Big Data Platform for Smart Aquaculture

Resources

Checklist for establishing a Smart Aquaculture Big Data Platform

This checklist is a practical tool for institutions and partners that plan to establish or strengthen a Smart Aquaculture Big Data Platform. It guides users through the main preparation steps, including defining the platform scope, mapping stakeholders, assessing institutional and technical capacity, developing a data management plan, preparing a pilot, collecting user feedback, and reviewing governance arrangements before scaling.

FAQs

National governments play a critical enabling role in the establishment of Big Data Platforms. Key actions with them include:

Legal and regulatory framework: Enact or adapt legislation that addresses data privacy, ownership, and sharing for aquaculture. Define the legal status of digitally recorded farm and farmers' data and disease reports.
Institutional mandates: Designate a lead institution with a clear mandate to host and operate the platform and establish inter-agency coordination mechanisms.
National farm registry: Ensure that a digital, spatially referenced farm registry exists and can be integrated with the platform. This is foundational for all other platform functions. A lot of countries are making progress in digitizing their land-registries, and without a clear mandate to use that data a Big Data Platform will struggle.
Budget allocation: Include platform operation and maintenance costs in national aquaculture programme budgets. These costs easily amount to 50% of the total development costs over a period of 5 years.
Incentives for data sharing: Consider regulatory incentives or services that encourage farmers to share data with the platform, such as preferential licensing, access to subsidized inputs, or extension services. Even programs where farmers can keep sensors and / or mobile phones after a deprecation period can be considered.
Coordination with digital government initiatives: Link the platform to national digital identity systems, land registries, and health data infrastructure where applicable. Underscore the importance of farm inventories for effective early warning systems, post disaster recovery mechanisms (including insurance payouts based un UNDP's Post-Disaster Needs Assessment (PDNA) tool and loans to provide rapid liquidity).

International organizations, bilateral donors, and development banks can support the establishment and strengthening of Big Data Platforms through:

Technical assistance: Provision of expertise in platform design, AI/ML model development, geospatial services, and data governance.
Funding: Project grants and loans for platform infrastructure, capacity building, and pilot programmes.
South-South cooperation: Facilitating exchanges between countries with operational platforms and countries in earlier stages of development.
Standards development: Support for harmonization of data standards across national platforms to enable regional and global data sharing. This includes standards for data used for regulatory & legal Reform: Providing technical assistance to align domestic laws with international standards, such as the Anti-Money Laundering (AML/CFT) frameworks to improve financial integrity or specific operational guidelines for AI auditing, risk assessment, and incident reporting (e.g., based on guidelines for the EU AI Act).
Evaluation and learning: Independent assessment of platform performance and impact to generate evidence for scale-up through e.g. participatory Monitoring and Evaluation (M&E).

The use of AI/ML and geospatial technologies in aquaculture monitoring raises important questions about equity, safety, and data sovereignty. International stakeholders can help address these by:

Promoting open-source tools and publicly available data (such as the Copernicus programme) to reduce dependence on proprietary systems.
Supporting the adoption of AI governance frameworks such as the NIST AI Risk Management Framework, ISO 42001, and the EU AI Act, which help ensure that AI/ML methods used in national platforms are validated, transparent, and accountable.
Utilizing frameworks like the UNESCO Recommendation on the Ethics of AI or the OECD AI Principles to provide clear, actionable, and non-binding "soft law" that helps countries establish local AI governance.
Targeted Capacity-Building Programs: Implementing training initiatives such as the ITU's AI Skills Coalition and the FAIR Forward AI for Policymakers Training, focusing on AI literacy for public servants, regulators, and civil society.
Ensuring that AI/ML models are trained on data that is representative of local conditions, rather than transferred directly from other contexts without validation.
Advocating for data sovereignty: national governments should retain ownership of aggregated aquaculture data and have the right to govern its use in international research and commercial applications.
Supporting equitable access: ensuring that platform services are accessible to small-scale and extensive producers, not only commercial intensive farms, through appropriate interface design, connectivity support, and extension services.
Support with resources for development: support the customization of existing services to new contexts in order to make the service transferable to other contexts, e.g. through South-South collaboration initiatives.