Data Cleansing and Simple Business Rules

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

By David Loshin

Having worked as a data quality tool software developer, rules developer, and consultant, I am relatively familiar with some of the idiosyncrasies associated with building an effective business rules set for data standardization and particularly, data cleansing. At first blush, the process seems relatively straightforward: I have a data value in a character string that I believe to be incorrect and I want to use the automated transformative capability of a business rule to correct that incorrect string into a correct one.
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Where in the End-to-End Should Address Standardization and Correction Happen?

Blog Administrator | Address Standardization, Address Validation, Data Management, Data Quality | , , , , , ,

By David Loshin

In my last post, I shared a story about how rampant address validation actually can transform accurate (if not 100% standardized) addresses into inaccurate ones. My client actually noted that with some of the tools they have seen, addresses that have been submitted to the product and standardized are then re-submitted to the product, which then reports their own corrected address as being invalid!
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A Comparison of Microsoft Data Quality Services (Denali) vs. Melissa Data’s Data Quality Components for SSIS

Blog Administrator | Address Correction, Data Integration, Data Management, Data Quality | , , , ,

By Ira Whiteside

Recently Microsoft released a new Beta Release of SQL Server codenamed “Denali,” which included the Data Quality Services (DQS) feature. Here at Melissa Data, we are partners with Microsoft and also participate in the Azure Data Services Market for DQS providing Address Correction references.
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