Data Quality Analyst/MVP Channel Manager
Two truths about data: Data is always changing. Data will always have problems. The two truths become one reality–bad data. Elusive by nature, bad data manifests itself in ways we wouldn’t consider and conceals itself where we least expect it. Compromised data integrity can be saved with a comprehensive understanding of the structure and contents of data.
… Read More
By David Loshin
In my last two posts we looked at the distribution of information about entities and the use of record linkage to find corresponding data records in different data sets that can be linked together. Record linkage can be used for a number of processes that we bundle under the concept of “data enhancement,” which we’ll use to describe any methods for
improving the value and usefulness of information.… Read More
By Elliot King
Beauty is in the eyes of the beholder, but that is not the case when it comes to data quality or, at least, it is not the whole story. Data quality can be measured along several different dimensions. But in the final analysis data quality depends on the context within which the data is used.
Perhaps the most obvious criteria by which to measure data quality is
accuracy.… Read More
By Joseph Vertido
The process of finding approximate matching records in your data to get rid of
duplicates is precisely that – fuzzy. It raises as many questions as answers. Am
I using a good matching algorithm? Am I matching on the right fields? Is it a
true match or a false one?
The problem begins when inconsistent data enters from multiple sources.… Read More