Big Data Analytics is the science of uncovering insights, patterns and correlations and discovering meaningful information from mounds of data that exists in various forms, structured and un-structured, using techniques from different scientific fields such as statistics, data modelling, machine learning, mathematics, computer science, neuroscience, visualisations, business intelligence and many others. It could be characterised as the process of turning data into insights and insights into meaningful actions, enhancing in this way the decision making capabilities of a company and as a result reducing costs and improving upon performance.
Data science or data art could be characterized as an attempt to shift away from the traditional empirical-based reasoning to a formal, scientific, data-driven way of thinking and operational tactic. The research firm IDC’s estimate of the size of big data market for last year, 2016, was $136B and this is expected to grow exponentially in the coming years with more and more companies deploying data science related projects and new startups that offer products based on data rising. Data science has already proved itself and analytics are extensively applied across many different sectors and industries such as retail, banking, financial services, security, telecom, healthcare, shipping and many others.
Data analytics is driving incremental value for ship owners and charters by influencing decision across different business, tactical, operational as well as strategic functions of the marine industry. As more and more data are collected, stored and analysed, shipping companies are beginning to appreciate and thus aim to utilise the value of this data in order to make informed decisions, managing in this way the company in a better and more efficient way. The shipping industry is inevitably undergoing a massive but beneficial change driven by Big Data capabilities across different areas:
- Fuel consumption: Combination of the appropriate sensors and optimisation techniques can be applied in order to understand under what conditions a given ship has optimised fuel consumption at maximum performance. This can be translated into huge savings.
- Route and supply-chain optimisation: Advanced analytics and optimisation techniques can be applied on the data related to the routes followed by the ships in order to derive an optimal strategy related to the order of the different destinations across different routes to be followed.
- Operational efficiency: Optimize marine operations, manage staff time efficiently and identify cost savings through comprehensive maritime data that include information about ships, ownership, builder, company, ports and route details.
- Threat management: Identify companies that pose credit and security risks, with extensive ship, company and Automatic Identification System (AIS) data.
- Market size and competition: Understand the world fleet, ship and ship ownership information, as well as new markets.
- Maintenance prediction: Through sensors on the ships combined with advanced predictive analytics techniques can be applied in order to identify which areas of the ship need priority in terms of maintenance. This will ensure that maintenance is considered at the optimum moment, preventing delays, increasing efficiency and reducing the time required for a ship to be in maintenance mode.
- Cargo tracking: A big problem in shipping industry is that many shipping containers are lost every year due to different factors. This costs a lot of amount of money and time for investigation. A solution is to apply data analytics on a datasets related to these lost containers and derive some special characteristics or features about those containers and their environment. This might help to reduce similar problems in the future and thus avoiding extra costs due to losses.
- Regulatory compliance: Use ship and ownership/registration data to determine any connection to sanctioned countries or countries posing legal or financial risk.