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.
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Reduce Fraud and Data Entry Errors with Full Contact Authentication

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Melissa Data recently launched Personator – a flagship data quality Web service designed to provide instant identity verification and fraud prevention for e-commerce and call center applications.

Personator compares incoming customer and prospect records against multi-sourced data sets – including telecom data, USPS datasets, title and deed information, financials and GIS – to confirm that a name matches an address, corrects and updates address, telephone and email information.… Read More

The Meaning of Nothing

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By David Loshin

What does it mean when a data element has a null value? In my previous posts, I sort of suggested that the data value was “not available” but that is a bit presumptive. At earlier stages in my data career, I spent a lot of time thinking about the meaning of a null value, and considering the reasons that a data element’s value was missing.
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Low Cost Ways to Improve Data Quality

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By Elliot King

In many organizations, when one side of the house starts talking about improving data quality, the other side of the house starts hearing one thing and one thing only–costs. They assume that initiating a data quality program is going to be a heavy lift financially, requiring consultants, investments in technology, training and more.
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All or Nothing?

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By David Loshin

One of the most frequently referenced dimensions of data quality is completeness. At a formal level, completeness implies rules specifying mandatory assignment of values to particular data elements. In layman’s terms, that specifies rules to make sure critical attributes are populated with values.
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