Customer Record Accuracy Depends on Quality External Data
An accurate address only means you found an existing mailbox. An accurate customer record means you've determined that an actual customer with a rich and detailed history of purchasing decisions occupies a validated, deliverable location. A data-driven data quality management solution uses external data resources to correct, update and validate your high-impact data so that you have truly accurate customer records.
The Power of External Data
The only way to validate a name—a customer—is to know whether there is some type of verified reason for that name to be paired with the address to which it is tied. Using a highly verified set of external data resources to vet a customer file can provide meaningful business measurements for data quality by supplying transactional evidence that a customer lives at an address at a specific point in time.
Screening customer data files with this highly verified external reference data also takes the address-hygiene process to new levels. For example, an address such as "#19867," which would be uncorrectable using typical address-hygiene software, can be corrected to a verifiable address such as "19867 SW 62nd Avenue." This is achieved by using additional customer attributes, such as name and postal code, matching them to a reference data source and then applying the missing address elements, hence vastly improving the underlying quality of the data.
Without a doubt, data quality management is a taxing job. Companies spend years implementing software matching algorithms in an attempt to eliminate duplicate customer records, to create accurate single customer views and to understand customer value. At the end of that process, it seems there is always a wall that is hit where matching algorithms simply cannot resolve every problem. Therein lies the beauty of using data-driven external resources, which can provide intelligent linkages that no matching algorithms could ever achieve.