GDS Data Architecture

• About

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

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

8. Share data transparently

Continuity principle

Statement

We are as transparent as possible about how and why data is shared.

Why does this matter?

Being transparent about how and why data is shared matters to government for one reason above all: it is a necessity for fostering trust in our data-sharing initiatives. Only by understanding the reasons behind data sharing, the safeguards in place and the extent of sharing can people feel confident that their information is being handled responsibly. A guarantee of transparency reassures the public that their privacy is respected and demonstrates a commitment to ethical governance (Principle 9).

From a policy perspective, transparency is essential for understanding if data sharing initiatives are having the desired effect and addressing problems early. By making the elements and processes within of a data sharing system observable as widely as possible, we stand a better chance of addressing risks and failures quickly and avoiding unnecessary harms.

Data sharing transparency must also go hand-in-hand with a proportionate approach to security (Principle 10). Striking a balance between openness and confidentiality will be the art of a successful data sharing adoption campaign.

How do we do this?

Overall, we should follow and expand upon the government’s guidance on working in the open and publishing service data detailed in the Technology Code of Practice (Point 3) and the Service Standard (Point 10 and Point 12).

Data providers should

• Document what data is being shared, with whom, and for what purposes.
• Make data dictionaries and metadata available to describe the structure, meaning, and provenance of shared data.
• Publish registers or records of approved access requests from data consumers.
• Provide open channels for feedback, questions, or concerns about data sharing activities, and respond constructively to such engagement.
• Participate in forums, workshops, or public consultations to discuss data sharing practices and listen to community perspectives.

Data consumers should

• Document sources, methods, and rationale for obtaining shared data.
• Engage in regular communication with data providers and stakeholders, providing updates on how shared data is being utilised.
• Participate in public forums, workshops, or consultations to discuss data usage practices and receive feedback from the wider community.
• Respond openly to questions, concerns, or requests for clarification regarding their use of shared data.

Data sharing enablers should

• Identify metrics to indicate how well a data sharing service is solving the problem it’s meant to solve, and track performance against them.
• Publish data about the performance and usage of data sharing services.
• Publish technical documentation, including code repositories, data models, and infrastructure diagrams, to allow others to understand and review their work.
• Disclose the criteria and rationale used for selecting technologies, platforms, and standards involved in data sharing initiatives.
• Report openly on challenges faced, lessons learned, and the outcomes of their activities, including both successes and setbacks.
• Respond constructively to questions or requests for clarification, and proactively address concerns raised by stakeholders or community members.