Big Data is important to telecom operators, but the task of implementation seems daunting. This Insider covers the possibilities enabled by Big Data initiatives, guiding telecom operators through the role of market conditions (personalization, customer selection), some exciting forms of Big Data analytics, the organizational challenges (availability of skills, differences between business units) and regulatory concerns. The report contains three case studies, on Turkcell's m-commerce analytics, Netflix's recommender system and Weze, an m-commerce platform for marketing and payments in the UK.
Telecom operators all consider Big Data initiatives important, but they currently feel neither ready to take on this task nor very willing to have someone else do it for them. Despite the absence of clearly defined expectations, they sense the potential innovation that can be unleashed through Big Data analytics and hope to become a central component in this new engine of service development. In this Insider, we aim to inform our readers about the meaning of Big Data and the possibilities enabled by Big Data initiatives, as well as to guide telecom operators through four areas that need to be considered in Big Data implementations. After briefly describing the context and the nature of Big Data, we focus on (1) market conditions, discussing issues of personalization and customer selection. Then we explain in more detail (2) some exciting forms of Big Data analytics and demonstrate through simple hypothetical cases how operators can draw insights from their data. We also describe (3) the organizational challenges, such as the availability of skills and the need to reconcile the deep differences between various business units. We finish by visiting the (4) regulatory concerns that are likely to surface as Big Data applications become commonplace. The report also contains three case studies, on Turkcell's m-commerce analytics, Netflix's recommender system and Weze, an m-commerce platform for marketing and payments in the UK.
Table Of Contents
Introduction: Big Data
Personalization, rate of innovation, customer selection
Case: Turkcell personalizes m-commerce through analytics
Data management, analytics, storage and computing
Case: Netflix uses recommender systems for its viewers
Human resources, RandD prioritization, governance
Privacy, data ownership, net neutrality
Case: UK operators overcome their differences over the Weve platform
List Of Tables
List Of Figures