HIGH PERFORMANCE ANALYTICS: THE OPPORTUNITY AND THE CHALLENGE
PILLAR 3: IN MEMORY ANALYTICS
Quickly Responding to Market Preferences and Trends
Macy’s, one of the world’s largest and best- known retailers, has amassed a huge and loyal base of customers who shop at its stores, by mail order, and online at Macys.com. Like Family Dollar, Macys.com thrives on its ability to analyze its data at the SKU level. “We were aggregating away from prod- ucts and trying to extrapolate and understand what product assortments are more readily available,” said Kerem Tomak, Macy.com’s vice president of analytics. “But with high- performance analytics, you can run hundreds
or thousands of models at the product level—the SKU level—because you have the big data and the analytics to support those models.
“That’s a huge breakthrough for us. Now we can see and understand how the business is performing in the marketplace. We can see how prod- ucts are selling on Macys.com, for instance, versus how they’re selling in stores. Or we can see the impact of our marketing efforts on sales results in both channels. The challenge boils down to the ability to gather big data and turn it into daily insights so that we can respond to any consumer- preference or marketplace changes. High- performance analytics is the way we make that happen.”
From 167 Hours to 84 Seconds
Imagine it’s your job to manage billions of dollars in consumer mortgages. You’d better know your current risk position pretty much all the time. But what if you had to wait a whole week just to find out where you stand right now? That’s how it was for many lenders during the period leading up to the financial crash of 2008. As their portfolios continued to grow, so did their data volumes, meaning they were capturing much more informa- tion than they could process. And risk teams simply could not work fast enough to keep pace with demands for new and refined models.
At one industry giant, the risk- management team operated a sepa- rate hardware environment to run a perfor mance- intensive routine that identifies characteristics and candidates for modeling. Unfortunately, the average processing time was 6.5 hours, leading most analysts to limit their data explorations due to simple pragmatics. They “settled” because they didn’t have time to do their best. Worse, when the modeling team executed the same routines in its production environment, it required 167 hours of processing time—essentially, a full week.
High- performance analytics has turned all that around. Risk assess- ments that used to take a week are now ready in just 84 seconds—more
than 7,000 times faster! Analysts now actually have the time—and motiva-
tion—to iterate models many more times than previously possible, and they no longer have to make modeling shortcuts to meet computational limitations. And that increased capacity to iterate and experiment is sav- ing the company tens of millions of dollars because better models are being produced.
The company faced similar big data challenges in its marketing opera- tions. To minimize churn, maximize customer lifetime value, and execute
more profitable cross- sell and upsell campaigns, the marketing team needs to target as many as 15 million recipi ents. But it couldn’t process all that data without high- performance analytics.
Now, using HPA, the lender has achieved tremen dous gains in its data- base marketing—as much as 215 times faster—dramatically compressing the model- development life cycle and allowing teams to test and validate additional variables for greater reliability in their models. The result: Team productivity has improved dramatically, and the models are more reli- able. With 15 million prospects, even a minor improvement to the typical 1 percent response rate quickly translates into tens of millions of dollars in revenue.
Tackling Complex Challenges
In- memory analytics is the pinnacle of HPA. The key is its ability to
divide analytic processes into easily manageable pieces with computa- tions distributed in parallel across a dedicated set of processing blades. With in- memory analytics, you can use sophisticated analytics on the biggest data sets ever to tackle complex problems quickly and solve dedi- cated, industry- specific business challenges faster than ever. Sometimes, the computational breakthroughs come not from the volume of the data involved but also from the CPU- intensive techniques that are required.
In- memory analytics give you concurrent, in- memory, and multiuser access to data, no matter how big or small. This type of HPA software is optimized for distributed, multithreaded architectures and scalable pro- cessing, so you can run new scenarios or complex analytical computations extremely fast. You can instantly explore and visualize data and tackle problems you could never feasibly approach due to computing constraints. In- memory analytics lets you
• Make decisions faster—You get quick access to more targeted infor- mation so you can seize opportunities and mitigate threats in near- real time.
• Gain more precise answers from complete data—You can run more sophisticated queries and models using all your data to generate more precise models that can improve business performance.
• Establish a reliable, scalable analytics infrastructure—Overcome tra- ditional IT constraints, and get answers to difficult business ques- tions quickly, with speed and flexibility.
In- memory analytics was designed expressly to address the complex que- ries and analyses that leverage big data or need large amounts of com- putational horsepower such as data exploration, visualization, descriptive statistics, model building with advanced algorithms, and scoring of new data—all at breakthrough speeds. This is the preferred framework for risk management, revenue optimization, text analytics, marketing campaign optimization, analysis of social networks, and other compute- intensive, data- intensive problems.