Data manipulation is the process of adjusting, organizing, transforming, and managing data to make it more useful or usable in analysis and reporting procedures. It consists of many practices and processes that can be applied to data stored in any kind of file or database. Manipulating data is vital to data analysis processes. It consists of six key aspects which are
- Data cleaning: the process of correcting and removing corrupt, inaccurate, or duplicated records from a dataset.
- Data transformation: changing the format or structure of a dataset to allow its use in different formats or programs.
- Data aggregation: combining multiple datasets together to create one record that can then be analyzed and worked on more quickly.
- Data integration: combining parts of datasets from different sources into one dataset that can then be analyzed as if it were always a full set.
- Data sorting: arranging or sorting data into a specific order, such as by date or value.
- Data filtering: filtering a dataset in order to show only data that fits a specific criteria or requirement.
Data manipulation is a series of procedures that can make datasets more accurate and usable for organizations or entities. Any of the six key aspects can be combined to create a dataset that fits the exact need of the entity looking to use it. Effective data manipulation leads to a higher-quality dataset, which leads to more accurate and better data based decisions and insights.