Data Cleansing

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Huang, Fang
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2019-01-01
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Fang Huang (2019) Data Cleansing. In: Schintler L., McNeely C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_300-1
Abstract
Data cleansing, also known as data cleaning, is the process of identifying and addressing problems in raw data to improve data quality (Fox 2018). Data quality is broadly defined as the precision and accuracy of data, which can significantly influence the information interpreted from the data (Broeck et al. 2005). Data quality issues usually involve inaccurate, unprecise, and/or incomplete data. Additionally, large amounts of data are being produced every day, and the intrinsic complexity and diversity of the data result in many quality issues. To extract useful information, data cleansing is an essential step in a data life cycle.
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Encyclopedia of Big Data
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