The issues associated with performing QA and data validation in an enterprise-wide business intelligence initiative are complex, and in many ways transcend the QA tenets that are appropriate for online transaction processing systems (OLTP). Attempts at establishing effective data validation often fail due to the enormity of the task.

The growth of business intelligence in insurance is driven by a number of key factors; one of the most important being the compelling need for an insurer to have a "single version of the truth." In order to achieve this lofty goal, an overall BI strategy must be defined, organized, developed and tested. It is the testing component or more specifically, the validation of the data that is the subject of this paper. We use the term "QA" throughout the paper. For our purposes, QA refers to the process of testing and validating the data that is used to populate the BI system that is being implemented. It does not refer to functional validation that is more appropriate to an OLTP environment.

A BI initiative is difficult by definition. It typically requires a multi-faceted process that takes disparate data from multiple, heterogeneous sources and organizes it in a way that it becomes useful to all levels and functional reaches within the organization. To be useful, this data must earn credibility and must be beyond reproach. Sounds like a reasonable goal, but why is that so difficult?