OLAP and Multidimensional Databases

Finance departments use OLAP for applications such as budgeting, activity-based costing (allocations), financial performance analysis, and financial modeling. Sales departments use OLAP for sales analysis and forecasting. Marketing departments use OLAP for market research analysis, sales forecasting, promotions analysis, customer analysis, and market/customer segmentation. Typical manufacturing OLAP applications include production planning and defect analysis.

Important to all of these applications is the ability to provide managers the information that they need to make effective decisions about an organization's strategic directions. A successful OLAP application provides information as needed; that is, it provides “just-in-time” information for effective decision-making.

Providing such information requires more than a base level of detailed data. Just-in-time information is computed data that usually reflects complex relationships and is often calculated on the fly. Analyzing and modeling complex relationships are practical only if response times are consistently short. In addition, because the nature of data relationships may not be known in advance, the data model must be flexible. A truly flexible data model ensures that OLAP systems can respond to changing business requirements as needed for effective decision making.

Although OLAP applications are found in widely divergent functional areas, all require the following key features:

• Multidimensional views of data

• Calculation-intensive capabilities

• Time intelligence

Key to OLAP systems are multidimensional databases, which not only consolidate and calculate data; but also provide retrieval and calculation of a variety of data subsets. A multidimensional database supports multiple views of data sets for users who need to analyze the relationships between data categories. For example, a marketing analyst might ask following questions:

• How did Product A sell last month? How does this figure compare to sales in the same month over the last five years? How did the product sell by branch, region, and territory?

• Did this product sell better in particular regions? Are there regional trends?

• Did customers return Product A last year? Were the returns due to product defects? Did the company manufacture the products in a specific plant?

• Did commissions and pricing affect how salespeople sold the product? Did certain salespeople sell more?

In multidimensional databases, the number of data views is limited only by the database outline, the structure that defines all elements of the database. Users can pivot the data to see information from a different viewpoint, drill down to find more detailed information, or drill up to see an overview.