Record Matching Made Easy with MatchUp Web Service

MatchUp®, Melissa's solution to identify and eliminate duplicate records, is now available as a web service for batch processes, fulfilling one of most frequent requests from our customers - accurate database matching without maintaining and linking to libraries, or shelling out to the necessary locally-hosted data files. Now you can integrate MatchUp into any aspect of your network that can…

Continue Reading

A 6-Minute MatchUp for SQL Server Tutorial

In this short demo, learn how to eliminate duplicates and merge multiple records into a single, accurate view of your customer - also known as the Golden Record - through a process known as survivorship using Melissa Data's advanced matching tool, MatchUp for SQL Server. Watch our video to learn more!

Continue Reading

Centricity and Connections: Clearing the Air

By David Loshin There are opportunities for adjusting your strategy for customer centricity based on understanding the grouping relationships that bind individuals together (either tightly or loosely). And in the last post, we looked at some examples in which linking customer records into groups was straightforward when the values to be compared and weighted for similarity are exact matches. When…

Continue Reading

Customer Centricity and Connections: Establishing the Link

By David Loshin In my last post, we began to look at the value proposition for grouping individual customers into logical groupings. We began by looking at a grouping that generally appears naturally, namely the traditional residential household. We talked about householding in a previous blog posting, but it is worth reviewing the basic approaches used for determining that a…

Continue Reading

Content Standards for Data Matching and Record Linkage

By David Loshin As I suggested in my last post, applying parsing and standardization to normalize data value structure will reduce complexity for exact matching. But what happens if there are errors in the values themselves? Fortunately, the same methods of parsing and standardization can be used for the content itself. This can address the types of issues I noted…

Continue Reading

Normalizing Structure Using Data Standardization for Improved Matching

By David Loshin In my last few posts, I discussed how structural differences impact the ability to search and match records across different data sets. Fortunately, most data quality tool suites use integrated parsing and standardization algorithms to map structures together. As long as there is some standard representation, we should be able to come up with a set of…

Continue Reading

Improving Identity Resolution and Matching via Structure, Standards, and Content

By David Loshin One of the most frequently-performed activities associated with customer data is searching - given a customer's name (and perhaps some other information), looking that customer's records up in databases. And this leads to an enduring challenge for data quality management, which supports finding the right data through record matching, especially when you don't have all the data…

Continue Reading

Modeling Issues and Entity Inheritance

By David Loshin In our last set of posts, we looked at matching and record linkage and how approximate matching could be used to improve the organization's view of "customer centricity." Data quality tools such as parsing, standardization, and business-rule based record linkage and similarity scoring can help in assessing the similarity between two records. The result of the similarity…

Continue Reading

Approximate Matching

By David Loshin Actually, my first name is not David - that is really my middle name, but it is the given name my parents used when talking to me. This has actually led to a lot of confusion over the years, especially when confronted with a form asking for me "first name" and my "last name." For official forms…

Continue Reading