Dublin, Aug. 16, 2019 (GLOBE NEWSWIRE) -- The "Big Data Market by Leading Companies, Solutions, Use Cases, Business Cases, Infrastructure, Technology Integration, Industry Verticals, Regions and Countries 2019-2024" report has been added to ResearchAndMarkets.com's offering.
Big Data Market by Leading Companies, Solutions, Use Cases, Business Cases, Infrastructure, Technology Integration, Industry Verticals, Regions and Countries 2019 - 2024 provides an in-depth assessment of the global Big Data market, including business case issues/analysis, application use cases, vendor landscape, value chain analysis, and a quantitative assessment of the industry with forecasting from 2019 to 2024. This report also evaluates the components of Big Data infrastructure and security framework.
Additional topics covered in this report include:
- Big Data Technology: Analysis of infrastructure and important issues such as security and privacy
- Big Data Use Cases: A review of investments sectors and specific use cases for the Big Data market
- The Big Data Value Chain: An analysis of the value chain of Big Data and the major players involved within it
- The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
- Big Data Vendor Assessment: Assessment of the vendor landscape of leading players within the Big Data market
- Market Analysis and Forecasts: Global and regional assessment of the market size and forecasts for 2019 to 2024
This report also includes analysis and forecasts for streaming data analytics. IoT facilitates vast amounts of fast-moving data from sensors and devices. For many use cases, data flows constantly from the device or sensor to the network and sometimes back to the device. In some cases, these streams of data are simply stored (for potential later use) and in other cases, there is a need for real-time data processing and analytics.
Report Findings:
- Big data in cognitive computing will reach $12.6B USD globally by 2024
- Big data application infrastructure will reach $9.1B USD globally by 2024
- Big data in public safety and homeland security will reach $5.3B USD globally by 2024
- Real-time data will be a key value proposition for all use cases, segments, and solutions
- Market leading companies are rapidly integrated big data technologies with IoT infrastructure
Report Benefits:
- Detailed forecasts 2019-2024
- Identify leading market segments
- Learn about Big Data technologies
- Identify key players and strategies
- Understand market drivers and barriers
- Identify opportunities in IoT data analytics
- Understand regulatory issues and initiatives
- Understand business case for enterprise Big Data
Target Audience:
- IoT companies
- Network service providers
- Systems integration companies
- Big Data and Analytics companies
- Advertising and media companies
- Enterprise across all industry verticals
- Cloud and IoT product and service providers
Key Topics Covered:
1.0 Executive Summary
2.0 Introduction
2.1 Big Data Overview
2.1.1 Defining Big Data
2.1.2 Big Data Ecosystem
2.1.3 Key Characteristics of Big Data
2.1.3.1 Volume
2.1.3.2 Variety
2.1.3.3 Velocity
2.1.3.4 Variability
2.1.3.5 Complexity
2.2 Research Background
2.2.1 Scope
2.2.2 Coverage
2.2.3 Company Focus
3.0 Big Data Challenges And Opportunities
3.1 Securing Big Data Infrastructure
3.1.1 Big Data Infrastructure
3.1.2 Infrastructure Challenges
3.1.3 Big Data Infrastructure Opportunities
3.1.3.1 Securing State Data
3.1.3.2 Securing APIs
3.1.3.3 Securing Applications
3.1.3.4 Securing Data for Analysis
3.1.3.5 Securing User Privileges
3.1.3.6 Securing Enterprise Data
3.2 Unstructured Data and the Internet of Things
3.2.1 New Protocols, Platforms, Streaming and Parsing, Software and Analytical Tools
3.2.2 Big Data in IoT will require Lightweight Data Interchange Format
3.2.3 Big Data in IoT will use Lightweight Protocols
3.2.4 Big Data in IoT will need Protocol for Network Interoperability
3.2.5 Big Data in IoT Demands Data Processing on Appropriate Scale
4.0 Big Data Technologies And Business Cases
4.1 Big Data Technology
4.1.1 Hadoop
4.1.1.1 Other Apache Projects
4.1.2 NoSQL
4.1.2.1 Hbase
4.1.2.2 Cassandra
4.1.2.3 Mongo DB
4.1.2.4 Riak
4.1.2.5 CouchDB
4.1.3 MPP Databases
4.1.4 Others and Emerging Technologies
4.1.4.1 Storm
4.1.4.2 Drill
4.1.4.3 Dremel
4.1.4.4 SAP HANA
4.1.4.5 Gremlin & Giraph
4.2 Emerging Technologies,Tools, and Techniques
4.2.1 Streaming Analytics
4.2.2 Cloud Technology
4.2.3 Google Search
4.2.4 Customize Analytical Tools
4.2.5 Internet Keywords
4.2.6 Gamification
4.3 Big Data Roadmap
4.4 Market Drivers
4.4.1 Data Volume & Variety
4.4.2 Increasing Adoption of Big Data by Enterprises and Telecom
4.4.3 Maturation of Big Data Software
4.4.4 Continued Investments in Big Data by Web Giants
4.4.5 Business Drivers
4.5 Market Barriers
4.5.1 Privacy and Security: The Big' Barrier
4.5.2 Workforce Re-skilling and Organizational Resistance
4.5.3 Lack of Clear Big Data Strategies
4.5.4 Technical Challenges: Scalability & Maintenance
4.5.5 Big Data Development Expertise
5.0 Key Sectors For Big Data
5.1 Industrial Internet and Machine-to-Machine
5.1.1 Big Data in M2M
5.1.2 Vertical Opportunities
5.2 Retail and Hospitality
5.2.1 Improving Accuracy of Forecasts and Stock Management
5.2.2 Determining Buying Patterns
5.2.3 Hospitality Use Cases
5.2.4 Personalized Marketing
5.3 Media
5.3.1 Social Media
5.3.2 Social Gaming Analytics
5.3.3 Usage of Social Media Analytics by Other Verticals
5.3.4 Internet Keyword Search
5.4 Utilities
5.4.1 Analysis of Operational Data
5.4.2 Application Areas for the Future
5.5 Financial Services
5.5.1 Fraud Analysis, Mitigation & Risk Profiling
5.5.2 Merchant-Funded Reward Programs
5.5.3 Customer Segmentation
5.5.4 Customer Retention & Personalized Product Offering
5.5.5 Insurance Companies
5.6 Healthcare and Pharmaceutical
5.6.1 Drug Development
5.6.2 Medical Data Analytics
5.6.3 Case Study: Identifying Heartbeat Patterns
5.7 Telecommunications
5.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization
5.7.2 Big Data Analytic Tools
5.7.3 Speech Analytics
5.7.4 New Products and Services
5.8 Government and Homeland Security
5.8.1 Big Data Research
5.8.2 Statistical Analysis
5.8.3 Language Translation
5.8.4 Developing New Applications for the Public
5.8.5 Tracking Crime
5.8.6 Intelligence Gathering
5.8.7 Fraud Detection and Revenue Generation
5.9 Other Sectors
5.9.1 Aviation
5.9.2 Transportation and Logistics: Optimizing Fleet Usage
5.9.3 Real-Time Processing of Sports Statistics
5.9.4 Education
5.9.5 Manufacturing
6.0 Big Data Value Chain
6.1 Fragmentation in the Big Data Value
6.2 Data Acquisitioning and Provisioning
6.3 Data Warehousing and Business Intelligence
6.4 Analytics and Visualization
6.5 Actioning and Business Process Management
6.6 Data Governance
7.0 Big Data Analytics
7.1 The Role and Importance of Big Data Analytics
7.2 Big Data Analytics Processes
7.3 Reactive vs. Proactive Analytics
7.4 Technology and Implementation Approaches
7.4.1 Grid Computing
7.4.2 In-Database processing
7.4.3 In-Memory Analytics
7.4.4 Data Mining
7.4.5 Predictive Analytics
7.4.6 Natural Language Processing
7.4.7 Text Analytics
7.4.8 Visual Analytics
7.4.9 Association Rule Learning
7.4.10 Classification Tree Analysis
7.4.11 Machine Learning
7.4.12 Neural Networks
7.4.13 Multilayer Perceptron (MLP)
7.4.14 Radial Basis Functions
7.4.14.1 Support Vector Machines
7.4.14.2 Nave Bayes
7.4.14.3 K-nearest Neighbors
7.4.15 Geospatial Predictive Modelling
7.4.16 Regression Analysis
7.4.17 Social Network Analysis
8.0 Standardization And Regulatory Issues
8.1 Cloud Standards Customer Council
8.2 National Institute of Standards and Technology
8.3 OASIS
8.4 Open Data Foundation
8.5 Open Data Center Alliance
8.6 Cloud Security Alliance
8.7 International Telecommunications Union
8.8 International Organization for Standardization
9.0 Key Big Data Companies And Solutions
For more information about this report visit https://www.researchandmarkets.com/r/pg7vsh
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