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Data processing is the process in which data is collected and translated into usable information. Typically it is performed by a data scientist or team of data scientists, it is important for data processing to be done in a good way as not to negatively affect the end product, results or data output.

Data processing is started with raw form of data and converted it into a more readable format such as graphs, documents, etc. giving it the form and context important to be interpreted by computers and used by employees throughout an organization.

The data processing has total 6 basic steps as;

  • Data collection
  • storage of data
  • Sorting of data
  • Processing of data
  • Data analysis
  • Data presentation
  • conclusions

There are three methods utilized to process that are Manual, Mechanical, and Electronic.

Stages of data processing

1. Data collection

The first step in data processing is the collection of data. Data is sent from available sources, including data lakes and data warehouses. It is important that the data sources available are trustworthy and well-built so the data collected is of the highest quality.

2. Data preparation

Once the data is collected, it is entered into the data preparation stage. Data preparation, often means “pre-processing” that is the stage at which raw data is cleaned up and organized for the following stage of data processing. During preparation, raw data is checked for any errors. The purpose of this step is to remove bad data (redundant, incomplete, or incorrect data) and begin to generate high-quality data for the best business intelligence.

3. Data input

The clean data is then entered into its destination, and translated into a language that it can understand.

4. Processing

During this stage, the data is given to the computer in the previous stage is actually processed for interpretation now. Processing is done by using machine learning  algorithms, though the process itself may vary change depending on the source of data being processed (data lakes, social networks, connected devices etc.) and its intended use (examining advertising patterns, medical diagnosis from connected devices, determining customer needs, etc.).

5. Data output/interpretation

This is the stage in which data is finally usable to non-data scientists. It is now in the translated, readable, and often in the form of graphs, videos, images, plain text, etc. Members of the organization or institution can now start to self-serve the data for their own data analytics projects in future.

6. Data storage

It is the final stage for data processing. After all of the data is processed, it is then stored for future use for many purposes. While some information may be put to use immediately, much of it will serve a purpose later on. Plus, properly stored data is a necessity for compliance with data protection legislation like GDPR.

Different Types of Data Processing

  • Data Processing by Application Type
  • Scientific Data Processing
  • Commercial Data Processing
  • Data Processing Types by Processing Method
  • Automatic versus Manual Data Processing
  • Batch Processing
  • Real Time Data Processing

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