It is a bold statement of the obvious to say that data warehouses are data centric. This is different from the way that order entry systems are process centric, payment card validation is real-time sensitive or airplane avionics is computationally intense. Yet, as data warehouses are incorporated into leading-edge business intelligence (BI) applications, they take on characteristics of many of the most demanding system profiles and service level agreements. The data-centric nature of data warehousing is on a collision course with other requirements unless a solution of reconciling and rationalizing the competing trade-offs can be found.
The suitability of a service-oriented architecture (SOA) to transform data warehouses into information as a service is addressing this challenge, though with important conditions and qualifications.1 For example, going forward, data access services should include the operations required by data warehousing, such as aggregation, multidimensional roll ups and complex joins. Look for SOA-enabled master data management functions to support both transactional systems and data warehousing. A SOA provides a method of abstracting from the underlying location of the master data, hiding the complexity under the hood as long as the defined service is accommodated. However, for the foreseeable future, do not forget to check on the location of the data when large data volumes or complex joins are invoked...
