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April 18, 2008

Wrong Approach to Data Quality, Right Approach to Data Quality.

Filed under: Data Quality — Alena Semeshko @ 2:24 am

As much as you hear about the importance of data quality being the determinant of your organization’s success, companies all over the world still use inaccurate and outdated data in their daily work. In the bulk of information that piles up over the months, even years, you usually can’t even identify what’s more urgent and important.

The traditional ETL approach that quite a few companies have come to use sure is helpful. But once you’ve gone through its stages, it’s important not to forget that your new cleansed data is still constantly being enriched and changed. So in less than no time a new challenge emerges as you get your cleansed data mixed with new data that isn’t necessarity as consistent and reliable as it should be. What do you do? Try implementing a unified and repeated data quality monitoring approach.

The steps you could follow while at that include:

  • Create a clear standard that your incoming data should match
  • Identify the main issues with incoming data by checking it against the created standard
  • Look for the ways to solve the identified problems (as a possibility you cold create a notification system to send out alerts whenever unvalid or inconsistent data is detected)

At the first glance this looks like it could solve your problems. But that’s just your incoming data. Another part of the problem lies in the clean data already stored in your warehouse. It’s validity isn’t everlasting, is it? Thus a few more things in your to-do list:

  • Identify the most appropriate time span for your data to be re-verified
  • Schedule your data verification system to conduct repeated checks according to the identified data validity time span

All in all, just keep in mind that data within any organization is a dynamic and constanly-changing asset, and data quality checking should become a repeated procedure, rather than a one-time practice.

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