Home > Bouwstenen > Data Delen > Data spaces > Quickstart guide > Phase 2: Design and create
Business topics cover the overall context, roles and responsibilities, fee structures and branding.
Examples of topics are compensation mechanisms, branding, and value proposition.
Consider the all relevant business, legal and operational topics and determine which are relevant to the specific context of your AI data space and make decisions on how your design will cater to the choices made. The resources provide a comprehensive overview of topics relevant for an AI data space. In implementing the AI data space make and formalise agreements with all involved stakeholders on each topic.
Legal topics cover the impact of regulation, governance structure and mutual responsibilities between stakeholders.
Examples of topics are liability, governance structure and penalties.
Consider the all relevant business, legal and operational topics and determine which are relevant to the specific context of your AI data space and make decisions on how your design will cater to the choices made. The resources provide a comprehensive overview of topics relevant for an AI data space. In implementing the AI data space make and formalise agreements with all involved stakeholders on each topic.
* = Relevant EU regulation might include GDPR, AI Act, Data Governance Act, Data Act, Digital Services Act, eIDAS Regulation, E-Privacy Regulation, Digital Markets Act, Database Directive, Cyber Security Act
Operational topics cover how processes and (centralised) services are operated in the future ecosystem.
Examples of topics are monitoring & reporting, version management and complaint & dispute management.
Consider the all relevant business, legal and operational topics and determine which are relevant to the specific context of your AI data space and make decisions on how your design will cater to the choices made. The resources provide a comprehensive overview of topics relevant for an AI data space. In implementing the AI data space make and formalise agreements with all involved stakeholders on each topic.
Functional topics describe the functions and services that will be offered to facilitate the goal use case.
Examples of topics are functional components of AI systems and customer control.
There are different functional implementations in an AI data space. The resources give examples of functional implementations for your AI data space. These implementations could be necessary for your AI applications. Towards a federation of AI data spaces and DSC UCIG give examples of functional implementations. Analyse the reference implementations in the NL AIC GitLab and IDSA GitHub to determine if they can be applied to your situation or make use of existing building blocks to improve interoperability and future scalability.
Technical topics describe the technical requirements to provide and control the functional components.
Examples of topics are protocols/standards, message formats, audit trails, the use of Privacy Enhancing Technologies, semantic agreements and AI technologies.
Designing your technical implementation based on existing architecture and reference implementations allows for an efficient and scalable implementation. Therefore analyse existing resources and determine whether they can be re-used or (partially) applied to your AI data space context. Build, test and validate the technical specifications and iterate on design based on practical learnings.