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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 sanitization methods are also applied for the cleaning of sensitive data, such as through heuristic-based methods, machine-learning based methods, and k-source anonymity. [ 2 ] This erasure is necessary as an increasing amount of data is moving to online storage, which poses a privacy risk in the situation that the device is resold to ...
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.
Data erasure (sometimes referred to as data clearing, data wiping, or data destruction) is a software-based method of data sanitization that aims to completely destroy all electronic data residing on a hard disk drive or other digital media by overwriting data onto all sectors of the device in an irreversible process. By overwriting the data on ...
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
Given the variety of data sources (e.g. databases, business applications) that provide data and formats that data can arrive in, data preparation can be quite involved and complex. There are many tools and technologies [5] that are used for data preparation. The cost of cleaning the data should always be balanced against the value of the ...
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Advisory actions typically allow data to be entered unchanged but sends a message to the source actor indicating those validation issues that were encountered. This is most suitable for non-interactive system, for systems where the change is not business critical, for cleansing steps of existing data and for verification steps of an entry process.