It’s time to clean up the data and start transforming it into information. We have created a program we call ‘Matchup’. Matchup employs state-of-the-art fuzzy matching algorithms that help us identify records in our databases that are the same business (like the example we showed you of Priceline with two different names); helps streamline insurance carriers where different spellings or abbreviations are employed; and helps recognize all of the different ways the BOR (broker of record) likes to submit details. It aggregates multiple records like BOR data into a single record. With Matchup we have created many matching rules that help us identify duplicate information, from the obvious to the not-so-obvious, so we can prune and merge the database.
We now want to standardize and validate company names, addresses, emails, and phone info. We use a third-party service that we feel is the best-in-class. We pass the information we have from the filing and the service returns to us standardized and validated information.
The service provides us full data quality by comparing company name, address, phone, and email information against multi-sourced datasets. The service enriches the data by updating addresses and adding latitude/longitude coordinates and comprehensive demographics. Basically the service is adding more awesomeness to our database.