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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]
Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. . The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data or produce summary data and statistics at different aggregation levels for various applications
[21] [22] The need for data cleaning will arise from problems in the way that the datum are entered and stored. [21] Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. [23]
Data scrubbing reduces the likelihood that single correctable errors will accumulate, leading to reduced risks of uncorrectable errors. Data integrity is a high-priority concern in writing, reading, storage, transmission, or processing of data in computer operating systems and in computer storage and data transmission systems.
The problem of database repair is a question about relational databases which has been studied in database theory, and which is a particular kind of data cleansing. The problem asks about how we can "repair" an input relational database in order to make it satisfy integrity constraints.
Example: You may have a sandbox or spreadsheet of state data using state abbreviations. Open this section for editing via the visual editor (VE). Delete the D.C. row if necessary, but do not save the changes. Select and copy (Ctrl+C) the full state names. You can usually paste them directly into the sandbox or spreadsheet over the abbreviations.