The term On-Line Analytical Processing (OLAP) was coined by E.F. Codd in 1993 to refer to a type of application that allows a user to interactively analyze data. An OLAP system is often contrasted to an OLTP (On-Line Transaction Processing) system that focuses on processing transaction such as orders, invoices or general ledger transactions. Before the term OLAP was, coined, these systems were often referred to as Decision Support Systems.
OLAP is now acknowledged as a key technology for successful management in the 90's.It describes a class of applications that require multidimensional analysis of business data.
OLAP systems enable managers and analysts to rapidly and easily examine key performance data and perform powerful comparison and trend analyses, even on very large data volumes. They can be used in a wide variety of business areas, including sales and marketing analysis, financial reporting, quality tracking, profitability analysis, manpower and pricing applications, and many others.
OLAP technology is being used in an increasingly wide range of applications. The most common are sales and marketing analysis; financial reporting and consolidation; and budgeting and planning. Increasingly, however OLAP is being used for applications such as product profitability and pricing analysis; activity based costing, manpower planning; quality analysis, in fact for any management system that requires a flexible, top down view of an organization.
Online Analytical Processing (OLAP) is a method of analyzing data in a multidimensional format, often across multiple time periods, with the aim of uncovering the business information concealed within the data'- OLAP enables business users to gain an insight into the business through interactive analysis of different views of the business data that have been built up from the operational systems. This approach facilitates a more intuitive and meaningful analysis of business information and assists in identifying important business trends.
OLAP is often confused with Data Warehousing. OLAP is not a data warehousing, methodology, however it is an integral part of a data warehousing solution. OLAP comes in many different shades, depending on the underlying database structure and the location of the majority of the analytical processing. Thus, the term OLAP has different meanings depending on the specific combination of these variables. This white paper examines the different options to support OLAP. It examines the strengths and weaknesses of each and recommends the analytical tasks for which each is most suitable.
OLAP provides the facility to analyze I the data held within the data warehouse in a flexible manner. It is an integral component of a successful data warehouse solution; it is not in itself a data warehousing methodology or system. However, the term OLAP has different meanings for different people, as there are many variants of OLAP. This article attempts to put the different OLAP scenarios into context.
OLAP can be defined as the process of converting raw data into business information through multi-dimensional analysis. This enables analysts to identify business strengths and weaknesses, business trends and the underlying causes of these trends. It provides an insight into the business through the interactive analysis of different views of business information that have been built up from raw operating data which reflect the business users understanding of the business.