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Data processing, is the Manipulation of data by a computer. It includes the conversion of raw data to machine-readable form, flow of data through the CPU and memory to output devices, and formatting or transformation of output. Any use of computers to perform defined operations on data can be included under data processing. In the commercial world, data processing refers to the processing of data required to run organizations and businesses.


1.Time Sharing

This kind of Data processing is entirely based on time. In this, one unit of processing data is used by several users. Each user is allocated with the set timings on which they need to work on the same CPU/processing Unit.

Intervals are divided into segments, and thus to users, so there is no collapse of timings which makes it a multi-access system. This processing technique is also widely used and mostly entertained in startups.

2.Multi Processing

This is the most commonly used data processing technique. However, it is used all over the globe where we have computer-based setups for Data capture and processing.

As the name suggests – Multiprocessing is not bound to one single CPU but has a collection of several CPUs. As the various set of processing devices are included in this method, therefore the outcome efficiency is very useful.

The tasks are broken into frames and then sent to the multiprocessors for processing. The result obtained is expected to be in less time and the output is increased. The additional benefit is every processing unit is independent, thus failure of any will not impact the working of other processing units.

3.Online Processing

This data processing technique is derived from Automatic data processing. This technique is now known as immediate or irregular access handling. Under this technique, the activity by the framework is prepared at the time of operation/processing. It can be viewed easily with the continuous preparation of data sets. This processing method highlights the fast contribution of the exchange of data and connects directly with the databases.

4.Data Virtualization

Data virtualization techniques are another important development in real-time data processing, where the data remains in its source form, the only information is pulled for the needs of data processing. The beauty of data virtualization is where transformation is not necessary, it is not done, so the error margin is reduced.

Data virtualization and stream processing mean that data analytics can be drawn in real-time much quicker, benefiting many technical and financial applications, reducing processing times and errors.

Other than these popular Data processing Techniques there are three more processing techniques which are mentioned below-

5.Batch Processing

To save computational time, before the widespread use of distributed systems architecture, or even after it, stand-alone computer systems apply batch processing techniques. This is particularly useful in financial applications or where data requires additional layers of security, such as medical records.

Batch processing completes a range of data processes as a batch by simplifying single commands to provide actions to multiple data sets. This is a little like the comparison of a computer spreadsheet to a calculator in some ways. A calculation can be applied with one function, that is one step, to a whole column or series of columns, giving multiple results from one action. The same concept is achieved in batch processing for data. A series of actions or results can be achieved by applying a function to a whole series of data. In this way, computer processing time is far less.

Batch processing can complete a queue of tasks without human intervention, and data systems may program priorities to certain functions or set times when batch processing can be completed.

Banks typically use this process to execute transactions after the close of business, where computers are no longer involved in data capture and can be dedicated to processing functions.

Methods of data processing in brief
  • Manual Data Processing – Manual data procession is totally depended upon human being’s brain power and efforts. Not a single or simplest device is used to calculate data. Every calculation is done by the staff and there is a big chance of errors. When you are running a complete setup, you need to switch yourself among different jobs. This increases the chances of error. Manual data processing is slow and there is a higher chance or errors and omissions.
  • Mechanical Data Processing – Secondly, there is another kind of data processing where all of the calculation is done by calculator. This type is called mechanical data processing. Though manual and mechanical data processing are obsoleted now a day, there are a few small organizations which cannot afford to have a complete computerized setup. This requires them to use mechanical data processing.
  • Electronic Data Processing – The third, modern and most common form of data processing is called electronic data processing. In this all the data is processed and calculated through computers and there are negligible chance of errors and mistakes. There are various applications which are designed to process data and this technique is the fastest of all three kinds. Electronic data processing methods makes it more organized, accurate, eases data analysis and is less time-consuming as well.

Automatic versus Manual Data Processing

It may not seem possible, but even today people still use manual data processing. Bookkeeping data processing functions can be performed from a ledger, customer surveys may be manually collected and processed, and even spreadsheet-based data processing is now considered somewhat manual. In some of the more difficult parts of data processing, a manual component may be needed for intuitive reasoning.

The first technology that led to the development of automated systems in data processing was punch cards used in census counting. Punch cards were also used in the early days of payroll data processing.

The Rise of Computers for Data Processing

Computers started being used by corporations in the 1970s when electronic data processing began to develop. Some of the first applications for automated data processing in the way of specialized databases were developed for customer relationship management (CRM) to drive better sales.

Electronic data management became widespread with the introduction of the personal computer in the 1980s. Spreadsheets provided simple electronic assistance for even everyday data management functions such as personal budgeting and expense allocations.


Importance of data processing includes increased productivity and profits, better decisions, more accurate and reliable. Further cost reduction, ease in storage, distributing and report making followed by better analysis and presentation are other advantages. The need to process data is now widely realized and reflected in every field of work. Let the work be done in a business atmosphere or for educational research purpose, data management systems are used by every business. It is a multidimensional process which is involved in almost every field of human life. Generally speaking, the term “Data processing” is used where you have to collect innumerable data files from different sources. You have to arrange them in a way that can be practically beneficial for the purpose you have gathered all that material. It is a task of synchronizing collected data from different sources and convert it to an organized form . This makes it easy to understand and retrieve the specific information anytime. There are various data processing methods which include manual data processing, mechanical data processing and electronic data processing. Data processing is one of the most important daily tasks especially when dealing with big data and performing data mining All those fields where we can expect a huge data available to settle down like education, banking or transportation now realises the importance of data processing. With the emergence of fields like data science, data analysis, big data etc. the need to process data and to understand the importance of processing the data is crucial.

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