5 Big Data Use Cases To Watch - InformationWeek

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Data Management // Big Data Analytics

5 Big Data Use Cases To Watch

Here's how companies are turning big data into decision-making power on customers, security, and more.

sitting in their customers' environment, and they phone home with information about the use, health, or security of the device," said Gallivan.

Storage manufacturer NetApp, for instance, uses Pentaho software to collect and organize "tens of millions of messages a week" that arrive from NetApp devices deployed at its customers' sites. This unstructured machine data is then structured, put into Hadoop, and then pulled out for analysis by NetApp.

3. Data warehouse optimization
This is an "IT-efficiency play," Gallivan said. A large company, hoping to boost the efficiency of its enterprise data warehouse, will look for unstructured or "active" archive data that might be stored more cost effectively on a Hadoop platform. "We help customers determine what data is better suited for a lower-cost computing platform."

4. Big data service refinery
This means using big-data technologies to break down silos across data stores and sources to increase corporate efficiency.

A large global financial institution, for instance, wanted to move from next-day to same-day balance reporting for its corporate banking customers. It brought in Pentaho to take data from multiple sources, process and store it in Hadoop, and then pull it out again. This allowed the bank's marketing department to examine the data "more on an intra-day than a longer-frequency basis," Gallivan told us.

"It was about driving an efficiency gain that they couldn't get with their existing relational data infrastructure. They needed big-data technologies to collect this information and change the business process."

5. Information security
This last use case involves large enterprises with sophisticated information security architectures, as well as security vendors looking for more efficient ways to store petabytes of event or machine data. In the past, these companies would store this information in relational databases. "These traditional systems weren't scaling, both from a performance and cost standpoint," said Gallivan, adding that Hadoop is a better option for storing machine data.

When it comes to managing data, don't look at backup and archiving systems as burdens and cost centers. A well designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems. Read our The Agile Archive report today. (Free registration required.)

Jeff Bertolucci is a technology journalist in Los Angeles who writes mostly for Kiplinger's Personal Finance, The Saturday Evening Post, and InformationWeek. View Full Bio

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