Polish Your Data Dictionary

As the saying goes, Cleanliness is next to godliness. And in direct marketing, data cleanliness leads to improved profitability. But because data hygiene is about as exciting as, well, dental hygiene, too many marketers give it only cursory attention.

And that, says Brent Bissell, president of Minneapolis-based consultancy Direct Target One, is a costly oversight. Poor hygiene results in an inconsistent data dictionary—the naming conventions for fields, values, and other database elements—which in turn yields an underachieving database. To polish your data dictionary and your database, Bissell suggests reviewing your data records and making the following changes where necessary:

* Place each data element in the correct field. This sounds basic, but it’s especially problematic for companies that sell to both businesses and consumers: The company name and/or the address data often end up in incorrect fields.

* Parse names—place title, first name, and surname in separate fields.

* Parse any other data elements that may be incorrectly combined in one field

* Standardize names, addresses, functional titles, and other categorical information.

* Find, mark, and in some cases eliminate duplicate records

* Create unique ID numbers.

* Create exception reports for unusual findings—go back to your previous data-handling rules to see what exceptions have cropped up in the past.

If you plan to overlay outside data onto your records, hold off until your own data are clean. This will ensure higher match rates and minimize the wasted expense matching data to duplicate records.

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