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Ensuring Data Integrity

"Garbage in, garbage out," is an accurate phrase connoting that even our most efficient supercomputer will not produce quality analysis if it is not supplied with quality data in the first place.

By Michael Mattix, Steven Henning | Updated on April 01, 2010
In the claim-management arena, we have evolved from simple life forms only able to spit out closing ratios to super users who are able to extract reports from our claim systems on any subject senior management can conjure.

As anyone who has spent time on the computer knows, “garbage in, garbage out,” is an accurate phrase connoting that even our most efficient supercomputer will not produce quality analysis if it is not supplied with quality data in the first place. An apocryphal tale involves two co-workers examining the output of their new computer. Said one to the other, “Do you realize it would take 400 people at least 200 years to make a mistake this big?”

We spend a great deal of time manipulating data from our systems, looking for loss and expense drivers, claim trends, and various other uses. A whole cottage industry is developing in the area of predictive analytics—the process of analyzing current and historical data to make predictions about future events. The insurance industry has been an early adopter of these methods, especially in the fields of fraud and credit scoring. The uses of predictive analytics are limited only by the imagination of those employing the techniques.

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