Data processing is the collection and manipulation of digital data to produce meaningful information. It is a form of information processing, which is the modification of information in any manner detectable by an observer. Data processing involves transforming raw data into valuable information for businesses. It is usually performed in a step-by-step process by a team of data scientists and data engineers in an organization. The raw data is collected, filtered, sorted, processed, analyzed, stored, and then presented in a readable format. The following are the stages of data processing:

1. Data Collection: Data is gathered from reliable sources, including databases such as data lakes and data warehouses. It is crucial that the data sources are accurate, dependable, and well-built to ensure that the data collected and the information gathered is of superior quality and functionality.

2. Data Preparation: During this stage, the data inputted to the computer in the previous stage is actually processed for interpretation. Processing is done using machine learning algorithms, though the process itself may vary slightly depending on the source of data being processed (data lakes, etc.).

3. Data Input: Data in its raw form is not useful to any organization. Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organization.

4. Data Processing: Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output.

5. Data Analysis: This stage involves the use of statistical and other analytical techniques to extract insights from the data.

6. Data Output: The last step of the data processing cycle is storage, where data and metadata are stored for further use. This allows for quick access and retrieval of information whenever needed, and also allows it to be used as input in the next data processing cycle directly.

Data processing may involve various processes, including validation, sorting, filtering, and summarizing. It is distinct from word processing, which is the manipulation of text specifically rather than data generally. Data processing is crucial for companies to gain access to valuable insights and maintain a competitive edge[[2]](https://www.talend.com/resources/wh...