MIM3: Exchanging Data
Introduction
Data is the lifeblood of urban innovation. Many cities and communities are now leveraging data to make better decisions about prioritising scarce public funding, improving service delivery to their citizens, and improving the overall quality of life in the city. Increasingly relevant data is not only sourced and shared across different city departments but also exchanged with external organisations to improve local innovation capacity and to access critical information not available in-house.
Over the past decade or more, many cities and communities used open data initiatives or ad-hoc data sharing agreements to advance data use and sharing across their organisation and with relevant stakeholders. However, they find these approaches are no longer sufficient. Publishing data openly on a public data portal does not suit the many types of sensitive data (e.g., commercial or personal data) cities and communities are now working with. Bespoke bilateral data sharing agreements are cumbersome to agree, set up and maintain, and do not scale well. As the diversity and complexity of data collaborations grow, cities and communities need to find more differentiated ways of data sharing.
Cities and communities are now exploring new ways of managing their local data ecosystem. This includes ensuring that:
data is discoverable in a local data ecosystem, no matter what parties the data comes from or what systems it resides on
data is accessible in a local data ecosystem to all parties who are entitled to access the data.
data is adequately permissioned in a local data ecosystem, so that parties using the data are fully aware of what they can or cannot do with it.
data and its exchange can be trusted in a local data ecosystem. This includes both trust in the data provided as part of a data exchange and trust in the parties participating in the exchange.
The MIM3 working group aims to help cities and communities create common guidelines that make data sharing and collaboration more effective in a local data ecosystem.
It tackles the challenges of how data can be stewarded and used across diverse systems of different organisations into a common data ecosystem that enables data providers and users to exchange data in a trusted way with confidence and derive value from it. It also aims to ensure that interactions related to data exchange in different local data ecosystems of other cities and communities are interoperable.
Glossary of terms
Data
Information encoded in a structured or unstructured format, suitable for processing, analysis, or storage
Data source
The origin from which data is generated or collected, such as devices, systems, applications, or external entities
Data asset
A distinct unit of data made available for use, which may take the form of a static dataset, a real-time data stream, or any other structured or unstructured data format.
Dataset
A collection of related data, typically organised in a structured format such as tables, files, or records.
Data stream
A continuous flow of data records generated over time, typically delivered in real-time or near real-time through a protocol or API, enabling ongoing processing or analysis as data is received.
Data provider
An entity that generates, collects, or curates data and makes it available for use by others.
Data user
An individual or system that accesses and utilises data for analysis, decision-making, or service delivery.
Data ecosystem
A coordinated environment of stakeholders, technologies, policies, and processes that enable the collection, sharing, management, and use of data across organisational or sectoral boundaries.
Data ecosystem orchestrator
The organisation or entity responsible for governing, coordinating, and maintaining the overall functioning of the data ecosystem.
Data rights owner
The legal or contractual holder of rights over the data. This entity determines how the data can be used, shared, or monetised, and is ultimately responsible for its governance and compliance.
Data ecosystem administrator
An entity or role responsible for the operational management of a data ecosystem, including user access control, system configuration, data catalogue maintenance, monitoring, and enforcement of governance policies.
Objectives
To enable the setting up and management of effective and trustworthy data-sharing ecosystems, ensuring that provided data assets can be discovered by data users and made available under the asserted terms of the data providers.
To ensure that data-sharing ecosystems that comply with this MIM have a basic level of interoperability with each other and are based on clear governance rules that are transparent to all participants.
Capabilities and Requirements
1
Define governance rules for the data-sharing ecosystem
Guiding ecosystem participants by setting the overall contractual framework and policies for compliant data sharing.
2
Define terms and conditions for data sharing
Empowering data providers to share data under their terms (self-determination) and providing legal certainty to data users for further data use
3
Validate compliance with data sharing terms and conditions
Giving trust to data providers and users that the supply and use of data follows agreed-upon terms and conditions.
4
Discover available data assets
Allowing data users to find available data assets in a data ecosystem across different providers based on their needs.
5
Discover ecosystem participants
Provide additional trust for data exchange where the identity of the data provider and users matter.
6
Agree on data exchange
Provide the means to agree on a data exchange between the data provider and the user so that access to otherwise restricted data can be enabled.
7
Request new data that is currently not available
Allow data users who cannot find the data they need in a data ecosystem to express their demand for potential future supply.
Capability C1: Define governance rules for the data sharing ecosystem
RC1.1: The orchestrator of a data ecosystem should be able to define a governance model (e.g. rulebook) that outlines membership rules, the roles, responsibilities and obligations of ecosystem participants and overall principles that govern the data exchange between them.
RC1.2: Members who participate in a data ecosystem should be aware of its governance model and any changes made to it.
RC1.3: The governance model should be easily comprehensible by all ecosystem participants.
Capability C2: Define terms and conditions for data sharing.
RC2.1: Data providers are free to define terms and conditions under which they want their data to be exchanged. (Before sharing data assets, data providers should ensure they have adequate permission from data owners.)
RC2.2: Terms and conditions for a data exchange should be described clearly and unambiguously so that they are easily understandable for data users.
RC2.3: Terms and conditions for a data exchange should be expressed through data licenses/data sharing agreements based on well-defined templates or standards.
RC2.4: Terms and conditions should be in line with the overall governance model of the data ecosystem and not conflict with it. (Note: A data provider can decide not to participate in a data ecosystem if the underlying governance model is too restrictive or permissive.)
Capability 3: Validate compliance with data sharing terms and conditions
RC3.1: The orchestrator of a data ecosystem must be able to ascertain compliance of its participants with the underlying governance model.
RC3.2: Data providers should be able to ascertain that the use of data by a data user complies with previously agreed-upon terms and conditions.
RC3.3: Data users should be able to ascertain that the data provider's supply of data follows previously agreed-upon terms and conditions.
Capability 4: Discover available data assets
RC4.1: Data users should be able to discover what data is available in a data sharing ecosystem without prior knowledge of it.
RC4.2: Data assets should be adequately described with metadata so that data users can make reliable decisions about their further intended use.
RC4.3: Metadata used to describe data assets should be based on well-defined, easy-to-understand description formats that are both human—and machine-readable.
Capability 5: Discover ecosystem participants
RC5.1: Ecosystem participants (e.g., data users and providers) should be able to discover each other within a data-sharing ecosystem
RC5.2: Sufficient metadata about each ecosystem participant should be available to ascertain their trustworthiness relevant for data exchange.
RC5.3: Data providers should be able to reliably identify data users who want to access their data assets and verify their metadata before granting access to their data.
RC5.4: Data users should be able to identify data providers who share data in a data ecosystem before accessing or using data.
RC5.5: Ecosystem participants may also choose to remain anonymous if the governance model of the data-sharing ecosystem allows for it. In this case, RC5.1 to RC5.4 can be ignored.
Capability 6: Agree on data exchange
RC6.1: Data users and providers should be able to agree to a data exchange and the terms and conditions associated with it (e.g. acceptance of terms or the legal signature of the agreement, negotiations).
RC6.2: Data users and providers should be able to terminate a previously agreed-upon data exchange in accordance with the previously agreed-upon terms and conditions associated with the data exchange
RC6.3: Where possible, electronic means for agreement and termination of a data exchange may be provided.
Capability 7: Request new data currently not available
RC7.1: Data users should be able to express their interest in data assets currently unavailable in the data sharing ecosystem.
RC7.2: Data providers should be able to obtain requests made by data users for new data assets.
Mechanisms
TBD
Specifications
TBD
Interoperability Guidance
TBD
Conformance and compliance testing
TBD
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