On-Line Analytical Processing (OLAP) is a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.
OLAP functionality is characterized by dynamic multi-dimensional analysis of consolidated enterprise data supporting end user analytical and navigational activities including:
calculations and modeling applied across dimensions, through hierarchies and/or across members
trend analysis over sequential time periods
slicing subsets for on-screen viewing
drill-down to deeper levels of consolidation
reach-through to underlying detail data
rotation to new dimensional comparisons in the viewing area
OLAP stands for On Line Analytical Processing, a series of protocols used mainly for business reporting. Using OLAP, businesses can analyze data in all manner of different ways, including budgeting, planning, simulation, data warehouse reporting, and trend analysis. A main component of OLAP is its ability to make multidimensional calculations, allowing a wide and lightning-fast array of possibilities. In addition, the bigger the business, the bigger its business reporting needs. Multidimensional calculations enable a large business to complete in seconds what it otherwise would have waited a handful of minutes to receive.
In computing, online analytical processing, or OLAP (pronounced /ˈoʊlæp/) is an approach to swiftly answer multi-dimensional analytical queries. OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture. The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).
Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. They borrow aspects of navigational databases and hierarchical databases that are faster than relational databases.
The output of an OLAP query is typically displayed in a matrix (or pivot) format. The dimensions form the rows and columns of the matrix; the measures form the values.
One main benefit of OLAP is consistency of calculations. No matter how fast data is processed through OLAP software or servers, the reporting that results is presented in a consistent presentation, so executives always know what to look for where. This is especially helpful when comparing information from previous reports to information contained in new ones and projected future ones. "What if" scenarios are some of the most popular uses of OLAP software and are made eminently more possible by multidimensional processing.
Another benefit of multidimensional data presentation is that it allows a manager to pull down data from an OLAP database in broad or specific terms. In other words, reporting can be as simple as comparing a few lines of data in one column of a spreadsheet or as complex as viewing all aspects of a mountain of data. Also, multidimensional presentation can create an understanding of relationships not previously realized. All of this, of course, can be done in the blink of an eye.
Producers of OLAP software are familiar, including Oracle, IBM, and Hyperion Solutions. Oracle, which has a reputation for being different, refers to OLAP software as Business Intelligence. IBM and Hyperion Solutions, wishing to remain consistent with industry standards, call their software OLAP.
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