Enabling High Quality Analytics Through a Data Validity Dimension

website@infoadvisors.com — Fri, 26 May 2006 21:41:00 GMT

I've added a new article by Peter Stiglich on Enabling High Quality Analytics Through a Data Validity Dimension. Peter's approach is interested -- who knew that using the cartesian product was going to be so helpful?

Data Quality

While working on an Enterprise Data Warehouse for a state court system the issue of poor data quality in the source systems became apparent. Referential integrity was not strictly enforced and there was very little in the way of attribute level constraints. One normally expects that these types of constraints be enforced for an OLTP application, whether through the application, in the database, or both.

Of course, one should never be surprised when there is poor data quality in the source systems – poor data quality is the norm rather than the exception. According to The Data Warehouse Institute (TDWI) over $600 billion a year is lost due to poor data quality.

If you are interested in submitting an articled, send it to website@infoadvisors.com for review.

Karen Lopez: Musings on Data, Process, and Architecture

Enabling High Quality Analytics Through a Data Validity Dimension