Many years ago I worked in a midsize insurance company. At that time IT made a huge change in insurance pricing since it now was possible to differentiate prices based on a lot of factors known to the databases.
The CEO decided that our company should also make some new pricing models based on where the customer lived, since it was perceived that you were more exposed to having your car stolen and your house ripped off if you live in a big city opposite to living in a quiet countryside home. But then the question: How should the prices be exactly and where are the borderlines?
We, the data people, eagerly ran to the keyboard and fired up the newly purchased executive decision tool from SAS Institute. And yes, there were a different story based on postal code series, and especially downtown Copenhagen was really bad (I am from Denmark where Copenhagen is the capital and largest city).
Curiously we examined smaller areas in downtown Copenhagen. The result: It wasn’t the criminal exposed red light district that was bad; it was addresses in the business part that hurt the most. OK, more expensive cars and belongings there we guessed.
Narrowing down more we were chocked. It was the street of the company that was really really bad. And last: It was a customer having the very same house number as the company that had a lot of damage attached.
Investigating a bit more case was solved. All payments made to specialists doing damage reporting all over the country was made attached to a fictitious customer on the company address.
After cleansing the data the picture wasn’t that bad. Downtown Copenhagen is worse than the countryside, but not that bad. But surprisingly the CEO didn’t use our data; he merelthese companies did the analysis. They all had head quarter addresses in the same business area.
Liliendahl on Data Quality 