Discover PerformanceHP Software's community for IT leaders // October 2013
Taking the Guess-work out of big data
Bruce Yen, director of business intelligence at Guess, explains how the company is benefitting from its HP Vertica data and analytics platform.
The fashion industry might be glamorous, but it’s also competitive, fast changing, and fickle. Guess realized this when it handed its big data challenges to its director of business intelligence, Bruce Yen, who then created an enterprise-level analytics program from scratch, based on the HP Vertica platform.
SiliconANGLE CEO John Furrier and Wikibon CEO Dave Vellante sat down with Yen at the recent HP Vertica Big Data Conference in Boston to ask him about his take on analytics and how Guess has benefitted from implementing a robust data analytics platform.
“At the core, I think we’re still trying to solve the same problems: how do we make data actionable?” Yen says. “I think now, with all this convergence with new technology, we’re actually able to do something with it.”
Fast enough for retail
Guess’s previous, Oracle-based data warehouse was slow and constantly being tuned for performance by the IT staff. And the system didn’t allow Guess end users and analysts to ask the right questions to get to the information they needed to perform their analyses. As a result, staff was relying on manually compiled reports and vast spreadsheets listing reams of style numbers.
“We looked at a couple of other databases, but settled on Vertica because [it] had a very clean approach to technology—the speed at which you could query, the way it compressed the data. There’s very little fine tuning that you have to do,” Yen says.
After migrating from Oracle databases to Vertica, one of Yen’s first projects was to create analytics dashboards that his end users could use to efficiently accomplish data tasks, such as quickly identifying best sellers across various markets. Guess buyers and merchants could then spend their time actually analyzing the information, rather than compiling it, pasting images into spreadsheets, and manually sharing it with their teams.
Pretty enough for fashion
But Yen faced an additional challenge working in an industry driven by visuals. “We’re in the fashion industry, and people like pretty things,” says Yen, which is why he used Guess’s own culture as a reference point for addressing its data issues.
Yen understood that the visual presentation of information was an important part of getting users to adopt the new analytics platform. So Guess brought in a graphic designer to re-skin the dashboards and make them as visually engaging as possible.
“There’s a host of visual analytics,” Yen says. “You can put colors and styles and fabrics side by side. To us, that’s huge. No report with tons of written data is going to do that for you.”
Focused enough for each customer
The third business goal for Yen was to address the customer. “Our take is: How do you use technology to enhance the customer experience? When they walk into our stores or shop online, how do we better market to them?” Yen says.
These aren’t new challenges for the retail world. In fact, Yen acknowledges that they’re the same issues traditionally faced by retail. We just have a more sophisticated set of tools available to make data actionable, make it accessible, work with larger data sets, and get faster insights on the customer and the marketplace.
“It’s very promising,” Yen says. “I see how, from a company standpoint, we’re beginning to look at analytics as something that’s a lot more valuable.”
For a full perspective on formulating a big data strategy for your enterprise, sign in or register now to download our free, original ebook, “Connecting Data for New Business Insight.”
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