Sometimes Data Quality is the Law

By Elliot King We have all read the statistics about the real costs that poor data quality represents. And intuitively, we know that bad data is, well, bad. But, in many cases, bad data is more than just bad for business. Increasingly, good data is required by law. In 2001, the U.S. Congress added two lines to its major appropriations…

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Clean Data is Good Data

By Elliot King The cliché is as old as computing itself--garbage in, garbage out. And that cliché is as true now as ever, if not more so. Unfortunately, with information flowing into companies from so many sources including the Web and third-party providers, mistakes should not just be expected; they are basically inevitable. Garbage data is going to get in…

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The Relative Distinction of Address Validation, Precision, and Accuracy

By David Loshin One nice thing about addresses, especially in the United States, is that they have well-defined standards. In previous blog series, I have looked at the process of address standardization and correction, so I won't belabor that point. However, many people confuse the differences among a  valid address, a precise representation of an address, and an accurate address.…

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Standardizing Your Approach to Monitoring the Quality of Data

By David Loshin In my last post, I suggested three techniques for maturing your organizational approach to data quality management. The first recommendation was defining processes for evaluating errors when they are identified. These types of processes actually involve a few key techniques: 1) An approach to specifying data validity rules that can be used to determine whether a data…

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Garbage In …

By Elliot King We all know that dirty data is not really dirty; it is just incorrect. Data cleansing consists of correcting mistakes in the data. Mistakes make their way into contact data in several different ways. It may just be wrong or incomplete; it may not be updated; and it may be duplicated if small variations are entered into…

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