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Worldwide Mobile Big Data Market 2016-2020

  • March 2016
  • -
  • TeleResearch Labs
  • -
  • 120 pages


Now-a-days, a trend is fast catching on as how telecom big data is being increasingly harnessed in disease control thereby bettering healthcare system. Various ways and methods are being employed to harness telecom big data in this direction. Recently, various studies and instances (in which telecom big data has been used in preventing the epidemic) came to the fore substantiating the claims. For example, such an episode came into the light when telecom big data was used to stem the spread of the Chikungunya virus in the Caribbean.

United Nations Economic Commission for Latin America and the Caribbean (ECLAC) recently published report titled An assessment of big data for official statistics in the Caribbean in which it noted that the use of big data through geospatial (or location) of mobiles was used to support healthcare, and to design measures to tackle the eruption of Chikungunya transversing the region.

“Geospatial applications for smart phones backed by the Ministry of Health
in detecting the location of infected persons and to contain the epidemic in Trinidad and Tobago.”
(Source: Worldwide Mobile Big Data Market 2016, by Washington based Teleresearchlabs Inc.)

Similarly, another instance also came into the light when Telenor Research, in cooperation with the Harvard TH Chan School of Public Health and the University of Peshawar, Pakistan published a report signifying the ability of Big Data to predict and track the proliferation of dengue disease. The research titled Impacts of human mobility on the emergence of dengue epidemics in Pakistan scrutinized anonymized call data from more than 30 million Telenor’s subscribers of Pakistan during the 2013 dengue outbreak, making use of the large volume of data to perfectly chart the geographic spread and timing of the epidemic.

Table Of Contents

Worldwide Mobile Big Data Market 2016-2020
1 Prologue: A fresh perspective for Big Data
2 Big Data: Simplified technical concepts, market drivers, challenges, value creation, risk assessment and investment priorities
2.1 Prime reasons for telcos to explore big data
2.1.1 Market saturation slowing conventional growth prospects
2.1.2 ARPU is continuously falling
2.1.3 Telcos need to preserve existing revenue sources
2.1.4 Overcoming churn
2.1.5 Declining profitability from mobile data business: Dumb pipe scenario
2.1.6 OTTs are hurting telcos' business bottom lines
2.1.7 Telcos continuously losing their relevance in the value chain
2.1.8 Telcos need to add new business and create new sources of revenue
2.2 Telecom perspective on Big Data
2.3 Gauzing Big data strength of telco operators
2.4 Big data monetisation challenges for telco operators
2.5 Advanced analytics and big Data can create big business value
3 Big Data: Modern business use cases, analysis and decision making
3.1 Big data Innovative Business models
3.1.1 Enhanced API Enablement and NextGen VAS
3.1.2 Explore transaction data for boosting sales
3.2 Leverage Big Data for Precision Marketing
3.2.1 Offer Optimisation at Individual level
3.2.2 Enhanced Churn Prediction and Prevention
3.2.3 Profitable Product Packaging for Specific OTT
3.3 Improved Operational Efficiency
3.3.1 Smart and pre-emptive Customer care
3.3.2 Intelligent Network Planning and monetisation
3.3.3 Cell-Site Optimisation
3.3.4 Subscriber-Centric Wireless Offloading
3.4 Real Time Network and Subscriber Intelligence
3.4.1 Enhanced prediction and management of temporary/ sudden Network Congestion
3.4.2 Enable Location Based and Personalized Advertising
3.4.3 Social Media and Sentiment Analysis
3.5 Quality of service (QoS) enhancement
3.5.1 Dynamic Subscriber Profiling and Segmentation
4 Worldwide Significant Telco Case Studies: Projects, Evolution, Timelines and future strategies
4.1 Verizon's Precision Market Insights
4.2 Telefonica Dynamic Insights
4.3 Weve, O2
4.4 China Mobile Guangdong billing and customer service.
4.5 LIVE Singapore!
4.6 Singtel's DataSpark
5 Top telco-focused big data vendors: Profile, Market positioning, Investments and solutions
5.1 Accenture
5.2 Argyle Data
5.3 Capgemini
5.4 Cloudera
5.5 CSC
5.6 Ericsson
5.7 Hewlett-Packard (HP)
5.8 Huawei
5.9 IBM
5.10 Microsoft
5.11 Nokia Networks
5.12 Platfora
5.13 SAP
5.14 TIBCO
6 Global Big Data Market Forecast 2015-2020
6.1 Europe
6.2 North America
6.3 Latin America
6.4 Asia-Pacific
6.5 Middle East and Africa
7 Big Data Best Practices and Recommendations for Telco Operators
7.1 Setting investment priorities for Big Data
7.2 Network Optimization and multilayer Monetization
7.3 Subscriber Insight data and Personalized Services
7.4 Mobile Advertising and LBS
7.5 Data Risks and Regulations are highly crucial

Figure 1 Levels of big data operator
Figure 2 Shift in telecom market leadership and competition
Figure 3 ARPU per year in (In US$), 2015
Figure 4 Global Voice and Messaging Revenues Lost to OTT
applications (In US$ Billion), 2014-2020
Figure 5 Global Voice Revenue (In US$ Billion), 2012-2014
Figure 6 Global Voice Revenue by Region (In US$ Billion),
Figure 7 Global SMS Revenue (In US$ Billion), 2012-2014
Figure 8 Global SMS Revenue by Region (In US$ Billion), 2012-
Figure 9 Common telco data sources and information
Figure 10 Formulating steps to use big data
Figure 11 The progression to next-generation customer
Figure 12 Global big data revenue forecasts (In US$ Million),
Figure 13 North America big data revenue forecasts (In US$
Million), 2016-2020
Figure 14 Europe big data revenue forecasts (In US$ Million),
Figure 15 Latin America big data revenue forecasts (In US$
Million), 2016-2020
Figure 16 Asia-Pacific big data revenue forecasts (In US$
Million), 2016-2020
Figure 17 Middle East and Africa big data revenue forecasts (In
US$ Million), 2016-2020

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