Businesses are increasingly finding that traditional query and reporting tools are not performant or scalable enough to support all of users' complex ad-hoc queries, not efficient for analysis beyond two or three "dimensions", and not functional enough for sophisticated trend analysis or forecasting. By contrast, a good online analytical processing (OLAP) solution -- essentially a database plus special indexes and operations for in-depth querying -- enables rapid, in-depth analysis and forecasting involving difficult-to-anticipate ad-hoc queries for both immediate tactical and longer-term strategic decision-making. OLAP also differentiates itself by its ability to analyze data across more than two or three dimensions. For example, CFOs can exploit the analytical and calculation power of OLAP not only for traditional analysis but also for areas such as budgeting, financial reporting, consolidation, planning, and business forecasting.
An architecture involving a separate data store can complicate and increase IT infrastructure, administrative costs and security problems; introduce data quality and latency issues associated with data movement; slow down analysis when data is not in the "right" database; inevitably result in inconsistent data and business rules between systems, reducing users' ability to make sound judgments; and force constant reconciliation and synchronization between OLAP and source data. Thus, users are increasingly seeking to do OLAP "inside" enterprise databases such as DB2.