What it is, who’s using it and why you should care
Financial institutions are tackling mammoth risk management analyses involving billions of calculations; retailers must determine optimal prices and assortments for vast numbers of products across hundreds of store locations. Organizations in every industry are struggling to analyze massive amounts of data to quickly answer complex business questions. Traditional computing infrastructures and techniques simply can't keep up.
In response to this growing problem, high-performance analytics is gaining traction. In a nutshell, high-performance analytics breaks highly complex computational problems into groups of much smaller problems, distributing those across IT resources (i.e., grid, in-memory or in-database architectures).
SAS is investing in high-performance analytics and recently unveiled an addition to its offerings: SAS In-Memory Analytics. The SAS high-performance analytics portfolio also includes grid computing and in-database processing. Here's an explanation of each component:
• SAS® Grid Computing: Computations are split up and each piece runs in parallel across a pool of server resources assigned in a grid environment.
• SAS® In-Database: Computations run in parallel inside a database to leverage the massive parallel processing (MPP) architecture offered by databases.
• SAS® In-Memory Analytics: Computations are done in-memory and threaded across a dedicated grid of server nodes.
Answers in minutes, not hours
In a recent demonstration, SAS slashed the time required to analyze a complex financial portfolio against 100,000 possible market states and two time horizons. What took 18 hours on a single server required only three minutes in the SAS High-Performance Analytics environment.
"As the saying goes, time is money, and there is a huge competitive advantage when organizations reduce the time in making business decisions – as long as the insight one has is valid," says TowerGroup Senior Research Director Rodney Nelsestuen. "The ability to process complex risk calculations at a faster speed without sacrificing accuracy is critical to supporting enterprise risk strategies at financial institutions."
The SAS High-Performance Analytics offerings for retail are showing similar speed advantages. In tests, SAS generated optimal markdown prices on 200 million product-location combinations over 26 weeks, an impossible task with first-generation systems.
Positive creativity solves complex risk puzzle
United Overseas Bank partnered with SAS on a high performance computing solution that combines grid computing, matrix-based calculations and in-memory analytics to calculate risk. See what UOB's Chief Risk Officer has to say about the interplay between risk classes and developing better risk controls for banking – in near-real time
Who else is using high-performance computing?
Bank of America and iconic retailer Macy's are both trying out SAS High-Performance Analytics solutions. Here's what they have to say about the new technology:
"Without SAS, processing times would be longer, hedging decisions would be delayed and, ultimately, the bank would be behind the market," says Russell Condrich, Senior Vice President, Corporate Investment Group, Bank of America.
"We are very excited with the early benchmark results of SAS High-Performance Analytics. The unique method by which SAS is distributing complex workloads is allowing us to perform even the most complex analysis in a fraction of the time. We view this as a major breakthrough for quickly analyzing and modeling color-/size-intensive product data across hundreds of store locations concurrently," says Larry Lewark, Chief Information Officer, Macy's.
Nobel Laureate Myron Scholes on high-performance analytics
You've heard what UOB, Bank of America and Macy's have to say: now Nobel Laureate Myron Scholes, originator of the Black-Scholes options pricing model, weighs in. During this interview with Tom Kimner, leader of the Global Risk Practice for SAS, Scholes offers insights on the advantages of high-performance analytics.