More time for analytics

With SAS^® Grid Computing, SunTrust analysts spend less time moving data

"Analysts should spend their time doing analysis," says Dudley Gwaltney of SunTrust's Client Information Group. They shouldn't, for example, be spending time copying files from one place to another because of capacity constraints, waiting for a batch job to be completed or for a server to come back online from maintenance. They should be working in a centrally managed computing environment that most effectively addresses workload management, high availability and performance.

For precisely those reasons, SunTrust is now converting to a SAS 9.3 business intelligence and analytics platform enabled by SAS Grid Computing. Using this grid-enabled computing architecture, Gwaltney and his colleagues will be working in an environment where SAS applications and services are guaranteed to be available, where resources are prioritized according to demand, and jobs are accelerated through parallel processing.

Bottom line: Gwaltney will more effectively do what he's paid to do.

All data in one place

Gwaltney leads the Analytical Modeling Team within SunTrust's Client Information Group that supports all lines of business within SunTrust. Its predictive models for up-sell, cross-sell, attrition and other marketing activities are data- and process-intensive. Gwaltney's staff is first-rate; the computing environment in which they've been working, not quite so.

Under SunTrust's old configuration, servers were allocated based on business function, resulting in sufficient computing resources for some, not enough for others—and an overall lack of flexibility in reassigning servers when more space was needed. Depending on which initiative they were supporting at the moment, analysts on Gwaltney's team might have to retrieve data from one or two different locations and transfer it to other servers for processing.

Gwaltney, a longtime SAS^® Enterprise Guide^® and SAS^® Enterprise Miner™ user, says that he regularly deals with files that are as large as 10 gigabytes. "It takes quite a while to move that much data between servers, and we're constantly having to move data around." In SunTrust's new grid-enabled computing architecture and cluster file sharing, all the data will be in one place. "We also won't be wasting space; we won't have all these duplicate files," Gwaltney says. "That will be a real advantage both to IT and to my analytics team."

Answers today, not tomorrow

The previous server configuration also meant time lost waiting for a processing resource. Gwaltney and his colleagues regularly have jobs that take seven or eight hours to run. Narrowing that to three or four hours, Gwaltney says, “is the difference between providing an answer today rather than tomorrow. And in our business, that makes quite a difference.”

SAS Grid Computing addresses the issue of slow response times by providing a tool for standardized workload management and centralized administration of servers. The workload for SAS programs can be distributed across multiple CPUs automatically, and the ability to prioritize jobs based on business rules ensures that necessary resources are freed up when they are needed. "Jobs in the grid environment will now run more efficiently," Gwaltney says. "Processors will be better managed; it distributes the work based on available capacity and priority."

Gwaltney believes that grid computing will allow for time to do more with the data – more in-depth analytics. "The things that used to take me several hours to do, now take me an hour," he says. "They happen so much faster. The data is all in one place, and it's on a system that processes things faster." "It will give the analyst time to ask more questions, to spend more time with analytics, which is what they pay us to do."

Doing work differently, more efficiently

Gwaltney says it will be interesting to see how the end-user perspective will evolve as the new SAS Grid Computing environment continues to be rolled out. He believes SAS users at SunTrust will follow a natural learning curve from "How can I do what I've always done?" to "How can I do it more efficiently?" to "How can I take advantage of this new system to find new opportunities?"

“I think once users get past that point," Gwaltney predicts, "they'll start thinking about the analyses that they could never do before and taking advantage of new capabilities.” All because there's additional time for analysts to conduct analytics.