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What is data processing?

Data processing refers to essential operations executed on raw data to transform the information into a useful format or structure that provides valuable insights to a user or organization. The outcomes of data processing operations flow into various data outputs as designed by a data scientist, including data analytics, business intelligence, machine learning (ML) and artificial intelligence (AI).

With data processing, raw data is collected from various data input sources and means, from real-time data streaming to more static batch processes from which data is collected and processed at specific intervals.

The raw data is transformed or processed in a series of steps that validate, format, sort, aggregate and store data. The term big data is used when particularly large volumes of data are processed. The raw data that is processed can be pulled from a data lake, and the output can land in a data lake, data lakehouse, data warehouse or database.

Data processing, a broad topic, intersects with the related concepts of data preprocessing and data preparation. Both options commonly occur before an actual data processing activity, and both ensure proper data collection to enable the most effective data processing operations.

6 steps of the data processing cycle

The data processing cycle commonly includes these six primary steps in the following order:

Types of data processing

Data processing can be categorized into several types based on the method and technologies used. Manual data processing, the most basic form of data processing, involves humans collecting, filtering and organizing data without a machine or electronic device. On the other hand, electronic data processing (EDP) means data is collected, organized, processed and stored digitally.

There are different types of EDP-based processing types, including the following:

Examples of data processing

Data processing spans myriad industries and applications, reflecting both its versatility and fundamental role in digital operations. The following examples illustrate the critical importance of data processing:

Data processing analytics

While data processing transforms raw data into something usable, data analytics is often the critical technology for interpreting the meaning of data patterns. Data processing combined with analytics leads to fact-based decisions.

Data analyticsgoes beyond preparing and organizing data. It involves scrutinizing datasets to find trends, draw conclusions and make predictions based on their information. It involves applying statistical analysis and modeling techniques to already-processed data to uncover insights that foster informed decision-making.

The future of data processing

These innovative trends and technologies are shaping the future of data processing:

This was last updated in April 2024

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