Data Cleansing
Data Cleansing is the process of identifying and correcting or removing inaccurate, incomplete, irrelevant, duplicated, or improperly formatted data from a dataset. It is an essential step in data preparation before analysis, as it ensures that the data is accurate, consistent, and reliable. Data cleansing involves various techniques such as parsing, standardization, validation, and enrichment. Parsing involves breaking down the data into its component parts, such as separating first and last names. Standardization involves converting the data into a consistent format, such as converting all dates to a standard format. Validation involves checking the data for accuracy and completeness, such as verifying that all addresses have a valid postal code. Enrichment involves enhancing the data by adding additional information, such as appending demographic data to a customer database.
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