Full form of DP
"Data Processing" is the full form of DP. DP technology involves a computer program for processing and organizing large amounts of numerical data. It can also be used to manipulate, analyze, measure, sort, and store data. Simply, it transforms raw data into actionable information through a mechanism involving computer networks, software, and so on.
Organizations use computing devices and applications to perform various activities by manipulating raw data to extract details. It presents insightful output data in the form of diagrams, documents and graphics, and so on. A wide variety of data processing applications are commercially available. Some of them are MS Excel and Word, PowerPoint, and so on.
Some processes include data processing
- Validation – A method guarantees the safe, accuracy, and usefulness of the data supplied.
- Sorting – It’s being used to organize increasing or decreasing objects in some series.
- Summary – Used to minimize the comprehensive data to its key points.
- Aggregation – It combines several pieces of information.
- Analysis – It uses advanced and very accurate algorithms and mathematical calculations.
- Classification – It is used to classify data into various groups.
Stages of Data Processing
The following are the stages of Data Processing-
- Data collection
Data collection is the first step in data processing. Data is obtained from the available sources, including data lakes and data warehouses. The available data sources must be credible and well-compiled so that the data collected (and then used as information) is of the highest possible quality.
- Data Preparation
Once the data is collected, it enters the data preparation phase. Data preparation often referred to as "pre-processing," is the stage in which raw data is cleaned and organized for the next data processing stage. During preparation, the raw data is carefully checked for possible errors. This step aims to remove bad data (redundant, incomplete, or incorrect data) and create high-quality data for the best business intelligence.
- Data input
The clean data is then fed into its target (a CRM like a data warehouse or Salesforce like Redshift) and translated into a language it understands. Data input is the first stage, where the raw data start to take the form of a usable information.
- Processing
During this stage, the data entered into the computer in the previous stage is processed for interpretation. Processing is done using machine learning algorithms, although the process itself vary slightly depending on the source of the processed data (social networks, data lakes, connected devices, etc.) and its intended use (exploring advertising patterns, a determining customer needs, medical diagnosis from connected devices etc.).
- Data output/interpretation
The output/interpretation phase is where the data is finally usable by non-data scientists. It is readable, translated and often in the form of graphs, images, videos, plain text, etc.). Members of a company or institution can now start handling the data independently for their data analysis projects.
- Data storage
The last stage of data processing is storage. After all the data is processed, it is stored for future use. While some information can be used immediately, much of it will serve a purpose later. In addition, properly stored data is a must for compliance with data protection legislation such as GDPR. When data is stored correctly, it can be quickly and easily accessed by organization members when needed.