  Communication Service Providers (CSPs) are in a unique position to utilize
their vast transactional data assets generated from interactions of subscribers
with network elements as well as with other subscribers. CSPs could leverage
its data assets for a gamut of applications such as service personalization,
predictive offer management, loyalty management, revenue forecasting, network
capacity planning, product bundle optimization and churn management to gain
significant competitive advantage. However, due to the sheer data volume,
variety, velocity and veracity of mobile subscriber datasets, sophisticated
data analytics techniques and frameworks are necessary to derive actionable
insights in a useable timeframe. In this paper, we describe our journey from a
relational database management system (RDBMS) based campaign management
solution which allowed data scientists and marketers to use hand-written rules
for service personalization and targeted promotions to a distributed Big Data
Analytics platform, capable of performing large scale machine learning and data
mining to deliver real time service personalization, predictive modelling and
product optimization. Our work involves a careful blend of technology,
processes and best practices, which facilitate man-machine collaboration and
continuous experimentation to derive measurable economic value from data. Our
platform has a reach of more than 500 million mobile subscribers worldwide,
delivering over 1 billion personalized recommendations annually, processing a
total data volume of 64 Petabytes, corresponding to 8.5 trillion events.
