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.

As long as there is some standard representation, we should be able to come
up with a set of rules that can help to rearrange the words in a data value
to match that standard.

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Data Quality Management Mistakes to Avoid

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By Elliot King

Everyone wants high quality data and it seems that goal should not be so hard to achieve. The need seems obvious and there are plenty of good tools that can be put to work in the effort. Unfortunately, it is just not that easy to set up a successful, ongoing data quality program.

The first mistake companies make as they attempt to manage their data is that
they don’t take the time to understand exactly what they want regarding their
data quality.… Read More

Structural Differences and Data Matching

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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, but with a lot of duplication.
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Moving to Action

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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 that
it can’t be the last.… Read More

Non-technical Employees Need to Understand Data Quality

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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 fix the problems they find.
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