Peter Buxbaum writes how some large companies are trying to deal with their "large" data in the article from Big Data Republic below.
Many people think of big data analytics as the province of relatively young, online companies like Google and Facebook. But a new report (registration required), "Big Data in Big Companies," co-authored by Tom Davenport, Research Director of the International Institute for Analytics, and Jill Dyché, Vice President for Best-Practices at SAS, shows how 20 large, old-school companies are benefiting from big data projects.
Only the name is new
There are several interesting aspects to the report. One is that the big data phenomenon cuts across industry lines, from finance to transportation to retailing, healthcare, and entertainment. Another is that many of these companies have been struggling with big data -- although not necessarily under that name -- for some time. UPS, for example, began to capture and track millions of package movements daily in the 1980s.
The report reports several case studies in the use of big data analytics from a diverse array of companies. Those same case studies illustrate a diversity of uses to which analyzing big data may be put.
A selection of case studies
United Healthcare, an insurer, has moved from analyzing structured data to capturing data on customer attitudes from customer complaints recorded at call centers. United is turning the voice data into text and analyzing it with natural language processing software. The analysis can identify customers who use terms suggesting strong dissatisfaction, prompting an intervention by a United representative who can try to rectify the problem.
Bank of America has been involved with big data for years, but newer systems allow for better analysis of the data. The bank has always captured large amounts of customer data across multiple channels, but was unable to analyze all of its customers at once, relying instead on systematic samples. "With big data technology," says the report, "it can increasingly process and analyze data from its full customer set. The primary focus of the bank’s big data efforts is on understanding the customer across all channels and interactions, and presenting consistent, appealing offers to well-defined customer segments."
Macys.com focuses on customer-oriented analytical applications involving personalization, ad and email targeting, and search engine optimization. Macys.com uses a variety of technologies for big data, most of which are not used in the rest of the company. But that will eventually be changing. Kerem Tomak, head of the analytics organization at Macys.com, noted that there "will be increasing integration between Macys.com and the rest of Macy’s systems and data on customers, since... an omnichannel approach to customer relationships is the right direction for the future."
Caesars Entertainment, the casino operator, has long used analytics, collecting information on customers from its loyalty program, from web clickstreams, and from real-time play in slot machines. Today, Caesars is looking to enhance its capabilities to be able to respond to customers in real time. To that end it has acquired Hadoop clusters and open-source and commercial analytics software and has also added data scientists to its analytics group. Caesars is also beginning to analyze mobile data, and is experimenting with pushing real-time offers to customers' mobile devices.
General Electric presents a different kind of example. The company instruments locomotives and jet engines to monitor the health of the equipment. One sensor on a single gas turbine in a jet engine generates 500 gigabytes of data daily. GE crunches that data to monitor the health of engine blades and to discover patterns on when blades break, allowing GE to better tune its manufacturing and repair processes.
Does the report provide any new ideas on how your company can use big data? Which case studies did you find most interesting, and why? Let me, and all of us, know by commenting here after you read the report.
View the original article here
Source: Big Data Republic