A Guide to Better Survivorship – A Melissa Data Approach

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. In this article, we will take a look at how…

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Reduce Fraud and Data Entry Errors with 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,…

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The Meaning of Nothing

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…

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Low Cost Ways to Improve Data Quality

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…

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All or Nothing?

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…

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Get it Right the First Time

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…

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Normalizing Structure Using Data Standardization for Improved Matching

By David Loshin In my last few posts, I discussed how structural differences impact the ability to search and match records across different data sets. Fortunately, most data quality tool suites use integrated parsing and standardization algorithms to map structures together. As long as there is some standard representation, we should be able to come up with a set of…

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Data Quality Management Mistakes to Avoid

By Elliot King Everyone wants high quality data and it seems that goal should not be so hard to achieve. The need seems obvious and there are plenty of good tools that can be put to work in the effort. Unfortunately, it is just not that easy to set up a successful, ongoing data quality program. The first mistake companies…

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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,…

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Moving to Action

By Elliot King The first step in a data quality program is to assess your data. Whether you opt for data profiling or some other assessment mechanism, this part of the process consists of systematically identifying exactly where the problems can be found in your data sets. While assessment is obviously the first step, it should be just as obvious…

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