The maritime industry relies extensively on data to optimise operations, from engine performance monitoring to tracking shipping routes and cargo delivery. As advanced sensors and Internet of Things (IoT) devices proliferate across vessels and ports, the amount of data available to leverage continues growing exponentially. However, collecting large datasets alone does not necessarily translate into extractable value or competitive advantage. Companies must have the infrastructure and capabilities to progress collected data through multiple stages. Together known as the data value cycle, to derive actionable insights.
The first stage in the maritime data value cycle is data acquisition. This refers to identifying valuable data streams natively available on vessels through onboard sensors and externally through satellites, weather sensors and market trackers and setting up methods to record them. For example, a shipping firm may tap into a ship’s AIS transceiver, radar, LIDAR and fuel monitoring systems to collect positional, obstacle detection, weather readings and engine performance data while tracking freight market rates and container positioning data from ports. The acquisition stage is foundational – while companies may later augment a dataset, they can only extract insights from information successfully captured initially.
After collecting datasets via data acquisition processes, the following requirement is scalable and secure data storage. Vessels often record vast granular IoT telemetry data streams from multiple onboard systems over months-long shipping routes. The storage architectures must facilitate cost-effective data warehousing with little performance degradation and accountability via access controls, activity logs and redundancies that protect against data loss. Most firms leverage hybrid cloud infrastructure, taking advantage of affordability and retaining control over proprietary data assets unsuitable for third-party, public cloud hosting.
With the raw data warehoused, processing mechanisms clean, validate, sort and prepare datasets for analysis. Domain teams define relevant schemas that enrich data by adding headers, units and descriptors that provide essential context. Specialised algorithms may also identify and adjust potentially skewed readings from a subset of older, less-calibrated sensors. The processing stage optimises data for analysis. Here outdated legacy formats transform into analysis-ready structured sets.
The data analysis component lies at the core of converting processed data streams into extractable business value. Data scientists and analysts apply statistical modelling, machine learning algorithms and AI-powered predictive analytics to identify correlations, trends and patterns of interest from the collated vessel telemetry, cargo delivery data and other datasets. The insights derived at this stage are directly applicable to enhancing maritime operations. Improved predictive maintenance allows ship engineers to repair or replace components likely to fail soon. Software analytics that alert crew about ideal operational parameters for minimal fuel consumption enable noteworthy energy savings over entire voyages. The analysis also tracks indicators of unforeseen shipping delays or emerging inventory overstock or understock scenarios.
Firms must contextualise the findings into easily digestible formats using descriptive visualisations, performance dashboards, interactive journey data mapping. That is more tailored to different maritime business roles to facilitate productive discussion and planning around the operational insights uncovered through data analytics. Contextualised data insights enable diverse stakeholders, from cargo load planners to finance directors, to quickly understand critical trends and performance drivers without combing through technical data science models or query results.
The final component, closing the data value cycle loop, focuses the data journey towards action. Contextualised vessel performance reports, cargo and inventory indicators and other shipping analytics feed into planning meetings, management decisions and operational changes that improve efficiency, resilience against disruptions and sustainability. Data tracking arrival times may reveal correlating weather conditions causing extensive delays so ships reroute journeys. Analysing energy consumption data identifies modifications maintaining engine peak efficiency. The action phase activates data’s progress from passive digital records into active drivers of greater maritime performance.
While the six phases of the data value cycle unlock vital intelligence for enhanced decision-making, they also have implications for data ownership and usage rights. As multiple external third parties may participate in cloud storage, analytics and data sharing, clarifying data control becomes essential, especially regarding proprietary ingested datasets. Can a port leverage vessel fuel telemetry for its own broader analytics? If so, does the shipping company retain any value from enabling such usage.
As maritime operations continue adopting data-centric frameworks to accelerate toward smart mega-ships and ports, optimising data’s value cycle necessitates parallel innovations in agreeing and assigning economic value across involved data stakeholders fairly through innovative contracting frameworks or usage fee structures – vital components beyond just technology alone.
For a more in depth understanding of the topics covered in this article, refer to our latest report titled ‘Common Interests; How the maritime industry can share data, collaborate with trust, and build a mutually beneficial digital ecosystem.’ This comprehensive guide benchmarks shipping’s progress on using digital solutions to collaborate on decarbonisation goals and shows how industry frontrunners are breaking down the technical, legal, financial and cultural barriers.