Why OLAP?

Relational databases have dominated database technology by providing the online transactional processing (OLTP) that is essential for businesses to keep track of their affairs. Designed for efficient selection, storage, and retrieval of data, relational databases are ideal for housing gigabytes of detailed data.

The success of relational databases is apparent in their use to store information about an increasingly wide scope of activities. As a result, they contain a wealth of data that can yield critical information about a business. This information can provide a competitive edge in an increasingly competitive marketplace.

Analytical Processing Answers Business Questions

The challenge is in deriving answers to business questions from the available data, so that decision makers at all levels can respond quickly to changes in the business climate. While a standard transactional query might ask, "When did order 84305 ship?" a typical series of analytical queries might ask, "How do sales in the Southwestern region for this quarter compare with sales a year ago? What can we predict for sales next quarter? What factors can we alter to improve the sales forecast?"

The transactional query involves simple data selection and retrieval. However, the analytical queries involve inter-row calculations, time series analysis, and access to aggregated historical and current data. This is online analytical processing -- OLAP.

The data processing required to answer analytical questions is fundamentally different from the data processing required to answer transactional questions. Table 1-1 highlights the major differences.

Table 1-1  Characteristics of Transactional And Analytical Queries

Characteristic	Transactional Query	Analytical Query
Typical operation	Update	Analyze
Age of data	Current	Historical
Level of data	Detail	Aggregate
Data required per query	Minimal	Extensive
Querying pattern	Individual queries	Iterative queries

Types of OLAP Applications

Applications that support business analyses fall into these major groups:

• Standard reporting
• Ad-hoc query and reporting
• Multidimensional analytical reporting
• Predictive analysis and planning

Oracle provides the technology for all of these types of applications. Oracle OLAP and its development tools are particularly suited to analytical reporting and predictive analysis applications. This guide will introduce you to the tools for developing these types of applications.

Analytical Reporting

Analytic applications can support many facets of a business and offer high returns on the investment. Here are just a few examples of analytical applications:

• Accounting. Forecasting, budgeting, cost and profitability analyses, and consolidation
• Human Resources. Skills consolidation, labor scheduling and optimization
• Distribution. Scheduling and optimization
• Sales Force Automation. Cross-selling and territory analyses
• Marketing. Churn and market-based analyses
• Retailing. Site location and demographic analyses
• Manufacturing. Demand planning and forecasting
• Health Care. Outcomes analysis
• Financial Services. Risk assessment and management

Predictive Analysis

Planning applications allow organizations to predict outcomes. They generate new data using predictive analytical tools such as models, forecasts, aggregation, allocation, and scenario management. Some examples of this type of application are corporate budgeting and financial analyses, and demand planning systems.

Budgeting and financial analyses systems allow organizations to analyze past performance, build revenue and spending plans, manage toward profit goals, and model the effects of change on the financial plan. Management can determine spending and investment levels that are appropriate for the anticipated revenue and profit levels. Financial analysts can prepare alternative budgets and investment plans contingent on factors such as fluctuations in currency values.

Demand planning systems allow organizations to predict market demand based on factors such as sales history, promotional plans, pricing models, and so forth. They can model different scenarios that forecast product demand and then determine appropriate manufacturing goals.