Why Data Governance and Collaboration Are Essential for the Future of Urban Digital Twins

By Begoña G. Otero (Research Fellow, The GovLab; Affiliated Research Fellow MPI Innovation & Competition, Munich) and Stefaan G. Verhulst (Co-Founder The GovLab and The DataTank)

The concept of digital twins has quickly become the new darling of the smart city world. By 2030, more than 500 cities plan to launch some kind of digital twin platform, often wrapped in dazzling promises: immersive 3D models of entire neighborhoods, holographic maps of traffic flows, real-time dashboards of carbon emissions. These visuals capture headlines and the political imagination. But beneath the glossy graphics lies a harder question: what actually makes a digital twin useful, trustworthy, and sustainable?

Having recently worked directly on a U.S. metropolitan digital twin pilot, we know the answer is not just shiny and sophisticated imagery. A genuine twin is a living ecosystem of different stakeholders and diverse datasets — integrating maps, open government data, IoT sensors, predictive AI models, synthetic data, and mobility data into a single responsive platform. Done right, a digital twin becomes a decision-making sandbox: where planners can simulate how pedestrianizing a street shifts congestion, for example, or how a Category 3 hurricane might inundate vulnerable neighborhoods.

Technology alone is not enough

If the initial rush of digital twin projects has taught anything, it’s that technology alone is not enough. Building a functional digital twin is as much an institutional and governance challenge as a technical one. The platform must integrate data from multiple sources, including government departments, private firms, utilities, researchers, and other relevant entities. Global leaders in the field, from Singapore’s Virtual Singapore to Orlando’s much-publicized holographic twin, have all discovered the same truth: the long-term value of a twin depends not on its graphics but on its data governance. Singapore’s twin works because its government mandated cross-agency data sharing. Orlando’s flashy prototype only turned serious when planners acknowledged that its future hinges on becoming an open ecosystem where utilities, agencies, and even residents can contribute data.

In practice, however, the necessary data is often scattered and siloed. European pilots have shown this clearly: the obstacle was not imagining use cases but finding and accessing the data to make them possible. In the OASC pilot regions, such as Athens and Pilsen, project teams reported that the biggest hurdle was that much of the relevant data sat in silos — owned by private firms, higher levels of government, or agencies unused to thinking of themselves as data stewards. Even when data existed, municipalities often lacked clear mandates, agreements, or technical workflows to integrate it responsibly.

The same applied in Helsinki, which today runs one of the most advanced city twins in Europe. Before reaching that point, the city had to spend years building a reliable data repository, common standards, and trust agreements with residents to ensure equitable use of information. Similarly, in the UK, the Gemini Principles and the subsequent National Digital Twin programme were born out of recognition that without shared governance, data would remain fragmented across sectors such as energy, transport, and the environment. Both cases show that even resource-rich contexts face governance hurdles first; technology comes later.

The lesson is clear: digital twins will only move beyond hype if we treat them as governance infrastructures, not visual spectacles. That means aligning the concept with frameworks of data governance, collaboration, and digital self-determination — in the process, ensuring that digital twins serve public purposes, respect local contexts, and empower communities to shape how their data is used.

Recent examples underscore this point. Fujitsu’s recent digital twin project (2024) highlights how private innovation can align with public trust frameworks. In partnership with Tsuda University, the project demonstrates how open standards and collaborative agreements can transform corporate projects into shared civic assets. In Bologna, the experimental governance model Civic Digital Twin shows how governance models can give residents structured ways to contest or shape the data that feeds urban simulations, linking digital twins directly to democratic participation.

This is why we developed a 4Ps framework of data governance (Purpose, Principles, Processes and Practices), which we explain further below. The four Ps represent an actionable framework designed to anchor digital twins in rights-based governance. Here, concepts like Digital Self-Determination (DSD) and AI Localism become essential. DSD departs from the notion that individuals and communities must be able to influence how their data is collected, used, and reused — not as one-off consent, but as an ongoing capability to revoke, amend, or participate in the process. AI Localism translates this into practice by demonstrating how cities — from Las Vegas to Bologna — can integrate local norms, transparency registers, and public feedback loops into their AI and data governance. In other words, digital twins only thrive when they are treated not just as datasets, but as living infrastructures of self-determination and accountability.

The Four “P’s” of a Sustainable Data Governance Framework for Digital Twins

For many cities data governance feels abstract and complex, as it is spread across different departments, technical standards, privacy rules, and institutional agreements. We developed a simple heuristic that can help cut through the complexity: the “4Ps” of governance — Purpose, Principles, Processes, and Practices.

• Purpose anchors the twin in a clear mission and public value proposition. For example, ss the goal to reduce emissions, improve mobility, or prepare for disasters? Purpose defines the why and ensures that the twin is aligned with urgent community priorities. In Helsinki, the city’s twin was explicitly tied to carbon neutrality goals, allowing residents to simulate insulation or solar investments for their homes. This purposeful framing made the twin not a gadget but a climate tool. In the EU’s flagship Destination Earth (DestinE) programme, Purpose is set at continental scale: harnessing federated Earth system models to support the Green Deal and climate adaptation policies, not just create visualizations. Likewise, in Bologna, researchers have shown how linking twin development to digital self-determination, giving residents an explicit say in how mobility and environmental data are used, builds legitimacy from the outset.
• Principles provide the normative compass. Values such as participation, transparency, accountability, and equity should guide decisions at every stage. Singapore embedded these principles when it required all agencies to contribute datasets to Virtual Singapore, while also offering anonymization and opt-out options to protect privacy. Principles only matter if they subsequently are applied in local contexts through for instance, indexes of datasets or algorithms (transparency), community engagement forums (participation), or grievance mechanisms (accountability).
• Processes involve the means of making decisions, in line with the principles to meet the purposes. They are also the operational backbone: the workflows for data collection, validation, sharing, and retirement. Without responsible and effective processes, governance remains rhetoric. In our pilot, we used a simple but powerful tool: the 5W+H framework (What data, Why needed, Who can access, When updated, Where stored, How processed). This operational clarity echoes AI Localism’s call for accountability and oversight, ensuring that when a new dataset is added — such as ride-hailing data — it is assessed for bias, privacy, and community impact before integration. Processes enable the system to remain adaptive and resilient.
• Practices put principles and decisions made through processes into enforceable action. This means policies, formal agreements, interoperability standards, audits, and oversight bodies. Fujitsu’s 2024 digital twin initiative exemplifies how the private sector can also adopt this approach. By designing twins around open standards and contractual guarantees of data use, the project illustrates how practices can bridge corporate innovation and civic trust. Australia’s ANZLIC guidelines play a similar role: setting cross-sector rules of the road that allow twins to interconnect securely.

The 4Ps are mutually reinforcing. Purpose and Principles establish direction, while Processes and Practices operationalize and enforce that direction. What makes this framework powerful is that it creates an infrastructure for data collaboration: once the scaffolding of rights, roles, and rules is in place, collaboration among agencies, companies, and citizens becomes feasible and sustainable. In this way, the 4Ps make digital twins not just technically robust, but also socially legitimate — embodying the ideals of digital self-determination and local accountability. This kind of resilient governance foundation is what separates a pilot that fizzles out from a platform that delivers public value over time.

Data Collaboration: The Secret Sauce of Successful Digital Twins

Experience with digital twins shows that collaboration is essential — and it is strong data governance frameworks that make such collaboration work. A digital twin is never the product of one agency, company or lab: it is a team effort. No single organization possesses all the data or expertise required to replicate a complex digital twin system. Success depends on building a data collaborative — a structured partnership across government, private sector, academia, and civil society to pool data for the common good.

This is not business-as-usual. Most organizations, whether public or private, are used to hoarding data, guarding it for competitive advantage, or fearing legal exposure. Overcoming that culture requires governance scaffolding, and this is where the 4Ps come alive. Purpose provides the shared mission that convinces partners to share data (“we are building flood resilience, not just a 3D map”). Principles embed trust through commitments to equity, privacy, and transparency. Processes specify who can access which datasets under what conditions. And Practices & Policies turn those promises into binding agreements, audits, and accountability structures. Collaboration without such scaffolding quickly unravels. Together, these help create an enabling context for greater data collaboration and sharing.

Global examples highlight the importance of collaboration. An important contributor to Virtual Singapore was the fact that the government required agencies to contribute data into a unified platform and gave citizens opt-outs to ease privacy concerns. Amsterdam augmented its twin with crowdsourced air-quality sensors contributed by residents, proving that communities can be active collaborators if governance respects their rights. Bologna took it a step further by embedding digital self-determination directly into its urban twin experiment, allowing residents to challenge or amend how mobility and environmental data are used in planning. This is governance not just of data, but with people.

Industry–government dialogues are converging on the same point: scaling digital twins will depend less on features and more on trust, interoperability, and governance muscle — a theme now echoed in the UK, Singapore, Australia, and beyond.

Here, the link to AI Localism is critical. Municipalities are increasingly designing governance “from below”: creating transparency registers, public forums, and publish-or-explain routines to keep digital systems accountable to local communities. Digital twins thrive when they adopt this ethos. Helsinki’s Climate Atlas, for example, lets homeowners test the energy impact of insulation or solar panels, giving citizens an immediate role in shaping climate policy. What looks like a technical dashboard is, in practice, an exercise in local accountability.

Collaboration also requires data stewardship capacity–people and institutions with the necessary skills and authority to manage data responsibly. This is the missing middle that organizations like The Data Tank and The GovLab have emphasized. Stewards build trust between actors, ensure adherence to principles, and translate technical standards into human agreements. Without them, even the best policies remain paper promises. With them, a twin becomes a living collaborative where rights and responsibilities are continuously balanced.

These stewardship capacities are not just local experiments; they are beginning to scale globally. At the continental level, Europe’s Destination Earth (DestinE) is institutionalizing collaboration by linking models and data lakes across ESA, ECMWF, and EUMETSAT under a shared governance framework. In the UK, the National Digital Twin programme embeds data stewards into assurance regimes that cut across sectors. Beyond Europe, Singapore’s Virtual Singapore made data stewardship a government-wide responsibility, mandating agency contributions, while Australia’s ANZLIC principles promote federated twins where stewardship and standards travel across jurisdictions. Even private-sector initiatives show this evolution: Fujitsu’s digital twin policy technology has been designed not as a closed corporate tool but as a platform that aligns with open standards and contractual guarantees of responsible data use. The message across contexts is consistent: data collaboratives last when stewardship is institutionalized — through standards, roles, and incentives — rather than left to voluntary goodwill.

Add all of this up, and the result is a model in which Digital Self-Determination, AI Localism, and collaborative data governance meet. Instead of one-off consent boxes, individuals and communities gain ongoing influence over how data is used. Instead of siloed institutions, partners operate under a shared purpose and enforceable agreements. Instead of flashy prototypes, digital twins become durable infrastructures of trust and problem-solving, ultimately allowing cities (and their residents) to tackle some of the most thorny public problems of our time.

Lessons for the Future: Making Digital Twins Last and Matter

As cities and countries venture deeper into the digital twin era, a few lessons stand out from early experiences:

• Start with Purpose and the Problem, Not Tech for Tech’s Sake: It’s easy to be enchanted by sophisticated visualizations or the mere idea of a “metaverse for cities.” But successful projects, from Singapore to Helsinki, began by focusing on urgent pain points and clear objectives — whether it was flooding, traffic congestion, or carbon reduction. Purpose provides a narrative that attracts support and funding, while ensuring the digital twin is more than a technological showcase.
• Invest in Data Governance from Day One: Do not treat data governance and data sharing as an afterthought or paperwork to be done once the shiny model is built. They are the foundation of the entire endeavor. A robust governance framework — like the 4P’s discussed above — is essential to ensure the digital twin’s outputs are trusted and used in decision-making. This includes addressing legal issues (privacy laws, data ownership) early, setting up a governance team or board, and establishing processes for ongoing oversight.
• Build Collaborative Capacity and Trust: Digital twins require structured collaboration across public agencies, private firms, academia, and communities. Establishing a data collaborative is therefore essential. Such collaboratives should be underpinned by shared principles, clear agreements, and inclusive governance mechanisms that give all partners a meaningful role. Small pilot projects can build confidence by demonstrating value. International experience — from Singapore’s government-wide mandate to Australia’s ANZLIC framework — shows that twins are sustainable only when collaboration is institutionalized through rules, standards, and incentives.
• Operationalize Ethical Principles: Principles such as transparency, accountability, and equity must be embedded in daily practice. This involves open dashboards that allow residents to inspect data, feedback and grievance mechanisms for correcting errors, and measurable indicators to assess whether benefits reach underserved groups. In our project, community representation was mandated in the governance charter, and new datasets underwent ethics review for bias and privacy risks. Barcelona’s “data justice” experiment illustrates how principles can be operationalized: by analyzing aggregated mobile phone data to evaluate urban mobility equity, the city identified underrepresented districts and targeted them for improved data collection and policy attention.
• Plan for Sustainability and Scale: Digital twins often begin as short-term pilots; sustaining them requires long-term funding, institutional anchoring, and adaptability. Governance structures should periodically update membership, processes, and policies as technology and regulation evolve. Reliance on open standards avoids technological lock-in and facilitates scaling. Cultural barriers can be greater than financial ones: early demonstrators that prove value are critical for justifying ongoing investment. As the Rotterdam experience highlights, the challenge is less about resources than about cultivating a mindset of continuous improvement.
• Obtain a social license: Governance infrastructures for digital twins must anchor technology in public legitimacy. As previously noted in The Case for Local and Regional Public Engagement in Governing Artificial Intelligence, public trust emerges when communities are empowered to shape the very systems that affect them. This requires new ways to engage communities as to obtain a social license for data re-use. If we approach it in that spirit, focusing as much on governance and people as on models and code, we can move beyond the hype and truly deliver on the promise of digital twins: more informed, inclusive, and proactive decisions for the future of our communities.

In conclusion, the excitement surrounding digital twins is well-founded — these virtual city replicas have the potential to revolutionize urban planning, make infrastructure more efficient, and enhance resilience to threats such as climate change. But realizing that potential requires digging below the shiny surface. It requires getting the plumbing right: the governance agreements, the data standards, the collaborative relationships, and public trust. Those elements may not generate splashy headlines, yet they ultimately determine whether a digital twin remains an eye-catching demo or becomes a lasting tool for better governance. The experiences of early adopters from Florida to Finland all sing a similar tune: technology is only half the equation. Equally important is creating a framework that allows data to flow responsibly across boundaries and be applied in a manner that aligns with public values.

The next generation of digital twins will thrive only if we build them on principles of openness, fairness, and shared purpose. The task is one of turning data into a public problem-solving tool. A digital twin should be viewed not as a technical artifact, but as a living, evolving data collaborative that brings together many partners to improve their city.