Telcos are sitting on rich data mines in their signaling traffic and their users’ smartphones. The challenge, says George Zhao, marketing director of ZSmart Big Data at ZTEsoft, is knowing how to extract it and how to use it effectively.
Big Data Insights: Operators are clearly sitting on lots of useful data - what’s preventing them from using it effectively?
George Zhao: Even though all operators have been talking about big data, most of them are still facing inevitable challenges when exploiting the value of big data. One such challenge is lack of organization readiness. Due to the disruptive changes brought by big data, operators have to evolve their organizations accordingly - department structure, business model, decision-making process, cross-team interaction strategy, orchestration mechanism of the entire eco-system, skilled resource, etc.
Another inevitable challenge is isolated and/or tangled ownership of the data. Under the traditional business model, the different aspects of data usually belong to separate objects - locations, departments, or systems. Therefore, the isolated silos of data cannot provide a comprehensive view to operators.
Yet another challenge is the immaturity of privacy and security control. When cyber life becomes an important part of our real life, customers are really concerned about their privacy and security, because big-data analytics may bring side effects threatening their real life.
One source of actionable data for operators is signaling traffic - how can they leverage that data and what can they use it for?
The signaling data is obviously valuable, because it contains first-hand and real-time raw data of per-session or per-user traffic: customer, terminal, region, duration, experience, traffic, etc. This data can be used in several aspects, such as network O&M, network optimization marketing & sales and customer service support.
For example, the network department can use signaling data to enable more comprehensive CEI (based on KQIs and KPIs) in real-time to cover multiple dimensions, such as illustrating customer behavior via historical statistics, simplifying the process of tracing and locating the problem and monitoring service usage of specific customer segmentations, as well as, of course, network optimization.
Meanwhile, the marketing/sales department can use it for providing real-time insight of customer behaviors, personalizing offers for specific customers, and Improving the customer experience accordingly.
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