Being able to design and produce electronic reports automatically has been an important software success for today's businesses, enabling them to streamline their information gathering and distribution processes.
While the need for quick and easy access to information in data warehouses is important, it is crucial to be able to take the next step and analyze that data for use in real-time strategic business decisions.
Typical discussions about a data warehouse return on investment (ROI) evolve from focusing on final project cost vs. time saved in IT labor, to addressing how to save time and make better decisions, to quantifying business decisions more accurately with an OLAP (Online Analytical Processing) system. The end result is that organizations with an existing data mart and an OLAP system in place will go on to conduct "advanced analytics" once the data warehouse has been built.
OLAP and business intelligence (BI) are key elements of reporting and offer tremendous value to businesses. Without these systems available, many companies may head toward reduced profitability. While some would say that OLAP applications are commodities that can be trivialized as "count dashboards," others would argue that OLAP is a critical function that cannot be replaced -- even by advanced analytics. OLAP and advanced analytics are complementary and serve the same purpose: to use the data warehouse to increase ROI.
OLAP is often confused with advanced analytics because both are forms of analysis, but "advanced analytics" is a broad marketing term that means different things to different people, even within the software vendor community. For
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the purpose of this article, data analysis is defined as:
Advanced analytics is commonly used as a "catch all" category for techniques of statistical, machine learning or mathematical roots. This includes such activities as descriptive modeling, predictive modeling, structural modeling, forecasting, quality assurance and optimization (whereas data mining concerns descriptive and predictive modeling alone). Figure 1 shows a landscape of the analytics space.
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The component terms listed above are described in more detail below:
Recent trends indicate that advanced analytics will continue to become more mainstream, particularly as large numbers of OLAP developers look for the next challenge. In addition, the desire to derive greater returns from data warehouse spending will also cause more organizations to use advanced analytic techniques that truly deliver on the promise to provide greater ROI when intelligently implemented.
Existing information analysis methods are becoming outdated and unable to support sustained growth, requiring companies to adopt new analytic power that provides them with a competitive edge over rivals. Accurate, in-depth analytic analysis provides the underlying value in helping a company make informed and insightful decisions – ultimately leading toward sustained success.
About the Author
John Wallace is an analytical consultant in the San Francisco office of SAS. He has worked with clients in the automotive, ISP, grocery, retail, PC/server and consumer software industries. He can be reached at john.wallace@sas.com.
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