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MatchUp was built with contact- domain specific knowledge. We have leveraged our 27 years of experience working with addresses and names to build in contact-specific rules so you don’t have to. In addition to contact data, MatchUp also has a full toolbox of general string fuzzy algorithms. These algorithms provide a flexible engine for any type of data quality project.
Domain Specific Knowledge: We know all about contact data from their idiosyncrasies to common mistakes. We have built rules and logic specifically from this knowledge.
- Address Obscurities: Handle addresses no matter what format they are in. Not all parts of an address are createdFuzzy Algorithms: MelissaData has also included a toolbox of over 12 general string matching algorithms. These include industry- accepted algorithms like Levenstein and Jaro-Winkler, as well as proprietary Melissa Data ones like MD_Keyboard.
equal. We isolate and match on the important elements of the address while accounting for the elements where mistakes happen the most.
- Nicknames and Abbreviations: We have databases and logic built over decades to find matches for nicknames and abbreviations.
- Different Formatting: Don’t worry if your data is in different formats. MatchUp will take the data and parse it out correctly to find the best match.
However, MatchUp is capable of much more than just the aforementioned features. The following are a few use cases that demonstrate the power of MatchUp in different scenarios.
Householding is a concept that allows you to group data by pre-defined criteria. Often, this criterion is a household (all members of a household count as one group). However, the same concept can be applied to any other criteria whether it’s a workplace, a department, a last name, or even a location.
Proximity Matching: One powerful feature that sets MatchUp apart from all other tools is the ability to set the geographic distance as a criterion. Often, a large building or company will have entrances on different streets, or a physical mail box along with a PO Box. You can use Proximity matching to group records that are geographically close together.
MatchUp makes matching between multiple lists easy. Sometimes, your project requires you not to consolidate multiple lists, but just to find common or unique records between them. MatchUp’s list intersection ability allows you to find all the common data between multiple lists. Conversely, MatchUp’s list suppression ability allows you to find just the data that is unique to each individual list. The applications for these abilities are nearly endless, especially with MatchUp’s flexible matching capabilities which allow you to customize the matching to your exact data set.
Flexible Matching: MatchUp has incredible customization and can adapt to any type of data that can be thrown at it. It can handle over 35 pre-defined data types (like address, email, SSN…) and allow up to 16 cascading rule sets. For example, you can match on address and full name. However, if that fails, you can cascade down to match on address, first name, initial, and last name up to 16 match attempts.
Point Of Entry/Batch
As a developer, system admin, or database admin tool, MatchUp can be implemented at point of entry or in batch. Point- of- entry processing provides real-time monitoring of incoming records allowing you to accept unique records while rejecting duplicate records while they are being entered. Batch processing enables you to dedupe an entire database at once.
Speed/Scalability: MatchUp is a fast and scalable tool. It can handle millions of records an hour in batch and find any matches in under a second for point of entry. Built for the enterprise, MatchUp is multi-thread safe and scalable for data sets of any size.
MatchUp provides you with the opportunity to take control of your data. You can finally have a single, consolidated view of the data that drives your business. Data itself is not power. Correct data is information that provides knowledge. Knoweldge is power. Empower your business and take the next step in contact data quality with Melissa Data’s MatchUp.