What is the definition of OLAP?

Definition of OLAP, Advantages and Uses

Easy OLAP Definition

OLAP (Online Analytical Processing) is the technology behind many Business Intelligence (BI) applications. OLAP is a powerful technology for data discovery, including capabilities for limitless report viewing, complex analytical calculations, and predictive “what if” scenario (budget, forecast) planning.

 

How is OLAP Technology Used?

OLAP is an acronym for Online Analytical Processing. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. It is the foundation for may kinds of business applications for 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.

Advantages of OLAP

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 technology has been defined as the ability to achieve “fast access to shared multidimensional information.”  Given OLAP technology’s ability to create very fast aggregations and calculations of underlying data sets, one can understand its usefulness in helping business leaders make better, quicker “informed” decisions.

 

OLAP for Multidimensional Analysis

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 two dimensions (standard spreadsheet) or three dimensions (for example, a workbook with tabs of the same report, by one variables), 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.

Implementing an OLAP Solution

OLAP technology implementations depend not only on the type of software, but also on underlying data sources and the intended business objective(s). Each industry or business area is specific and requires some degree of customized modeling to create multidimensional “cubes” for data loading and reporting building, at minimum. An OLAP solution might be intended for dynamic reporting for finance professionals, with source data originating in an ERP system. Or a solution might address a medical institution’s activities as concerns patient analysis. All of which is to say that customers need to have clear objectives in mind for an intended solution, and start to consider product selection on that basis. Another factor to consider in an OLAP implementation is the delivery to end users: does the initial user base want to adopt a new front end, or is there a preference for utilizing a dashboard? Or  perhaps users are better served by a dynamic spreadsheet “delivery” system to achieve, for example, a collaborative budgeting and forecasting solution. PowerOLAP® from PARIS Tech [the sponsor of OLAP.com] is one such product that features Excel as a front end, for a wide variety of uses.

See Also

Types of OLAP Systems

OLAP and Business Intelligence History

OLAP and Business Intelligence Terms

OLAP Tutorials

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