In most cases, the foundation for a data warehouse is a relational data base. Relational data bases, which store data in two-dimensional rows and columns, are powerful tools, but they are not the most efficient method for complex decision making, Workman admits. He adds, however, that relational data bases are a mature, robust environment with replication and distribution capability. Relational data bases have been effective at managing operational data, Workman says. Operational data include transaction-driven operations such as inventory control and airline ticketing and scheduling.
Firms generate mountains of reports from such data.
Increasingly, however, companies are demanding more sophisticated multidimensional decision support, which aims to convert disparate sources of data into valuable information. Operational support focuses on organizing posted transactions, while decision support examines trend analysis, Workman offers. Companies need multidimensional analysis. Multidimensional analysis is much more powerful than typical spreadsheet-type analysis, Workman states. For example, using multidimensional analysis, a retail store's sales easily could be compared to its historical sales data or to current or past sales at any other store or aggregated region.