Press release
The Big Data Market Witnessing Robust Growth and Growing at the CAGR of 14% during forecast period
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.Download Sample Report Copy From Here@ https://www.researchreporthub.com/report/the-big-data-market/39259/#requestforsample
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 scientific R&D.
Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Telecom & IT estimates that Big Data investments will account for over $65 Billion in 2018 alone. These investments are further expected to grow at a CAGR of approximately 14% over the next three years.
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The Big Data Market: 2018 2030 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 profiles, market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2018 till 2030. The forecasts are segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.
The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
Table of Content
1 Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 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: Big Data Analytics
3.1 What are Big Data Analytics?
3.2 The Importance of Analytics
3.3 Reactive vs. Proactive Analytics
3.4 Customer vs. Operational Analytics
3.5 Technology & Implementation Approaches
3.5.1 Grid Computing
3.5.2 In-Database Processing
3.5.3 In-Memory Analytics
3.5.4 Machine Learning & Data Mining
3.5.5 Predictive Analytics
3.5.6 NLP (Natural Language Processing)
3.5.7 Text Analytics
3.5.8 Visual Analytics
3.5.9 Graph Analytics
3.5.10 Social Media, IT & Telco Network Analytics
4 Chapter 4: Big Data in Automotive, Aerospace & Transportation
4.1 Overview & Investment Potential
4.2 Key Applications
4.2.1 Autonomous & Semi-Autonomous Driving
4.2.2 Streamlining Vehicle Recalls & Warranty Management
4.2.3 Fleet Management
4.2.4 Intelligent Transportation
4.2.5 UBI (Usage Based Insurance)
4.2.6 Predictive Aircraft Maintenance & Fuel Optimization
4.2.7 Air Traffic Control
4.3 Case Studies
4.3.1 Boeing: Making Flying More Efficient with Big Data
4.3.2 BMW: Eliminating Defects in New Vehicle Models with Big Data
4.3.3 Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data
4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data
4.3.5 Groupe Renault: Boosting Driver Safety with Big Data
4.3.6 Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data
5 Chapter 5: Big Data in Banking & Securities
5.1 Overview & Investment Potential
5.2 Key Applications
5.2.1 Customer Retention & Personalized Products
5.2.2 Risk Management
5.2.3 Fraud Detection
5.2.4 Credit Scoring
5.3 Case Studies
5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data
5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data
5.3.3 OTP Bank: Reducing Loan Defaults with Big Data
5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data
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