Catch Bad Data Before It Wrecks Your Business
1. Data Cleansing
Data cleansing combines the definition of business rules in concert with software designed to execute these rules. Yet there are some idiosyncrasies associated with building an effective business rules set for data standardization and, particularly, data cleansing.
At first blush, the process seems relatively straightforward: We have a data value in a character string that we believe to be incorrect and we'd like to use the automated transformative capability of a business rule to correct that incorrect string. For example, a rule might transform the abbreviation "ST" into the word "street." It is a simple data cleansing process; but, in reality, it is too simple to provide the best results. Without further controls, an address such as "St. Charles St." would be transformed to "Street Charles Street."
In order to correctly transform the data, a bit more control is required in regard to how, where and when the rule is applied. One approach to resolving rule conflicts is the introduction of contextual constraints for application of the rules. This is more complex, but assists in differentiating the application of rules. Another approach somewhat adjusts the rule set to ensure distinction of abbreviation and then phasing the application of rules. Most importantly, businesses must evaluate the ways data cleansing tools define rules as a way to determine the best option for their particular dataset.
2. Address Data Quality
One aspect of managing the quality of master address and location data involves reviewing much of the existing documentation collected from a number of different operational systems, as well as reviewing the business processes to see where location data is either created, modified or read. There are likely to be many references to operations or transformations performed on addresses—mostly with the intent of improving the quality of the address.
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.