FEBRUARY 2013

When it comes to building a big data analytics strategy, organizations face two major and immediate challenges: how to manage and store the large influx of data that comes in while they decide the questions they hope to answer using it; and finding experienced staff with the skill to make sense of this exploding wealth of information. And big data is no longer important only to large enterprises. It’s becoming increasingly important for small and medium-sized companies to tackle big data projects in order to stay competitive.

Making Sense of the Variety of Structured and Unstructured Data When an organization deploys modern cluster technologies, it increases its ability to analyze all of its data. That company can now stash all data as it comes in and process it when the business decides to look for new insight. The desire to take a schema and apply it just in time is an important influence on SAS big data analytics. So is the ability to use a cluster of computers—a massively parallel processing (MPP) infrastructure—rather than a single computer, and teach the software to work across this infrastructure.

SAS offers its own analytics platforms, which include both high-performance analytics and visual analytics software. This software, which can be applied to a wide range of domains, works to make

sense of the variety of structured and unstructured data sources. For example, it can be employed to help catch credit card, banking, or insurance fraud as well as help the government sector to pinpoint the misuse of social services. In the retail industry, it’s becoming possible to build models and do forecasts to understand business right down to the individual stock item level in a store. Pricing strategy can be fluid and very dynamic, almost real time—especially for an online business such as a store or a hotel.

Information that’s being captured and stored can be used in innovative and unanticipated ways. For example, analyzing security photos at a grocery store resulted in the ability to understand that if the parking lot was, for example, 50 percent full, then in 20 minutes the checkout lines would be that much longer, and would require additional cashiers. This insight brought benefit beyond improved customer service, as well: With more notice that they’ll be needed at the checkout line, workers had the ability to better plan their day, increasing job satisfaction.

There are many more undiscovered ways that data can be pieced together to show us interesting patterns of behavior or provide insight we can take advantage of. And companies of all sizes can take advantage of the ability to use their existing data in more meaningful and interesting ways.

Paul Kent, vice president of big data at SAS, explains how SAS* High-Performance Analytics solutions are helping business address big data challenges and gain actionable insight from massive amounts of information.

Solution Spotlight

Discovering Innovative Ways to Make Use of Big Data with SAS* High-Performance Data Analytics Paul Kent, Vice President of Big Data, SAS

Analytics Infrastructure

SAS* High-Performance Analytics

SASGrid Computing

Deployment flexibility:

Massively parallel processing

(MPP)

Symmetric multiprocessing

(SMP)Appliance

CloudOnsite

Architecture flexibility:

SASIn-Database

SASIn-MemoryAnalytics

International Institute for Analytics

The International Institute for Analytics (IIA) is a leading global analytics research organization and is the only research firm dedicated exclusively to supporting enterprises as they build their analytics programs. IIA defines the path to analytics excellence by guiding enterprises on how to best fund, staff, manage, evaluate, and refine their analytics programs. SAS and Intel are committed supporters of the tremendous work being done by IIA in helping educate the marketplace on the value of analytics. For more information, visit iianalytics.com.

High-Performance Analytics from SAS Combining industry-leading analytics software with high-performance computing technologies produces fast and precise answers to previously unsolvable problems—and enables our customers to gain greater competitive advantage.

• SAS Grid Computing creates a centrally managed, shared environment for processing large jobs and supporting a growing number of users efficiently.

• SAS In-Database executes analytic logic into the database itself for improved agility and governance.

• SAS In-Memory Analytics eliminates the need for disk-based processing, allowing for much faster analysis.

Together, the components of this integrated platform can help an IT manager change the decision-making landscape—and redefine how an organization solves big data business problems.

SAS High-Performance Analytics

Available for EMC* Greenplum*, Oracle*, Teradata*, and Apache Hadoop* software, SAS High-Performance Analytics is an in-memory offering that processes sophisticated analytics and big data to produce time-sensitive insights very quickly. With SAS High-Performance Analytics, business can apply high-end analytical techniques to solve complex business problems.

For optimal performance, data is pulled and placed within the memory of a dedicated database appliance for analytic processing. Because the data is stored locally in the database appliance, it can be pulled into memory again for future analyses in a rapid manner. With it, businesses can perform analyses ranging from descriptive statistics and data summarizations to model building and scoring new data at breakthrough speeds.

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Copyright © 2013 Intel Corporation. All rights reserved. Intel, the Intel logo, Intel Sponsors of Tomorrow., the Intel Sponsors of Tomorrow. logo, and Xeon

are trademarks of Intel Corporation in the U.S. and other countries.

*Other names and brands may be claimed as the property of others.

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SAS and Intel At SAS, the basis of our MPP cluster strategy is Intel® Xeon® processors running the 64-bit Linux* operating system. And as a part of the Intel compiler team for more than a decade, SAS provides input that helps Intel refine and sharpen the code compiler on successive generations of Intel microprocessors. This enables increasingly better object code to be created over time to exploit the instruction sets as they evolve. Intel actively provides new technology for our labs

that we can use for research and discovery, and we provide feedback. That not only makes us good business collaborators, it brings a strong benefit to our customers in terms of the strength and usability of our solutions.

For more information about SAS big data analytics solutions, visit SAS.com/HPA.