Advances in data capture has resulted in the proliferation in the types of reporting platforms available for the fishing industry to report to the Government and third parties resulting in duplication of processes, collection of multiple data sets, and a requirement for fishing boats to operate multiple systems to meet their reporting requirements. It is estimated for licensing systems alone around four times the necessary spend to create a national system has been expended or will be expended within the next few years. Coupled with an increased drive for fishers to participate in traceability schemes a new data architecture is required to enable access to data that links currently disparate data sets and in particular creating a unique event linking identifier.
To support the changing needs there is a need to develop a design for a fisheries data architecture which:
• allows for the linkage or integration of currently disparate data
• allows for multiple methods of data transfer
• is adaptable to changing needs allowing for future expansion and changes to data sets and collection methodology
• supports sharing of data with third parties in real time.
While single integrated box solutions initially appear an attractive solution for solving this problem, they limit the ability to adapt quickly to changing needs and reduce long term market competition. There is a need to develop a data architecture that allows for future adaptation and provides industry flexibility to choose equipment they employ on their boats. The key aspect is linking a wide range of data, collected from different sources to a single event and sharing this data quickly across different platforms and for different purposes.
Consistent with the recommendation from “accelerating precision agriculture to decision agriculture” report it offers an opportunity to demonstrate benefits of digital initiatives.
1. Linked data – Data sets are inherently linked in a way that allows ease of use.
2. Modern data sharing – Data sets should be exposed to external users through an easy to maintain and minimal touch solution such as application programming interfaces (APIs).
3. Ensure data integrity – Data is clean and validated with minimal errors. Data is stored according to predefined elements maintained in an agency or industry wide taxonomy.
4. Standardised data collection – Data is received in a uniform approach. Care is taken to not duplicate data where it is unnecessary to do so.
5. System capability fit for purpose – Implemented systems directly support various business outcomes of fisheries stakeholders.