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What is Data Processing: Definition, Cycle and Types

Read Time: 3 minutes

What is Data Processing?

Data Processing refers to converting raw data into meaningful information, and these data are machine-readable as well. Thus, data processing involves collecting, Recording, Organizing, Storing, and adapting or altering to convert the raw data into useful information.

Stages of the Data Processing Cycle

The data processing cycle consists of steps to convert your raw data into actionable and meaningful information. Generally, Data Processing Cycle consists of the following SIX stages.

  1. Data collection
  2. Data preparation
  3. Data input
  4. Data Processing
  5. Data output
  6. Data storage

7 Types of Data Processing based on What needs to be captured

Types of Data processing have phenomenally grown with rising demands from manual data processing to automation.

Let’s dive into the SEVEN types of data processing techniques that depend on the information that needs to be captured and how soon you need it.

  1. Manual Data Processing
  2. Mechanical Data Processing
  3. Electronic Data Processing
  4. Batch Data Processing
  5. Real-time Data Processing
  6. Online Data Processing
  7. Automatic Data Processing

1. Manual Data Processing

The Manual Data processing method is where data entry specialists record and process data manually through the ledger, paper record systems, and more manual data entry process. Though it is one of the earliest data processing methods, manual data entry is costly, time-consuming, error-prone, and labor-intensive.

For instance, imagine a company where employee entry is permitted only by signing a ledger instead of today’s access cards.

2. Mechanical Data Processing

Mechanical data processing processes data through mechanical devices such as typewriters, mechanical printers, and other devices. Albeit being faster than the manual data processing method, it started to fade away with the future evolutions.

3. Electronic Data Processing

In 1980, with the birth of computers, electronic data processing (EDP) marked its existence. In EDP, the computer seamlessly processes the data automatically with pre-defined instructions from the data specialists.

For instance, the use of spreadsheets to record student marks was prevalent during this time.

Though this data processing method is accurate, reliable, and faster than its predecessor, it still required data specialists for manual data entry and calculations.

4. Batch Data Processing

Batch data processing, process data by providing actions to multiple data sets through a single command. For example, in spreadsheets, data entry specialists can enter the formula for a single cell and apply it for the whole column. This type of data processing accelerates the processing time and can complete a series of tasks without human intervention.

5. Real-time Data Processing

Real-time processing came into existence with the advent of the internet. By utilizing the internet, this processing method receives and processes data at the same time. Simply put, it captures data in real-time and generates quick or automatic reports. Hence this is one of the fastest data processing methods.

For example, take GPS tracking systems where sensors detect heavy traffic and give input on a real-time basis. Though the process saves time and labor, it is expensive and requires heavy maintenance.

6. Online Data Processing

Online data processing is often confused with real-time data processing; both receive and process data simultaneously, but with online processing, the user can extract data anytime, anywhere. The bar code system is the best example of online processing. When buying a book in a bookstore, with the bar code scanning, the book’s data is automatically changed as sold. Another concrete example is access cards.

7. Automatic Data Processing

Today’s millennials are entering the new age of data processing with the entry of Artificial Intelligence. Data processing cannot be made better, with no human intervention, data entry on a real-time basis, error-free, and secure than any processing methods.

To illustrate this type of data processing, consider the automation of billions and billions of invoices in the logistics sector. It not only reduces the grunt work but also helps to focus on great work.


From the above overview of data processing methods, it is no surprise organizations find automatic data processing as the best fit. But not all organizations have joined the AI bandwagon!

Now is always the best time to act!

With a proven track record of delivering personalized services, iTech, as a data outsourcing partner with a constant focus on innovation, has added automation services to its arsenal to aid the customers in today’s digital arena.

Our automation services include SAP robot process automation, OCR/ICR services, ML-based data entry services, and more. Let us e-meet to talk more.

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