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Big data and analytics: Telco strategies, investments and use cases

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Big data and analytics: Telco strategies, investments and use cases


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Big data and analytics: Telco strategies, investments and use cases



Executive 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.

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 isn't 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

Table of Contents

Executive Summary

Section 1-Big data / analytics market context Report scope and definitions

Global market context 7 Participants in big data / analytics in the telecoms market

Legal and regulatory framework

Section 2-Industry survey results and analysis Status of service provider implementation of big data/analytics

Drivers of big data / analytics strategy

Level of financial investment in BDA and operational structure

Perceived challenges to BDA implementation 14 Perceived benefits of BDA implementation

Extent to which perceived benefits have been realized 16 BDA operator / suppliers survey-key insights

BDA operator / supplier survey-challenges to BDA implementation

Recommendations on how to address BDA implementation challenges

Section 3-Telco BDA strategies, investments and use cases Telco BDA case studies

AT&T

Orange

SingTel

Telefonica 26 BDA use cases in telecom environment

Network optimization use cases: bell mobility, sprint

Fraud detection use cases: vodafone and argyle data

Churn reduction /customer experience management-MTS India, XO communications

BDA as a service to customers / data monetization use cases: Pinsight Media+, Telefonica Dynamic Insights

Use cases: Key insights

Section 4-Key findings and recommendations Key findings

Recommendations

Appendices

Companies mentioned About Pyramid Research Pyramid Research contact information

To know more information on Purchase by Section, please send a mail to sales@kenresearch.com

Exhibit 1: Key definitions

Exhibit 2: Mobile and Internet penetration, 2015-2020

Exhibit 3: Global ARPS, 2015-2020 7 Exhibit 4: Leading BDA vendors

Exhibit 5: Players in BDA in telecoms

Exhibit 6: Status of big data analytics implementation

Exhibit 7: Drivers of big data/ analytics implementation

Exhibit 8: Extent of financial investment in BDA

Exhibit 9: BDA structure within organizations

Exhibit 10: Challenges to BDA implementation

Exhibit 11: Perceived benefits of BDA implementation

Exhibit 12: Extent of delivery of benefits

Exhibit 13: BDA in telco sector schematic

Exhibit 1: Key definitions

Exhibit 2: Mobile and Internet penetration, 2015-2020

Exhibit 3: Global ARPS, 2015-2020 7 Exhibit 4: Leading BDA vendors

Exhibit 5: Players in BDA in telecoms

Exhibit 6: Status of big data analytics implementation

Exhibit 7: Drivers of big data/ analytics implementation

Exhibit 8: Extent of financial investment in BDA

Exhibit 9: BDA structure within organizations

Exhibit 10: Challenges to BDA implementation

Exhibit 11: Perceived benefits of BDA implementation

Exhibit 12: Extent of delivery of benefits

Exhibit 13: BDA in telco sector schematic

Single User License:
Report can be used by individual purchaser only

Site License:
Report can be shared by unlimited users within one corporate location, e.g. a regional office

Corporate User License:
Report can be shared globally by unlimited users within the purchasing corporation e.g. all employees of a single company

Accenture

Amazon

Amdocs

Argyle Data

AT&T

Bell Mobility

China Unicom

Cisco

Coral Group

DataSpark

Dell

EMC

Facebook

Global Communications Fraud Association

Google

Hewlett-Packard Enterprise

HP Vertica

IBM

KAI Square Pte Ltd

MTS India

Oracle

Orange Business Services

Palantir

Pinsight Media+

PWC

SAP

SAS

SingTel

Sprint

Synergic

Tableau

Telefónica

Teradata

Vodafone

XO Communications

Big data, analytics, real-time analytics, telecoms analytics, telecoms big data, telco analytics, telco big data, telco BDA, sentiment analysis, telco big data value chain, telco big data and customer experience, telco analytics and churn reduction, data management, telecom operators, mobile operators, personalization, m-commerce, payments, customer selection, recommender system, data sets, storage, computing, governance, social network analysis, multidimensional scaling, data mining, anomaly detection, association rule learning, fitness landscapes, multivariate statistics, factor analysis, content analysis, collaborative filtering, content filtering, expert analysis, sentiment analysis


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