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Building Data Spaces that work: A Stakeholder Perspective

Prof.dr. Bige Tunçer - Professor

Dr. Dujuan Yang - Assistant Professor

Information System in the Built Environment, Eindhoven University of Technology

The CitzenCity is project has received funding from the European Union’s Horizon Europe Research and Innovation Programme

CitizenCity

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Why a Data Space?

CitizenCity

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

The Rulebook: Before any data flows, participants have to agree on a legal contract, which is the Institutional part of the system.

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

The Rulebook: Before any data flows, participants have to agree on a legal contract, which is the Institutional part of the system.

Digital Sovereignty: This is a social value. It’s the human desire to share data but still act as data holders.

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What is a Data Space?

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Ref: IDSA Data Space Connector Report

The Rulebook: Before any data flows, participants have to agree on a legal contract, which is the Institutional part of the system.

Digital Sovereignty: This is a social value. It’s the human desire to share data but still act as data holders.

Create value by solving problems

CitizenCity

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What is a Data Space?

Socio-technical system

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Ref: IDSA Data Space Connector Report

The Rulebook: Before any data flows, participants have to agree on a legal contract, which is the Institutional part of the system.

Digital Sovereignty: This is a social value. It’s the human desire to share data but still act as data holders.

Create value by solving problems

CitizenCity

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CitizenCity: A Data Space in Practice

CitizenCity builds 3 different use cases

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Eindhoven 🇳🇱	Oulu	Vitoria-Gasteiz 🇪🇸
⚡ Energy Transition	🤖 Liveability & Behaviour	🚗 Mobility & Safety
Positive Energy District (PED)	Cross-domain data space	Winter road management
Municipal data platform	AI interaction (city + citizens)	Data-driven decision support
Multi-department collaboration	Real-time urban data	Services for planners & citizens

Different domains. Different cities.�Same challenge: stakeholders must collaborate to create value through dataspace

CitizenCity

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WP2: Stakeholder engagement model

Common Engagement Model

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A structured methodology bridging internal consortium coordination and external scalability, enabling public entities, municipalities, and EU-level stakeholders to replicate or extend CitizenCity's approach within the DS4SSCC objective.

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Stakeholder Complexity

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CitizenCity

Let me show you what a real stakeholder landscape looks like in one of our use cases.

Here, we tried to identify all stakeholders involved in a data-driven service. We have governments, police, mobility labs, data providers, companies, and also citizens.

And they are not isolated—there are many interactions between them.

Data flows, responsibilities, and decisions are distributed across these actors. For example, one stakeholder provides data, another processes it, and another uses it for decision-making.

So the system is not just complex—it is interdependent. If we try to design a data space without understanding this structure, we risk building something that does not fit how these stakeholders actually work together.

So the next step is to bring structure into this complexity.

We organise stakeholders based on their role and level of involvement. At the center, we have the core team—those directly building and operating the system. Then, the involved stakeholders—they actively contribute, but are not leading.

And finally, the informed stakeholders—they are affected and need to be engaged, but less intensively. The key point is: not all stakeholders are equal—and treating them the same is a mistake.

Once we understand who the stakeholders are and how they are positioned, the next question becomes: what value does this system create for them?

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Why does value matters?

Data spaces succeed when technology, stakeholders, and value align

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Stakeholders

Public actors
Private actors
Citizens
Communities

Technology

Interoperability
Governance infrastructure
Rulebook

Value

Social value
Business value

A data space does not create value automatically; value emerges when technical design, stakeholder roles, and incentives are aligned

CitizenCity

Before we talk about value, it is important to recognise that data spaces operate in complex stakeholder environments. In urban contexts, different actors are involved, including public authorities, service providers, technology partners, and citizens. These actors do not always have the same goals, responsibilities, or incentives. That means the success of a data space is not only a matter of technical interoperability. It also depends on whether stakeholders can coordinate, trust each other, and work around a shared purpose. So when we discuss whether a data space works, we need to ask not only whether the infrastructure functions, but also whether the stakeholder configuration supports value creation. This brings us to the question of value. A data space does not create value automatically just because data can be shared. Value emerges when technical design, stakeholder roles, and incentives are aligned around concrete problems and meaningful services. In other words, a data space is only useful if it helps solve problems in ways that are relevant for both people and organisations. This is why value assessment matters. It helps us move beyond technical capability and ask whether the data space is actually producing outcomes that are socially meaningful and institutionally sustainable

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Social value and business value – both matter

The two dimensions both are needed for long-term legitimacy and sustainability of data services, while reflect different and complementary logics of value creation

What do we mean by “value”?

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Business

value

Social

value

Social value — value as experienced by people

Experienced benefits for citizens and communities
Trust, fairness, quality of life, acceptance

Business value — value that enables organizations to act and sustain

Organizational and system capacity to deliver and sustain services
Governance, investment, ecosystem, scalability

Social and business value are interdependent, but not identical.

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From literature and co-creation workshop to an indicator set

Literature:

Social Impact Assessmen (SIA), Social Value Assessment (SVA), public value

Practice: European smart city projects (such as, Triangulum, SynchroniCity, Ruggedised)

Co-creation

Workshop-based refinement with consortium partners and relevant stakeholders

So the indicator set is not purely theoretical and not purely case-specific; it is designed as a reusable, practice-informed assessment logic.

Where do the indicators come from?

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Indicator clusters

Five clusters

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Value outcomes

Behavioural responses

Value proxies

Citizen experience, acceptance, and adoption

How people experience and respond to data-driven services

Participation, inclusion, and procedural fairness

How decisions are made and whose voices are included

Economic & financial effects

How households, firms, and markets are affected

Ecosystem, institutional capacity, and scaling

Whether solutions can be sustained and replicated

Governance & organisational legitimacy

How decision-makers assess value and readiness to act

Social value

indicators

Business value

indicators

CitizenCity

One important refinement in this work is that we do not treat all indicators as if they play the same role. Some indicators capture actual value outcomes. Others reflect behavioural response, such as whether people use a service or whether organisations collaborate. A third group represents pre-conditions or proxies for value creation, such as awareness or enabling arrangements. This distinction is useful because it avoids double counting and supports clearer causal reasoning. By distinguishing these roles, we can interpret the indicators more carefully and avoid treating all evidence as equivalent. To make the framework more usable, the indicators are grouped into five clusters. On the social value side, we focus on citizen experience, acceptance, and adoption, and also on participation, inclusion, and procedural fairness. On the business value side, we focus on economic and financial effects, ecosystem and institutional capacity for scaling, and governance and organisational legitimacy. These clusters help us move from a long list of possible indicators to a clearer structure that reflects the main dimensions of value creation in data-driven urban services.

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Indicator framework: Social value

Citizen experience, acceptance, and adoption

Examples

Perceived quality of life impact
Perceived usefulness of services
Trust in data handling, privacy, and security
Active end-users / awareness

Participation, inclusion, and procedural fairness

Examples:

Meaningful citizen participation
Complaint and feedback mechanism

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CitizenCity

Let me now make this more concrete, starting with social value. The first cluster focuses on citizen experience, acceptance, and adoption. This includes indicators such as perceived quality of life impact, perceived usefulness of services, trust in data handling, privacy and security, active use of services, and awareness among the target population. Together, these indicators help us understand whether services are visible, used, trusted, and seen as beneficial.

The second cluster focuses on participation, inclusion, and procedural fairness. Here the interest is not only whether citizens are present, but whether they can participate meaningfully and whether there are mechanisms for complaints, feedback, and accountability. So the social side is not just about user satisfaction. It is also about whether the process is perceived as fair and whether value is experienced in ways that matter to citizens and communities.

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Indicator framework: Business value

Economic and financial effects

Examples

Public-private partnerships generated
Jobs created
Business creation and growth
Leverage effect/cost reduction

Ecosystem, institutional capacity, and scaling

Examples:

Replication and continuation of project initiatives
SMEs involved
Ecosystem collaboration capacity

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Governance and organisational legitimacy

Examples:

perceived value by city decision makers

CitizenCity

On the business value side, the first cluster covers economic and financial effects. This includes indicators such as jobs created, business creation and growth, public-private partnerships, leverage of funding, and reductions in essential living costs. These indicators help us understand whether the initiative creates economic effects for market actors.

The second cluster focuses on ecosystem, institutional capacity, and scaling. Here we look at whether SMEs are involved, whether collaboration capacity is growing, and whether project initiatives are continued or replicated beyond the pilot.

The final cluster is governance and organisational legitimacy. This is where we ask whether city decision makers actually see the solutions as valuable and relevant. That matters because even when a service performs well technically, it will not continue unless the organisations responsible for governing and funding it also see strategic value in it.”

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In your work environment, what are the main challenges in engaging stakeholders?

The Slido app must be installed on every computer you’re presenting from

Do not edit�How to change the design

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What questions would you like to have answered during the pannel discussion

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Do not edit�How to change the design

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Thanks!

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TU/e team:

Lieke van Weereld

Milan van Bussel

Kirsten Visser

Benjamin van Berg

Dr. Dujuan Yang

Prof. Bige Tuncer

The CitzenCity is project has received funding from the European Union’s Horizon Europe Research and Innovation Programme

CitizenCity