What is OLAP?
Online Analytical Processing (OLAP) is a method of retrieving answers to complex analytical queries from multi-dimensional data cubes. OLAP is attributed to business intelligence, which is similarly attributed to relational reporting and data mining. However, databases that function with OLAP use a multi-dimensional data model, enabling users to conduct analytical reports with quick execution time.
In the past, the bulk of investment in corporate computing was in business departments that generated or captured data, such as accounting, order processing, manufacturing and customer relations. Data warehousing developed out of a need to add value to this data by collecting, cleansing and sifting data from a variety of operational systems, and making the resulting information available to a wide audience of end users for analysis and reporting.
Today, organizations are investing in applications and technologies that deliver additional value from this collected data. Most information in a relational data warehouse is too arcane or unwieldy for the average business user. Advanced query tools hide the database complexity to some degree, but for a large class of applications where the end user is viewing multi-dimensional data, Online Analytical Processing (OLAP) technology provides a better solution.
The OLAP Data Model
In an OLAP data model, information is viewed conceptually as cubes, which consist of quantitative values (measures) and descriptive categories (dimensions).
For example, typical measures could include dollar sales, unit sales and inventory; typical dimensions could include time, geography and product. Within each dimension, data can be organized into a hierarchy that represents levels of detail in the data. A time dimension, for example, could include levels for years, months and days. The multi-dimensional data model makes it simple for users to formulate complex queries, arrange data on a report, switch from summary to detail data, and filter or slice data into subsets.
All organizations have some form of multi-dimensional data, and the complexity of this data is not necessarily a function of company size. Even the smallest company would like to track sales by product, salesperson, geography, customer and time. OLAP applications also deliver information more quickly to end users by preparing some computed values in advance, rather than at execution time. The combination of easy navigation and fast performance lets end users view and analyze data more quickly and efficiently than is possible with relational database technology alone.
OLAP Analysis Capabilities
In the past, OLAP tools have been used for specialized financial applications, such as budgeting, forecasting and reporting. Today, OLAP technology is used to accelerate analysis and decision making for business critical applications throughout the enterprise, such as Enterprise Resource Planning (ERP), manufacturing, Customer Relationship Management (CRM), and supply chain management.
Advanced OLAP capabilities include data mining and data visualization. Advanced visualization is useful for comprehending complex data by displaying it from several dimensions at once using color, shapes, maps, charts, perspective and animation. Visual queries make it easier and faster to navigate and select complex multi-dimensional data in a purely visual way.
In addition to advanced analysis capabilities, pre-built analytical applications for budgeting, sales analysis and performance management are widely available. These applications are quicker to install and are usually more functional than applications developed in-house.
The cost and availability of OLAP technology is no longer a barrier to deployment, but selecting the appropriate OLAP technology requires a good understanding of particular business needs, performance requirements, data volumes, user skills and system architecture. Simba offers comprehensive OLAP solutions that enable the utmost data connectivity and interoperability for actionable business intelligence. Contact us to learn more.