Whether you use the internet to learn about a certain topic, complete financial transactions online, order food, etc., data is being generated every single second. The use of social media, online shopping and video streaming services have all added to the increase in the amount of data. A study by Domo estimates that 1.7MB data is created every second for every human being on the planet in 2020. And in order to utilize and get insights from such a huge amount of data - data processing comes into play.

Moving forward, let us understand what is data processing.

What Is Data Processing?

Data in its raw form is not useful to any organization. Data processing is the method of collecting raw data and translating it into usable information. It is usually performed in a step-by-step process by a team of data scientists and data engineers in an organization. The raw data is collected, filtered, sorted, processed, analyzed, stored, and then presented in a readable format.

Data processing is crucial for organizations to create better business strategies and increase their competitive edge. By converting the data into a readable format like graphs, charts, and documents, employees throughout the organization can understand and use the data.

Post Graduate Program in Data Engineering

Your Gateway To Becoming a Data Engineering ExpertView Course
Post Graduate Program in Data Engineering

Now that we understood what is data processing, let’s understand its cycle process. 

Data Processing Cycle

The data processing cycle consists of a series of steps where raw data (input) is fed into a process (CPU) to produce actionable insights (output). Each step is taken in a specific order, but the entire process is repeated in a cyclic manner. The first data processing cycle's output can be stored and fed as the input for the next cycle. 


Fig: Data processing cycle (source)

Generally, there are six main steps in the data processing cycle:

Step 1: Collection

The collection of raw data is the first step of the data processing cycle. The type of raw data collected has a huge impact on the output produced. Hence, raw data should be gathered from defined and accurate sources so that the subsequent findings are valid and usable. Raw data can include monetary figures, website cookies, profit/loss statements of a company, user behavior, etc.

Step 2: Preparation

Data preparation or data cleaning is the process of sorting and filtering the raw data to remove unnecessary and inaccurate data. Raw data is checked for errors, duplication, miscalculations or missing data, and transformed into a suitable form for further analysis and processing. This is done to ensure that only the highest quality data is fed into the processing unit. 

The purpose of this step to remove bad data (redundant, incomplete, or incorrect data) so as to begin assembling high-quality information so that it can be used in the best possible way for business intelligence.

Step 3: Input

In this step, the raw data is converted into machine readable form and fed into the processing unit. This can be in the form of data entry through a keyboard, scanner or any other input source. 

Step 4: Data Processing

In this step, the raw data is subjected to various data processing methods using machine learning and artificial intelligence algorithms to generate a desirable output. This step may vary slightly from process to process depending on the source of data being processed (data lakes, online databases, connected devices, etc.) and the intended use of the output.

Step 5: Output

The data is finally transmitted and displayed to the user in a readable form like graphs, tables, vector files, audio, video, documents, etc. This output can be stored and further processed in the next data processing cycle. 

Step 6: Storage

The last step of the data processing cycle is storage, where data and metadata are stored for further use. This allows for quick access and retrieval of information whenever needed, and also allows it to be used as input in the next data processing cycle directly.

Free Course: Big Data Hadoop and Spark Developer

Learn Big Data Basics from Top Experts - for FREEEnrol Now
Free Course: Big Data Hadoop and Spark Developer

Now that we have learned what is data processing and its cycle, now we can look at the types.

Types of Data Processing

There are different types of data processing based on the source of data and the steps taken by the processing unit to generate an output. There is no one-size-fits-all method that can be used for processing raw data.



Batch Processing

Data is collected and processed in batches. Used for large amounts of data.

Eg: payroll system

Real-time Processing

Data is processed within seconds when the input is given. Used for small amounts of data.

Eg: withdrawing money from ATM

Online Processing

Data is automatically fed into the CPU as soon as it becomes available. Used for continuous processing of data.

Eg: barcode scanning


Data is broken down into frames and processed using two or more CPUs within a single computer system. Also known as parallel processing.

Eg: weather forecasting


Allocates computer resources and data in time slots to several users simultaneously. 

Data Processing Methods

There are three main data processing methods - manual, mechanical and electronic. 

Manual Data Processing

In this data processing method, data is processed manually. The entire process of data collection, filtering, sorting, calculation and other logical operations are all done with human intervention without the use of any other electronic device or automation software. It is a low-cost method and requires little to no tools, but produces high errors, high labor costs and lots of time.

Mechanical Data Processing

Data is processed mechanically through the use of devices and machines. These can include simple devices such as calculators, typewriters, printing press, etc. Simple data processing operations can be achieved with this method. It has much lesser errors than manual data processing, but the increase of data has made this method more complex and difficult.

Learn Data Science with R for FREE

Master Basics of Data Science with R for FREEEnrol Now
Learn Data Science with R for FREE

Electronic Data Processing

Data is processed with modern technologies using data processing software and programs. A set of instructions is given to the software to process the data and yield output. This method is the most expensive but provides the fastest processing speeds with the highest reliability and accuracy of output.

Examples of Data Processing

Data processing occurs in our daily lives whether we may be aware of it or not. Here are some real-life examples of data processing:

  • A stock trading software that converts millions of stock data into a simple graph
  • An e-commerce company uses the search history of customers to recommend similar products
  • A digital marketing company uses demographic data of people to strategize location-specific campaigns
  • A self-driving car uses real-time data from sensors to detect if there are pedestrians and other cars on the road

That was all about the article what is data processing.

Want to begin your career as a Big Data Engineer? Check out the Big Data Engineer Training Course and get certified.

Here’s What You Can Do Next

Data contains a lot of useful information for organizations, researchers, institutions, and individual users. With the increase in the amount of data being generated every day, there is a need for more data scientists and data engineers to help understand these data. Simplilearn’s Data Engineering Certification Course in collaboration with IBM and partnership with Purdue University offers the highest learning experience to help you master crucial data engineering skills. By leveraging Purdue University’s academic excellence in data engineering and IBM’s industry-relevant and hands-on training experience, this program will help fast-track your career as a data engineering professional.

Hope you liked the article “what is data processing” in case of doubts ask your questions in the comment section.

About the Author

Nikita DuggalNikita Duggal

Nikita Duggal is a passionate digital nomad with a major in English language and literature, a word connoisseur who loves writing about raging technologies, digital marketing, and career conundrums.

View More
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.