Stages of Data Processing ||

Different Types of Data Processing

1. Manual Data Processing: manual data processing refers to physically collecting, cleaning, and then, analyzing the information. This traditional method has been pervasive for for years. Bookeeping, balance sheet, cash flow statements, and other banking & accounting statement are its finest examples.

The processing experts manually calculate and place values. However, all of these things are time-consuming. Even, this method processes erroneous data, which takes more time, expertise, and effort for the reconciliation of that data.

2. Automatic data processing: this is one of the most effective methods of drawing insights. This requires an automatic data processing device or equipment. This is connected or integrated with other systems or equipment. Once connected, the automatic platform becomes ready to acquire, store, manipulate, manage, move, control, display, switch, transit, and receive datasets as information. Punched Cards are its biggest example. 

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: As the name suggests, batch processing is when chunks of data, stored over a period of time, are analyzed together, or in batches. Batch processing is required when a large volume of data needs to be analyzed for detailed insights. For example, sales figures of a company over a period of time will typically undergo batch processing. Sincere there is a large volume of data involved, the system will take time to process it. By processing the data in batches, it saves on computational resources.

Batch processing is preferred over real-time processing when accuracy is more important than speed. Additionally, the efficiency of batch processing is also measured in terms of throughput. Throughput is the amount of data processed per unit time.

5. Multiprocessing: multiprocessing is the method of data processing where two or more than two processors work on the same dataset. It might sound exactly like distributed processing, but there is a difference. In multiprocessing, different processors reside within the same system. Thus, they are present in the same geographical location. If there is a component failure, it can reduce the speed of the system.

Distributed processing, on the other hand, uses servers that are independent of each other and can be present in different geographical locations. Since almost all systems today come with the ability to process data in parallel, almost every data processing system uses multiprocessing.

However, in the context of this article, multiprocessing can be seen as having an on-premise data processing system. Typically, companies that handle very sensitive information might choose on-premise data processing as opposed to distributed processing. For example, pharmaceutical companies or businesses working in the oil and gas extraction industry.