What Is Data Processing?

data processing

What Is Data Processing? The data is created every second. The popularity of social websites, media, and videos streaming have contributed to the growth in the volume of data. A study conducted by Domo suggests that 1.7MB of data is generated every second for every human around the globe by 2020. In order to make use of and gain insight from this huge amount of data, processing of data is required.

Data Processing

In its raw form, data is of no use for any business. The process of data processing involves the process of collecting data in raw form and turning it into usable data. It’s usually done in a step-by-step manner by a group consisting of data scientists and engineers in a company. The data that is collected is then filtered, sorted through processing, and stored. It is then analyzed, stored, and finally, presented in a format that is readable.

Data processing is essential for businesses to develop better business strategies and enhance their competitive advantage. Through the conversion of data into a format that is readable, such as charts, graphs, and documents, all employees within the organization are able to comprehend and utilize the information.

We now know the concept of data processing, let’s look at its cycle.

Data Processing Cycle

The process of processing data is comprised of a sequence of steps during which the raw information (input) gets fed to an algorithm (CPU) to generate useful information (output). Each step is performed in a certain order, but the whole process repeats itself in a continuous method. The process’s data may be saved and used as an input to the subsequent cycle.

In general, there are six primary stages of the process of data processing:

Data Processing

Step 1: Collection

The gathering of data in raw form is the very first step in the process of processing data. The nature of the raw data gathered will have a major influence on the output that is produced. Thus, raw data must be gathered from specific and precise sources to ensure the results are reliable and useful. Raw data may include financial numbers, website cookies, profit/loss reports of a firm, and user behavior, among others.

Step 2: Preparation

Data preparation, also known as data cleaning can be described as the act of sorting or separating the raw data in order to eliminate unneeded and incorrect data. Raw data is scrutinized for duplicates, errors mistakes, missing data, or miscalculations and then converted into a format that is suitable to be further analyzed and processed. This process is carried out to ensure only the most accurate data is fed to an analysis unit.

Step 3: Input

In this process, all raw information is transformed to machine-readable form and then fed to the unit for processing. This could be in its form as data input using a scanner, keyboard, or another input device.

Step 4: Data Processing

In this stage, this raw information is then subjected to various data processing techniques that employ artificial intelligence and machine learning to produce the desired output. The process may differ between processes based on the source of the data to be processed (data lakes or online databases, connected devices, etc.) and the intended usage of the results.

Step 5: Output

The data is then transferred and presented before the end-user. The data is displayed in an easily readable format such as tables, graphs and videos, audio documents, and more. The output is then stored and processed further in the next cycle of data processing.

Step 6: Storage

The final step in the process of data processing is storage, in which data and metadata are kept for later use. This permits rapid access and retrieval of data whenever required, and allows the data to be used as input into the next processing cycle immediately.

After we’ve understood what data processing is and its process, we can examine the kinds.

Types of Data Processing

There is a variety of data processing, based on the data source and the process followed by the unit processing it to create an output. There isn’t a universal method that is suitable to process raw data.

Batch ProcessingData is processed and collected in batches. This is used to process large quantities of data. For example, payroll systems.
Real-time ProcessingData processing takes just seconds after the input has been provided. This is useful for small quantities of data. Example: withdrawing cash from ATM
Online ProcessingData is automatically fed to the CPU when it is available. It is used to process continuous data. Examples include barcode scanning
MultiprocessingData is broken into fragments and processed by two or more processors within one computer system. Also called parallel processing.For example, weather forecasting.
Time-sharingAllocates computer resources and data time slots to multiple users at the same time.

There are various types of data processing methods dependent on the purpose for which the data is required to be used. In the article below we’re going to look at the five major kinds that are used for data processing.

1 . Commercial Data Processing

Commercial data processing is the application of conventional relational databases. Additionally, it also involves batch processing. It involves supplying huge amounts of quantities of data into the system and then producing an enormous amount of output using fewer computational processes. It essentially combines computer and commerce, making it practical for businesses. The information processed by this system is typically standardized and has a lower risk of being erroneous.

A lot of manual tasks are now automated with the help of computers that make the process effortless and error-free. Computers are employed in business to gather data and convert it into data that can be useful for the business. Accounting programs are a good example of applications that process data. An Information System (IS) in the area of study that studies topics like the computer systems that support organizations.

2 . Scientific Data Processing

In contrast to commercial data processing, Scientific data processing involves extensive use of computational processes, but smaller quantities of inputs as well outputs. The computational processes comprise arithmetical and comparators. 

When processing this kind of data there is a risk of error. are unacceptable because they could lead to incorrect decision-making. Thus, validation, sorting, and standardizing data is performed with great care and a range of methods of science are used to ensure there are no incorrect results and relationships are established.

This is more time-consuming than commercial processing. The most common examples of processing scientific data include the processing, management of the distribution of science data products, and facilitating analytical research of algorithms such as calibration data, products for data and also keeping all software and calibration data under strict control over the configuration.

3. Batch Processing

Batch Processing is a kind of Data Processing that involves a variety of cases that are processed at the same time. The data is processed in batches and is typically utilized when the data is homogeneous and is in large amounts. Batch Processing is defined as concurrent, simultaneous, or the execution of a task in a sequential fashion. 

Simultaneous Batch processing happens when they are carried out with the exact same resources for each of the cases simultaneously. Sequential Batch processing happens when they are carried out using the same source in various cases. They are executed either immediately following each other.

Concurrent Batch processing refers to that they are run with the same resources and only partially overlap in time. It is typically used in financial applications, or in those instances where additional security levels are needed. This processing computation time is considerably smaller because applying an algorithm to the entire data, it extracts the results. It can complete the task with a minimal amount of human involvement.

4. Online Processing

In the language of today’s databases “online” is a reference to “interactive” and “within the boundaries of potential patients.” Processing online is the reverse of the “batch” process. Online processing is built from a variety of simpler operators like the traditional queries processing engine is constructed. Online processing Analytical operations usually comprise large parts of large databases. Therefore, it shouldn’t be a surprise that the current Online analytical systems can provide an interactive experience. The key to their performance is precomputation.

In the majority of Online Analytical Processing systems, the answer to every single click and point is calculated well before the user begins the application. 

Indeed, many online processing systems compute that information quite poorly, however, because the computation is performed ahead of time, the user is not aware of the issue with performance. This kind of processing is utilized for data that needs to be processed in a continuous manner and is then processed automatically by the system.

5. Real-Time Processing

Data Processing

The present data management system is typically unable to handle processing data on an as and when it is needed, as the system relies on periodic updates to batches, which means there is a delay of many hours between the occurrence of an event, and then recording or changing it. 

This led to the demand for a system that can track, update, and process data on an as-and-when basis, i.e. in real-time. This would aid in reducing the time between the time of occurrence and processing virtually zero. Large amounts of information are transferred into the systems of organizations which is why the storage and processing of it in real-time could alter the situation.

The majority of organizations wish to gain real-time data insights so that they can understand the external and internal environment of their own organization incomplete. This is why the need for a system is born that can manage real-time data processing and analytics. This kind of processing gives results when and as it occurs. 

The most popular method is to extract the data straight from the source, which can also be described as stream and draw conclusions, without actually downloading or transferring it. Another key technique used in live processing is Data virtualization where valuable information is pulled in order to meet the purposes of data processing. However, the data is kept in its original form.

6. Data processing distributed to the public

Distributed Data Processing (DDP) can be described as a method to break down large data sets and store them across several servers or computers. In this kind of processing, the job is shared between multiple resources or machines and executed in parallel, rather than being executed synchronously and then arranged in an orderly queue.

 Since the data is processed in a shorter amount of time this is cost-effective for companies and permits faster processing. Additionally, the reliability of a distributed processing system is very high.

7. Multi-Processing

Multiprocessing is a kind of processing that involves several processors are working on the same data simultaneously. The multiple processors in this case are integrated into an identical system. Data is divided into frames. Each frame will be processed by at least two CPUs in the same computer system, which is all operating simultaneously.

8. Time-Sharing Processing

Central processing units (CPU) in a massive-scale digital computer communicates with many users using different programs nearly all at once in this kind of process. This allows you to resolve many distinct problems in the process of input and output because the CPU is much more efficient than peripheral equipment (e.g. printers, printers, or video monitors ). 

The CPU tackles each user’s challenge in a sequential fashion however remote terminals give an impression that access or retrieval via the system is instantaneous since the solutions are readily available once the issue is properly filled in.

Data Processing Methods

There are three primary techniques for processing data including mechanical, manual, and electronic.

Manual Data Processing

In this process of data processing that data is processed manually. The whole procedure that includes gathering data and sorting, filtering calculations and other operations that are logical are carried out by a human being without the need for any other electronic gadget or automation software. This is a cost-effective method and requires no tools, yet results in significant errors, high labor costs, and a lot of time.

Mechanical Data Processing

Data processing is done mechanically with the machinery and devices. They can be simple devices like typewriters, calculators printing presses, etc. Simple data processing tasks are possible with this method. It is much less error-prone as compared to manual processing however, the growing number of records has made the technique more complicated and complex.

Electronic Data Processing

Data is processed using the latest technologies by using programs for data processing and software. A set of instructions are provided to the program for processing the information and generating output. This is the method that is costliest, yet it offers the fastest processing speed and the highest accuracy and reliability of the output.

Data Processing

Examples of Data Processing

Data processing happens in our everyday lives, regardless of whether we are conscious about it, or even aware. Here are some examples

  • A stock trading program that transforms millions of stock information into simple graphs
  • An online retailer makes use of the search history of its customers to suggest products that are similar to theirs
  • Digital marketing companies use the data on people’s demographics to plan location-specific marketing campaigns
  • The self-driving car utilizes real-time information from sensors to identify if there are motorists, pedestrians and pedestrians on the road.

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Hello, My name is Ruchika and I am a Full Stack Developer from Delhi. I am final year Computer Science student from SLIET University. My technologies are Nodejs, React, MongoDB, and I am also familiar with Python, C, and C++. Apart from technical skills, My hobbies are reading, writing, and traveling. I consider myself a very focused person and I always work towards my goals in a very efficient manner. I am a team player and very optimistic in tough times.



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