How to Do It All with Melissa

Blog Administrator | Address Check, Address Correction, Address Quality, Address Standardization, Address Validation, Address Verification, Analyzing Data Quality, Data Enhancement, Data Enrichment, Data Governance, Data Integration, Data Management, Data Matching, Data Profiling, Data Quality, Data Quality Components for SSIS, Full Contact Authentication, Geocoding, Global Address Verification | , , , ,

With
Melissa, you can do it all – see for yourself with the brand new Solutions
Catalog. This catalog showcases products to transform your people data (names,
addresses, emails, phone numbers) into accurate, actionable insight. Our
products are in the Cloud or available via easy plugins and APIs.
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Melissa Data’s Listware Online Brings Efficient Cloud-Based Data Quality to Business Users

Blog Administrator | Address Quality, Data Cleansing, Data Enhancement, Data Enrichment, Data Quality, Listware Online | , , , ,

Take
the headache out of maintaining clean contact data with our new Listware
Online! With a simple data upload (there’s no software to install), this
Cloud-based service verifies, corrects, and standardizes U.S. and Canadian
addresses, and adds missing name, address, phone, and email information.
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A Guide to Better Survivorship – A Melissa Data Approach

Melissa Team | Address Quality, Analyzing Data, Analyzing Data Quality, Data Enhancement, Data Integration, Data Management, Data Quality, Survivorship | , , , , , , ,

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.
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Structural Differences and Data Matching

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 | , , ,

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, but with a lot of duplication.
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Improving Identity Resolution and Matching via Structure, Standards, and Content

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Enhancement, Data Enrichment, Data Integration, Data Management, Data Profiling, Data Quality, Duplicate Elimination, Record Linkage | , , , ,

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 values, or if the values are incorrect.
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