What Is Automated Data Processing?

Date First Published: 19th September 2023

Topic: Computer Systems

Subtopic: Computer Software

Article Type: Computer Terms & Definitions

Difficulty Level: 4/10

Learn about what automated data processing is in this article.

Automated data processing, also known as electronic data processing, is a type of data processing that relies on automated systems to handle and process the data with little user interaction. The role of data entry is taken away from individual users, allowing large amounts of data to be processed at once to achieve a specific goal. Specialist devices usually recognise and interpret data and convert it from its physical source to digital form.

Examples Of Automated Data Processing

Below are two examples of automated data processing.

A barcode scanner is a type of automated data processing as the scanner translates the barcode into numbers and/or letters to the computer which is translated into readable information. When barcodes on items are scanned, they are matched to a database of products on the computer, returning the price.

OMR is a way of entering in data by recognising pencil or pen marks on pre-printed forms. The scanner looks at the different levels of reflectivity of the light at certain positions on the form. It will recognise the black marks because they reflect less light than the blank areas on the form. This is a way of extracting useful data from fill-in fields and checkboxes very quickly and accurately. It can be used for lottery tickets, tests, quizzes, exams, medical forms, and surveys.

Advantages and Disadvantages Of Automated Data Processing

• Increased accuracy - Automated data processing reduces the chances of human errors, like typos, as data does not have to be manually entered.
• Increased speed - Automated data processing is quicker than manual data processing as forms can be quickly read through by automated systems without breaks.
• Reduced costs - Automated data processing is cheaper than paying someone to manually enter data.

• It lacks flexibility. Since only very specific data can be automatically inputted, it is less suitable for handling more complex data. Handling complex data will require some manual data processing.
• Staff may not be familiar with automated data processing. This may require training, which can be time-consuming and expensive.
• Data can be prone to misinterpretation. For example, if handwriting is difficult to read, mistakes can happen during the data entry process, like the scanner misreading the text

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