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What is Data Processing and What are its Types and Method ?

An essential component of managing information to extract insightful knowledge is data processing. It involves turning unprocessed data into valuable and useable information. Data processing is now an essential part of many different sectors and businesses in the modern digital age, facilitating well-informed decision-making.

Using a calculator to find out the results of mathematical operations is considered a type of data processing. Editing video clips in their raw form in order to obtain smaller clips is also considered data processing, as are requests to withdraw and deposit money in ATM machines.

But how is the data processed? Is it a simple process? Or complicated? In this article, we delve into the field of data processing to answer these questions and more, and explain its stages and types in detail, but in a simplified manner.

What is Data Processing ?

What is Data Processing ?

Data processing refers to the systematic manipulation, organization, and analysis of raw data to extract meaningful information. It involves various steps, including collection, sorting, cleaning, and transformation of data into a more understandable format. Through the use of specialized software and algorithms, data processing aims to uncover patterns, trends, and insights that can aid decision-making and problem-solving in various fields such as business, science, healthcare, and more.

For example, governments and giant economic institutions process the data they collect to be able to plan for the future. E-commerce sites depend on processing the data they collect from users to help them find the products they need.

It is also relied upon primarily in the field of technology to determine what users need, whether now or in the future.

Data processing begins with the data in its raw form, then converting it into a form that can be easily read and understood.

Among the most popular forms that are relied upon to understand data after processing it are:

  • Graphs.
  • documents.
  • Tables.

The previous forms give the data the form and context necessary for its interpretation by computers. These forms also facilitate the possibility of using the data by workers in companies and concerned parties.

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Stages of Data Processing

The stages of data processing encompass:

  1. Data Collection: Gathering raw data from different sources.
  2. Data Input: Converting collected data into a format suitable for processing.
  3. Data Processing: Analyzing and transforming the input data.
  4. Data Storage: Storing processed data for future use.
  5. Data Output: Presenting the processed data in a readable form for users.

Types of Data Processing

Types of Data Processing

Is all data processed the same way? Yes and no, the data processing process is similar to each other, but the size and nature of the data may require relying on one of the following methods to process the data. 

1. Batch Processing:

In this process, data is processed by collecting it into batches or groups. This method is considered the most appropriate for processing the huge amounts of data that large companies deal with, such as processing data on credit card company customers’ operations.

2. Real-time Processing

In this type, the raw data is processed at the same time as it is obtained. Operations that require processing a small amount of data depend on this type of processing, and its most famous uses are automated teller machines that process customer request data in real time and execute them.

Real-time data processing also depends on sensors to receive the data, then process it in real-time and output it. The most famous systems that rely on this type are protection systems and alarm devices.

3. Online Data Processing :

Data processing via the Internet is similar to real-time processing. The fundamental difference between the two types is that the first relies heavily on the Internet to send and receive data, and is also subject to the power of the system that manages this data, which sometimes exposes it to delays.

The difference between the two types can also be seen when depositing cash amounts in an ATM. The machine will add the amount that was deposited to its owner’s account instantly, but the bank’s system may take time to process the amount and add it to the account.

4. Online Analytical Processing :

Focuses on complex queries and analysis of data for decision-making purposes. OLAP systems are optimized for read-heavy operations, allowing users to analyze large volumes of data quickly.

5. Distributed Processing:

Involves the use of multiple interconnected computers to work on a task. It helps in handling large datasets by distributing the workload across multiple nodes, improving efficiency and reducing processing time.

6. Time Sharing

Processing different sets of data simultaneously (or time sharing) is closely related to multiprocessing, in both types where data is processed by more than one central processing unit simultaneously.

The difference is that in processing data through time division, one processes the data almost simultaneously. Every user who communicates with the central processor to process his own data gets a share of the processor time called a time slice.

The processor arranges the segments and processes the data sequentially with a small time difference between each processing operation and the next. This difference is approximately a fraction of a second, and thus the user does not feel any significant delay in processing his data.

7. Stream Processing:

Involves handling and processing continuous streams of data in real-time. It’s used for applications that require immediate analysis and actions based on incoming data, such as IoT (Internet of Things) devices or financial trading systems.

8. Multiprocessing

In multiprocessing, processing is done using more than one central processing unit. Here, each unit processes a specific part of the data and then devotes itself to processing another part, and so on.

This type is usually used to process huge amounts of data and relies on a large number of central processors. In the past, this type was only used on giant computers that had more than one central processing unit.

Now central processors have become cheaper than before, which has contributed to their use in home computers as well. Note that currently central processors contain more than one processing unit inside them, or cores, that can process data easily.

This type is used to process weather data from different regions to predict weather conditions in specific areas.

9. Near-line Processing:

Combines elements of both online and offline processing, where data is stored in a way that allows relatively quick access but not as immediate as online processing.

Data Processing Method

1. Manual method

The manual method is considered one of the oldest methods of data processing. As is clear from the name, data is processed manually by one or more people. Here, the human element is relied upon in all the stages that we explained previously.

This method has many advantages, such as its low cost and not relying on any tools of any kind. However, this method has disadvantages that should be taken into account when choosing a method for processing data… the most important of which is that it requires a great deal of time and effort to obtain information.

Also, the biggest disadvantage of this method is the high error rate as a result of relying on the human element alone in the process. Currently, this method is rarely used, as the electronic method has replaced it even in small data processing operations.

2. Mechanical method

This type relied on automated or mechanical tools in most stages of data processing. These tools may include, but are not limited to:

  • Calculators (mechanical ones that were used in the past).
  • Typewriters.
  • Pressure printing machines.

The error rate in this method is much lower than its previous counterpart, but the biggest drawback of this method is its inability to deal with the huge amounts of data that exist today. Like the previous method, this method is not currently relied upon to process data.

3. Electronic method

The technical development that brought us computers also brought with it enormous amounts of data that needed to be processed. These quantities took advantage of the advantages provided by the computer, such as automating processes and providing reliable information quickly and without errors.

You can run an electronic application that collects data, classifies it, and outputs it in the required format within minutes and without any significant effort. Large companies prefer to rely on this method because it is the most effective, and its outputs can be relied upon to make successful growth decisions.

Finally, each of the previous methods was the main method for processing data in one era. Currently, all data processing operations are based on the electronic method, due to the fact that electronic devices have become accessible to everyone.


In conclusion, data processing serves as the backbone of modern businesses and industries. Its efficient execution leads to improved decision-making and streamlined operations, but it also demands addressing challenges for optimal utilization.


What role does data processing play in business analytics?

Data processing is important in business analytics as it helps derive insights from raw data, aiding in strategic decision-making.

Is manual data processing still relevant in today’s technological era?

manual data processing remains relevant in specific industries requiring human intervention for data accuracy.

How does data processing enhance cybersecurity measures?

Effective data processing includes security protocols to protect sensitive information, thereby bolstering cybersecurity measures.

What are the potential risks associated with data processing?

Risks involve data breaches, data manipulation, or misinterpretation leading to flawed decision-making.

Can data processing be entirely error-free?

Achieving absolute error-free data processing is challenging due to complexities in handling vast amounts of information, but stringent quality checks mitigate errors significantly.



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