Maritime data screening and improvement plan

We help ensure your data is fit for data-driven decision-making processes, and has the quality required by your stakeholders.

Efficient data collection and effective utilization are major maritime challenges. Introducing new sensors or software can cause disruptions, and adding new use cases or end users may require new data collection intervals.

Efficient maritime data collection with DNV

Our systematic approach for maritime data assessments includes: 

  1. Mapping data collection processes to create a baseline: First, the data collection processes are mapped in terms of on-board data, connectivity, shore data processing and distribution to other stakeholders – creating a baseline.  
  2. Determining gaps – comparing the baseline with your digital ambitions and operational needs: Once the overview of current processes (or baseline) is established, this can be compared with the digital ambitions or short and long-term operational needs to ultimately determine any gaps. 
  3. Creating an improvement plan that closes the gap and fits with your specific ambitions, needs and requirements: After the gaps have been determined, they form the basis for an improvement plan with concrete follow-up actions of the implementation according to your needs. 

Maritime emission data as an example

Emission data represents one example where efficient and effective data collection and processing are essential. New regulatory requirements are leading to increased reporting needs and new use of this data. This is challenging the existing data streams. Fuel consumption is a key input factor and is monitored, collected and stored by different means both manually and digitally. The data is processed and shared differently by different ships and companies, often allowing room for errors. The traditional way of collecting the data may not meet the new requirements for frequency and accuracy in stakeholder reporting. 

Benefits from using DNV to streamline your maritime data collection:

  • Reduce cost of data management
  • Confidence in your data, ensuring trust in decisions made 
  • Improved data collection processes, reducing time and effort spent in collection and presentation of data
  • Making sure your data is fit for purpose and reusable, unlocking new potential