Big data: easy to collect, difficult to use

Staff writer
26 Mar 2015
00:00
News
Features

Smartphones are also a major touch point in terms of data gathering - how can telcos tap into that data, and what could they do with it?

To start, the telco can attract customers by integrating customer-care related functions (self-care, promotion, complaints, billing, usage monitoring, etc.) into an eye-catching app, which could have game-like and social interaction features.

As the customer base grows, the ecosystem of the telco gets healthier, and the customers rely on it more and more. Meanwhile, the telco can collaborate with other players such as OTTs and ISPs to make its ecosystem more attractive. As a result, customer loyalty is enhanced.

The data collected from the smartphones is really helpful to analyze customer behaviors, especially CEI and ARPU related topics. As a result, corresponding actions - promotion, customer-care calls, etc. - will be triggered to the specific customers via unobjectionable means, which is to say preferred contact channels, time, methods, etc.

What are the challenges of implementing any big data solution?

Well, first, there’s no singular approach when implementing a big data solution due to different requirements for every organization, whether it’s because of infrastructure, needs, targets, strategy etc.
Also, big data-related solutions are impacted by other advanced technology developments. Technologies like NFV/SDN, cloud, & IOT, for example, are developing fast. These impact each other, and sometimes, these technologies are tangled, which makes system design & implementation very complex.

In order to successfully implement a big-data project, there is also a certain amount of organization transformation required. But this is very difficult, since vested interests definitely become obstacles.
Implementing big-data solutions is also a complex undertaking. Unlike traditional OLAP/OLTP kinds of applications, big data requires different strategies to take care of both storage and processing. In addition, big data and business analytics must clean up data to ensure that incomplete, inaccurate, and duplicate data is removed, but this is always ignored.

This article first appeared on Telecom Asia Big Data Insights March 2015 edition

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