Part 1: There are many measurements in data quality that can be useful. We describe seven measurements and how they can be used to measure your data quality. The descriptions of the seven dimensions are part of this series of blog posts (eight including this one.)
Why measure data quality? Like anything you may want to improve for the long-term a measurement is needed so you can gauge what action you must take in order improve. Data degrades day-by-day, so a consistent approach to entering the right data, cleaning data and importing good data will ensure that quality remains the same.
Acuate’s Seven Dimensions of Data Quality:
- Completeness (Part 2 of 8)
- Accuracy (Part 3 of 8)
- Consistency (Part 4 of 8)
- Conformity (Part 5 of 8)
- Currency (Part 6 of 8)
- Duplication (Part 7 of 8)
- Integrity (Part 8 of 8)
There are other measures that can be used, but we find these are sufficient in analysing data quality. A before and after spreadsheet of results can be produced as shown below, clearly identifying the areas of weakness and providing action plans to solve the on-going problems.
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