When you use the internet to learn about a topic, order food, or complete a financial transaction, data is generated every single second. Businesses use data processing to gain insights from such a large amount of data. 

So, let’s understand this concept of data processing in more detail along with its different types and methods. 

Define Data Processing

Data processing happens when data is collected and translated into usable information. The data processing operations are usually performed by data scientists. It is extremely important to complete the processing of data carefully to achieve the right output. When done incorrectly, it can negatively affect the end product. 

Some of the examples of data processing are:

  • A digital marketing company uses demographic data of users to strategize location-specific campaigns
  • A stock trading software that converts numerous stock data into simple graphs
  • An e-commerce company uses the search history of users to recommend similar products

What is Data Processing Cycle?

A data processing cycle begins by collecting data in raw form. It is then converted into a more readable format such as documents and graphs. This gives it the necessary form and context which can then be interpreted by computers and used by employees throughout an organization. 

What are the Different Data Processing Stages?

The data processing cycle consists of six stages: 

  • Data Collecting

The first task in data processing is data collection. The process of data collection is done by pulling data from different available sources, including data lakes and data warehouses. It is crucial to collect data from credible sources so that you get the highest quality outcome. 

  • Data Preparation
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The second step is the preparation of data, also known as the pre-processing stage. Here, data is cleaned and organized for the next stages. In this stage, raw data is carefully checked for any errors, and bad data such as incomplete, redundant, or incorrect data are eliminated. 

  • Data Input

In this stage, the cleaned data is entered into the destination which can be a CRM or a data warehouse. This helps in translating the data into an understandable language, making it the first stage in which raw data begins its transformation into usable information.

  • Data Processing

In the fourth stage, the raw data is subjected to various data processing methods using machine learning and artificial intelligence algorithms to generate desirable output. The process may vary based on the source of data being processed and the purpose it would be used for. 

  • Data Output or Data Interpretation

The data is finally transmitted and displayed to the user in a readable format in this stage. It can be in the form of graphs, videos, charts, documents, and audio files, among others. This output can also be stored and further processed in the next data processing cycle. 

  • Data Storage

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

Different Types of Data Processing

There are different data processing types based on their sources and steps in data processing unit for output generation:

  • Batch Processing: In this type of processing, data is collected and processed in batches. It is a common type for processing large amounts of data. Example: Payroll system.
  • Real-time Processing: Here data is processed within seconds when the input is given. This type of processing is usually used for small amounts of data. Example: Withdrawing money from the ATM.
  • Online Data Processing: Data is automatically fed into the CPU whenever available. This processing type is used for continuous processing of data. Example: Barcode scanning.
  • Multiprocessing: Also known as parallel processing, where data is broken into frames and processed using two or more CPUs within a single computer system. Example: Weather forecasting.
  • Time-sharing: In this type, computer resources and data are allocated in time slots to several users simultaneously.
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Method of Data Processing

The three major methods of data processing are:

  • Manual Data Processing

In this process, data is handled manually. The entire data cycle is completed through the aid of human intervention and without the use of any electronic device or automation software. It is a low-cost method, however, has the scope to produce high errors. It also has a high labor cost and requires a significant amount of time for completion. 

  • Mechanical Data Processing

Data is processed mechanically through machines and devices. This can include the use of simple devices like typewriters, and calculators, among others. You can complete simple data operations using this method. It has fewer chances of errors as compared to the manual process, however, the increase in data has made this method more complex.

  • Electronic Data Processing

Data is processed with modern technologies using data processing software and programs. Using the set of instructions, the software processes the data and yields the output. Although this method is expensive, it provides the fastest and most accurate results. 

Understanding the Future of Data Processing

The future of data processing lies in cloud computing. While the six stages of data processing remain unchanged, cloud technology has advanced the data processing method, making it faster, advanced, and cost-effective. 

 With the help of cloud technology, companies can blend their platforms into one centralized system, making it easy to work and adapt. This can also be used for smooth interaction of new updates which can aid big and small organizations to scale easily. 

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Additionally, it is crucial to understand the importance of big data in today’s world. The impact of big data is huge on how businesses are conducted. It has helped the journey from data processing to analytics more seamless. 

Analytics is essentially the process of finding, interpreting, and communicating meaningful patterns in data, the next logical step after processing of data. It helps you to make sense of the data after it has been processed. Therefore, both big data and analytics are crucial to achieving quality results from data processing. 

Conclusion 

Thus, data processing helps in identifying useful information that can be used by businesses to make data-driven decisions. You can complete data processing steps by following the six stages. Choose the type and method suitable for your business to gain in-depth insights to elevate your business.