Bringing order to big-data chaos

The term “big data” has taken many different forms over the years. Telecom operators realize that big data (aka high performance analytics) is a key competitive advantage and the enabler of future business models.

But only a few have experienced a fraction of the benefits promised by big data evangelists. Some Asian operators are already ahead of the curve, either leveraging analytics for better campaign management or monetizing them in anonymized forms. Examples:

• Malaysia’s Celcom Axiata leveraged high performance analytics to optimize its marketing campaigns and deliver over $50 million in annual incremental revenue.
• Singtel’s DataSpark develops data analytics, which leverages (anonymized) location analytics to derive mobility patterns and customer insights. DataSpark has established joint research collaboration with research institutes such as A*STAR’s I2R and MIT to develop advanced data analytics.
• SK Telecom’s Geovision offers a statistical database/map combination for analyzing the purchasing patterns of customers based on age/time/location. Since Geovision’s launch, SKT also applies analytics to enhance the quality of its existing services, like the T-Map navigation system.

Most Asian operators are still far from mainstream data-driven operations - either through third party monetization or internal management improvements. They embrace big data in a chaotic manner, largely driven by vendors’ promises and pitches, unclear development roadmaps and a quick-commercialization mindset. These types of big data strategies typically fail. For success, operators must embark on a long-term journey to incorporate new technological platforms and attributes into their organizational DNA.

While pressured to commercialize big data efforts quickly to compete with internet players, operators also discover that a fully integrated solution isn’t the answer. Rather, an operator needs to develop required capabilities, technical skills, and a differentiated mindset.

The foundation for high-performance analytics begins with the setup of a R&D unit that performs beyond the typical Business Intelligence (BI) functions within the organization. Ideally, this unit is positioned centrally within the corporation and coordinated among multiple internal stakeholders such as the strategy, commercial, networks and IT units.