What Is Data Processing: Types and Examples

What Is Data Processing?

Data in its raw form is useless to any organization. Data processing is the method of collecting raw data and translating it into usable information. It is usually performed step by step by a team of data scientists and engineers in an organization. The raw data is collected, filtered, sorted, processed, analyzed, stored, and then presented in a readable format.

Data processing is essential for organizations to create better business strategies and increase their competitive edge. By converting the data into readable formats like graphs, charts, and documents, professionals can understand and use the data.

Now that we’ve defined what data processing is, let’s explore the data processing cycle, which outlines the steps involved in transforming raw data into valuable insights.

Six Stages of Data Processing

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

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 is to remove flawed data (redundant, incomplete, or incorrect data) 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 cleaned and prepared data is converted into a machine-readable format and entered into the processing system. This can involve data being input manually through a keyboard, scanned from physical documents, or imported from other digital sources such as APIs or databases. The input phase ensures that the data is properly structured and ready for the next stage of processing.

Step 4: Data Processing

In this step, the raw data is subjected to various data processing methods using AI and machine learning 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

Finally, the data is transmitted and displayed to the user in readable form, such as 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 directly as input in the next data processing cycle.

Once the data has gone through the various phases of the processing cycle, it is important to understand the different types of data processing methods that can be applied to achieve specific objectives and outcomes. Let us go through each of them.

Types of Data Processing

Different types of data processing exist 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 for processing raw data.

In batch processing, data is collected over a period and then processed in batches. It’s suitable for handling large amounts of data where immediate output is not necessary. A common example of this is a payroll system, where data is collected throughout the month and processed at the end.

Real-time processing handles data instantly as soon as the input is received. It’s ideal for scenarios requiring quick responses and works best with small volumes of data. A typical use case is withdrawing money from an ATM, where the system needs to respond within seconds.

This type of processing involves data being automatically fed into the CPU as soon as it becomes available. It’s used for continuous and immediate data processing, making it perfect for applications like barcode scanning at checkout counters.

Also known as parallel processing, multiprocessing breaks down data into smaller frames and processes them simultaneously using two or more CPUs within a single computer system. A real-world example of this is weather forecasting, which demands high processing power and speed.

Time-sharing is a form of computing in which computer resources and data are divided into time slots, allowing several users to work together simultaneously. This allows systems to be utilized by multiple users simultaneously without conflict.

After the data has been successfully input into the system, the next step involves applying various data processing methods to transform this raw data into meaningful and actionable insights. Let us learn more about the methods.

Data Processing Methods

Five data processing methods exist: manual, mechanical, electronic, distributed, and automatic. Let’s learn more about each of them.

1. Manual Data Processing

This data processing method is handled manually. The entire process of data collection, filtering, sorting, calculation, and other logical operations are all done with human intervention and 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 and tedium.

2. Mechanical Data Processing

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

3. 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.

4. Distributed Processing

Distributed processing refers to distributing the processing power across multiple computers or devices. This methodology increases the speed and reliability of your operations by drawing on the collective strength of numerous systems. It’s particularly effective for handling large-scale processing tasks that a single computer might struggle with.

5. Automatic Data Processing

Automatic data processing relies on software to carry out routine operations without human intervention. By automating repetitive tasks, this method not only boosts efficiency but also reduces the chances of human error. It allows teams to focus more on strategic efforts rather than manual data handling.

Once the appropriate data processing methods have been implemented, various data processing tools are utilized to streamline and enhance the efficiency of transforming raw data into valuable insights. Let us learn about 5 such data processing tools.

Here are some of the most popular data processing tools that help businesses manage, process, and analyze large amounts of data:

Apache Hadoop is an open-source tool used to store and process large datasets across many computers. It can handle huge amounts of data and process it quickly. Hadoop uses a system called MapReduce to split tasks into smaller chunks, making it faster and more efficient for big data projects.

Apache Spark is another open-source tool that processes data quickly. It’s known for its speed because it works in memory rather than writing data to disk. Spark can handle both batch and real-time data, making it a versatile tool for different kinds of data tasks. It also works well with machine learning and other tools like Hadoop.

Google BigQuery is a cloud-based tool that allows users to analyze large datasets quickly. It can process massive amounts of data in seconds and integrates well with other Google Cloud services. BigQuery is scalable, meaning it can grow with the data needs of any business.

Talend is a tool for connecting and managing data from different sources. It helps businesses clean, process, and move data easily. Talend is known for its user-friendly interface, allowing data professionals to design and manage data processing tasks without complex coding.

• Microsoft Azure Data Factory

Microsoft Azure Data Factory is a cloud-enabled service that allows companies to design and govern data pipelines. It has the capability to handle data in both batch and streaming modes, and is well-integrated with various services in Microsoft Azure. The tool’s drag-and-drop interface makes it easy to design data tasks without needing to write code.

Examples of Data Processing

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

1. Stock Trading Platforms: These platforms process real-time market data, analyzing thousands of transactions per second to generate actionable insights like stock trends and price predictions.
2. E-commerce Personalization: Online stores use customer browsing and purchase history to process data, offering personalized product recommendations that enhance user experience and drive sales.
3. Ride-Hailing Apps: Apps like Uber process geolocation and traffic data in real time to optimize routes, set dynamic pricing, and match drivers with passengers efficiently.

The Future of Data Processing

Data processing has moved towards cloud computing. It offers faster, more efficient, and cost-effective ways to handle large volumes of data. Cloud platforms bring everything together instead of relying on separate systems, making data easier to manage and update. These platforms also support growth, allowing both large and small businesses to handle increasing data needs. As data grows, cloud computing will keep playing a significant role in managing, processing, and storing it with greater speed and reliability.

Conclusion

Data is growing rapidly, and the need for skilled professionals to process and manage it is also increasing. As tech advances, cloud computing and other modern methods are making data processing faster and more efficient. This means businesses and organizations can use data better to make smarter decisions.

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