Get Smart with Next-Gen Data Analysis

By: Jesse Cryderman

There is a problem with big data, and it's not that it's big. 

In ninth grade, John Myers discovered that he was wearing the same underwear on the six occasions when he scored a perfect 100 on his math exams. His eyes widened at the apparent discovery: his magic underwear granted him unparalleled acuity. The following week, John went to his exams, dutifully wearing his magic underwear. When the grades came back, he only scored a 78. What happened? John fell victim to a logical fallacy called illusory correlation. John had a pool of data, but drew a fallacious conclusion. That's the problem with data--without context, it's valueless.

Communications service providers (CSPs) are applying big data solutions to better understand their networks, customers, business opportunities, and more, but they face the same problem as John and his magic underwear. The issue with big data, is not that it's big. It's that the analytical framework(s) used to draw insight from big data face inherent problems rooted in logic.  Big data is only as useful as the actionable insight it provides, and that requires advanced analytics suites that can separate the wheat from the chaff in real time.

Goal-driven analysis

Simply collecting more and more data and accessing it in faster ways does not replace business decision making. At the end of the day, a CSP is judged on its ability to perform as a business, not its ability to be a number-crunching machine. Therefore, it is imperative that big data solutions are approached in the context of the specific business goals they are required to achieve.

According to ABI Research, "...key aspects of a successful mobile big data analytics offering include the ability to easily identify the key data sets, hook them efficiently and retain relevancy. The most successful implementations will start with a clear understanding of the project goals and will be tailored to individual needs. It is imperative that Telcos and Telecom providers understand that it is not one-size-fits-all but they take a more customer-defined and results-driven approach."

Each application of big data should be tied to a concrete, measurable goal. Is it a wise decision to throttle mobile video streams on Android smartphones during a Major League Baseball game at, say, Dodger Stadium? Let’s say a cell site in Boston is overloaded or malfunctioning, and capacity won’t be restored until the corresponding CSP can reprovision a neighboring tower. What kind of incentive should the company include in a text message to its customers in order to maintain and, possibly, even improve their loyalty? Solution providers such as Nokia, InfoVista, Seven Networks, Allot and many others are coming to the forefront as the management of wireless network and traffic visibility and real-time reactivity is becoming paramount to CEM (customer experience management).

The immediate value that advanced analytics represent for a CSP includes more efficient network planning, faster response times, improved security, better-targeted sales opportunities, and enhanced service provisioning, plus corollary improvements in the customer experience and loyalty. The long-term value includes greater agility, faster time to market (TTM) with highly desirable services and a leaner, smarter business strategy.

Gartner and Forrester Research point to advanced analytics as one of the leading applications of Big Data and, according to IBM, corporate IT executives rate business intelligence (BI) and analytics as their top priorities. Numerous solutions providers speckle the advanced-analytics landscape with offerings of varying complexity, specialization and cost. Large IT-legacy players like HP, SAP, Oracle and IBM have been very active in the market. Ericsson has been expanding its telecom-specific big data offering, and many niche solutions exist from companies such as Flytxt, Cloudera, Quiterian, cVidya and Pervasive Software.


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