The Meaning of Nothing

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

By David Loshin

What does it mean when a data element has a null value? In my previous posts, I sort of suggested that the data value was “not available” but that is a bit presumptive. At earlier stages in my data career, I spent a lot of time thinking about the meaning of a null value, and considering the reasons that a data element’s value was missing.
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Normalizing Structure Using Data Standardization for Improved Matching

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By David Loshin

In my last few posts, I discussed how structural
differences impact the ability to search and match records across different
data sets. Fortunately, most data quality tool suites use integrated parsing
and standardization algorithms to map structures together.
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Data Cleansing and Simple Business Rules

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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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Maximize Value and Mitigate Risk

Blog Administrator | Data Governance, Data Quality | , , , , ,

By Elliot King

It seems like everybody knows, or should know, that data is a company’s most important asset. Data is the primary commodity employees, customers and suppliers generate and consume everyday–it is the corporate lifeblood. Data is the raw material that let’s organizations know if they are doing well or falling short.
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