Working with a client recently, I came across a classic big-data scenario -- in other words, one where "big-data" could have helped. There was a need to match geographic network rollout data with customer take-up information and customer profile data in order to analyze which types of customer were being reached by the operator's marketing, geographically, and which were proving resistant to marketing efforts. The data was coming from three different places: the CRM system, a third-party customer survey, and the network inventory system. Manual intervention was required to extract the data and clean it (no mean feat given the availability of qualified resources). It required further person-power and time to bring the data sources together. This process took days, and in the end it transpired that the data needed further processing and improvement before any detailed analysis could take place.
This was a relatively simple, offline big-data challenge. There was no requirement to analyze vast volumes of data in real time, no detailed service usage information to process and no requirement to directly link the analytics output to company processes or systems. But even this comparatively simple piece of analysis was not easy.
The big-data technologies, strategies, platforms, and methodologies being variously promoted by big-data vendors all offer ways of streamlining and improving this sort of process; a process that is being played out all around the world, by all sorts of telecom operators and by various different types of data user (and often multiple data users per organization) every day. And indeed vendors propose that operators can make use of real-time data sets much more detailed and comprehensive than those in the example I highlighted.
The potential returns for telecom operators from effective use of the information sitting within their organizations are tremendous, and range from vastly improved operational efficiency, better use of deployed assets, improved service development and management, better customer services, and, ultimately, increased sales or profitability.
However, operators that have embarked on the big-data journey have not always enjoyed immediate success, and they have a great deal of work to do to unlock the value on which they are sitting. Many early deployments have struggled due to lack of planning, lack of experience, over-focus on some aspects of the big-data solution and under-emphasis on the ways the outputs will be used. Many other operators -- such as the client I worked with -- are a long way from having an all-singing, all-dancing big-data machine.
Operators have the choice of developing the capability in-house, but -- equally -- there is a range of providers now ready to deliver support. Operators must choose their partners carefully, deciding between technology and training providers, application/solution vendors or professional and managed services firms. And they need an approach that can deliver identifiable swift wins and investment returns.
The latest Heavy Reading Service Provider IT Insider, "Big Data: Big Cost or Big Opportunity?," explores the progress telecom operators are making in terms of their efforts to exploit their data assets. It identifies the key uses for big-data, and looks at what operators have been doing so far. It also analyzes the types of companies that are emerging/have emerged to support telecom big-data projects, the products and services they provide, and how they compare.
— Simon Sherrington, Analyst, Heavy Reading Service Provider IT Insider
Big Data: Big Cost or Big Opportunity?, a 26-page report in PDF format, is available as part of an annual subscription (6 bimonthly issues) to Heavy Reading Service Provider IT Insider, priced at $1,595. Individual reports are available for $900.