GDS Data Architecture
Data Sharing Principles
- Overview
- 1. Treat data as an asset
- 2. Federate first
- 3. Prepare for Once Only
- 4. Reuse sharing solutions
- 5. Support automation
- 6. Design for all data stakeholders
- 7. Use common standards for sharing
- 8. Share data transparently
- 9. Share data lawfully and ethically
- 10. Secure shared data proportionately
Capability Model
5. Support automation
Statement
We support automation across data sharing processes, including discovery, access, linking and interpretation.
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 in tandem with other good data management practice 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?
Relevant work in progress:
- Data Sharing Sandbox
Overall, we should follow government guidance on automated decision-making, machine-readability and ethical AI practices when automating data sharing processes.