Reduce Fraud and Data Entry Errors with Full Contact Authentication

Blog Administrator | Address Quality, Data Cleansing, Data Management, Data Quality, Full Contact Authentication | , , , , ,

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

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Management, Data Quality | , , ,

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. And even if those

responsible for containing costs agree that improving data quality is important and can have a real impact on the bottom line, they often wonder if the impact will be big enough to justify the investment.… Read More

All or Nothing?

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Quality | , , , , ,

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.

Now there are a few things to think about here regarding the critical nature of
completeness rules for data validity, from the data creation side and from the
data consumption side.… Read More

Get it Right the First Time

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Quality, Data Quality Assessment | , , , , ,

By Elliot King

People generally think of data quality as a remedial exercise. During the ongoing course of business, for a variety of reasons, companies find themselves with incorrect data. The goal of a data quality program is to identify the incorrect data and fix it.

And while data errors inevitably do occur, an essential element of a data
quality program is putting technology and processes in place that will ensure as
much as possible that the data captured initially is correct.… Read More