Fed up, a cellphone customer was ready to switch his service because of all the dropped calls. Like many of us, he'd stewed in silence, not yet complaining to his telecommunications provider. So it seemed almost telepathic when his telecom sent him a text message apologizing for unexpected equipment issues and offering him a coupon to reduce his next bill.
This high level of customer service is really a testament to the savvy use of data. Telecoms can, for example, see which customers are experiencing dropped calls by analyzing cell-tower logs, call records or even comments on social media sites. Then the telecoms can react before frustration sets in.
Many people have talked about the power of Big Data — the trend of using huge amounts of different types of data (customer-loyalty programs, emails, video, call-center records and more) to quickly glean hidden insights from mountains of information. But equally important is the ability to act on these insights quickly. This often requires use of predictive analytics — tools that can crunch data and find patterns that suggest what's about to happen — and a network that can handle a large flow of data.
Last year, attendees at two Big Data conferences were asked how quickly their organizations needed to take action on analyzed data; 41% said minutes or even seconds.
Real-time data — the techie phrase for information that can be acted upon quickly — can eliminate problems before anyone even notices them. For instance, a computer onboard a car could send information to a vendor, which immediately crunches the data and determines an engine component is about to go bad. The driver could be alerted and have it fixed before he's on the side of the road waiting for a tow truck.
That's only one example. Police departments are dramatically reducing the crime rates in certain areas by using real-time Big Data to find crime patterns that would otherwise go unnoticed, according to a study from the TechAmerica Foundation. With this information in hand, police officers are being positioned to prevent and preempt crime, rather than just deal with the problems after the fact.
Predictive analysis requires the right infrastructure to support such rapid insights. As the need for real-time decision-making increases, companies are rethinking how hardware, software and networking components need to work together, so they can aggregate data, filter it and analyze it at lightning speed. Achieving the benefits of real-time data isn't just about the latest computer software; it's also about eliminating bottlenecks as the data zip around the network.
Once this is accomplished, the amount and kinds of problems that real-time data could solve might surprise many people. Take security professionals who monitor computer networks for data breaches, which are a growing concern. Some 39% of networks are challenged by a "lack of adequate staffing," and 35% are unable to identify problems — "too many false positives," according to a report by the Enterprise Strategy Group. Analyst Jon Oltsik, who conducted the study, said one possible solution is real-time Big Data tools that use advanced intelligence to pinpoint problems more accurately and eliminate manual processes. Think about what this means: To protect the network better, the network must be faster in processing the data that reveal the network is being attacked.
This may seem like a logical loop question on a philosophy test, but it's really a statement about what Big Data can accomplish with the right analytic tools and network infrastructure to make use of the data rapidly.
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