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
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
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 https://www.jigsawacademy.com/blogs/data-science/types-of-data-processing/)