Data processing is used 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.
It 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.
Stages of Data Processing
- Data collection: It is the first step in the data processing. Data is pulled from available sources, including data lakes and data warehouses. The data sources available must be trustworthy and well-built so the data collected is of the highest possible quality.
- Data preparation: Once the data is collected, it then enters the data preparation stage. Data preparation often referred to as “pre-processing” is the stage at which raw data is cleaned up and organized for the following stage of data processing. During preparation, raw data is diligently checked for any errors. The purpose of this step is to eliminate bad data and begin to create high-quality data for the best business intelligence.
- Data input: The clean data is then entered into its destination, and translated into a language that it can understand. Data input is the first stage in which raw data begins to take the form of usable information.
- Processing: 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 and its intended use (examining advertising patterns, a medical diagnosis from connected devices, determining customer needs, etc.).
- Data output/interpretation: At this stage, data is finally usable to non-data scientists. It is translated, readable, and often in the form of graphs, videos, images, plain text, etc.). Members of the company or institution can now begin to self-serve the data for their own data analytics projects.
- Data storage: It is the last stage in this process. After all of the data is processed, it is then stored for future use. When data is properly stored, it can be quickly and easily accessed by members of the organization when needed.
Data Processing Functions
It involves various processes, including:
- Validation: Ensuring that supplied data is correct and relevant.
- Sorting: “arranging items in some sequence and/or in different sets.”
- Summarization: Reducing detailed data to its main points.
- Aggregation: Combining multiple pieces of data.
- Analysis: The “collection, organization, analysis, interpretation and presentation of data.”
- Reporting: List detail or summary data or computed information.
- Classification: Separation of data into various categories.