Data Quality: How would you DEFINE it?

by Davina V
This article discusses on how difficult it is to define quality, especially data quality in this case. It compares the difference between the data quality between the source data and the dependent data. It seems that in a majority of the cases is that “in the world of business intelligence, the dependent data often is expected to be of higher quality than the source data” (Schraml, 2010).  That is because the source data that impacts the dependent data must not change, or the changes in the data may expose something detrimental, or the change is too costly for the company. So that data quality can improve “data placed within the dependent mart or data warehouse must be altered from the source. Sometimes these alterations become codified within the process migrating data from the source… Either way, this alteration leads to a situation in which the dependent data will no longer equate one-for-one to the source data” (Schraml, 2010). Nonetheless data quality affects all corners of any company, so no person is shy from the the responsibility.

In class this week, the topic is Data Quality and Integration so I thought because integration is self-explanatory enough I would search for what data ‘qualify’ would be defined as.

I thought it was interesting, I am a business major and the article refers to business intelligence. I learned a little more about the difficulties of having ‘good’ data quality from this article.

Schraml, T. (2010). Data quality issues leave everyone holding the bag. Database Trends and Applications24(2), 31. Retrieved from