paper, desk calculator/adding machine and human brain. This mode of data processing is suitable in a business environment where volume of data to be processed is small and delivery time is not critical.

Mechanical (or electro-mechanical) method: This involves the use involves the use of machines that are activated by an operator

e.g. punch cards equipment, Abacus, accounting machine etc. This method is faster than the manual method of processing data. The rate of error is less than the error rate in manual mode.

 Advantages                                      Disadvantages       

                    

Electronic Data Processing Method: This involves the use of computer to process data. EDP has the highest processing speed among all the three modes of data processing. Input devices are used to feed data into the computer; microprocessors are used to process the data and output devices are used to pass the result of processing (i.e. output or information) to the user.

Advantages                                             

1. It produces accurate results.
2. It is very fast.
3. It improves efficiency.
4. It handles complex computation.
5. It has large storage facility.
6. Handles large volume of data in short time.

   Disadvantages

1. High     initial     set-up cost.
2. Requires            regular power supply.
3. Required            skilled personnel.
4. Data is susceptible to virus attack.
5. Cost of maintenance may be high.

• Data Processing Stages

Stages of data processing include: origination, input, processing, storage, output and distribution. The data processing cycle is depicted in the following diagram.

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Figure 7a.      Data Processing stages

1. Origination: This is the collection of original (primary) data. It is possible to collect data and store it away in a secondary storage medium pending the time the data would be required for processing.
2. Input: This means feeding the data into the computer. Data is prepared in a convenient/suitable form for processing. The form of input device used depends on the data processing method. e.g. punch cards are used in electromechanical data processing while magnetic tape, disk, keyboard (terminal) are used in electronic data processing.
3. Processing: This is a planned series of actions and operations performed on data to convert the data to a more meaningful form called information.

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1. Storage: storage of data/Information could take place at any stage for future purposes. The double-headed arrows show the storage of data/information at one time, and the stored data/information serving as input for processing at another time.
2. Output: This is the result of the processed data (information). At times, the information produced today could be distributed to the appropriate end-users; at some other times, the information could be used as input for another data processing cycle.
3. Distribution: This is the dissemination of information produced, to the appropriate quarters for decision-making. The decision-makers are the end-users of information.

• Data Processing Techniques

Various Data processing techniques available are discussed as follows.

1. Batch Processing: This is a processing technique whereby many individual jobs/transactions are collected together (i.e. batched) and periodically processed against a master file using a single program loading. For instance, before the advent of online banking, many banks adopted batch processing where records of customer deposits made throughout the day are collected and batched for processing in the night (nightly processing) against the master file of account balances. However, if batch processing is used to process withdrawals, criminals would not have to use guns to rob banks! Batch processing is also applicable in payroll system which processes the different employee’s payslip individually in a single continuous operation. It is also applicable in billing system.

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1. Online-Processing: On-line processing: is the technique of using computer to process data by means of terminals connected to and controlled by a central processor. On-line systems involve the receipt of data directly from the point of origin through a variety of input/output terminal devices with telecommunication capabilities. On-line processing provides direct access to information files by terminal users and also enables them to update files with transaction data.

1. Time Sharing: Time-sharing: This processing technique allocates several small equal time slices to terminal users as their jobs are being processed. For instance, the computer might allocate 100 milliseconds (ms) to user 1, then 100ms to user 2, and then 100ms to user 3 and so on. If there are 100 users on the computer, it will allocate 100ms to each user before it returns to the top of the job queue and gives each user another round of 100ms. By so doing, the computer is able to work so quickly that each user feels as if he/she has exclusive use of the computer system. As new user jobs are presented to the computer or are finished, they are respectively added to or deleted from the job queue.

1. Real-Time processing: Real-time processing: is an on-line technique which is able to receive input data from various sources and which is able to process that data sufficiently rapidly to be capable of influencing the sources of the data.

• Distributed vs. Centralized Processing: A combination of local and remote computer terminals in a network connected to a central computer to divide the workload. A Distributed data processing facility is one in which computers, usually smaller computers, are dispersed throughout the organization. The objective of such disperse is to process

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information in a way that is most effective based on operational. Centralized data processing is a Computer data processing architecture where data processing support is provided by one or a cluster of computers, generally a large. Dedicated computers, located in a central data processing facility. In a centralized architecture, each person is provided with a local terminal that is connected by a communications facility to the central data processing facility.