Real world alignment is often seen as a competing measure of data quality opposite to the popular approach of data quality being seen as fitness for purpose of use.
When we try to narrow down what constitutes quality of data we may use data quality dimensions. So, how does data quality dimensions look like in the light of real world alignment? Here is a few thoughts:
- Uniqueness is probably the data quality dimension that most closely relates to real world alignment as the opposite of uniqueness is duplication which in the data quality world means that two or more different data records describes the same real world entity.
- Accuracy is best measured as in what degree data describes something in the real world.
- Credibility was recently proposed as an important data quality dimension by Malcolm Chisholm on Information Management in the article called Data Credibility: A New Dimension of Data Quality? Here credibility is if data is without any malicious manipulation performed to fulfill an evil purpose of use.