![]() ![]() ![]() If the data is missing, the information cannot be validated and if it’s not validated, it cannot be considered accurate. For businesses, it translates to poor customer insights, inaccurate business intelligence and the loss of ROI.ĭata completeness, therefore, is an essential component of the data quality framework and is closely related to validity and accuracy. Incomplete data can result in flawed reports and skewed conclusions in the research sector. Real-world data will always have incomplete or missing values, especially it is gathered from several sources.
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