It's a fact, smaller companies would be out of business if they utilized data quality management processes like their bigger brothers!  My mailbox is full of Fortune 500 companies' duplicate mailings of irrelevant offers.  Even basic work like Move Update (National Change of Address) do not seem to matter to companies like Allstate Insurance, Ford Motor Company and Countrywide Mortgage (no wonder a bailout was needed).  It is time for all of us to look at our marketing database, asses data quality from even the lowest common denominator; "does that person live at that address (NCOA)?" and "does that address even exist (CASS, DPV, etc)?"  Only after this basic assesment can marketers pretend to have a chance at driving a true marketing ROI. 

I, for one, am thrilled with the USPS change in regulations on NCOA requirements for Standard Mail and the frequency of Move Update requirements for First Class mail.  In my world of database marketing, I rely on quality data management as the core to delivering marketing ROI to my clients.  Their message sometimes finds itself in the trash beside a piece of true "junk mail" due to other database marketers refusal to adhere to data quality management best practices.  Recently my mail box included mail addressed to a former Tennessee Titan player who built my house 11 years ago and then was traded and moved the next year!  The Doctor who bought the house from the Titan also has mail from his former life, he has been gone for 7 years!  Not only will these new regulations save the USPS untold millions of dollars in UAA mail, the by product should include better results for my client's direct mail marketing programs and less trash in our landfills.  As Sarah Palin would say, that is just a great win for all of us!

Advanced data processing occurs once data is cleaned, standardized and consolidated to ensure that data is up to date.  Move updates should be applied periodically with services such as National Change of Address (NCOA).  Providers that perform NCOA updates often will also provide phone number updates and appends.  It is vital to have the accurate addresses and phone numbers to reach customers. 

Now that the customers can be reached, it is important to determine if they want to be reached.  If you are planning telemarketing efforts, you need to bounce your customer database against the National Do Not Call directory.  This will enable you to identify those who do not want to receive phone calls. 

For direct mail marketing it is important to determine if your customers want to be contacted via mail.  Data providers can flag customers on national do not mail lists.  Your company should also maintain its own do not mail indicator. 

Finally, it is essential to determine customers that are deceased.  Data providers can supply flags to indicate those customers that are deceased.  Communicating to deceased customers is upsetting to family members and will burn their goodwill with your company. 

Once data is up to date and solicitation preferences noted, the third step to continue turning data into information is data enhancement...


With a slowing economy, it is important to use information to drive marketing communications. Data collection and analysis needs to create business intelligence that marketers can use to identify the right opportunity at the right time and guides marketers to deliver the right message.

Data is everywhere. It is with sales reps, in departmental databases, in market research departments, and list processing services to name a few. The first step to turning this data into marketing information is to utilize data management services.   

A key element to data management services is electronic data processing. Automatic data processing is used to consolidate data into a 360 view of the customer. Data quality management uses hygiene procedures to clean and standardize the data. Data consultants can assist in recommending data management solutions to address specific needs and concerns.

Once data is clean and consolidated, the second step is using advanced data processing to continue turning data into information . . .