Earlier in my blog, Slowly Changing Dimensions – Special Attention Needed, I touched upon the need to pay special attention to slowly changing dimensions. Organizations have three variants of implementing solutions for slowly changing dimensions.

Type 1: in these implementations, the latest data is retained. This is implemented when there would be no need to do historic analysis. For example, an online transactional system that needs to display the latest list of values in the pull-down pick lists may use this type.

Type 2: in these implementations the history or the validity period for the changes is persisted.

I’ve recently posted a couple of articles at this site on data quality, this is the final one in a series of three.  Previous posts presented these ideas:

• Yes, there is a business case for improving data quality, and we’ve got business value examples. If you look for real money where you anecdotally know there are data quality problems, you’ll likely find it in high costs of data correction and rework, and savings related to business process improvements that reliable data enables.
• There are distinct things an organization can do to reap benefits of improved data management and data quality.  (1) Get started in the first place, (2) find the tangible benefits, (3) cross the departmental silos that exist in every large organization, and (4) promote sound data management practices.
• Impacts of poor data quality can seem abstract in a large organization. They aren’t for a small business owner.  Say you have a carpet cleaning business.  What if you knew 10% of your customer bills were wrong, but you weren’t sure by how much or in which direction?  First you’d panic. Then you’d rush to fix the problem.