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What is OLAP?
OLAP is an acronym for On Line Analytical Processing. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. It is quickly becoming the fundamental foundation for Intelligent Solutions including Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making.
Knowledge is the foundation of all successful decisions. Successful businesses continuously plan, analyze and report on sales and operational activities in order to maximize efficiency, reduce expenditures and gain greater market share. Statisticians will tell you that the more sample data you have, the more likely the resulting statistic will be true. Naturally, the more data a company can access about a specific activity, the more likely that the plan to improve that activity will be effective. All businesses collect data using many different systems, and the challenge remains: how to get all the data together to create accurate, reliable, fast information about the business. A company that can take advantage and turn it into shared knowledge, accurately and quickly, will surely be better positioned to make successful business decisions and rise above the competition.
OLAP—The Multidimensional Solution
Business is a multidimensional activity and businesses are run on decisions based on multiple dimensions. Businesses track their activities by considering many variables. When these variables are tracked on a spreadsheet, they are set on axes (x and y) where each axis represents a logical grouping of variables in a category. For example, sales in units or dollars may be tracked over one year’s time, by month, where the sales measures might logically be displayed on the y axis and the months might occupy the x axis (i.e., sales measures are rows and months are columns).
To analyze and report on the health of a business and plan future activity, many variable groups or parameters must be tracked on a continuous basis—which is beyond the scope of any number of linked spreadsheets. These variable groups or parameters are called Dimensions in the On-Line Analytical Processing (OLAP) environment.
Nowadays, many spreadsheet users have heard about OLAP technology, but it is not clear to them what OLAP means. Unlike relational databases, OLAP tools do not store individual transaction records in two-dimensional, row-by-column format, like a worksheet, but instead use multidimensional database structures—known as Cubes in OLAP terminology—to store arrays of consolidated information. The data and formulas are stored in an optimized multidimensional database, while views of the data are created on demand. Analysts can take any view, or Slice, of a Cube to produce a worksheet-like view of points of interest.
Rather than simply working with three dimensions, companies have many dimensions to track—-for example, a business that distributes goods from more than a single facility will have at least the following Dimensions to consider: Accounts, Locations, Periods, Salespeople and Products. These Dimensions comprise a base for the company’s planning, analysis and reporting activities. Together they represent the “whole” business picture, providing the foundation for all business planning, analysis and reporting activities.
The capability to perform the most sophisticated analyses—-specifically, the multidimensional analysis provided by OLAP technology—is an organizational imperative. Analysts need to view and manipulate data along the multiple dimensions that define an enterprise—essentially, the dimensions necessary for the creation of an effective business model.
OLAP implementation depends not only from technological basis software that is going to be used – but more from the business models and data sources. Each industry sector or business area is specific and required detailed modeling to create multidimensional cubes for easy data load and reporting building. For instance, approach for OLAP technology based system for medical institutions activities analysis or OLAP based data warehouse model development for police relevant activities analysis. OLAP model could also be build when information sources are common and not in a company landscape, but analysis of such kind of public, for instance economic, data is much valuable. In this case approach for web-services provided information analysis by use of OLAP technologies could be used.