What is it about?
Potential data issues (i.e., smelly data) are typically hard to grasp and detect. We address this by first providing a sound concept of such smelly data. Secondly, we present a catalog of the most common types of such issues together with concrete detection techniques.
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Why is it important?
Potential data issues can have serious consequences for almost every data-driven application. Being aware of them and detecting them is thus of utmost importance.
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This page is a summary of: Data smells, May 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3522664.3528590.
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