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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.

How to Create a Golden Record

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How to Create a Golden Record Blog Image.jpg


Some common goals in database administration include maintaining clean and effective patient data. A step that is often overlooked comes as the final step of the process and is importance for the purposes of survivorship, it is known as the "Golden Record". A Golden Record is the creation of a single, accurate, and complete version of a patient record.

Here is how you can determine the most accurate data to use in establishing the Golden Record:

Once you've gone through the matching process, you will most likely end up with duplicated records bundled into duplicate groups that are ready for consolidation. What comes this is where the unique or "winning" Golden Record becomes the next logical step. The process of selecting the best possible record as the surviving candidate can be tricky. However, techniques can be applied to perform the selection of the record that will be selected for survivorship.

How do you know which record you should keep? There are three commonly used techniques to selecting the surviving record:

1.       1. The most recent methodology is based on date-stamped records. Order them from most recent to least recent and then consider the most recent eligible as the survivor.

2.      2.  The most frequent approach matches records that contain the same information as an indication of their accuracy. When more than one record obtains the information it is an indication of their correctedness.

3.       3. The most complete method considers field completeness as its primary factor of correctness. The records that obtain the most filled out fields are considered to be the best record for survivorship.

Golden Records should be the goal of for all databases. Obtaining data quality is key to having the best connection and insight into your database.


By Natalia Crawford

Data Profiling: Pushing Metadata Boundaries

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By Joseph Vertido
Data Quality Analyst/MVP Channel Manager


Two truths about data: Data is always changing. Data will always have problems. The two truths become one reality--bad data. Elusive by nature, bad data manifests itself in ways we wouldn't consider and conceals itself where we least expect it. Compromised data integrity can be saved with a comprehensive understanding of the structure and contents of data. Enter Data Profiling.


Throw off the mantle of complacency and take an aggressive approach to data quality, leaving no opening for data contamination. How? Profiling.

More truths: Profiling is knowledge. Knowledge is understanding. That understanding extends to discovering what the problems are and what needs to be done to fix it.


Armed with Metadata

Metadata is data about your data. The analysis of gathered metadata with Profiling exposes all the possible issues to its structure and contents, giving you the information--knowledge and understanding--needed to implement Data Quality Regimens.


Here are only a few of the main types of Generic Profiling Metadata and the purpose of each:

  • Column Structure - Maximum/Minimum Lengths and Inferred Data Type - These types of metadata provides information on proper table formatting for a target database. It is considered problematic, for example, when an incoming table has values which exceed the maximum allowed length.

  • Missing Information - NULLs and Blanks - Missing data can be synonymous to bad data. This applies for example where an Address Line is Blank or Null, which in most cases is considered a required element.

  • Duplication - Unique and Distinct Counts - This allows for the indication of duplicate records. De-duplication is a standard practice in Data Quality and is commonly considered problematic. Ideally, there should only be a single golden record representation for each entity in the data.


Other equally important types of Generic Profiling Metadata include Statistics for trends data; Patterns (ReqEx) allow for identifying deviations from formatting rules; Ranges (Date, Time, String and Numbers); Spaces (Leading/Training Spaces and Max Spaces between Words); Casing and Character Sets (Upper/Lower Casing and Foreign, Alpha Numeric, Non UTF-8) Frequencies for an overview of the distribution of records for report generation on demographics and more.


Metadata Revolution & New Face of Profiling

Right now the most powerful profiling tool for gathering Metadata is the Melissa Data Profiler Component for SSIS, which is used at the Data Flow level, allowing you to profile any data type that SSIS can connect with, unlike the stock Microsoft Profiling Component, which is only for SQL Server databases.

More importantly the Melissa Data Profiler offers over 100 types of Metadata including all the Generic Profiling Metadata mentioned here.

The innovative Melissa Data's Profiler Component gathers Data Driven Metadata, which goes beyond the standard set of profiling categories. By combining our extensive knowledge on Contact Data, this allows us to get information not simply based on rules, norms, and proper formatting. Rather, it provides metadata with the aid of a back-end knowledge base. We can gather unique types of metadata such as postal code, State and Postal Code Mismatch, Invalid Country, Email Metadata, Phone and Names.


Take Control

The secret to possessing good data goes back to a simple truth: understanding and knowledge of your data through profiling. The release of Melissa Data's Profiler for SSIS allows you to take control of your data through use of knowledge base driven metadata. The truth shall set you free!

For more information on our profiling solutions, please visit our website


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!


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



Rancho Santa Margarita, CALIF - May 8, 2014 - Melissa Data, a leading provider of contact data quality and integration solutions, today announced its TechEd 2014 exhibit will feature new matching and de-duplication functionality in the company's MatchUp Component for SQL Server Integration Services (SSIS). Based on proprietary logic from Melissa Data, MatchUp consolidates duplicate customer records objectively, unlike any other data quality solution. Uniquely assessing the quality of individual data fields, MatchUp determines the best pieces of data to retain versus what to discard - enabling a smart, consistent method for data integrators to determine the best customer contact information in every field.

"A single, accurate view of the customer, known as the golden record, is the ideal for any business relying on customer data - reducing waste, optimizing marketing outreach and improving customer service. Yet common methods for matching and eliminating duplicate customer records involve subjective rules that don't consider the accuracy of the data itself," said Bud Walker, director of data quality solutions at Melissa Data. "MatchUp's intelligent rules offer a smarter, more consistent method for determining what information survives in the database and why. It's a critical data quality function that dramatically improves business operations."

MatchUp assesses the content within the customer record, in 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 information 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. MatchUp relies on deep domain knowledge of names and addresses for survivorship operations, used to granularly identify matches between names and nicknames, street/alias addresses, companies, cities, states, postal codes, phones, emails, and other contact data components.

MatchUp is part of Melissa Data's Data Quality Components for SQL Server Integration Services (SSIS), a suite of custom data cleansing transformation components for Microsoft SSIS, used to standardize, verify, correct, consolidate and update contact data for the most effective business communications. The suite further includes selected Community Editions for simple data quality procedures, free to developers and downloadable with no license required. Community Editions include Contact Verify CE for address, phone and name parsing, and email syntax correction; MatchUp CE provides simple matching and de-duplication without advanced survivorship operations, for up to 50,000 records using nine basic matchcodes.

Melissa Data will be demonstrating its MatchUp Component for SSIS at booth #1934 during Microsoft TechEd, May 12-15, 2014 at the George R. Brown Convention Center in Houston, TX. To download a free trial of Melissa Data's MatchUp Component for SSIS, click here; to request access to Melissa Data's free Community Editions, click here or call 1-800-MELISSA (635-4772).

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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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