One of the biggest ways that I've shown the value of data quality is to show an example of where it has failed. By this I mean going to a plant manager and telling him that, according to his data, all product has been checked for leaks. Then, show him the data from the field for that product where the biggest customer complaint is leaks. She'll quickly conclude that either the product was not checked ("pencil-whipped") or that the product was checked incorrectly.
Often, I've tried showing what happens when data quality is poor by using other industries (NASA, aviation, etc.). I've often gotten the response, "Well, we ain't building airplanes here!" So, showing them data within their own organization and speaking their language (financials). "Customer complaints cost us 10% of revenue last quarter due to this issue which our data says isn't an issue." The contradiction and the cost get leadership's and decision-makers' attention very quickly.
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