"Data that contain errors or cause problems when accessed and used. Some examples of dirty data are: Values in data elements that exceed a reasonable range, e.g., an employee with 4299 years of service. Values in data elements that are invalid, e.g., a value of 'X' in a gender field, where the only valid values are 'M' and 'F'. Missing values, e.g., a blank value in a gender field, where the only valid values are 'M' and 'F'. Incomplete data, e.g., a company has 10 products but data for only 8 products are included." (Margaret Y Chu, "Blissful Data ", 2004)
"Data that contain inaccuracies and/or inconsistencies." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)
"Poor quality data." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)
"Data that is incorrect, out-of-date, redundant, incomplete, or formatted incorrectly." (Craig S Mullins, "Database Administration", 2012)
"Data with inaccuracies and potential errors." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)
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