Understanding Data Quality Services

Blog Administrator | Analyzing Data Quality, Data Cleansing, Data Integration, Data Management, Data Quality | , , , , , ,
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. In this presentation, Joseph Vertido from Melissa Data will discuss the key concepts behind Knowledge Driven Data Quality, implementing a Data Quality Project, and will demonstrate how to build and improve your Knowledge Base through Domain Management and Knowledge Discovery.… Read More

Contact Concepts and Customer Centricity

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

By David Loshin

I recently taught a half-day seminar on an introduction to master data management, and every time I teach that course, I preface the discussion though an informal poll of the attendees with three requests. The first is to differentiate between the attendees who represent the interests of their corresponding organization’s “business side” and those who are on the IT or data management teams.
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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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The Format of Nothing

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Governance, Data Management, Data Profiling, Data Quality | , , , , ,

By David Loshin

The first question I always wonder about missing data is about the format of the missing data, especially in systems that predate the concept of the “system null” value. For example, early systems maintained files storing tables with fixed-width columns. When one of a record’s field was missing a value, something had to be fitted into that field to ensure that the rest of the columns lined up correctly.
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Content Standards for Data Matching and Record Linkage

Blog Administrator | Address Standardization, Analyzing Data, Data Management, Data Matching, Data Quality, Record Linkage | , , , , ,

By David Loshin

As I suggested in my last post, applying parsing and standardization to normalize data value structure will reduce complexity for exact matching. But what happens if there are errors in the values themselves?

Fortunately, the same methods of parsing and standardization can be used for the content itself. This can address the types of issues I noted in the first post of this series, in which someone entering data about me would have used a nickname such as “Dave” instead of “David.”

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