GDS Data Architecture

• About

Data Sharing Principles

• Overview
• 1. Treat data as an asset
• 2. Federate first
• 3. Reuse sharing solutions
• 4. Support automation
• 5. Design for all data stakeholders
• 6. Use common standards for sharing
• 7. Share data transparently
• 8. Share data lawfully and ethically
• 9. Secure shared data proportionately

Capability Model

• Overview
• Channels
• Infrastructure
• Data Access
• Services
• Governance
• Security
• Trust

4. Support automation

Directional principle

Statement

We deliver data sharing activities via responsibly managed automated processes as much as possible.

Why does this matter?

In a fast-paced digital world, there is more pressure than ever to increase the efficiency at which we make use of data. Automation-allowing machines to perform tasks with minimal human input-is key to making use of data at higher volumes and speeds. To help us distribute the value of data across the public sector, we should automate data sharing processes systematically and responsibly.

Automation of data sharing processes allows us to scale the value of data through easier discovery and interpretation, and improved access and linking. Performed correctly, increasing the proportion of automated tasks within the data lifecycle increases the time skilled people have to derive value from data. Moreover, as task automation is developed into advanced process automation with the use of emerging technologies like AI agents, improving the legibility of data to “machines users” now will allow us to experiment and get the best out of these technologies sooner.

How do we do this?

Overall, we should follow government guidance on automated decision-making, machine-readability and ethical AI practices when automating data sharing processes.

Data providers should

• Adhere to the Framework for Automated Decision-Making where data sharing decisions are automated or assisted by automation.
• Ensure that data and metadata are written and stored in machine-readable formats for sharing.
• Ensure API connectors adhere to API technical and data standards.
• Continuously monitor and review automated data sharing processes to mitigate risks and ensure good outcomes for data consumers.
• Apply human oversight and governance where automation introduces risk.
• Explore novel and scalable automated solutions for data sharing-such as Model Context Protocol (MCP)-with data sharing enablers.

Data consumers should

• Be aware of how automation and artificial intelligence is used to provide access to data.
• Use the Data and AI Ethics Framework to identify and mitigate the risks associated with using data that has been provided with the assistance of AI.

Data sharing enablers should

• Identify tasks across data-sharing processes where suitable, and where clear benefits can be demonstrated.
• Adhere to the Framework for Automated Decision-Making where data sharing decisions are automated or assisted by automation.
• Adhere to the Data and AI Ethics Framework where artificial intelligence is used in data sharing processes.
• Continuously monitor and review automated data sharing processes to mitigate risks and ensure good outcomes for data consumers.
• Explore novel and scalable automated solutions for data sharing-such as Model Context Protocol (MCP)-with data providers.