With a piping hot cup of coffee in hand, you’re ready to dive into your day and tackle a new project. First on the agenda? Design a process to convert all variations of US state names to their associated United States Postal Service standard two-character abbreviation. It’s the simplest of transformations—take one word or phrase and transform it into a known standard. Unfortunately, real world data can make this easy manipulation difficult. You grab another cup of coffee.
While plopping down back to your seat, you think about the crazy misspellings or rearranged text your program will encounter when it begins to evaluate US state names. Luckily different SAS applications give you many options to minimize the amount of work needed to achieve high quality results with your address data. Let’s look at the pros and cons of your choices when it comes to transforming data in a SAS environment – starting with the most robust solutions first.
The good news? You have options.
Using US state values as an example, we explored many data standardization approaches. Know that the same tools and techniques can be applied to other data and to other processes beyond data standardization. If you aren’t familiar with the QKB, take a look at what it offers for standardization, parsing, match code generation, identification analysis and more.
With a dedicated Customize application that lets you extend standard definitions and add new ones, you have complete control over the quality of your data. Even better, the QKB integrates with several SAS applications so you can choose the execution environment that is right for you. The core difference lies in having a centralized repository of standardization rules versus keeping transformation rules buried in custom code, and scattered across the enterprise.
Are you now ready to start your standardization project?
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