Summary
As the volume of data being created has grown exponentially in the past decade new technologies have emerged and been embraced by organizations in all industry sectors for a number of use cases. The amount of data being generated in the telecoms sector is colossal and there is a clear opportunity for communications service providers (CSPs) to analyze this data themselves and also, subject to legal and regulatory conductions, to sell it to other organizations. Telecoms operators are embracing big data analytics (BDA) with enthusiasm, but while many report that BDA is fully operational and already contributing benefit to their organizations others are still at the exploratory stage of addressing its potential. The key challenges operators face are a lack of BDA skills, organizational issues and siloed data.
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Key Findings
In the past few years CSPs across the world have been working with technology providers and consulting firms on a range of BDA projects. While there are strong indications that virtually every CSP has a BDA initiative, it is still early days for BDA in the telecoms sector.
Improved customer satisfaction is key driver. Customer satisfaction / churn reduction is the most important driver of BDA implementations among the firms surveyed for the purposes of this report. The ability to personalize services for individual customers is the next highest and also linked to revenue generation. Of importance too as a driver but less so is network optimization, which is focused on cost savings.
No pattern to how BDA is structured within organizations. A total of 40% of the organizations surveyed opted for a centralized approach when setting up the BDA function in their organization. A third opted for a decentralised approach while just 20% set up a separate business unit.
The main challenge for telecoms operators nowadays who seek to implement BDA within their organisations is to make important changes to the culture of the organization to allow this to work. These changes may not be the same for every organization but there will be some common themes. Telecoms firms tend to be large organizations with traditional hierarchical structures that present a challenge to any new initiative to be deployed across the organization.
A key factor in a successful implementation is to demonstrate success at an early stage. With this is mind it is essential that the organization does not fall into the trap of setting too many goals and trying to please everyone at once. Even with senior level support for the initiative and a senior person leading the project it is important to be able to show positive results in order to fully demonstrate the benefits of the process. Conversely, by taking the analytical process in small steps it allows the team the flexibility to change direction if any early objective isnt achieved
Synopsis
Big data and analytics: Telco strategies, investments and use cases examines the current situation with regard to big data and analytics among communications service providers, the use cases these organizations are harnessing the technology for and to what extent they are being successful. The Report provides an analysis of the industry survey results that obtained insights from service providers and their technology partners on their experiences with BDA implementations. Built around numerous telco case studies from across the world, this report sheds light on best practices for effective use of BDA that may soon mean the difference between winners and losers in the CSP market.
The Report is structured as follows:
Section 1: Big data / analytics market context
Section 2: Industry survey results and analysis
Section 3: Telco BDA strategies, investments and use cases
Section 4: Key findings and recommendations.
Reasons to Buy
Explore key drivers and challenges to telco big data and analytics implementations
Assess big data and analytics adoption
Examine telco strategies and best practices for commercializing big data and analytics solutions
Gain insights on telco investment plans for big data and analytics implementations over the next 2-3 years
Identify the main use cases for current and emerging big data and analytics implementations
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