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.”

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access