Big Data 2018 Global Market – Opportunities, Challenges, Strategies & Forecasts 2020

Description:      
                                                                                           
“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.

Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to R&D.
 
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Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for nearly $30 Billion in 2014 alone. These investments are further expected to grow at a CAGR of 17% over the next 6 years.

The “Big Data Market: 2014 – 2020 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies.

The report also presents market size forecasts for Big Data hardware, software and professional services from 2014 through to 2020. Historical figures are also presented for 2010, 2011, 2012 and 2013. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 34 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
 
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Table Of Contents – Major Key Points

1              Chapter 1: Introduction
1.1          Executive Summary
1.2          Topics Covered
1.3          Historical Revenue & Forecast Segmentation
1.4          Key Questions Answered
1.5          Key Findings
1.6          Methodology
1.7          Target Audience
1.8          Companies & Organizations Mentioned
               
2              Chapter 2: An Overview of Big Data
2.1          What is Big Data?
2.2          Key Approaches to Big Data Processing
2.2.1      Hadoop
2.2.2      NoSQL
2.2.3      MPAD (Massively Parallel Analytic Databases)
2.2.4      In-memory Processing
2.2.5      Stream Processing Technologies
2.2.6      Spark
2.2.7      Other Databases & Analytic Technologies
2.3          Key Characteristics of Big Data
2.3.1      Volume
2.3.2      Velocity
2.3.3      Variety
2.3.4      Value
2.4          Market Growth Drivers
2.4.1      Awareness of Benefits
2.4.2      Maturation of Big Data Platforms
2.4.3      Continued Investments by Web Giants, Governments & Enterprises
2.4.4      Growth of Data Volume, Velocity & Variety
2.4.5      Vendor Commitments & Partnerships
2.4.6      Technology Trends Lowering Entry Barriers
2.5          Market Barriers
2.5.1      Lack of Analytic Specialists
2.5.2      Uncertain Big Data Strategies
2.5.3      Organizational Resistance to Big Data Adoption
2.5.4      Technical Challenges: Scalability & Maintenance
2.5.5      Security & Privacy Concerns
               
3              Chapter 3: Vertical Opportunities & Use Cases for Big Data
3.1          Automotive, Aerospace & Transportation
3.1.1      Predictive Warranty Analysis
3.1.2      Predictive Aircraft Maintenance & Fuel Optimization
3.1.3      Air Traffic Control
3.1.4      Transport Fleet Optimization
3.2          Banking & Securities
3.2.1      Customer Retention & Personalized Product Offering
3.2.2      Risk Management
3.2.3      Fraud Detection
3.2.4      Credit Scoring
3.3          Defense & Intelligence
3.3.1      Intelligence Gathering
3.3.2      Energy Saving Opportunities in the Battlefield
3.3.3      Preventing Injuries on the Battlefield
3.4          Education
3.4.1      Information Integration
3.4.2      Identifying Learning Patterns
3.4.3      Enabling Student-Directed Learning
3.5          Healthcare & Pharmaceutical
3.5.1      Managing Population Health Efficiently
3.5.2      Improving Patient Care with Medical Data Analytics
3.5.3      Improving Clinical Development & Trials
3.5.4      Improving Time to Market
3.6          Smart Cities & Intelligent Buildings
3.6.1      Energy Optimization & Fault Detection
3.6.2      Intelligent Building Analytics
3.6.3      Urban Transportation Management
3.6.4      Optimizing Energy Production
3.6.5      Water Management
3.6.6      Urban Waste Management
3.7          Insurance
3.7.1      Claims Fraud Mitigation
3.7.2      Customer Retention & Profiling
3.7.3      Risk Management
3.8          Manufacturing & Natural Resources
3.8.1      Asset Maintenance & Downtime Reduction
3.8.2      Quality & Environmental Impact Control
3.8.3      Optimized Supply Chain
3.8.4      Exploration & Identification of Wells & Mines
3.8.5      Maximizing the Potential of Drilling
3.8.6      Production Optimization
3.9          Web, Media & Entertainment
3.9.1      Audience & Advertising Optimization
3.9.2      Channel Optimization
3.9.3      Recommendation Engines
3.9.4      Optimized Search
3.9.5      Live Sports Event Analytics
3.9.6      Outsourcing Big Data Analytics to Other Verticals
3.10        Public Safety & Homeland Security
3.10.1    Cyber Crime Mitigation
3.10.2    Crime Prediction Analytics
3.10.3    Video Analytics & Situational Awareness
3.11        Public Services
3.11.1    Public Sentiment Analysis
3.11.2    Fraud Detection & Prevention
3.11.3    Economic Analysis
3.12        Retail & Hospitality
3.12.1    Customer Sentiment Analysis
3.12.2    Customer & Branch Segmentation
3.12.3    Price Optimization
3.12.4    Personalized Marketing
3.12.5    Optimized Supply Chain
3.13        Telecommunications
3.13.1    Network Performance & Coverage Optimization
3.13.2    Customer Churn Prevention
3.13.3    Personalized Marketing
3.13.4    Location Based Services
3.13.5    Fraud Detection
3.14        Utilities & Energy
3.14.1    Customer Retention
3.14.2    Forecasting Energy
3.14.3    Billing Analytics
3.14.4    Predictive Maintenance
3.14.5    Turbine Placement Optimization
3.15        Wholesale Trade
3.15.1    In-field Sales Analytics
3.15.2    Monitoring the Supply Chain

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