On-Line Analytical Processing (OLAP) uses a multidimensional approach to organize and analyze business data. By storing data in highly optimized structures, businesses can very quickly explore the data and uncover important insights that would otherwise remain hidden. As a result, OLAP enables companies to achieve key organizational goals, including wide-ranging business intelligence and analytics.
OLAP & Business Intelligence
To explain how OLAP technology contributes to business intelligence (BI), we first need to define BI itself. BI means different things to different people. For some people, BI is only the data warehouse. Others see BI as the dashboards on their desktops. For this discussion, we define BI as all of the processes and technologies used to help businesses make better decisions. In addition to OLAP, these include, but are not limited to, the following:
- Enterprise Performance Management (EPM)
- Data Warehousing
- Business reporting, including dashboards and scorecards
- Predictive analytics and data mining
Together, the above support an organization’s ability to create, maintain, analyze, and report accurate information about the business, and use that information for forward-looking activities such as budgeting and forecasting.
Let us examine each in a bit more detail.
Enterprise Performance Management
Enterprise Performance Management (EPM) is a set of processes and related software that supports management excellence. EPM organizations are smart, agile, and aligned.
Smart organizations recognize that they must rationalize their analytical tools and data management systems to eliminate the noise and provide actionable insights to all the stakeholders of the enterprise.
Agile organizations are able to detect deviations between plans and execution quickly, find the root causes, and take fast corrective actions. They use best-of-breed technologies that offer advanced integration with operational systems, yet can be used easily with a company’s existing architecture and information technology (IT) investments.
Aligned organizations address the needs of all stakeholders and share information through integrated systems and processes so that all stakeholders are working from the same set of facts—that is, the same data.
The objective of a data warehousing system is to provide business users with a time-based, integrated view of cross-functional data. To create a data warehouse, we start with data that may exist in different formats across several systems. We transform the data, cleanse it, and create an integrated view of the data.
Data warehousing provides historical data, as opposed to the current snapshot of data that can be found in an online transaction processing (OLTP) system. A data warehouse does not answer the question “What orders are shipping now?” but rather reporting questions such as “How many orders did we ship last month?” and analytical questions such as “When have we shipped orders the fastest?”
A data warehouse offers a central, reliable repository of historical business data that all stakeholders can use. End users can write queries to pull data from this single source of data, so that regardless of who asks the question, they will get consistent answers.
Business reporting is about conveying information that is important to the organization and using that data to manage the business. Business reports have been around since the first data management systems were implemented.
The original medium of reports was paper documents. Today, many organizations implement business reports online through dashboards and scorecards. Business reports often require current data, and they can be widely distributed within an organization.
Predictive Analytics and Data Mining
Predictive analytics is concerned with examining historical data using statistical tools and techniques, such as regression or data mining, to forecast or predict future events and to determine the factors that best predict an event.
For example, using historical data, a company could forecast a customer’s price point for a certain product. By determining each customer’s profile, the company could manage its revenue stream better by charging different customers different prices. This would allow the company to increase revenue while maintaining customer satisfaction. After these models are developed, analysts can look for exceptions to the model for activities such as anomaly and fraud detection.
OLAP is a technology that supports activities ranging from self-service reporting and analysis to purpose-built management applications such as planning and budgeting systems. What differentiates OLAP from regular business reporting is the analytics. For example, metrics are often compared with a baseline, such as last year’s numbers or the performance of the whole United States.
In addition, OLAP enables one to analyze data in a business centric fashion. Business Users or Analysts, as opposed to IT Professionals, tend to think of their functions dimensionally; i.e. customers, products, markets, etc. Additionally, the ability to view summary data instantly allows for a much more conversational approach to analysis. Business rules and data elements are fused together, creating a model that mimics business function.