Structural Differences and Data Matching

By David Loshin Data matching is easy when the values are exact, but there are different types of variation that complicate matters. Let's start at the foundation: structural differences in the ways that two data sets represent the same concepts. For example, early application systems used data files that were relatively "wide," capturing a lot of information in each record,…

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Moving to Action

By Elliot King The first step in a data quality program is to assess your data. Whether you opt for data profiling or some other assessment mechanism, this part of the process consists of systematically identifying exactly where the problems can be found in your data sets. While assessment is obviously the first step, it should be just as obvious…

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Non-technical Employees Need to Understand Data Quality

By Elliot King Too often, data quality is seen as a technical issue. You know the drill: profile, measure, remediate, integrate, augment, control and so on. In many ways, a company's data is like an ecosystem, and the data quality team is analogous to the environmental specialists. They conceptualize an ideal state for data, measure the actual state, and then…

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Data Cleansing and Simple Business Rules

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…

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In a Global Economy, a Global Solution is Vital

By Patrick Bayne Data Quality Tools Software Engineer Heidelberglaan 8 3584 CS Utrecht If you were given this address, how would you know it was valid? Is it formatted correctly? How long will it take you to verify? For years businesses have understood a need for address validating solutions, because clean, accurate data is essential. Without accurate and consistent data,…

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Communication is a Key to Data Quality

By Elliot King Too often, data quality is seen as a strictly technical issue. Data quality problems must be identified, assessed and then rectified, and that process is best managed by experts using the right tools. But communication may be the most important element in a data quality program. A data quality program can only succeed if all the stakeholders…

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Address Quality – Take 2

By David Loshin We have dealt with some of our core address quality concepts, but not this one: The intended recipient must be associated with the deliverable address. The problem here is no longer address quality but rather address correctness. The address may be complete, all the elements may be valid, the ZIP+4 is the right one, and all values…

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Postal Standards and Address Quality – Take 1

By David Loshin The USPS Postal Standard (Publication 28) provides at least some of the specifications we need for address quality. For example,  "The Postal Service defines a complete address as one that has all the address elements necessary to allow an exact match with the current Postal Service ZIP+4 and City State files to obtain the finest level of…

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