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Worldwide Mobile Big Data Market 2016-2020

  • ID: 3641413
  • Report
  • Region: Global
  • 120 pages
  • Researchica
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Big Data in Mobile Used to Stem ‘Chikungunya Virus’ in the Caribbean


  • Accenture
  • China Mobile
  • Ericsson
  • IBM
  • O2
  • Singtel
  • MORE
Taking into perspective, all—Innovations, Business Models, and Transformations going on around Mobile & Big Data, supported by case studies, and forecasts for 2020.

Key Questions Answered:

1. What is the current state of the global big data market?
2. Why big data management and utilisation is quickly turning to be a prerequisite for operators?
3. How are big data explorations affecting the telecom market dynamics?
4. What are the various big data use cases for operators?
5. How is big data currently being used by telco operators?
6. How is big data helping operators to improve operational efficiency and generate new revenue streams?
7. Which operators have exhaustively planned/ executed big data strategies?
8. What are the key advantages operators hold vis-à-vis big data?
9. How to identify the business problems that big data can solve?
10. How big data can be explored to create multiple fresh and innovative revenue streams?
11. Which operators are the pioneers/ leading in big data monetisation?
12. Who should develop in house and who should outsource?
13. What should be the criterion for big as well as small operators for vendor differentiation and assessment?
14. What are the recent developments vis-à-vis big data products and services?
15. What are the telcos’ best practices in big data?
16. Who are the top telco-focused big data vendors?

Research Methodology

Adding Value to our Research Projects

All our projects are led by senior analysts and consultants with proven track records. The research techniques applied by TeleResearch Labs’ teams of analysts and consultants combine multiple approaches, including:

1. Market Analysis: both qualitative and quantitative;

2. The application of most appropriate data and market analysis tools for market segmentation, competition analysis, data modelling, strategic evaluation, market assessment, and forecasts;

3. All our findings, projections, and suggestions are cross verified by our internal market experts as well as through interviews with external industry veterans.

Information Sources

Sources for our reports are a combination of both face to face and telephonic interviews with telecom industry experts and consumers – in developed & emerging markets. These include executives of MNOs, MVNOs, OEMs, ISPs Infrastructure Vendors, Mobile App Development firms as well as several M2M specialists.

In addition to the above, we spoke to several key managerial personnel in some of the adjunct industries such as Healthcare, Insurance, Banking, Automotive, Retail etc. with a view to develop holistic guidelines/ prognosis. It also includes various surveys that were conducted in different regions of the world. Other sources comprise of operators' websites and financial reports, books, trade journals, magazines, white papers, industry portals, and numerous independent studies of government and regulatory bodies.

Forecasting Methodology

We made use of extensive database of macroeconomic and sector specific data to generate industry forecasts. Judgment based methods like the Delphi method and Extrapolation; Time series methods like Exponential smoothing, Cyclical and seasonal trends and Statistical Modelling; as well as the Survey method was also a part of the process. The initial baseline projection is computed with the most recent market data. After an initial baseline forecast, all probable future macroeconomic and industry specific occurrences and assumptions are taken into consideration to generate the final forecast.

Methodology Specific to this Report

The report is a voluminous analysis conducted over a course of 6 months from the pattern study of industrial transformation in past 3 years by a team of 7 senior research analysts, specifically assigned for this research project. The study is packed with magnitude of evidence, supporting caseworks, semi structured interviews (with 22 mobile/ fixed-line operators and industry veterans), business models, market speculations, and examples representing archetypes of highly versatile and serendipitous B2B/ polysectoral B2B environs that top leaders/ business modellers/ decision-makers have either applied or missed. Our association with the reputed telecom research houses helped us throughout the journey to keep our study accurate, authentic, and futuristic.

Note: Product cover images may vary from those shown
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  • Accenture
  • China Mobile
  • Ericsson
  • IBM
  • O2
  • Singtel
  • MORE
Outline of the Report

Chapter 1: Prologue: A fresh perspective for Big Data

Chapter 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

Chapter 3: Big Data: Modern business use cases, analysis & 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

Chapter 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

Chapter 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

Chapter 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 & Africa

Chapter 7: Big Data Best Practices & Recommendations for Telco Operators

7.1 Setting investment priorities for Big Data
7.2 Network Optimization & multilayer Monetization
7.3 Subscriber Insight data & Personalized Services
7.4 Mobile Advertising and LBS
7.5 Data Risks and Regulations are highly crucial

List of Figures:

Figure 1 Levels of big data operator
Figure 2 Shift in telecom market leadership & competition
Figure 3 ARPU per year in (In US$), 2015
Figure 4 Global Voice & Messaging Revenues Lost to OTT applications (In US$ Billion), 2014-2020
Figure 5 Global Voice Revenue (In US$ Billion), 2012-2014
Figure 6 Global Voice Revenue by Region (In US$ Billion), 2012-2014
Figure 7 Global SMS Revenue (In US$ Billion), 2012-2014
Figure 8 Global SMS Revenue by Region (In US$ Billion), 2012- 2014
Figure 9 Common telco data sources and information
Figure 10 Formulating steps to use big data
Figure 11 The progression to next-generation customer analytics
Figure 12 Global big data revenue forecasts (In US$ Million), 2016-2020
Figure 13 North America big data revenue forecasts (In US$ Million), 2016-2020
Figure 14 Europe big data revenue forecasts (In US$ Million), 2016-2020
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 & Africa big data revenue forecasts (In US$ Million), 2016-2020
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  • Accenture
  • China Mobile
  • Ericsson
  • IBM
  • O2
  • Singtel
  • MORE
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.”

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.

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5 of 4
- Accenture
- Argyle Data
- Capgemini
- China Mobile
- Cloudera
- Ericsson
- Hewlett-Packard (HP)
- Huawei
- Microsoft
- Nokia Networks
- O2
- Platfora
- Singtel
- Telefonica
- Verizon

Note: Product cover images may vary from those shown