Contact Data and Identifying Information

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By David Loshin

When inspecting two records for similarity (or for differentiation), the values in the identifying attributes from each corresponding record are compared to determine whether the two records can be presumed to represent the same entity or distinct entities.
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Validation of Data Rules

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By David Loshin

Over the past few blog posts, we have looked at the ability to define data quality rules asserting consistency constraints between two or more data attributes within a single data instance, as well as cross-table consistency constraints to ensure referential integrity.
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Get Used to It: Inconsistent Data is the New Normal

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By Elliot King

Nobody is perfect and neither is corporate data. Indeed, data errors are intrinsic to IT’s DNA. Data inevitably decays. Errors can be caused when data from outside sources are merged into a system. And then, of course, the humans that interact with the system are, well, human.
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Understanding Hierarchies

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By David Loshin

Defining standards for group classification helps in reducing confusion due to inconsistencies across generated reports and analyses. In the automobile classification example we have been using for the past few posts, we might pick the NHTSA values (mini passenger cars, light passenger cars, compact passenger cars, medium passenger cars, heavy passenger cars, sport utility vehicles, pickup trucks, and vans) as the standard.
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