Decisions in today's organizations have become increasingly data-driven and real-time, so the systems that support business decisions must be of exceptional quality. People sometimes confuse testing data warehouses that produce business intelligence (BI) reports with backend or database testing or with testing the BI reports themselves. Data warehouse testing is much more complex and diverse. Nearly everything in BI applications involves the data that "drives" intelligent decision making.
Data integrity can be compromised at all DWH/BI phases: when data is created, integrated, moved, or transformed. However, testing of data warehouses is usually deferred until late in the cycle. If testing is shortchanged (e.g., due to schedule overruns or limited resource availability), there's a high risk that critical data integrity issues may slip through the verification efforts. Even if thorough testing is performed, it's difficult and costly to address any data integrity issues exposed by this late-cycle testing. At this phase, the cause of the error can be anything from a data quality issue stemming from when the data enters the data warehouse, to a data processing issue caused by a malfunction of the business logic along the layers of the data warehouse and its BI components. This is a painstakingly tedious task and often consumes considerable resources.