Data quality impacts model output in complex and deceitful ways. The common proverb “garbage in, garbage out” summarizes this, albeit in a rather trivializing fashion. Wrong data may produce wrong results, but this describes only the most straightforward case. Nonsensical data can produce incorrect results that are not obviously garbage: the effects of poor data are often surreptitiously difficult to detect. In addition, just as with any more common software bug, the effects of poor data sometimes only materialize far from the source. Since models can be chained, where the output of model X becomes the input of model Y (e.g. in market risk models) these effects propagate making the root cause very difficult to identify. In addition, models often depend on their data in a highly non-linear fashion, so that even minor data errors can produce arbitrarily serious problems.