Performance Scalability

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By David Loshin

In my last post I noted that there is a growing need for continuous entity identification and identity resolution as part of the information architecture for most businesses, and that the need for these tools is only growing in proportion to the types and volumes of data that are absorbed from different sources and analyzed.
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Understanding Data Quality Services

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Knowledge Base, Knowledge Discovery, Domain Management,
and Third Reference Data Sets

PASS Virtual Chapter Meeting: Thursday, Jan. 31, 2013 at 9 am PDT, 12 pm EST.

With the release of Data Quality Services (DQS), Microsoft innovates its solutions on Data Quality and Data Cleansing by approaching it from a Knowledge Driver Standpoint.… Read More

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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Making Sense Out of Missing Data

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

By David Loshin

I have spent the past few blog posts considering different aspects of null values and missing data. As I mentioned last time, it is easy to test for incompleteness, especially when system nulls are allowed. And even in older systems, the variable ways that missing or null data is represented is finite, making it easy to describe rules for flagging incomplete records.
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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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