What is data processing and what are its types and stages yemenat 2023

Data processing or information processing is one of the most important technical processes on which our digital life depends! You may think that the data here means company data, numbers, sales, etc., and this is true, but it constitutes only part of this data.

Using a calculator to find out the results of calculations is a type of data processing. Editing video clips in their raw form in order to obtain smaller clips is also considered data processing, as well as requests to withdraw and deposit money at ATMs.

But how is the data processed? Is it a simple process? Or complicated? In this article, we navigate 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?

Data processing is a process in which a huge amount of data is converted into useful information that can be understood and analyzed. The conversion takes place through special programs and tools in the field of data analysis and processing.

Usually the task of data processing falls to a scientist or a team of data scientists.

Currently, most of the vital aspects of life depend on data processing, which makes it an important process that does not tolerate any errors in its outputs.

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 mainly in the field of technology to determine what users need, whether now or in the future.

Data processing begins with the data in its initial form, and then transforms it into an easily readable and understandable form.

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 necessary form and context for interpretation by computers. These forms also facilitate the possibility of using the data by employees in companies and stakeholders.

For more information about data analysis, see our article dedicated to explaining the basics of this field.

stages of data processing

1. Collection

In this step, data is collected from available data sources, which may include:

a) Data Lakes

Data lakes are a data store that can keep all the data of the entity in which it works, this data may include images and text files.

Data lakes can also maintain information from external sources such as those collected through:

• Internet of Things devices.
• User clicks within websites.
• User interaction on social media platforms (click, share, like, etc.).

Data lakes can also store data collected from cloud-based applications. Companies can analyze this information using a variety of tools including machine learning technology that automatically looks for patterns.

Finding patterns of behavior is the main goal of data processing, through which users’ actions are predicted and decisions are made.

b) Data Warehouse

Data warehouses are data stores just like lakes. The difference between the two extends to many aspects. However, the most important difference may be that data warehouses store processed data unlike lakes that store data in its raw form.

Also, repositories store data about specific topics, and they don’t store all data about everything about their organization.

Each of the fields depends on a specific source of all data sources, this is due to what each source provides of data forms that are suitable for certain fields more than others, and of course the advantages of each source in data storage in general.

Fields such as health, education, and transportation prefer to rely on data lakes to store their data. On the other hand, commercial and economic entities and institutions prefer to rely on data warehouses to store their data.

Finally, it is important that the available data sources are trustworthy and well-constructed. Quality is also an important factor that must be paid attention to during data collection, as this data must be of the highest possible quality.

Note that this data will be converted into information that contributes to making fateful decisions for giant entities and institutions, in other words, any low-quality or unreliable data that can cause huge disasters in the areas that depend on it.

2. Preparation

After being collected, the data enters the preparation stage (this stage is also known as the pre-processing or pre-processing stage. In this stage, the raw data (also called raw data) is cleaned and organized for the next stage.

During preparation, the data is checked for any errors. The purpose of this step is to remove bad data (redundant, incomplete, or incorrect data), and to obtain a high-quality amount of data for business intelligence to use.

Business intelligence or Business Intelligence is a set of processes and applications used that rely on data to make decisions for companies and organizations.

Business intelligence includes the following processes:

• Data collection.
• data analysis.
• Presenting information obtained through data processing in order to facilitate decision-making.

3. Input

The revised data is entered into the application designated for dealing with and displaying it. For example, e-commerce sites and those that deal with customers periodically collect their data, revise it, and then add it within the customer management program such as Hubspot CRM.

In other words, data is converted into easily understandable information. Data entry is the first stage in which raw data begins to transform into information that can be used and understood by the people who run the organizations and companies that collect it.

Data is usually entered for these programs through the components responsible for the input operations in the computer, such as:

• keyboard.
• optical reader.

The entry that takes place in this way is carried out by individuals, of course, and the data may also be automatically transferred from its source to the program that will carry out coordination and data processing tasks later.

4. Processing

During this stage, the data that was entered into the computer in the previous stage is processed in order to interpret and process it. The processing is done using artificial intelligence and machine learning algorithms .

Of course, the process itself differs based on the source of the data on which it relied (lakes or warehouses). Also, the purpose of all this data constitutes another difference in processing operations, as well as their size.

For example, data processing aimed at identifying customer behavior differs from that aimed at diagnosing a medical condition. With regard to the size of the data, it is directly proportional to the processing power required to process it, in other words, the greater the volume of data, the greater the processing power needed.

Any computer processes data through the central processing unit or CPU , but the difference between each device and another is the volume of data that it processes, so there are processors suitable for home use, and other giant ones such as those in data centers.

Finally, the programs used play an essential role in the rest of the steps of this process, such as interpretation and storage.

5. Data output and interpretation

The processing process then produces outputs, and to benefit from these outputs, it must be interpreted. The outputs – which are a set of information – are exported to the user in one of the following forms:

• Graph.
• reports.
• Video clips.
• documents.
• scales.
• audio clips.

Choosing the format to be relied upon for exporting data outputs is related to many factors such as the field and the function of the beneficiary. The format is an interpretation of the information that the institution or company has collected and then processed.

Analysts and data scientists can understand any of the previous forms of data, unlike individuals who are not specialized in this field, so some outputs take forms suitable for all individuals, and others take more complex forms as they are directed by specialists in a field.

In general, we see many forms of data interpretation around us. For example, major companies publish their budget for the past fiscal year in newspapers. The budget is the form closest to the table, and the interpretation here is tabulating each income and expense and writing its name.

In this way, a large number of individuals with different interests can understand this information or output (budget). Ordinary individuals can review this data as well as investors and decision makers, each according to his purpose from this data.

6. Storage

The process of storing processed data is the last stage. Storage in the computer world, its main purpose is to save for viewing later, and the field of data processing is not excluded from this rule.

Data storage serves several other important goals, the most important of which are:

a) Protection

Data protection is essential for any type of data and in any field. Data storage contributes greatly to protecting it from theft or manipulation, which are catastrophic risks that scare large companies and institutions because of their great impact on their future… Without storing data, it is difficult to protect it.

Imagine that the computer used by the data analyst gets damaged while looking at it. The damage may be due to a cyber risk, electrical failure, or anything else, and it may occur despite all the measures that companies take to secure and protect their devices.

If that data is not stored, it is lost forever and may need to repeat the processing process which is cumbersome and costly. Finally, data protection meets the requirements of the General Data Protection Regulation or GDPR, which all parties that deal with data adhere to.

b) Facilitate access

The parties that process the data rely on several departments to analyze and view it, and storage allows that. Viewing the data only without storing it makes the access to it when needed limited.

For example, corporate sales data must be stored so that other departments other than the sales department can view it. Departments such as marketing and purchases need to view sales data because it affects their performance and decisions.

c) Comparing it with previous and subsequent data and decisions based on it.

Decision makers in institutions and companies depend on data processing to make appropriate decisions for their companies and institutions, and to measure the effectiveness of these decisions, companies compare them and their results with the data that was relied upon to make them before making them.

Data processing is also used to compare data with each other, whether through a temporal or spatial framework. For example, companies compare their sales data in the current year with that of the previous year, or with that of another branch or geographical area.

Without data storage, data analysts would not have a reference to detect and properly analyze changes to it.

Of course, some data requires that it be viewed and decisions are made based on it immediately, and this usually relates to the scope of this data.

The most common type of data processing

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

1. Batch processing

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

2. Real-time data processing

In this type, the raw data is processed at the same time as it is obtained. Operations that need to process a small amount of data depend on this type of processing, and its most popular uses are ATMs that process and execute customer order data in real time.

Also, real-time data processing depends on sensors to receive data, then process it for real-time and output it. The most famous systems that depend on this type are protection systems and alarm devices.

3. Online processing of data

Data processing via the Internet is similar to real-time processing, the essential difference between the two types is that the first relies heavily on the Internet to send and receive data, and is 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 the owner’s account instantly, but the bank’s system may take time to process the amount and add it to the account.

4. 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.

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

Now central processors have become cheaper than before, which contributed to being used 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 certain regions.

5. Time Sharing

Processing different sets of data simultaneously (or time sharing) is closely related to multiprocessing, in both types data is processed by more than one CPU at the same time.

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

The processor arranges the slides and processes the data sequentially with a small time difference between each processing process and the other, this difference is almost a part of one second, and therefore the user does not feel any significant delay in processing his data.

data processing methods

1. Manual method

The manual method is one of the oldest methods of data processing, as is evident 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 its lack of dependence on any tools of any kind. However, this method has disadvantages that take it into account when choosing a method for processing data … the most important of which is that it requires great 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 factor alone in the process. Currently, this method is rarely used, as the electronic method has replaced it even in small data processing operations.

2. The mechanical method

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

• Calculators (mechanical ones, which were used in the past).
• Typewriters.
• Press printing machines.

The error rate in this method is much lower than its previous counterpart, but the biggest disadvantage of this method is its inability to deal with the huge amounts of data that exist in our time, like the previous method, this method is not currently relied upon in data processing.

3. Electronic method

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

You can run an electronic application that collects and classifies data and outputs it in the required format within minutes and without much effort. Large companies prefer to rely on this method as 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 an era from ages. Currently, all data processing operations are based on the electronic method, given that electronic devices have become within the reach of everyone.