6 Vital Parts of Data Processing 6 Vital Parts of Data Processing
Big data is becoming more prevalent in the business landscape. The global market is expected to reach an estimated $103 billion... 6 Vital Parts of Data Processing

Big data is becoming more prevalent in the business landscape. The global market is expected to reach an estimated $103 billion by 2027. The world of vast information is complex and often ambiguous to the average person.

Companies need to leverage data to remain competitive in their respective markets. Because technology is advancing rapidly, information has also become more advanced, and it must be processed so it can be used to make important business decisions.

Data processing requires specialists to turn unstructured or structured information into valuable insights. Many businesses across various industries use it to draw conclusions about their performance, employee productivity, and more.

Here’s what to know about the industry, the types of data processing professionals will rely on, and the stages it goes through.


Types of Data Processing

Below are five types of data processing methods that professionals use to make sense of the vast amounts of information from multiple sources.

  • Transaction processing: This method involves processing data during mission-critical situations. Specialists rely on hardware and software to complete the transaction processing method. If it’s interrupted, it can adversely affect business operations. 
  • Distributed processing: Large datasets are broken down and stored across multiple machines or servers. This method is cost-effective and has a high fault tolerance. 
  • Real-time processing: Data is processed at the time it’s retrieved. Think of GPS-tracking applications. Information is utilized much quicker than other methods.
  • Batch processing: Datasets are stored over some time and analyzed in batches. This method saves on computational costs. Batch processing is sometimes more accurate than real-time processing, where there may be room for error.
  • Multiprocessing: Many data processors use multiprocessing methods. This technique is similar to distributed processing, except servers are on-premise. It’s less cost-effective to use multiprocessing methods.

Use cases and positions continue to emerge as technology’s role becomes more sophisticated. Tech has become a cornerstone for every business, so it shouldn’t come as a surprise that data is constantly being generated. Without a viable management system that includes processing, companies would have no idea what to do with their information. 

6 Vital Stages of the Data Processing Cycle

Now that you’re familiar with the evolution of how information is gathered, let’s explore the six vital parts of data processing and what each step entails. 

1. Collection

It’s no surprise that collecting data would be the first step of the cycle. The quality of information collected can either positively or negatively impact how it’s processed. Data must be defined and accurate so the results are reliable at the end of the processing cycle.

2. Preparation

Once collected, data is transformed so it can be further analyzed and processed. For example, raw information must first be checked for accuracy. Data coming in from multiple sources needs to be placed into one defined dataset for easier processing. 

3. Input

In this step, verified data is converted into a machine-readable form. It is then processed through a computer application. Data entry can be done through a keyboard, scanner or directly from another source. This step must be performed quickly and accurately. It’s common for businesses to outsource this step of data processing. 

4. Processing 

Data is technically manipulated using artificial intelligence (AI) and machine learning (ML) algorithms. These algorithms generate an output or interpretation from the information. Some applications are tailored to process large sets within very short periods.

5. Output 

Processed data is finally transmitted and displayed to the specialist working on the processing. It can be presented in various formats during this stage, such as graphical reports, audio, document viewers or video. This is a critical stage, as it can be visualized and used to make company decisions.

6. Storage

The final stage of the processing cycle is storage, where data and metadata are kept for future use. Finding viable methods allows for easy retrieval. It’s also essential for companies to secure their information to prevent any cybersecurity incidents. 

As you can see, the data processing cycle requires more than a handful of steps. It’s vital for anyone working in big data to be aware of all the stages. 

Using Data Processing to Gain Value

Data processing is an increasingly invaluable resource for all types of companies as the business world becomes more competitive. It plays a significant role in the success of any organization. Using information to reach effective decisions can make or break a business, and companies will likely adopt innovative strategies to make the best use of what they have.

Shannon Flynn

Shannon Flynn

About the author: Shannon Flynn is a tech writer and Managing Editor for ReHack.com. She covers topics in biztech, IoT, and entertainment. Visit ReHack.com or follow ReHack on Twitter or to see more of Shannon’s posts.