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Record Matching Made Easy with MatchUp Web Service

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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.

New Company Magazine Features Data Quality Insights on Merging Duplicate Patient Records into a Golden Record, also Tips on Improving Healthcare Data Warehousing



Rancho Santa Margarita, CALIF. - September 9, 2014 - Melissa Data, a leading provider of global contact data quality and data enrichment solutions, today announced matching and de-duping functionality that solves duplicate records for healthcare database administrators (DBAs). Using tools based on proprietary logic from Melissa Data, healthcare DBAs can consolidate duplicate customer records objectively, unlike any other data quality solution. This and other healthcare data quality challenges are featured in Melissa Data Magazine, the company's new quarterly resource for DBAs and data quality developers.

Healthcare data is characterized by a steady stream of patient records and evolving contact points, warranting a smart, consistent method to determine the best contact information. Melissa Data Magazine highlights a new way to merge duplicate records, based on a unique data quality score that retains the best pieces of data from all of the various records.

"It's essential that healthcare data managers acknowledge data quality challenges up front, implementing processes to cleanse and maintain the trustworthiness of the information that goes into their master data systems," said Bud Walker, director of data quality solutions, Melissa Data. "Our new publication outlines how to ensure this high level of data precision, creating an accurate, single view of the patient. This is known as the Golden Record and is of critical value in healthcare settings - reducing costs, streamlining business operations and improving patient care."

Highlighting industry-specific data quality tools and solutions, Melissa Data Magazine will help DBAs and health information managers adapt to evolving challenges particularly as data becomes more global in nature. Future published issues will feature technologies such as SQL Server development tools, and markets such as retail, ecommerce, government and real estate.

Melissa Data Magazine will be available at the American Health Information Management Association (AHIMA) conference, Booth #723, starting September 27 in San Diego, Calif. Click here to download the healthcare issue of Melissa Data Magazine, or call 1-800-MELISSA (635-4772) for more information.

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A 6-Minute MatchUp for SQL Server Tutorial

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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!


Data Quality Tool Consolidates Duplicates into Single Golden Record of Customer Data; Uniquely Determines Most Accurate Information Based on Objective Data Quality Score


Rancho Santa Margarita, CALIF- April 23, 2014 - Melissa Data, a leading provider of contact data quality and integration solutions, today announced new matching and de-duplication functionality in its MatchUp Component for SQL Server Integration Services (SSIS), uniquely solving the business challenge of duplicate customer data. Based on proprietary logic from Melissa Data, MatchUp determines the best pieces of data to retain versus what to discard - consolidating duplicate records objectively, unlike any other data quality solution. By assessing the quality of individual data fields, MatchUp enables a smart, consistent method for database administrators (DBAs) to determine the best customer contact information in every field.

"The average database contains 8 to 10 percent duplicate records, creating a significant and costly business problem in serving, understanding and communicating with customers effectively. The ideal is a single, accurate view of the customer - known as a golden record - yet this remains one of the biggest challenges in data quality based on methodologies that don't adequately evaluate the content of each data field. As a result, DBAs either overlook duplicates or consistently struggle with determining what information survives in the database and why," said Bud Walker, director of data quality solutions, at Melissa Data. "By using intelligent rules based on the actual quality of the data, DBAs are much better positioned to retain all the best pieces of information from two or more duplicate records into a single, golden record that provides valuable insight into user behavior and helps boost overall sales and marketing performance."

MatchUp works in sharp contrast to matching and de-duplication methods that rely solely on subjective principles, such as whether the record is the most recent, most complete or most frequent. Instead, selection criteria for determining a golden record is based on a relevant data quality score, derived from the validity of customer data such as addresses, phone numbers, emails and names. Once the golden record is identified intelligently, MatchUp further references the data quality score during survivorship processes to support creation of an even better golden record; duplicate entries are then collapsed into a single customer record while retaining any additional information that may also be accurate and applicable.

Utilizing deep domain knowledge of names and addresses, survivorship operations with MatchUp can granularly identify matches between names and nicknames, street/alias addresses, companies, cities, states, postal codes, phones, emails, and other contact data components.

Melissa Data will be demonstrating its MatchUp Component for SSIS at booth #46 during Enterprise Data World, April 27-May 1, 2014 at The Renaissance Hotel in Austin, TX. To download a free trial of Melissa Data's MatchUp Component for SSIS, click here or call 1-800-MELISSA (635-4772).

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Melissa Data will demonstrate new "Golden Record" functionality in its MatchUp Component for SQL Server Integration Services (SSIS) at Enterprise Data World (EDW). MatchUp SSIS is a powerful tool for advanced matching and deduplication management. By integrating the Golden Record selection tool, MatchUp SSIS represents an industry breakthrough based on its ability to discern contact data information, and select the surviving record based on the level of quality of the information provided.

Melissa Data will also showcase its popular collection of contact data quality and integration solutions. The event will be held April 28 through May 2 in San Diego, Calif. at the Sheraton Hotel and Marina.

Come stop by Booth #412 and say hi!

CLICK HERE FOR MORE INFO

A Guide to Better Survivorship - A Melissa Data Approach

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By Joseph Vertido

The importance of survivorship - or as others may refer to as the Golden Record - is quite often overlooked. It is the final step in the record matching and consolidation process which ultimately allows us to create a single accurate and complete version of a record. In this article, we will take a look at how Melissa Data uniquely differentiates itself in approaching the concept of survivorship compared to some of the more conventional practices.

The process of selecting surviving records means selecting the best possible candidate as its representation. However, best in the perspective of survivorship can really mean a lot of things. It can be affected by the structure of data, where the data is gathered from, how data comes in, what kind of data is stored, and sometimes by the nature of business rules. Thus techniques can be applied in order to accommodate certain types of variations when performing survivorship. We find that there are three very commonly used techniques in determining the surviving record:

I. Most Recent

Date stamped records can be ordered from most recent to less recent. The most recent record can be considered eligible as the survivor.

II. Most Frequent

Matching records containing the same information are also an indication for correctness. Repeating records indicate that the information is persistent and therefore reliable.

III. Most Complete

Field completeness is also a factor of consideration. Records with more values populated for each available field are also viable candidates for survivorship.


Although these techniques are commonly applied in survivorship schemas, its correctness may not be as reliable in many circumstances. Because these techniques apply to almost any type of data, the basis in which a surviving record is created conforms only to "generic" rules. This is where Melissa Data is able to set itself apart from "generic" survivorship. By leveraging reference data, we can steer a way to generating better and more effective schemas for survivorship.

The incorporation of reference data in survivorship changes how rules come into play. Using the Most Recent, Most Frequent or Most Complete logic really has more of an aesthetic basis for selection. Ideally, the selection of the surviving record should be based off an actual understanding of our data.

And this is where reference data comes into play. What it boils down to at the very end is simply being able to consolidate the best quality data. Thus by incorporating reference data, we gain an understanding of the actual contents of data, and create better decisions for survivorship. Let's take a look at some instances on how reference data and data quality affect decisions for survivorship.

I. Address Quality

Separating good data from bad data should take precedence in making decisions for survivorship.

Address Quality Sample

In the case of addresses, giving priority to good addresses makes for a better decision in the survivorship schema.

II. Record Quality

It could also be argued that good data may exist in a single group of matching records. In cases like these, we can assess the overall quality of data by taking into consideration other pieces of information that affect the weight of overall data quality. Take for example the following data:

Record Quality Sample

In this case, the ideal approach is to evaluate multiple elements for each record in the group. Since the second record contains a valid phone number, it can be given more weight or more importance than the third record despite it being more complete.

Whether we're working with contact data, product data or any other form of data, in summary, the methodologies and logic used for record survivorship become dependent primarily on data quality. And however we choose to define data quality, it is imperative that we keep only the best pieces of data if we are to have the most accurate and correct information. In the case of Contact Data however, Melissa Data changes the perspective as to how the quality of data is defined, therefore breaking the norm of typical survivorship schemas.


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