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.… Read More

Customer Centricity and Birds of a Feather

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

By David Loshin

Why do we care to establish physical locations for individuals? One reason should be patently obvious: in every interaction between a staff member from your company and customer, both parties are always physically located somewhere, and many business performance indicators are pinned to a location dimension, such as “sales,” “customer complaints,” or “product distribution” by region.
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Customer Centricity and Location Characteristics

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

By David Loshin

In my last set of posts I began to examine the integration of the concept of “customer centricity” into business processes, starting with the ability to uniquely differentiate individuals in relation to their “contact-based” identifying characteristics, such as street address and telephone number.
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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

Data Quality Management Mistakes to Avoid

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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.
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