This report 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 2020 to 2025. 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 2020 to 2025
The 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.
Big data in cognitive computing will reach $18.6B USD globally by 2025
Big data application infrastructure will reach $11.7B USD globally by 2025
Big data in public safety and homeland security will reach $7.5B USD globally by 2025
Real-time data will be a key value proposition for all use cases, segments, and solutions
Market leading companies are rapidly integrating big data technologies with IoT infrastructure
The big data market consists of infrastructure providers, data centers, data-as-a-service providers, and other vendors. Solutions for managing unstructured data are evolving beyond systems aligned towards primarily human-generated data (such as social networking, messaging, and browsing habits) towards increasingly greater emphasis upon machine-generated data found across many industry verticals.
For example, manufacturing and healthcare are anticipated to create massive amounts of data that may be rendered useful only through advanced analytics and various Artificial Intelligence (AI) technologies such as machine learning and cognitive computing. The long-term prospect for these technologies is that they will become embedded in many different other technologies and provide autonomous decision making on behalf of humans, both directly, and indirectly through many processes, products, and services.
Emerging networks and systems such as IoT and edge computing will generate substantial amounts of unstructured data, which will present both technical challenges and market opportunities for operating companies and their vendors. Emerging big data tools, such as open APIs, will be implemented to facilitate data capture and processing with the ability to perform localized processing and decision making.
Big data solution provider dynamics are evolving almost as much as the data management technologies themselves. While some companies rely upon proprietary solutions, many leading companies such as Hortonworks and Cloudera offer products and services primarily based on open-source Apache Hadoop technology. One important distinction between market leaders is collaboration vs. competition. For example, Cloudera competes with IBM, Microsoft, and others in data science and AI whereas Hortonworks partners with these companies.
In terms of data management and analytics technologies, the big data industry is experiencing profound changes across the entire stack including infrastructure, security, analytics, and the application layer. The data services industry as a whole is shifting from host-based network topologies to cloud-based, data-centric architectures, thereby creating enormous challenges and opportunities for transitioning and securing data systems. In concert with this shift, big data infrastructure will require strategic governance and framework for optimized security.
Advanced analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes. AI enhances the ability for big data analytics and IoT platforms to provide value to each of these market segments. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.
The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service. However, real-time data is anticipated to become a highly valuable aspect of all solutions as a determinant of user behavior, application effectiveness, and an identifier of new and enhanced mobile/wireless and/or IoT related apps and services.
Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) are perhaps best known as data-intensive immersive technologies that require high bandwidth for operations. One of the least evaluated opportunities is the market opportunities associated with visualizing data and information in AR, VR, and MR environments. Much of this data will be unstructured, requiring big data analytics tools to process, categorize, and display in a meaningful manner. This will allow the end-user to visualize and utilize information in ways previously inconceivable.
In addition, leading data management companies are developing tools for improved general data visualization, facilitating improved information interpretation and decision making. Coupled with AI and cognitive computing, the field of advanced data visualization and analytics known as augmented analytics is transforming otherwise useless data into highly valuable and actionable smart data, often enabling dynamic decision making that may positively impact business operations as processes, transactions, and other events occur. Much of this smart data will be monetized in a data as a service approach by enterprise thanks to leading big data service provider solutions.
Detailed forecasts 2020-2025
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
Key Topics Covered
1. Executive Summary
2. 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 18.104.22.168 Volume 22.214.171.124 Variety 126.96.36.199 Velocity 188.8.131.52 Variability 184.108.40.206 Complexity 2.2 Research Background 2.2.1 Scope 2.2.2 Coverage 2.2.3 Company Focus
3. 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 220.127.116.11 Securing State Data 18.104.22.168 Securing APIs 22.214.171.124 Securing Applications 126.96.36.199 Securing Data for Analysis 188.8.131.52 Securing User Privileges 184.108.40.206 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. Big Data Technologies and Business Cases 4.1 Big Data Technology 4.1.1 Hadoop 220.127.116.11 Other Apache Projects 4.1.2 NoSQL 18.104.22.168 Hbase 22.214.171.124 Cassandra 126.96.36.199 Mongo DB 188.8.131.52 Riak 184.108.40.206 CouchDB 4.1.3 MPP Databases 4.1.4 Others and Emerging Technologies 220.127.116.11 Storm 18.104.22.168 Drill 22.214.171.124 Dremel 126.96.36.199 SAP HANA 188.8.131.52 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. 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. 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. 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 184.108.40.206 Support Vector Machines 220.127.116.11 Nave Bayes 18.104.22.168 K-nearest Neighbors 7.4.15 Geospatial Predictive Modelling 7.4.16 Regression Analysis 7.4.17 Social Network Analysis
8. 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. Key Big Data Companies and Solutions 9.1 Vendor Assessment Matrix 9.2 1010Data (Advance Communication Corp.) 9.3 Accenture 9.4 Actian Corporation 9.5 AdvancedMD 9.6 Alation 9.7 Allscripts Healthcare Solutions 9.8 Alpine Data Labs 9.9 Alteryx 9.10 Amazon 9.11 Anova Data 9.12 Apache Software Foundation 9.13 Apple Inc. 9.14 APTEAN (Formerly CDC Software) 9.15 Athena Health Inc. 9.16 Attunity 9.17 Booz Allen Hamilton 9.18 Bosch Software Innovations: Bosch IoT Suite 9.19 BGI 9.20 Big Panda 9.21 Bina Technologies Inc. 9.22 Capgemini 9.23 Cerner Corporation 9.24 Cisco Systems 9.25 CLC Bio 9.26 Cloudera 9.27 Cogito Ltd. 9.28 Compuverde 9.29 CRAY Inc. 9.30 Computer Science Corporation (CSC) 9.31 Crux Informatics 9.32 Ctrl Shift 9.33 Cvidya 9.34 Cybatar 9.35 DataDirect Network 9.36 Data Inc. 9.37 Databricks 9.38 Dataiku 9.39 Datameer 9.40 Data Stax 9.41 Definiens 9.42 Dell EMC 9.43 Deloitte 9.44 Domo 9.45 eClinicalWorks 9.46 Epic Systems Corporation 9.47 Facebook 9.48 Fluentd 9.49 Flytxt 9.50 Fujitsu 9.51 Genalice 9.52 General Electric 9.53 GenomOncology 9.54 GoodData Corporation 9.55 Google 9.56 Greenplum 9.57 Grid Gain Systems 9.58 Groundhog Technologies 9.59 Guavus 9.60 Hack/reduce 9.61 HPCC Systems 9.62 HP Enterprise 9.63 Hitachi Data Systems 9.64 Hortonworks 9.65 IBM 9.66 Illumina Inc 9.67 Imply Corporation 9.68 Informatica 9.69 Inter Systems Corporation 9.70 Intel 9.71 IVD Industry Connectivity Consortium-IICC 9.72 Jasper (Cisco Jasper) 9.73 Juniper Networks 9.74 Knome, Inc. 9.75 Leica Biosystems (Danaher) 9.76 Longview 9.77 MapR 9.78 Marklogic 9.79 Mayo Medical Laboratories 9.80 McKesson Corporation 9.81 Medical Information Technology Inc. (MEDITECH) 9.82 Medio 9.83 Medopad 9.84 Microsoft 9.85 Microstrategy 9.86 MongoDB (Formerly 10Gen) 9.87 MU Sigma 9.88 N-of-One 9.89 Netapp 9.90 NTT Data 9.91 Open Text (Actuate Corporation) 9.92 Opera Solutions 9.93 Oracle 9.94 Palantir Technologies Inc. 9.95 Pathway Genomics Corporation 9.96 Perkin Elmer 9.97 Pentaho (Hitachi) 9.98 Platfora 9.99 Qlik Tech 9.100 Quality Systems Inc (QSI) 9.101 Quantum 9.102 Quertle 9.103 Quest Diagnostics Inc. 9.104 Rackspace 9.105 Red Hat 9.106 Revolution Analytics 9.107 Roche Diagnostics 9.108 Rocket Fuel Inc. 9.109 Salesforce 9.110 SAP 9.111 SAS Institute 9.112 Selventa Inc. 9.113 Sense Networks 9.114 Shanghai Data Exchange 9.115 Sisense 9.116 Social Cops 9.117 Software AG/Terracotta 9.118 Sojern 9.119 Splice Machine 9.120 Splunk 9.121 Sqrrl 9.122 Sumo Logic 9.123 Sunquest Information Systems 9.124 Supermicro 9.125 Tableau Software 9.126 Tableau 9.127 Tata Consultancy Services 9.128 Teradata 9.129 ThetaRay 9.130 Thoughtworks 9.131 Think Big Analytics 9.132 TIBCO 9.133 Tube Mogul 9.134 Verint Systems 9.135 VolMetrix 9.136 VMware (Part of EMC) 9.137 Wipro 9.138 Workday (Platfora) 9.139 WuXi NextCode Genomics 9.140 Zoomdata
10. Overall Big Data Market Analysis and Forecasts 2020-2025 10.1 Global Big Data Marketplace 2020-2025 10.2 Big Data Market by Solution Type 2020-2025 10.3 Regional Big Data Market 2020-2025
11. Big Data Market Segment Analysis and Forecasts 2020-2025 11.1 Big Data Market by Management Utilities 2020-2025 11.1.1 Market for Servers and Other Hardware 2020-2025 11.1.2 Market for Big Data Application Infrastructure and Middleware 2020-2025 11.1.3 Market for Data Integration Tools & Data Quality Tools 2020-2025 11.1.4 Big Data Market for Database Management Systems 2020-2025 11.1.5 Big Data Market for Storage Management 2020-2025 11.2 Big Data Market by Functional Segment 2020-2025 11.2.1 Big Data in Supply Chain Management 2020-2025 11.2.2 Big Data in Workforce Analytics 2020-2025 11.2.3 Big Data in Enterprise Performance Analytics 2020-2025 11.2.4 Big Data in Professional Services 2020-2025 11.2.5 Big Data in Business Intelligence 2020-2025 11.2.6 Big Data in Social Media and Content Analytics 11.3 Market for Big Data in Emerging Technologies 2020-2025 11.3.1 Big Data in Internet of Things 2020-2025 11.3.2 Big Data in Smart Cities 2020-2025 11.3.3 Big Data in Blockchain and Cryptocurrency 2020-2025 11.3.4 Big Data in Augmented and Virtual Reality 2020-2025 11.3.5 Big Data in Cybersecurity 2020-2025 11.3.6 Big Data in Smart Assistants 2020-2025 11.3.7 Big Data in Cognitive Computing 2020-2025 11.3.8 Big Data in CRM 2020-2025 11.3.9 Big Data in Spatial Information 2020-2025 11.4 Big Data Market by Industry Type 2020-2025 11.5 Regional Big Data Markets 2020-2025 11.5.1 North America Market for Big Data 2020-2025 11.5.2 South American Market for Big Data 2020-2025 11.5.3 Western European Market for Big Data 2020-2025 11.5.4 Central and Eastern European Market for Big Data 2020-2025 11.5.5 Asia-Pacific Market for Big Data 2020-2025 11.5.6 Middle East and Africa Market for Big Data 2020-2025