Catch Bad Data Before It Wrecks Your Business
Address standardization is actually not very difficult, especially when you have access to a proven standard. At the highest level, the process is to first determine where the address does not conform to the standard, then to standardize the parts that did not conform. One can define a set of rules to check if the address has all the right pieces, whether they are in the right place and if they use the officially sanctioned abbreviations. Rules can also move address parts around, to map commonly used terms to the standard ones and use lookup tables to fill in the blanks when data is missing. In many cases, it is straightforward to rely on tools and methods to automatically transform non-standard addresses into standardized ones.
4. Data Enhancement
Most business applications are originally designed to serve a specific purpose and, consequently, the amount of data either collected or created by any specific application is typically just enough to get the specific job done. In this case, the data is utilized for the specific intent, and we'd say that the "degree of utility" is limited to that single business application.
On the other hand, businesses often use data created by one application to support another application. As a simple example, customer location data (such as ZIP codes) collected at many retail points of sale is used later by the retail business to analyze customer profiles and characteristics by geographical region. In these kinds of scenarios, the degree of utility of the data is increased, because the data values are used for more than one purpose.
There are a number of different ways data sets can be enhanced, including adapting values to meet defined standards, applying data corrections and adding additional attributes. We can start with a very common use of enhancement: postal standardization and address correction. Another common example involves individuals' names, which appear in data records in more than a thousand different forms: first name followed by last name; last name with a comma, followed by first name; with or without titles such as "Mr." or "Professor;" or perhaps different generational suffixes.
Greg Brown is vice president of Melissa, provider of global contact data quality and identity verification solutions that span the entire data quality lifecycle and integrate into CRM, e-commerce, master data management and Big Data platforms. Connect with Greg at email@example.com or via LinkedIn.