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Data processing is more and more popular now because various uses associate with data such as personal information, customer data, location data, etc. Therefore, when we need to manipulate data, data processing is the means. This method converts the raw data which is the input into the meaningful information which becomes the output. There are some data processing techniques which are grouped into five main types which are based on the functions of data.
1.      Commercial data processing
This system deals with huge data as the input and generates a large volume of output. This process uses less computational operations which combine commerce and computers. Since the processed data are standardized, the possibility of data errors is low. Surely, this is quite effective and useful for business. The prototypical example of data processing application using this method is Accounting Program.

2.      Scientific Data Processing
Contrasted with commercial data processing, Scientific data processing involves a large use of computational operations with less volume of input and output. This method uses arithmetical and comparison operations. As the consequence of the required accuracy, the process of validating, sorting, and standardizing must be done carefully. Due to this, the process takes longer time. The great accuracy of the output helps us make the correct decisions or conclusions.

3.      Bath Processing
This processes a number of cases simultaneously. The data that are collected and processed in batches must be homogenous and in large quantities. The process can be simultaneous, concurrent or sequential depending on how the batches are executed by the same resource, either respectively at the same time, partially overlapping in time or immediately after one another. The less computational time results in completing work without much human intervention. This process is commonly used in financial applications.

4.      Online processing
In contrast to batch processing, online processing can be built by more simple operators. This process is used when the data to be processed continuously and is fed into the system automatically.

5.      Real time processing
It is for commercial uses which have large processing applications. They require to get results from data exactly as it happens. The most common example is stream processing where the data analytics are directly drawn from the source, so the conclusion can be drawn without transforming or downloading the data.

(Adapted by Hedwig Maria from
based on the function of data
be gotten at the same time as it happens
in low probability of data errors
with the help of scientific data processing
to get the accurate output
as large as the output
due to various use of data
by bath processing
able to build the process
than commercial data processing does
the process will be sequential
because of the less computational time
after the conversion of the raw data


The popularity of data processing is ...


The output can be gotten ...


The classification of data processing techniques is ...


In commercial data processing, the number of input is ...


Standardization on data processing results  ...


Scientific data processing involves larger computational operations ...


Conclusion or decision can be taken properly ...


Validation, sorting, and standardization of the input are required ...


processing a large quantity of cases is done ...


If the batches are executed by the same source immediately after one another, ...


Bath processing doesn’t need human intervention ...


The automatic system of online processing makes the simple operators ...


The data taken directly from the source makes the process of drawing conclusion ...

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