Tuesday, November 23, 2010

3common types of data quality flaws

Invalid data
You have data but it's obsolete. Wrong phone number, a nonexistent postal code, junk email address. These errors can be detected by proper scrutiny.

Incomplete data
Most often fields in your database are empty. Tracking such fields and finding valid data takes time and resources.

Inconsistent data
This happens mostly during data migration process or when you add bulk data from other sources. To avoid such inconsistencies, try to model your data for similar formatting.

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