In my previous post, I made the argument that many times it’s okay to call data quality as good as it needs to get, as opposed to demanding data perfection. However, a balanced perspective demands acknowledging there are times when nothing less than perfect data quality is necessary. In fact, there are times when poor data quality can have deadly consequences.
In his book “The Information: A History, a Theory, a Flood,” James Gleick explained “pharmaceutical names are a special case: a subindustry has emerged to coin them, research them, and vet them. In the United States, the Food and Drug Administration reviews proposed drug names for possible collisions, and this process is complex and uncertain. Mistakes cause death.”
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