Fuzzy Matching – Global Intelligence Blog

March 6, 2019March 5, 2019 Author Address Check, Address Correction, Address Quality, Address Standardization, Address Validation, Address Verification, Analyzing Data, Analyzing Data Quality, Article, Big Data, BusinessCoder, Canada Post, Canadian Change of Address, CASS Certification, Clean Suite, Data Cleansing, Data Enhancement, Data Enrichment, Data Matching, Data Profiling, Data Quality, Demographics, Digital Identity Verification, Direct Mail, Duplicate Elimination, eIDV, Email Append, Email Marketing, Email Verification, Firmographics, Fuzzy Matching, Geocoding, Global Address Verification, Global Data Quality, Global Email, Global ID Verification, Global IP Locator, Global Name, Global Phone Verification, Golden Record, HIPAA, Householding, Identity Resolution, International Address Verification, IP Location, Location Intelligence, Mailing & Shipping, Mailing Lists, Marketing, MAT, Matching, MDM, Melissa Developer, Melissa Informatics, NCOA, News & Events, PAVE Certified, Presorting, Press Release, Property Data, Quick Tips, Record Linkage, Tips & Solutions, U.S. Property Service, Web API

Melissa Offers Insight on How a Healthy Data Lifecycle Supports Smart Business Decisions

New Resource Touts the Importance of Full Spectrum Protection to Combat Data Pitfalls Rancho Santa Margarita, CALIF – March 6, 2019 – Melissa, a leading provider of global contact data quality and identity verification solutions, today announced availability of the 2019 Melissa Solutions Catalog, a new resource that shares a wealth of information on the importance of good data, including how…

January 17, 2019January 17, 2019 Author Address Verification, Customer Identities, Data Cleansing, Data Profiling, Digital Identity Verification, eIDV, Email Verification, FinTech, Full Contact Authentication, Fuzzy Matching, GDPR, Global Address Verification, Global Data Quality, Global Email, Global ID Verification, Global Phone Verification, Identity Resolution, International Address Verification, Personator, Personator World

The Money Laundering Bad Guys Never Quit

Businesses that Neglect ‘Know Your Customer’ Regulations are Messing with Fate and Government Regulations Here’s the weird thing about money laundering: The problem just doesn’t want to go away. According to statistics compiled by the IRS, the ways that crooks hide the sources of their ill-gotten gains appear to be infinitely creative, with investigations and prosecutions remaining steady over recent…

May 1, 2017 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

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…

August 8, 2014 Blog Administrator Data Matching, Data Quality, Duplicate Elimination, Fuzzy Matching, Golden Record, Press Release, Record Linkage, SQL Server Integration Services, Survivorship

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!

August 3, 2012 Blog Administrator Address Quality, Address Standardization, Data Cleansing, Data Enhancement, Data Enrichment, Data Governance, Data Integration, Data Management, Data Matching, Data Quality, Duplicate Elimination, Fuzzy Matching

Structural Differences and Data Matching

By David Loshin Data matching is easy when the values are exact, but there are different types of variation that complicate matters. Let's start at the foundation: structural differences in the ways that two data sets represent the same concepts. For example, early application systems used data files that were relatively "wide," capturing a lot of information in each record,…

January 27, 2012 Blog Administrator Data Management, Data Quality, Fuzzy Matching, Record Linkage

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…

January 9, 2012 Blog Administrator Analyzing Data Quality, Data Quality, Fuzzy Matching, Record Linkage

Entities and their Characteristics

By David Loshin How can you tell if two records refer to the same person (or company, or other type of organization)? In our recent posts, we have looked at how data quality techniques such as parsing and standardization help in normalizing the data values within different records so that the records can be compared. But what is being compared?…

December 27, 2011 Blog Administrator Data Enhancement, Data Management, Data Quality, Fuzzy Matching, Record Linkage

Inferred Knowledge and Customer Intelligence through Matching and Linkage

By David Loshin What I have found to be the most interesting byproduct of record linkage is the ability to infer explicit facts about individuals that are obfuscated as a result of distribution of data. As an example, consider these records, taken from different data sets: A: David Loshin 301-754-6350 1163 Kersey Rd Silver Spring MD 20902 B: Knowledge Integrity,…

October 4, 2011 Blog Administrator Data Management, Data Quality, Data Quality Components for SSIS, ETL, Fuzzy Matching

Are You A Dupe Detective?

By Joseph Vertido The process of finding approximate matching records in your data to get rid of duplicates is precisely that - fuzzy. It raises as many questions as answers. Am I using a good matching algorithm? Am I matching on the right fields? Is it a true match or a false one? The problem begins when inconsistent data enters…