says Wilson Raj, global customer intelligence director for SAS. These “digital traces,” in turn, can be harnessed to get a full view of that customer. Since this level of customer experience analysis remains in its early days, only the most adventurous clients combine Twitter feeds with YouTube headers, aggregate sentiment analysis from Web-generated data sets and the blogosphere, and wrap it all in a natural language processing (NLP) engine in order to deem a customer happy or sad. As these technologies mature and corporate
ine, says IBM’s director of Emerging Technologies David Barnes. “The cost of getting started with this isn’t that great. That’s the cool part of these massively scalable systems. I can start on my MacBook, decide I like it, take that exact same code and spread it across 100 servers.”
USING BIG DATA TO PREDICT NETWORK You can also use big data on the back-end to make sure your offerings are what you say they are. TMobile, for example, continuously analyzes 2 PB of network performance
prevent—network outages that could have been caused by faulty Android applications, Twiford says. (After all, a smartphone user without network access might make a customer service call similar to the one described at the beginning of this article.) Twiford and her team also use handset data to determine whether they may need to open the floodgates if, for example, they anticipate a large volume of video suddenly hitting a given geographic area—Boston on the Fourth of July, Times Square on New Years’ Eve
Journalists on the campaign trail have used Wisdom to track down Mitt Romney supporters at their favorite restaurants. How? If they “like” Mitt and they “like” Joe’s Diner, reporters know they can stake out Joe’s and get some interviews.
BIG DATA WILL MAKE BIG STRIDES Big data, while intriguing, remains in its early days. Big data doesn’t come easily—or, for that matter, cheaply, as Wisdom Pro starts as $25,000 (about Rs 13 lakh). Companies working to integrate the
Big data provides social data and other publicly available data that can be analyzed and used to understand the customers’ sentiment and needs before they become issues or problems that lead to churn. This is a significant advancement for organizations that, until now, had to rely on customers’ frankness and candor to understand the issues. IT departments find the time, talent and resources, they’ll catch up with this trend. They’ll have to, says Rita Sallam, Gartner’s BI analyst and research vice president. “Advanced analytics must be more pervasive to deliver significant value and competitive advantage to an organization,” Sallam wrote in her February report, Advanced Analytics: Predictive, Collaborative and Pervasive. “To date, use of tools and processes for building advanced analytic applications and deriving and consuming insights have been limited to a small number of highly trained and experienced statisticians, analysts and operations research professionals.” Moving these tools into the hands of line-ofbusiness users may not be as hard as you would imag-
data on its IBM Netezza data warehouse, loading nearly 20 billion rows and processing nearly 150,000 ELT jobs daily. In a fiveminute span, says Christine Twiford, who works in T-Mobile’s network engineering department, as many as 60 users may be executing load and query operations on the system in near-real time. The main focus of network engineering is optimal network performance in the name of customer experience, she says. “We use clickstream data to calculate the speed of downloaded songs. This gives us a great proxy for understanding throughput speeds at the tower level, as well as handset speeds.” With this data at the ready, T-Mobile has been able to predict—and
and so on. This is exactly the opposite of what happened at some Olympic venues, where usage overwhelmed the networks set up to serve both event organizers and fans. These streams can be used to get in front of an emerging trend—or a presidential candidate. Say a coffee chain using Wisdom to better understand its customers discovered they have an “affinity” (to use MicroStrategy’s parlance) for a new type of chocolate hitting the market. If the company wanted to make a quick decision based on this preliminary but compelling insight, it could stock that chocolate in the stores where Facebook “likes” are highest, notes Warren Getler, MicroStrategy’s vice president of Corporate Communications.
many silos of internal data with intelligence from the Web and sentiment analysis from NLP engines, then, are riding the curl of this wave. Such firms are in the minority. Fewer than one percent of companies are currently using big data this way, says independent customer strategist Esteban Kolsky. However, that will change, he adds. “Big data provides social data and other publicly available data that can be analyzed and used to understand the customers’ sentiment and needs before they become issues or problems that lead to churn,” Kolsky says. “This is a significant advancement for organizations that, until now, had to rely on customers’ frankness and candor to understand the issues.” n