In a business environment characterized by fierce competition and rapid change, you need to make decisions quickly and on the basis of reliable facts and figures. But what if that information is incomplete? Or simply unavailable? What happens if executives and knowledge workers are left stranded in an ivory tower, unable to obtain the answers to pressing questions on customers, products, profitability and potential new sources of revenue?

The information you need is somewhere within your organization, but it is often locked away in operational applications, and difficult to obtain. And when you do get hold of the statistics you need, it is often not in the form you require, and you have little scope to explore that information in more detail to identify the precise cause of a sudden surge in costs or the reasons for the unexpected popularity of a particular product in a particular region.

The recognized technical concept for meeting this challenge is OLAP. OLAP is a separate environment with a dedicated database drawing on diverse data sources and designed to support queries and analysis.
The first wave of data warehouses has encountered a number of difficulties, including technical integration and laborious and lengthy implementation. One major obstacle has been gearing the warehouse to the business processes of the user organization.

Online analytical processing (OLAP) is an increasingly popular technology that can dramatically improve business analysis, but that has been characterized historically by expensive tools, difficult implementation, and inflexible deployment. Many companies have tackled the OLAP problem and created a solution that makes multi-dimensional analysis accessible to a broader audience, and potentially, at a significantly lower cost of ownership.

In an OLAP data model, information is conceptually viewed as cubes, which consist of descriptive categories (dimensions) and quantitative values (measures). The multi-dimensional data model makes it simple for users to formulate complex queries, arrange data on a report, and switch from summary to detail data, and filter or slice data into meaningful subsets. For example, typical dimensions in a cube containing sales information would include time, geography, product, channel, organization, and scenario (budget or actual). Typical measures would include dollar sales, unit sales, inventory, headcount, income, and expense.

Within each dimension of an OLAP data model, data can be organized into a hierarchy that represents levels of detail on the data. For example, within the time dimension, you may have the levels years, months, and days; similarly, within the geography dimension, you may have the levels country, region, state/province, and city. A particular instance of the OLAP data model would have the specific values for each level in the hierarchy. A user viewing OLAP data will move up or down between levels to see more or less detailed information.

Many organizations know that they need OLAP-based solutions, but those tasked to select and implement them may be new to the area, or may have lost track of its rapid developments. Selecting the right OLAP product is hard, but very important, if projects are not to fail; many buyers struggle even to produce an appropriate shortlist. Now the most widely used specialist OLAP resource worldwide.

The need of end users to analyze corporate data for the purpose of making better decisions is of paramount importance. Fast, consistent response to end-user requests is critical to interactive, ad-hoc exploration, comparison and analysis of data, regardless of database size and complexity. End users must be able to manipulate and derive data for analysis purposes by applying analytical operations such as ratios, cumulative totals, trends and allocations across dimensions and across hierarchical levels. OLAP technologies are essential to delivering this end-user value and are a critical component of broader information technology architecture.