Market Overview
The global Maritime Big Data Market is expected to grow from USD 1.2 billion in 2020 to USD 4.0 billion by 2029, at a CAGR of 16.6% during the forecast period. The maritime industry has been rapidly adopting big data and analytics to improve operational efficiency and safety. Big data in the maritime industry includes data generated from vessel tracking, port operations, cargo handling, and other areas. The increasing adoption of the Internet of Things (IoT) and connected devices is providing a large amount of data that can be utilized to improve the efficiency of maritime operations.
The maritime industry is highly regulated, and there is a need for accurate and timely data for decision-making. Big data analytics helps in reducing the risk of accidents, optimizing routes, reducing fuel consumption, and improving turnaround times. Big data is also being used for the predictive maintenance of vessels and for predicting weather patterns. The growing demand for real-time data analysis is expected to drive the growth of the maritime big data market during the forecast period.
Big data is becoming increasingly important in the maritime industry as it provides decision-makers with the ability to monitor, predict, and improve various aspects of maritime operations.This growth is primarily driven by the increasing demand for maritime big data analytics solutions and services from shipowners and operators, port authorities, and other stakeholders involved in maritime operations. In addition, the growing need for real-time vessel tracking and monitoring, predictive maintenance of vessels, and improved safety and security of maritime operations are also fuelling the growth of the maritime big data market.
What is maritime big data?
Big data is a term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy.
The maritime industry is no different when it comes to big data – in fact, the sector presents unique big data challenges due to the vastness of the oceans and the diverse range of stakeholders involved. But with big data comes big opportunity, and maritime big data has the potential to revolutionize the way we manage shipping traffic, protect our oceans and safeguard our maritime heritage.
So what exactly is maritime big data? In short, it’s any digital information that can be used to track maritime activity. This includes everything from GPS tracking data and AIS signals to social media posts and weather reports. By harnessing the power of big data analytics, we can gain insights into ship movement patterns, identify areas of high risk and improve maritime safety overall.
There are already a number of initiatives underway to harness maritime big data for good. The International Maritime Organization (IMO) is working on a global ship reporting system that will use AIS signals to track ship movements in real time. The Global Marine Data Network (GMDN) is another project that aims to create a central repository of marine data that can be accessed by anyone with an interest in the ocean.
What are the benefits of maritime big data?
Maritime big data has the potential to revolutionize the way maritime industries operate. By collecting and analyzing large amounts of data, maritime big data can provide insights that can improve safety, efficiency, and sustainability in the maritime sector.
Some of the potential benefits of maritime big data include:
Improved safety: Maritime big data can be used to identify trends and patterns that can help improve safety in the maritime industry. For example, by analyzing ship traffic data, maritime authorities can identify areas where ship collisions are more likely to occur and take steps to avoid them.
Improved efficiency: Maritime big data can be used to optimize routes and schedules for ships, leading to reduced fuel consumption and emissions. For example, by analyzing weather patterns and sea conditions, ships can be routed around areas of bad weather, saving time and fuel.
Improved sustainability: Maritime big data can be used to reduce the environmental impact of the maritime industry. For example, by analyzing ship traffic data, maritime authorities can identify areas where ships are causing pollution or damaging sensitive marine habitats. By taking steps to avoid these areas, the impact of the maritime industry on the environment can be minimized.
Report Includes
An overview of the global Maritime Big Data market, and related technologies and developments. Analyses of global market trends, with historical data from 2018, 2019, and 2020 estimates and projections of CAGRs through 2029. It also includes breakdowns of the overall Maritime Big Data market along with various segments, and by geographic region. Analysis of the stakeholder value chain in the Maritime Big Data market and comprehensive profiles of leading companies in the industry
Report Scope
The report forecasts the size of the Maritime Big Data market for components from 2022 through 2029
The Executive Summary provides a snapshot of key findings of the report. The introduction chapter includes the research scope, market segmentation, research methodology, and definitions and assumptions. It involves extremely rigorous scientific methods, tools and techniques to estimate the market size. Exhaustive secondary research is being carried out to collect information related to the market, the parent market, and the peer market. Primary research is undertaken to validate the assumptions, findings, and sizing with industry experts and professionals across the value chain of the market. Both top-down and bottom-up approaches are employed to estimate the complete market size.
The chapter on market dynamics includes market drivers, restraints, and opportunities which helps familiarise with market potential and upcoming opportunities. The chapter on key insights includes emerging trends from major countries, the latest technological advancement, regulatory landscape, SWOT analysis, and porters five forces analysis. This chapter provides detailed insights into the market which derives the market trends, changing phase of investments, scope of profit potential, and helps to take appropriate business decisions. The chapter on competitive analysis includes profiling of leading companies in the global market to map the leading companies and their focus of interest in the market.
After deriving the market size from the market size estimation process, the total market has been split into several segments and sub-segments. To complete the overall market engineering process and arrive at the exact statistics for all segments and sub-segments, data triangulation and market breakdown procedures is being used. The data triangulation is carried out by studying various factors and trends from demand and supply perspectives.
Segmentation Analysis:
The major drivers for the growth of this market are the increasing need for real-time data analytics and the growing demand for predictive maintenance in the maritime industry.
The maritime industry is one of the most data-intensive industries in the world. The industry generates large amounts of data from various sources such as weather, ocean, vessels, and port operations. This data can be used to improve the efficiency of maritime operations, reduce costs, and enable better decision-making.
There are three main types of data that are used in maritime industry: positional data, environmental data, and vessel performance data. Positional data includes GPS coordinates, ship location, heading, speed, and draft. Environmental data includes wind speed and direction, wave height and period, current speed and direction, water temperature, air temperature, humidity, barometric pressure, etc. Vessel performance data includes engine performance parameters (such as RPMs), fuel consumption rates, rudder angles, propeller thrust, etc.
Maritime big data can be segmented into four main categories: route planning and optimization, vessel tracking and monitoring, port operations management, and supply chain management.
Route planning and optimization is the process of planning the most efficient route for a vessel to take based on factors such as weather conditions, currents, tides, etc.
Global Maritime Big Data market Competitive Analysis:
Key players in the Global Maritime Big Data market are Maritime International, Inc., Windward, Ltd, our oceans challenge, Big Data Value Associations, IHS Markit Ltd., Eniram Ltd., ABB Ltd, Laros, Inc., Inmarsat plc. and Ericsson Inc. among other players.
*All our reports are customizable as per customer requirements
This study forecasts revenue and volume growth at global, regional, and country levels from 2018 to 2029. The Global Maritime Big Data market is distributed on the basis of below-mentioned segments:
Global Maritime Big Data market, By Type:
The global Maritime Big Data Market is expected to grow from USD 1.2 billion in 2020 to USD 4.0 billion by 2029, at a CAGR of 16.6% during the forecast period. The maritime industry has been rapidly adopting big data and analytics to improve operational efficiency and safety. Big data in the maritime industry includes data generated from vessel tracking, port operations, cargo handling, and other areas. The increasing adoption of the Internet of Things (IoT) and connected devices is providing a large amount of data that can be utilized to improve the efficiency of maritime operations.
The maritime industry is highly regulated, and there is a need for accurate and timely data for decision-making. Big data analytics helps in reducing the risk of accidents, optimizing routes, reducing fuel consumption, and improving turnaround times. Big data is also being used for the predictive maintenance of vessels and for predicting weather patterns. The growing demand for real-time data analysis is expected to drive the growth of the maritime big data market during the forecast period.
Big data is becoming increasingly important in the maritime industry as it provides decision-makers with the ability to monitor, predict, and improve various aspects of maritime operations.This growth is primarily driven by the increasing demand for maritime big data analytics solutions and services from shipowners and operators, port authorities, and other stakeholders involved in maritime operations. In addition, the growing need for real-time vessel tracking and monitoring, predictive maintenance of vessels, and improved safety and security of maritime operations are also fuelling the growth of the maritime big data market.
What is maritime big data?
Big data is a term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy.
The maritime industry is no different when it comes to big data – in fact, the sector presents unique big data challenges due to the vastness of the oceans and the diverse range of stakeholders involved. But with big data comes big opportunity, and maritime big data has the potential to revolutionize the way we manage shipping traffic, protect our oceans and safeguard our maritime heritage.
So what exactly is maritime big data? In short, it’s any digital information that can be used to track maritime activity. This includes everything from GPS tracking data and AIS signals to social media posts and weather reports. By harnessing the power of big data analytics, we can gain insights into ship movement patterns, identify areas of high risk and improve maritime safety overall.
There are already a number of initiatives underway to harness maritime big data for good. The International Maritime Organization (IMO) is working on a global ship reporting system that will use AIS signals to track ship movements in real time. The Global Marine Data Network (GMDN) is another project that aims to create a central repository of marine data that can be accessed by anyone with an interest in the ocean.
What are the benefits of maritime big data?
Maritime big data has the potential to revolutionize the way maritime industries operate. By collecting and analyzing large amounts of data, maritime big data can provide insights that can improve safety, efficiency, and sustainability in the maritime sector.
Some of the potential benefits of maritime big data include:
Improved safety: Maritime big data can be used to identify trends and patterns that can help improve safety in the maritime industry. For example, by analyzing ship traffic data, maritime authorities can identify areas where ship collisions are more likely to occur and take steps to avoid them.
Improved efficiency: Maritime big data can be used to optimize routes and schedules for ships, leading to reduced fuel consumption and emissions. For example, by analyzing weather patterns and sea conditions, ships can be routed around areas of bad weather, saving time and fuel.
Improved sustainability: Maritime big data can be used to reduce the environmental impact of the maritime industry. For example, by analyzing ship traffic data, maritime authorities can identify areas where ships are causing pollution or damaging sensitive marine habitats. By taking steps to avoid these areas, the impact of the maritime industry on the environment can be minimized.
Report Includes
An overview of the global Maritime Big Data market, and related technologies and developments. Analyses of global market trends, with historical data from 2018, 2019, and 2020 estimates and projections of CAGRs through 2029. It also includes breakdowns of the overall Maritime Big Data market along with various segments, and by geographic region. Analysis of the stakeholder value chain in the Maritime Big Data market and comprehensive profiles of leading companies in the industry
Report Scope
The report forecasts the size of the Maritime Big Data market for components from 2022 through 2029
The Executive Summary provides a snapshot of key findings of the report. The introduction chapter includes the research scope, market segmentation, research methodology, and definitions and assumptions. It involves extremely rigorous scientific methods, tools and techniques to estimate the market size. Exhaustive secondary research is being carried out to collect information related to the market, the parent market, and the peer market. Primary research is undertaken to validate the assumptions, findings, and sizing with industry experts and professionals across the value chain of the market. Both top-down and bottom-up approaches are employed to estimate the complete market size.
The chapter on market dynamics includes market drivers, restraints, and opportunities which helps familiarise with market potential and upcoming opportunities. The chapter on key insights includes emerging trends from major countries, the latest technological advancement, regulatory landscape, SWOT analysis, and porters five forces analysis. This chapter provides detailed insights into the market which derives the market trends, changing phase of investments, scope of profit potential, and helps to take appropriate business decisions. The chapter on competitive analysis includes profiling of leading companies in the global market to map the leading companies and their focus of interest in the market.
After deriving the market size from the market size estimation process, the total market has been split into several segments and sub-segments. To complete the overall market engineering process and arrive at the exact statistics for all segments and sub-segments, data triangulation and market breakdown procedures is being used. The data triangulation is carried out by studying various factors and trends from demand and supply perspectives.
Segmentation Analysis:
The major drivers for the growth of this market are the increasing need for real-time data analytics and the growing demand for predictive maintenance in the maritime industry.
The maritime industry is one of the most data-intensive industries in the world. The industry generates large amounts of data from various sources such as weather, ocean, vessels, and port operations. This data can be used to improve the efficiency of maritime operations, reduce costs, and enable better decision-making.
There are three main types of data that are used in maritime industry: positional data, environmental data, and vessel performance data. Positional data includes GPS coordinates, ship location, heading, speed, and draft. Environmental data includes wind speed and direction, wave height and period, current speed and direction, water temperature, air temperature, humidity, barometric pressure, etc. Vessel performance data includes engine performance parameters (such as RPMs), fuel consumption rates, rudder angles, propeller thrust, etc.
Maritime big data can be segmented into four main categories: route planning and optimization, vessel tracking and monitoring, port operations management, and supply chain management.
Route planning and optimization is the process of planning the most efficient route for a vessel to take based on factors such as weather conditions, currents, tides, etc.
Global Maritime Big Data market Competitive Analysis:
Key players in the Global Maritime Big Data market are Maritime International, Inc., Windward, Ltd, our oceans challenge, Big Data Value Associations, IHS Markit Ltd., Eniram Ltd., ABB Ltd, Laros, Inc., Inmarsat plc. and Ericsson Inc. among other players.
*All our reports are customizable as per customer requirements
This study forecasts revenue and volume growth at global, regional, and country levels from 2018 to 2029. The Global Maritime Big Data market is distributed on the basis of below-mentioned segments:
Global Maritime Big Data market, By Type:
- Remote Sensing
- Intelligent Traffic Management
- Energy Management
- Vessel Safety and Security
- Automatic Mode Detection
- Performance Monitoring and Optimization
- Other
- Military
- Civilian
- Others
- North America
- US
- Canada
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Rest of Europe
- Asia-Pacific
- Japan
- China
- India
- Australia
- South Korea
- Rest of Asia-Pacific
- LAMEA
- Brazil
- Saudi Arabia
- UAE
- Rest of LAMEA
MARITIME BIG DATA MARKET STUDY GLOBAL MARKET ANALYSIS, INSIGHTS AND FORECAST, 2022-2029
1. Introduction
- 1.1. Research Scope
- 1.2. Market Segmentation
- 1.3. Research Methodology
- 1.4. Definitions and Assumptions
2. Executive Summary
3. Market Dynamics
- 3.1. Market Drivers
- 3.2. Market Restraints
- 3.3. Market Opportunities
4. Key Insights
- 4.1. Key Emerging Trends – For Major Countries
- 4.2. Latest Technological Advancement
- 4.3. Regulatory Landscape
- 4.4. Industry SWOT Analysis
- 4.5. Porters Five Forces Analysis
5. Global Maritime Big Data Market Analysis (USD Billion), Insights and Forecast, 2018-2029
- 5.1. Key Findings / Summary
- 5.2. Market Analysis, Insights and Forecast – By Segment 1
- 5.2.1. Sub-Segment 1
- 5.2.2. Sub-Segment 2
- 5.3. Market Analysis, Insights and Forecast – By Segment 2
- 5.3.1. Sub-Segment 1
- 5.3.2. Sub-Segment 2
- 5.3.3. Sub-Segment 3
- 5.3.4. Others
- 5.4. Market Analysis, Insights and Forecast – By Segment 3
- 5.4.1. Sub-Segment 1
- 5.4.2. Sub-Segment 2
- 5.4.3. Sub-Segment 3
- 5.4.4. Others
- 5.5. Market Analysis, Insights and Forecast – By Region
- 5.5.1. North America
- 5.5.2. Latin America
- 5.5.3. Europe
- 5.5.4. Asia Pacific
- 5.5.5. Middle East and Africa
6. North America Maritime Big Data Market Analysis (USD Billion), Insights and Forecast, 2018-2029
- 6.1. Key Findings / Summary
- 6.2. Market Analysis, Insights and Forecast – By Segment 1
- 6.2.1. Sub-Segment 1
- 6.2.2. Sub-Segment 2
- 6.3. Market Analysis, Insights and Forecast – By Segment 2
- 6.3.1. Sub-Segment 1
- 6.3.2. Sub-Segment 2
- 6.3.3. Sub-Segment 3
- 6.3.4. Others
- 6.4. Market Analysis, Insights and Forecast – By Segment 3
- 6.4.1. Sub-Segment 1
- 6.4.2. Sub-Segment 2
- 6.4.3. Sub-Segment 3
- 6.4.4. Others
- 6.5. Market Analysis, Insights and Forecast – By Country
- 6.5.1. U.S.
- 6.5.2. Canada
7. Latin America Maritime Big Data Market Analysis (USD Billion), Insights and Forecast, 2018-2029
- 7.1. Key Findings / Summary
- 7.2. Market Analysis, Insights and Forecast – By Segment 1
- 7.2.1. Sub-Segment 1
- 7.2.2. Sub-Segment 2
- 7.3. Market Analysis, Insights and Forecast – By Segment 2
- 7.3.1. Sub-Segment 1
- 7.3.2. Sub-Segment 2
- 7.3.3. Sub-Segment 3
- 7.3.4. Others
- 7.4. Market Analysis, Insights and Forecast – By Segment 3
- 7.4.1. Sub-Segment 1
- 7.4.2. Sub-Segment 2
- 7.4.3. Sub-Segment 3
- 7.4.4. Others
- 7.5. Insights and Forecast – By Country
- 7.5.1. Brazil
- 7.5.2. Mexico
- 7.5.3. Rest of Latin America
8. Europe Maritime Big Data Market Analysis (USD Billion), Insights and Forecast, 2018-2029
- 8.1. Key Findings / Summary
- 8.2. Market Analysis, Insights and Forecast – By Segment 1
- 8.2.1. Sub-Segment 1
- 8.2.2. Sub-Segment 2
- 8.3. Market Analysis, Insights and Forecast – By Segment 2
- 8.3.1. Sub-Segment 1
- 8.3.2. Sub-Segment 2
- 8.3.3. Sub-Segment 3
- 8.3.4. Others
- 8.4. Market Analysis, Insights and Forecast – By Segment 3
- 8.4.1. Sub-Segment 1
- 8.4.2. Sub-Segment 2
- 8.4.3. Sub-Segment 3
- 8.4.4. Others
- 8.5. Market Analysis, Insights and Forecast – By Country
- 8.5.1. UK
- 8.5.2. Germany
- 8.5.3. France
- 8.5.4. Italy
- 8.5.5. Spain
- 8.5.6. Russia
- 8.5.7. Rest of Europe
9. Asia Pacific Maritime Big Data Market Analysis (USD Billion), Insights and Forecast, 2018-2029
- 9.1. Key Findings / Summary
- 9.2. Market Analysis, Insights and Forecast – By Segment 1
- 9.2.1. Sub-Segment 1
- 9.2.2. Sub-Segment 2
- 9.3. Market Analysis, Insights and Forecast – By Segment 2
- 9.3.1. Sub-Segment 1
- 9.3.2. Sub-Segment 2
- 9.3.3. Sub-Segment 3
- 9.3.4. Others
- 9.4. Market Analysis, Insights and Forecast – By Segment 3
- 9.4.1. Sub-Segment 1
- 9.4.2. Sub-Segment 2
- 9.4.3. Sub-Segment 3
- 9.4.4. Others
- 9.5. Market Analysis, Insights and Forecast – By Country
- 9.5.1. China
- 9.5.2. India
- 9.5.3. Japan
- 9.5.4. Australia
- 9.5.5. South East Asia
- 9.5.6. Rest of Asia Pacific
10. Middle East & Africa Maritime Big Data Market Analysis (USD Billion), Insights and Forecast, 2018-2029
- 10.1. Key Findings / Summary
- 10.2. Market Analysis, Insights and Forecast – By Segment 1
- 10.2.1. Sub-Segment 1
- 10.2.2. Sub-Segment 2
- 10.3. Market Analysis, Insights and Forecast – By Segment 2
- 10.3.1. Sub-Segment 1
- 10.3.2. Sub-Segment 2
- 10.3.3. Sub-Segment 3
- 10.3.4. Others
- 10.4. Market Analysis, Insights and Forecast – By Segment 3
- 10.4.1. Sub-Segment 1
- 10.4.2. Sub-Segment 2
- 10.4.3. Sub-Segment 3
- 10.4.4. Others
- 10.5. Market Analysis, Insights and Forecast – By Country
- 10.5.1. GCC
- 10.5.2. South Africa
- 10.5.3. Rest of Middle East & Africa
11. Competitive Analysis
- 11.1. Company Market Share Analysis, 2018
- 11.2. Key Industry Developments
- 11.3. Company Profile
- 11.3.1. Company 1
- 11.3.1.1. Business Overview
- 11.3.1.2. Segment 1 & Service Offering
- 11.3.1.3. Overall Revenue
- 11.3.1.4. Geographic Presence
- 11.3.1.5. Recent Development
- 11.3.2. Company 2
- 11.3.3. Company 3
- 11.3.4. Company 4
- 11.3.5. Company 5
- 11.3.6. Company 6
- 11.3.7. Company 7
- 11.3.8. Company 8
- 11.3.9. Company 9
- 11.3.10. Company 10
- 11.3.11. Company 11
- 11.3.12. Company 12
- 11.3.1. Company 1
Research Process
Data Library Research are conducted by industry experts who offer insight on industry structure, market segmentations technology assessment and competitive landscape (CL), and penetration, as well as on emerging trends. Their analysis is based on primary interviews (~ 80%) and secondary research (~ 20%) as well as years of professional expertise in their respective industries. Adding to this, by analysing historical trends and current market positions, our analysts predict where the market will be headed for the next five years. Furthermore, the varying trends of segment & categories geographically presented are also studied and the estimated based on the primary & secondary research.
In this particular report from the supply side Data Library Research has conducted primary surveys (interviews) with the key level executives (VP, CEO’s, Marketing Director, Business Development Manager and SOFT) of the companies that active & prominent as well as the midsized organization
FIGURE 1: DLR RESEARH PROCESS
Extensive primary research was conducted to gain a deeper insight of the market and industry performance. The analysis is based on both primary and secondary research as well as years of professional expertise in the respective industries.
In addition to analysing current and historical trends, our analysts predict where the market is headed over the next five years.
It varies by segment for these categories geographically presented in the list of market tables. Speaking about this particular report we have conducted primary surveys (interviews) with the key level executives (VP, CEO’s, Marketing Director, Business Development Manager and many more) of the major players active in the market.
Secondary ResearchSecondary research was mainly used to collect and identify information useful for the extensive, technical, market-oriented, and Friend’s study of the Global Extra Neutral Alcohol. It was also used to obtain key information about major players, market classification and segmentation according to the industry trends, geographical markets, and developments related to the market and technology perspectives. For this study, analysts have gathered information from various credible sources, such as annual reports, sec filings, journals, white papers, SOFT presentations, and company web sites.
Market Size EstimationBoth, top-down and bottom-up approaches were used to estimate and validate the size of the Global market and to estimate the size of various other dependent submarkets in the overall Extra Neutral Alcohol. The key players in the market were identified through secondary research and their market contributions in the respective geographies were determined through primary and secondary research.
Forecast Model
