Record Matching Made Easy with MatchUp Web Service

Blog Administrator | Data Governance, Data Integration, Data Management, Data Matching, Data Quality, Data Quality Components for SSIS, Data Steward, Data Warehouse, Duplicate Elimination, Fuzzy Matching, Golden Record, Householding, Identity Resolution, Record Linkage, SQL Server Integration Services, SSIS, Survivorship | , , , , , , ,

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 communicate
with our secure servers using common protocols like XML, JSON, REST or SOAP.

 

Select
a predefined matching strategy, map the table input columns necessary to
identify matches to the respective request elements, and submit the records for
processing. Duplicate rows can be identified by a combination of NAME, ADDRESS,
COMPANY, PHONE and/or EMAIL.

 

Our
select list of matching strategies removes the complexity of configuring rules,
while still applying our fast and versatile fuzzy matching algorithms and
extensive datatype-specific knowledge base, ensuring the tough-to-identify
duplicates will be flagged by MatchUp. 

 

The output response returned by the service
can be used to update a database or create a unique marketing list by
evaluating each record’s result codes, group identifier and group count, and
using the record’s unique identifier to link back the original database record.

 

Since
Melissa’s servers do the processing, there are no key files – the temporary
sorting files – to manage, freeing up valuable hardware resources on your local
server.

 

Customers
can access the MatchUp
Web Service
license by obtaining a valid license from our sales team and
selecting the endpoint compatible to your development platform and necessary
request structures here.

A 6-Minute MatchUp for SQL Server Tutorial

Blog Administrator | Data Matching, Data Quality, Duplicate Elimination, Fuzzy Matching, Golden Record, Press Release, Record Linkage, SQL Server Integration Services, Survivorship | , , ,

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!

Centricity and Connections: Clearing the Air

Blog Administrator | Address Quality, Analyzing Data, Customer Centricity, Data Quality, Record Linkage | , , , ,

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 the values are not exact, it introduces some level of doubt into the decision process for including a record into a group.

Let’s revisit our example from my last post by adding in a new record for evaluation:

 

John Hansen, 1824 Polk Ave., Memphis TN 38177
Emily S. Hansen, 1824 Polk Ave., Memphis, TN 38177
Emily Stoddard, 1824 Polk Avenue, Memphis, TN

We had already decided that John and Emily shared a household, but all of a
sudden we have a third record with a name that shares some similarity, with one
of the existing names, and an almost exact street address match (note that the
third record is missing a ZIP code).

We could speculate that “Emily Stoddard” changed her name after she got married
to “John Hansen,” or that she changed an address somewhere as she moved form her
bachelorette pad to their newlywed home. But without exact knowledge of the
facts, it is only speculation, and one must exercise some care when relying on
speculation for business decisions.

If a few small differences pose a challenge to linkage, what would you think of
dozens, or even hundreds of variations for names, locations, or other data
values?

Just as a case in point: in a hallway conversation at the recent Data Governance
Conference, a colleague mentioned that one of his customers’ databases had over
one hundred variations for a certain big-box retailer’s name! The conclusion you
can draw from this is that a key part of the record linkage process involves
some traditional data quality tactics, namely appending a standardized version
of the data to help your linkage algorithms score record similarity as a prelude
to establishing connectivity.

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Customer Centricity and Connections: Establishing the Link

Blog Administrator | Customer Centricity, Data Management, Data Quality, Record Linkage | , ,

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 group of individuals
share a household. The general approach is to analyze a collection of data
records and examine sets of identifying attributes for degrees of similarity in
naming and residence locations. Many situations are relatively straightforward,
such as this example:


John Hansen, 1824 Polk Ave., Memphis TN 38177
Emily S. Hansen, 1824 Polk Ave., Memphis, TN 38177

In this example, two individuals share both a last name and a location address,
and although the data evidence does not guarantee truth of the inference, it
might be reasonable to suggest that because there is a link between the family
name and the residence location, these two individuals are members of the same
household. The algorithm, then, is to link records into a collection of similar
records based on similarity of the surname and residence characteristics.

However, the concept of grouping is not limited to conventional groups, since
there are many artificial groups formed as a result of shared interests or
similarities in profile criteria. For example, people interested in certain
sports car models often organize “fan clubs,” new mothers often organize toddler
play groups, and sports team fans are often rabid about their franchise
alliances.

In turn, your company might want to create marketing campaigns that target sets
of individuals grouped together by demographic or psychographic attributes. In
these cases, you would adjust your algorithms to link records based on
similarity of the values in other sets of data attributes.

Establishing the link goes beyond looking at the data that already exists in
your data set. Rather, you may need to append additional data acquired from
alternate sources.

And, interestingly enough, you will need to connect the acquired data to your
existing data, and that requires yet another record linkage effort. Apparently,
understanding customer collectives is pretty dependent on record linkage. And
while linking records is straightforward when all the data values line up
nicely, as you might suspect, there are some curious intricacies of linkage in
the presence of data with questionable quality.