The Big Data Market: 2016 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

Abstract
“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 scientific R&D.

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 over $46 Billion in 2016 alone. These investments are further expected to grow at a CAGR of 12% over the next four years.

The “Big Data Market: 2016 – 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 market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2016 through to 2030. The forecasts are further 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.

Content
Table of Contents

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

6 Chapter 6: Big Data in Defense & Intelligence
6.1 Overview & Investment Potential
6.2 Key Applications
6.2.1 Intelligence Gathering
6.2.2 Battlefield Analytics
6.2.3 Energy Saving Opportunities in the Battlefield
6.2.4 Preventing Injuries on the Battlefield
6.3 Case Studies
6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data
6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data
6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Threats
6.3.4 Ministry of State Security, China: Predictive Policing with Big Data
6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data

7 Chapter 7: Big Data in Education
7.1 Overview & Investment Potential
7.2 Key Applications
7.2.1 Information Integration
7.2.2 Identifying Learning Patterns
7.2.3 Enabling Student-Directed Learning
7.3 Case Studies
7.3.1 Purdue University: Improving Academic Performance with Big Data
7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data
7.3.3 Edith Cowen University: Increasing Student Retention with Big Data

8 Chapter 8: Big Data in Healthcare & Pharma
8.1 Overview & Investment Potential
8.2 Key Applications
8.2.1 Drug Discovery, Design & Development
8.2.2 Clinical Development & Trials
8.2.3 Population Health Management
8.2.4 Personalized Healthcare & Targeted Treatments
8.2.5 Proactive & Remote Patient Monitoring
8.2.6 Preventive Care & Health Interventions
8.3 Case Studies
8.3.1 AstraZeneca: Analytics-Driven Drug Development with Big Data
8.3.2 Bangkok Hospital Group: Transforming the Patient Experience with Big Data
8.3.3 Novartis: Digitizing Healthcare with Big Data
8.3.4 Pfizer: Developing Effective and Targeted Therapies with Big Data
8.3.5 Sanofi: Proactive Diabetes Care with Big Data
8.3.6 UnitedHealth Group: Enhancing Patient Care & Value with Big Data

9 Chapter 9: Big Data in Smart Cities & Intelligent Buildings
9.1 Overview & Investment Potential
9.2 Key Applications
9.2.1 Energy Optimization & Fault Detection
9.2.2 Intelligent Building Analytics
9.2.3 Urban Transportation Management
9.2.4 Optimizing Energy Production
9.2.5 Water Management
9.2.6 Urban Waste Management
9.3 Case Studies
9.3.1 Singapore: Building a Smart Nation with Big Data
9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data
9.3.3 OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data

10 Chapter 10: Big Data in Insurance
10.1 Overview & Investment Potential
10.2 Key Applications
10.2.1 Claims Fraud Mitigation
10.2.2 Customer Retention & Profiling
10.2.3 Risk Management
10.3 Case Studies
10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data
10.3.2 RSA Group: Improving Customer Relations with Big Data
10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data

11 Chapter 11: Big Data in Manufacturing & Natural Resources
11.1 Overview & Investment Potential
11.2 Key Applications
11.2.1 Asset Maintenance & Downtime Reduction
11.2.2 Quality & Environmental Impact Control
11.2.3 Optimized Supply Chain
11.2.4 Exploration & Identification of Natural Resources
11.3 Case Studies
11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data
11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data
11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data
11.3.4 Brunei: Saving Natural Resources with Big Data

12 Chapter 12: Big Data in Web, Media & Entertainment
12.1 Overview & Investment Potential
12.2 Key Applications
12.2.1 Audience & Advertising Optimization
12.2.2 Channel Optimization
12.2.3 Recommendation Engines
12.2.4 Optimized Search
12.2.5 Live Sports Event Analytics
12.2.6 Outsourcing Big Data Analytics to Other Verticals
12.3 Case Studies
12.3.1 Twitter: Cracking Down on Abusive Content with Big Data
12.3.2 Netflix: Improving Viewership with Big Data
12.3.3 NFL (National Football League): Improving Stadium Experience with Big Data
12.3.4 Baidu: Reshaping Search Capabilities with Big Data
12.3.5 Constant Contact: Effective Marketing with Big Data

13 Chapter 13: Big Data in Public Safety & Homeland Security
13.1 Overview & Investment Potential
13.2 Key Applications
13.2.1 Cyber Crime Mitigation
13.2.2 Crime Prediction Analytics
13.2.3 Video Analytics & Situational Awareness
13.3 Case Studies
13.3.1 DHS (Department of Homeland Security): Identifying Threats with Big Data
13.3.2 Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data
13.3.3 Memphis Police Department: Crime Reduction with Big Data

14 Chapter 14: Big Data in Public Services
14.1 Overview & Investment Potential
14.2 Key Applications
14.2.1 Public Sentiment Analysis
14.2.2 Tax Collection & Fraud Detection
14.2.3 Economic Analysis
14.2.4 Predicting & Mitigating Disasters
14.3 Case Studies
14.3.1 ONS (Office for National Statistics): Exploring the UK Economy with Big Data
14.3.2 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data
14.3.3 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data
14.3.4 City of Chicago: Improving Government Productivity with Big Data
14.3.5 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data
14.3.6 Ambulance Victoria: Improving Patient Survival Rates with Big Data

15 Chapter 15: Big Data in Retail, Wholesale & Hospitality
15.1 Overview & Investment Potential
15.2 Key Applications
15.2.1 Customer Sentiment Analysis
15.2.2 Customer & Branch Segmentation
15.2.3 Price Optimization
15.2.4 Personalized Marketing
15.2.5 Optimizing & Monitoring the Supply Chain
15.2.6 In-Field Sales Analytics
15.3 Case Studies
15.3.1 Walmart: Making Smarter Stocking Decision with Big Data
15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data
15.3.3 The Walt Disney Company: Theme Park Management with Big Data
15.3.4 Marriott International: Elevating Guest Services with Big Data
15.3.5 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data

16 Chapter 16: Big Data in Telecommunications
16.1 Overview & Investment Potential
16.2 Key Applications
16.2.1 Network Performance & Coverage Optimization
16.2.2 Customer Churn Prevention
16.2.3 Personalized Marketing
16.2.4 Tailored Location Based Services
16.2.5 Fraud Detection
16.3 Case Studies
16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data
16.3.2 AT&T: Smart Network Management with Big Data
16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data
16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data
16.3.5 Freedom Mobile: Optimizing Video Quality with Big Data
16.3.6 Coriant: SaaS Based Analytics with Big Data

17 Chapter 17: Big Data in Utilities & Energy
17.1 Overview & Investment Potential
17.2 Key Applications
17.2.1 Customer Retention
17.2.2 Forecasting Energy
17.2.3 Billing Analytics
17.2.4 Predictive Maintenance
17.2.5 Maximizing the Potential of Drilling
17.2.6 Production Optimization
17.3 Case Studies
17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data
17.3.2 British Gas: Improving Customer Service with Big Data
17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big Data

18 Chapter 18: Future Roadmap & Value Chain
18.1 Future Roadmap
18.1.1 Pre-2020: Towards Cloud-Based Big Data Offerings for Advanced Analytics
18.1.2 2020 – 2025: Growing Focus on AI (Artificial Intelligence), Deep Learning & Edge Analytics
18.1.3 2025 – 2030: Convergence with Future IoT Applications
18.2 The Big Data Value Chain
18.2.1 Hardware Providers
18.2.1.1 Storage & Compute Infrastructure Providers
18.2.1.2 Networking Infrastructure Providers
18.2.2 Software Providers
18.2.2.1 Hadoop & Infrastructure Software Providers
18.2.2.2 SQL & NoSQL Providers
18.2.2.3 Analytic Platform & Application Software Providers
18.2.2.4 Cloud Platform Providers
18.2.3 Professional Services Providers
18.2.4 End-to-End Solution Providers
18.2.5 Vertical Enterprises

19 Chapter 19: Standardization & Regulatory Initiatives
19.1 ASF (Apache Software Foundation)
19.1.1 Management of Hadoop
19.1.2 Big Data Projects Beyond Hadoop
19.2 CSA (Cloud Security Alliance)
19.2.1 BDWG (Big Data Working Group)
19.3 CSCC (Cloud Standards Customer Council)
19.3.1 Big Data Working Group
19.4 DMG (Data Mining Group)
19.4.1 PMML (Predictive Model Markup Language) Working Group
19.4.2 PFA (Portable Format for Analytics) Working Group
19.5 IEEE (Institute of Electrical and Electronics Engineers)
19.5.1 Big Data Initiative
19.6 INCITS (InterNational Committee for Information Technology Standards)
19.6.1 Big Data Technical Committee
19.7 ISO (International Organization for Standardization)
19.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange
19.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
19.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques
19.7.4 ISO/IEC JTC 1/WG 9: Big Data
19.7.5 Collaborations with Other ISO Work Groups
19.8 ITU (International Telecommunication Union)
19.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
19.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks
19.8.3 Other Relevant Work
19.9 Linux Foundation
19.9.1 ODPi (Open Ecosystem of Big Data)
19.10 NIST (National Institute of Standards and Technology)
19.10.1 NBD-PWG (NIST Big Data Public Working Group)
19.11 OASIS (Organization for the Advancement of Structured Information Standards)
19.11.1 Technical Committees
19.12 ODaF (Open Data Foundation)
19.12.1 Big Data Accessibility
19.13 ODCA (Open Data Center Alliance)
19.13.1 Work on Big Data
19.14 OGC (Open Geospatial Consortium)
19.14.1 Big Data DWG (Domain Working Group)
19.15 TM Forum
19.15.1 Big Data Analytics Strategic Program
19.16 TPC (Transaction Processing Performance Council)
19.16.1 TPC-BDWG (TPC Big Data Working Group)
19.17 W3C (World Wide Web Consortium)
19.17.1 Big Data Community Group
19.17.2 Open Government Community Group

List of Figures
Figure 1: Hadoop Architecture
Figure 2: Reactive vs. Proactive Analytics
Figure 3: Big Data Future Roadmap: 2018 – 2030
Figure 4: Big Data Value Chain
Figure 5: Key Aspects of Big Data Standardization
Figure 6: Global Big Data Revenue: 2018 – 2030 ($ Million)
Figure 7: Global Big Data Revenue by Submarket: 2018 – 2030 ($ Million)
Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2018 – 2030 ($ Million)
Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2018 – 2030 ($ Million)
Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2018 – 2030 ($ Million)
Figure 11: Global Big Data SQL Submarket Revenue: 2018 – 2030 ($ Million)
Figure 12: Global Big Data NoSQL Submarket Revenue: 2018 – 2030 ($ Million)
Figure 13: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2018 – 2030 ($ Million)
Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2018 – 2030 ($ Million)
Figure 15: Global Big Data Professional Services Submarket Revenue: 2018 – 2030 ($ Million)
Figure 16: Global Big Data Revenue by Vertical Market: 2018 – 2030 ($ Million)
Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2018 – 2030 ($ Million)
Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2018 – 2030 ($ Million)
Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 2018 – 2030 ($ Million)
Figure 20: Global Big Data Revenue in the Education Sector: 2018 – 2030 ($ Million)
Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2018 – 2030 ($ Million)
Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2018 – 2030 ($ Million)
Figure 23: Global Big Data Revenue in the Insurance Sector: 2018 – 2030 ($ Million)
Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2018 – 2030 ($ Million)
Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 2018 – 2030 ($ Million)
Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2018 – 2030 ($ Million)
Figure 27: Global Big Data Revenue in the Public Services Sector: 2018 – 2030 ($ Million)
Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2018 – 2030 ($ Million)
Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2018 – 2030 ($ Million)
Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2018 – 2030 ($ Million)
Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2018 – 2030 ($ Million)
Figure 32: Big Data Revenue by Region: 2018 – 2030 ($ Million)
Figure 33: Asia Pacific Big Data Revenue: 2018 – 2030 ($ Million)
Figure 34: Asia Pacific Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 35: Australia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 36: China Big Data Revenue: 2018 – 2030 ($ Million)
Figure 37: India Big Data Revenue: 2018 – 2030 ($ Million)
Figure 38: Indonesia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 39: Japan Big Data Revenue: 2018 – 2030 ($ Million)
Figure 40: Malaysia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 41: Pakistan Big Data Revenue: 2018 – 2030 ($ Million)
Figure 42: Philippines Big Data Revenue: 2018 – 2030 ($ Million)
Figure 43: Singapore Big Data Revenue: 2018 – 2030 ($ Million)
Figure 44: South Korea Big Data Revenue: 2018 – 2030 ($ Million)
Figure 45: Taiwan Big Data Revenue: 2018 – 2030 ($ Million)
Figure 46: Thailand Big Data Revenue: 2018 – 2030 ($ Million)
Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2018 – 2030 ($ Million)
Figure 48: Eastern Europe Big Data Revenue: 2018 – 2030 ($ Million)
Figure 49: Eastern Europe Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 50: Czech Republic Big Data Revenue: 2018 – 2030 ($ Million)
Figure 51: Poland Big Data Revenue: 2018 – 2030 ($ Million)
Figure 52: Russia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2018 – 2030 ($ Million)
Figure 54: Latin & Central America Big Data Revenue: 2018 – 2030 ($ Million)
Figure 55: Latin & Central America Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 56: Argentina Big Data Revenue: 2018 – 2030 ($ Million)
Figure 57: Brazil Big Data Revenue: 2018 – 2030 ($ Million)
Figure 58: Mexico Big Data Revenue: 2018 – 2030 ($ Million)
Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2018 – 2030 ($ Million)
Figure 60: Middle East & Africa Big Data Revenue: 2018 – 2030 ($ Million)
Figure 61: Middle East & Africa Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 62: Israel Big Data Revenue: 2018 – 2030 ($ Million)
Figure 63: Qatar Big Data Revenue: 2018 – 2030 ($ Million)
Figure 64: Saudi Arabia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 65: South Africa Big Data Revenue: 2018 – 2030 ($ Million)
Figure 66: UAE Big Data Revenue: 2018 – 2030 ($ Million)
Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2018 – 2030 ($ Million)
Figure 68: North America Big Data Revenue: 2018 – 2030 ($ Million)
Figure 69: North America Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 70: Canada Big Data Revenue: 2018 – 2030 ($ Million)
Figure 71: USA Big Data Revenue: 2018 – 2030 ($ Million)
Figure 72: Western Europe Big Data Revenue: 2018 – 2030 ($ Million)
Figure 73: Western Europe Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 74: Denmark Big Data Revenue: 2018 – 2030 ($ Million)
Figure 75: Finland Big Data Revenue: 2018 – 2030 ($ Million)
Figure 76: France Big Data Revenue: 2018 – 2030 ($ Million)
Figure 77: Germany Big Data Revenue: 2018 – 2030 ($ Million)
Figure 78: Italy Big Data Revenue: 2018 – 2030 ($ Million)
Figure 79: Netherlands Big Data Revenue: 2018 – 2030 ($ Million)
Figure 80: Norway Big Data Revenue: 2018 – 2030 ($ Million)
Figure 81: Spain Big Data Revenue: 2018 – 2030 ($ Million)
Figure 82: Sweden Big Data Revenue: 2018 – 2030 ($ Million)
Figure 83: UK Big Data Revenue: 2018 – 2030 ($ Million)
Figure 84: Big Data Revenue in the Rest of Western Europe: 2018 – 2030 ($ Million)
Figure 85: Global Big Data Workload Distribution by Environment: 2018 – 2030 (%)
Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2018 – 2030 ($ Million)
Figure 87: Big Data Vendor Market Share: 2017 (%)
Figure 88: Global IT Expenditure Driven by Big Data Investments: 2018 – 2030 ($ Million)
Figure 89: Global IoT Connections by Access Technology: 2018 – 2030 (Millions)