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The yearly cost of poor quality data in the US is estimated at $3.1 trillion, according to IBM. The primary cause of this issue is human error, not system glitches. Examples of dirty data include incorrect typing, inconsistent formatting, blank fields, and duplicate entries, which lead to incomplete, incorrect, or irrelevant data. Working with such data undermines the reliability of results, making it difficult to trust the outcomes of analyses.