Most data quality technology was born in relation to the direct marketing industry back in the good old offline days. Main objectives have been deduplication of names and addresses and making names and addresses fit for mailing.
When working with data quality you have to embrace the full scope of business value in the data, here being the names and addresses.
Back in the 90’s I worked with an international fund raising organization. A main activity was sending direct mails with greeting cards for optional sale with motives related to seasonal feasts. Deduplication was a must regardless of the country (though the means was very different, but that’s for another day). Obviously the timing of the campaigns and the motives on the cards was different between countries, but also within the countries based on the names and addresses.
Two examples:
German addresses
When selecting motives for Christmas cards it’s important to observe that Protestantism is concentrated in the north and east of the country and Roman Catholicism is concentrated in the south and west. (If you think I’m out of season, well, such campaigns are planned in summertime). So, in the North and East most people prefer Christmas cards with secular motives as a lovely winter landscape. In the South and West most people will like a motive with Madonna and Child. Having well organized addresses with a connection to demographic was important.
Malaysian names
Malaysia is a very multi-ethnic society. The two largest groups being the ethnic Malayans and the Malaysians of Chinese descent have different seasonal feasts. The best way of handling this in order to fulfill the business model was to assign the names and addresses to the different campaigns based on if the name was an ethnic Malayan name or a Chinese name. Surely an exercise on the edge of what I earlier described in the post What’s in a Given Name?
Liliendahl on Data Quality 