Melissa Data Wins Industry Accolades for Data Quality Leadership and Technical Contributions

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Named to DBTA 100: Companies that Matter Most in Data, as well as SD Times 100 for Innovation and Market Presence


Rancho Santa Margarita, CALIF, June 18, 2014 – Melissa Data, a leading provider of contact data quality and integration solutions, today announced it was named for the second consecutive time to the DBTA 100, Database Trends and Applications magazine’s list of the companies that matter most in data.… Read More

[WEBINAR] How to Seamlessly Cleanse and Enrich Your Customer Data

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Your contact data is perhaps your organization’s most valuable asset. But how good is your data?

At least 25 percent of most companies’ data is probably inaccurate, according to industry analyst Gartner. Bad data is likely caused by the fact that contact data is always in flux – as people move, get married, retire or pass away.… Read More

Data Quality Assessment: Column Value Analysis

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Enrichment, Data Profiling, Data Quality, Data Quality Assessment | , , , , ,

By David Loshin

In recent blog series, I have shared some thoughts about methods used for data quality and data correction/cleansing. This month, I’d like to share some thoughts about data quality assessment, and the techniques that analysts use to review potential anomalies that present themselves.
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Nobody’s Perfect

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By Elliot King

People of a certain age may remember the long-time marketing slogan for Ivory soap. Ivory was 99.44 percent pure (pure of what was left unsaid.) While it would be nice if our data could be 99.44 percent accurate–or better yet 100 percent accurate–that goal is simply impossible.
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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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