Non-technical Resources
On this page
- Example Stories - See potential pilot scenarios
- 📖 The LDT4SSC Methodology - Four-phase implementation framework
- Other Resources - Additional ecosystem resources
🎯 What you'll find here
These non-technical resources provide practical guidance for the organisational, strategic, legal, and governance aspects of the pilot journey.
Quick links:
- 📖 The LDT4SSC Methodology - Navigate the four-phase framework
- 💡 Example Stories - Fictional pilot scenarios for each work strand
- Other Resources - Additional ecosystem resources
The resources support pilots and applicants in aligning stakeholders, structuring responsibilities, anticipating risks, and creating the conditions for sustainable, interoperable, and widely adopted digital services. Together, these methods and workshops help build the shared culture, clarity, and coordination required to transform a use case into a long-lasting, impactful solution.
Work Strands Overview
The non-technical resources support pilots across three interconnected work strands, each requiring different organisational approaches:
WS1: Technical Interconnection of LDTs
Linking and scaling digital twins that are already in place
Focus on multi-stakeholder governance, legal frameworks for data sharing, cross-organisational coordination, and partnership models for federated LDT ecosystems.
WS2: Creation of LDTs Based on Common Needs
Addressing shared urban or regional challenges (e.g., cross-border traffic, air pollution)
Emphasis on use case co-creation, stakeholder alignment, data governance frameworks, business model design, and responsible digital principles for new LDT initiatives.
WS3: Adding New Advanced AI-Based Tools to existing LDTs
Integrating AI-driven, value-added services to enhance existing LDTs and new services to the LDTs Toolbox
Focus on change management, user training and adoption, ethical AI considerations, and preparing organisations for advanced analytics and decision-support capabilities.
Example stories
To illustrate the potential of each WS, we present a set of Pilots’ Example Stories, i.e. fictional scenarios that demonstrate how future pilots could emerge and operate within the LDT4SSC project framework. These examples are intended to help prospective applicants envision their own roles and opportunities within the initiative.
The example stories developed for WS1 show how cities and communities with an existing level of digital maturity (as described in the Requirements section) can build on their Local LDTs to form a federated, EU-wide ecosystem. Each example illustrates how interoperability, cross-border collaboration, and shared data platforms can enable scalable and replicable solutions. The realistic use cases highlight the concrete benefits of interconnected LDTs, such as improved decision-making, resource optimisation, and faster adoption of open-source tools, while also addressing common challenges like technical integration, governance, and stakeholder coordination. Their purpose is to inspire replication, lower entry barriers for less digitally advanced regions, and foster a shared community of practice around federated LDTs, advancing the project’s goal of a unified and interoperable digital infrastructure for Europe.
Traffic Management and Air Quality
Two municipalities of similar size, population, and density have each developed a descriptive Local Digital Twin (LDT) to monitor traffic, air quality, and their interaction. They partner with a specialised private service provider to interconnect their systems and exchange data and related services using the NGSI-LD standard. By linking their context brokers, the partners establish a shared real-time dashboard that enables coordinated management of transport corridors and supports efforts to reduce congestion and pollution.
To ensure seamless data and service interoperability, the partners align their API documentation with the MIM1. The outcomes include both operational improvements such as optimised traffic flows and reduced emissions, and reusable assets such as standardised data models and dashboards that can be adopted by other cities.
This example reflects the ambition of WS1: to scale up EU-wide datasets and open-source solutions within the LDT ecosystem, demonstrating how federated approaches can transform local innovations into collective impact.
Interconnected Networks Management and Service Marketplace
Cities of a DS4SSCC Pilot projects interconnect their Local Digital Twins (LDTs) with those of another group of communities to create a shared marketplace for essential public services, aligned with the priorities of each local authority. These services may include areas such as mobility, land-use planning, and energy or electricity management, reflecting the themes of the New European Bauhaus (NEB) and the European Green Deal. By leveraging the SIMPL building blocks and the FIWARE ecosystem of technologies, this new consortium integrates waste, energy, and water management systems, enabling local authority syndicates to exchange data, optimise resource allocation, and deliver new services across regions. The main challenge lies in ensuring the maturity of the connector, which must reliably bridge diverse platforms while maintaining data consistency and security.
The outcomes include operational efficiencies such as streamlined network management and cross-city service coordination, as well as contributions to the SIMPL framework, reinforcing its role as means for interoperable LDT ecosystems. This example illustrates how federated LDTs can move beyond individual city silos, fostering a collaborative economy of shared services and scalable, cross-regional solutions.
Regional Energy and Climate Data Space
Several municipalities in a cross-border region with varying digital maturity levels create a shared DS to combine energy production and consumption data. The goal is to monitor regional energy use and evaluate its climate impact. Together with a data space specialist and a local energy supplier, the municipalities deploy a FIWARE Data Space Connector and use the EU LDT Toolbox to model scenarios on energy demand, renewable integration, and the environmental impact of alternative energy sources. The main challenge is to manage data access rights among diverse stakeholders (municipalities, regional alliances, and private partners) while ensuring secure data exchange and consistent decision-making across governance levels.
The project delivers a real-time regional overview of energy supply and demand, enabling policymakers, businesses, and citizens to track consumption patterns and make informed choices. It demonstrates how federated LDTs can consolidate fragmented local data into a unified, actionable resource for advancing climate resilience and the energy transition.
📖 The LDT4SSC Methodology
Ideation (EXPLORE)
Many digital projects fail because they jump too quickly to a solution without clarifying the underlying need. The ideation phase establishes a shared foundation by defining a clear use case, identifying relevant data, anticipating impacts, and aligning all stakeholders. It also embeds responsible digital principles early on, questioning the project's purpose, ethics, and long-term value.
📋 Prerequisites
- A clearly identified problem rooted in the organisation's mission
- Political/strategic support and a committed sponsor
- A dedicated project manager
- Ability to mobilise internal and external stakeholders
🎯 Objectives
- Clarify and contextualise the problem
- Identify beneficiaries, expected impacts, context, actors, available data, constraints, and risks
- Produce a project framework to secure resources (funding, support, platforms)
👥 Stakeholders to Engage
- Operational/business teams
- Cross-functional or "bridge" agents
- Decision-makers (managers, elected officials)
- IT and data teams
- End-users or their representatives
- Technical partners, experts, or peer local authorities
EXPLORE Resources
Map your use case from a functional point of view
This workshop helps participants build a shared understanding of the use case by collaboratively drawing the current process, data flows, and pain points. By making an abstract idea tangible and envisioning an ideal future state, it aligns stakeholders early on and forms a visual foundation for the rest of the project.
Workshop A: Map Your Use Case from a Functional Point of View
Question your why
This workshop helps teams clarify the deeper purpose of their digital project by exploring the operational, user, strategic, economic, and socio-environmental benefits it aims to deliver. By identifying the real objectives and expected impacts, stakeholders can better articulate the project, prioritise functionalities, and avoid solutions that fail to address the core problem.
Workshop B: Question Your Why
Questioning your project from the perspective of digital responsibility
This workshop invites teams to evaluate their project through the lens of sustainable design for digital services, questioning its necessity, relevance, and potential ecological, social, and economic impacts. It provides a quick, collective assessment of both positive and negative effects across the project lifecycle, helping decision-makers make more responsible and informed choices.
Workshop C: Question your project from a sustainable design for digital services perspective
✨ Achievements at the end of the phase
- A drawing of the use case identifying the data sources, their path, the Information System bricks, the players, etc.
- The desired impacts by type
- The reason why for the project
- The costs and benefits, and the indicators for monitoring them
- The project stakeholders
Specifications (VALIDATE)
After ideation clarifies the need, motivations, stakeholders, and data, the specification phase focuses on defining how the solution should work. It translates the shared use case into functional and technical requirements, taking into account real-world constraints (data, architecture, users, security). This phase prevents misunderstandings between business and technical teams, embeds interoperability early on, and provides the basis for selecting or prototyping the appropriate solution.
📋 Prerequisites
- A clear and validated use case with precise objectives
- Identified data requirements and understanding of data accessibility
- A committed project team with experience in data-related issues
- A realistic view of available resources (time, budget, technical support)
🎯 Objectives
- Identify and describe personas (future users of the solution)
- Translate functional needs into operational technical specifications
- Identify required data and define its lifecycle
- Determine necessary indicators, dashboards, and potential simulations
- Define data access conditions, legal constraints, and governance arrangements
- Produce documentation to guide development or prototyping
👥 Stakeholders to Engage
- Business referents (use case owners, service managers)
- End-users of the solution
- Data engineers or data architects
- IT or infrastructure managers
- Legal experts or DPOs when personal data or data-sharing clauses apply
VALIDATE Resources
Implementing the Ideal fit-for-purpose Data Governance
This workshop helps participants build a shared, strategic vision of what fit-for-purpose data governance should look like in their organisation by exploring political, legal, organisational, and technical dimensions and imagining ideal and undesirable future scenarios. It then translates these insights into a concrete action plan, clarifying roles, processes, and conditions for success to ensure sustainable and coordinated data governance across projects.
Workshop H: Implementing the Ideal fit-for-purpose Data Governance
Implementing data governance
This section presents data governance as a cross-functional framework encompassing technical, legal, organisational, and strategic dimensions, essential for creating digital twins and connecting them within shared data infrastructures. It outlines a set of common practices and workshops that help project leaders and partners clarify roles, establish processes, and build sustainable, collaborative governance structures that support long-term reuse and interoperability.
A focus on data governance
Workshop O: Identify stakeholders and their status/legitimacy to act
This workshop helps identify all stakeholders involved in data governance—both internal and external—and clarify their roles, expertise, responsibilities, and ability to act or contribute. By visually mapping actors and defining collaboration and data-sharing conditions, it lays the groundwork for cross-functional and multi-partner governance essential to implementing the project.
Workshop P: Documenting each person's responsibilities in data governance (mission statement/job profile)
This workshop focuses on clarifying and formally documenting each stakeholder’s responsibilities in data governance, ensuring that roles are explicit and aligned across the consortium. By defining missions or adapting job profiles, it strengthens engagement, accountability, and the internal structures needed to support effective, long-term data governance.
Workshop I: Question the legal framework and define its data
This workshop helps participants examine the legal framework needed to govern data securely and consistently, identifying regulatory requirements and the contractual clauses or internal processes needed to meet them. By comparing needs with existing resources, it supports the development of clear, robust legal foundations for data use and sharing.
Workshop Q: Exploring each stage of creating a digital twin (through mini-questionnaires and the use of resources: experience reports, clauses, standard contracts)
This workshop equips participants to anticipate legal, contractual, and interoperability requirements at every stage of their digital twin or data platform project by examining typical clauses, risks, and resources. By mapping legal needs across the project lifecycle and drawing on standard contracts and experience reports, teams gain the ability to secure long-term viability, manage intellectual property, and negotiate effectively with suppliers and partners.
Describing your dashboard and indicators
This workshop guides participants in designing dashboards and indicators that genuinely meet the needs of different user personas, ensuring each metric is meaningful, usable, and aligned with strategic objectives. By mapping the path from source data to dashboard outputs, it helps avoid overly complex or irrelevant indicators and provides solid foundations for prototyping or deployment.
Workshop E: Describe your dashboard and indicators
Defining the lifecycle and interoperability of your data
This workshop expands the initial use-case diagram by mapping all data sources, transformations, flows, and actors involved, ensuring a complete view of the data lifecycle from creation to reuse. It also assesses interoperability needs—such as API-based access and integration requirements—to support future reuse, system alignment, and robust data infrastructure planning.
Workshop N: Identify the life cycle and interoperability of your data
Raising awareness and putting interoperability into practice
This workshop introduces participants to the principles and importance of interoperability, helping them recognise obstacles, levers, and the different interoperability layers that affect a data project. Through a collective diagnostic exercise, it builds a shared understanding of priorities and equips teams to prevent compatibility issues, ensuring smoother integration and sustainable data use in later phases.
Workshop J: Raising awareness and implementing interoperability
Workshop R: Lego Serious Play
This workshop uses the Lego Serious Play method to help participants model complex ideas—such as governance, processes, or system architecture—in a tangible and easily adjustable form. By enabling creative, visual co-construction, it reveals gaps and shared solutions more effectively than traditional discussions, supporting alignment before moving into implementation.
✨ Achievements at the end of the phase
At this stage of the method, the following elements have been achieved or identified:
- The personas
- The desired indicators, data visualisations and dashboards, based on the personas
- The desired functionalities, based on the personas
- The data lifecycle, and the action plan for accessing and using the data effectively
Prototyping (DEFINE)
Prototyping turns a theoretical concept into a tangible proof of feasibility. Rather than building a finished service, this phase focuses on rapidly testing a solution on a limited scope, using real data and confronting assumptions with technical, functional, and human realities. While not always mandatory—especially if existing market solutions already fit the need—prototyping often provides valuable insights before large-scale deployment. Its purpose is twofold: to show that the idea works and to confirm that it brings real value to users. This iterative, frugal approach enables quick learning while preparing the foundations for a scalable deployment.
📋 Prerequisites
- A prioritised and formalised use case validated internally
- Clear functional and technical specifications
- Identified and accessible datasets in usable form
- Identified human and technical resources to build the prototype
- Agreement from a small group of end-users to test the solution under semi-real conditions
🎯 Objectives
- Experiment with concrete solutions using real data and representative users
- Validate organisational hypotheses
- Confirm technical feasibility (data access, processing, flow integration, result display, etc.)
- Confirm or refine design choices based on practical feedback
- Prepare the conditions for future deployment (technical, organisational, legal)
👥 Stakeholders to Engage
- A project manager
- Business referents to test and validate functionalities
- A data engineer or developer to integrate data and build the prototype
- A UX/UI designer or facilitator (if possible) to ensure clear user journeys
- An IT or infrastructure consultant to ensure compatibility with the existing environment and support implementation
DEFINE Resources
Produce a prototype of your project
This method ensures that prototypes are designed with interoperability at their core by first formalising a multi-dimensional representation of the use case—capturing objectives, users, features, data, and constraints—to guide collaboration with technical teams. It then structures and contextualises the data through knowledge-graph modelling and existing ontologies, creating a simplified, standards-aligned semantic model that supports integration, reuse, and long-term scalability.
Methodology. Step-by-step process of building a use case
✨ Achievements at the end of the phase
At this stage of the method, the following elements have been produced or identified:
- A prototype that meets the use case
- Reusable knowledge model(s)
- The code used to produce the algorithms or models
- Documentation of the prototype
Deployment (IMPLEMENT)
Deployment marks the transition from experimentation to full implementation. Once the prototype has demonstrated feasibility, the goal becomes widespread adoption across the organisation. This phase focuses on stabilising and securing the solution, establishing maintenance processes, and ensuring long-term sustainability. It also finalises the technical architecture, economic model, and legal framework, while preparing an action plan for training, onboarding, and organisational change. Ideally, many of these topics will have been explored earlier; this stage formalises and operationalises them. Deployment may also open opportunities for scaling, replication, or pooling with other local authorities.
📋 Prerequisites
- A prototype validated by target users and tested in near-real conditions
- A political or strategic decision to make the solution sustainable
- A defined target technical architecture (hosting, tools, security, interconnections)
- An analysis of implementation and maintenance costs
- A clear vision for the future governance of the solution (technical, organisational, partnership)
🎯 Objectives
- Establish governance for the data and service (updating, sharing, accountability)
- Choose an appropriate management structure where relevant
- Organise maintenance, support, and long-term evolution of the solution
- Technically stabilise the solution for production use
- Ensure user adoption through training and ongoing adjustments
- Document and capitalise on lessons learned to support reuse internally or externally
👥 Stakeholders to Engage
- The business department responsible for the project, to steer deployment and support adoption
- IT/digital teams to integrate the solution into the existing information system
- Functional managers to monitor user feedback and prioritise improvements
- External service providers for industrialisation, hosting, and technical support
- Public purchasers and legal experts to formalise contracts and clarify rights of use and ownership
- Potential partner local authorities for pooling or scaling initiatives
IMPLEMENT Resources
Onboard, train, and involve agents
This workshop supports staff and elected representatives in understanding the value of a data project by identifying the audiences to be mobilised and defining tailored awareness and training actions. By mapping stakeholder groups and shaping clear messages and engagement strategies, it strengthens commitment, fosters collaboration, and ensures the project is sustainably adopted.
Workshop K: Onboard, train and acculturate agents in a digital project
Designing and drafting an action plan
This workshop helps teams turn their use case into a concrete action plan by identifying key steps, organising them on a timeline, and exploring alternative implementation scenarios. By visualising the sequence of actions and anticipating constraints, it supports better planning, facilitates decision-making, and aligns stakeholders around a realistic path forward.
Workshop G: Designing and drafting an action plan
Defining your business model
This workshop helps participants explore and structure the financial mechanisms that will support the deployment and long-term sustainability of their data or digital project, from funding models to partnerships and value-added services. By identifying resources, revenue levers, and feasibility constraints, it ensures the project’s economic viability and provides a solid basis for decision-makers to evaluate and support the initiative.
Workshop L: Define the Business Model for your Project
Workshop M: Refine your Business Model
Other resources
This table summarises other important resources from the initiatives in the ecosystem.
| Title | Small Description | Source | Thematic Area |
|---|---|---|---|
| DSSC's Co-Creation Method – Align Stakeholders on the Data Space Scope | Helpful to build consensus on the data space's boundaries and goals, ensuring all stakeholders share a clear vision | DSSC | Data Spaces |
| The Economics of Data Sharing: Insights from a Legal-Economics Workshop | Provides insights on economic incentives and legal frameworks that can help design sustainable data sharing models. | DSSC | Data Spaces |
| MIMS Specification Introduction | Introduces standards for interoperable data management, essential for connecting platforms. | OASC | Interoperability / MIMs |
| DS4SSCC-DEP Blueprint | DS4SSCC | Data Spaces | |
| DS4SSCC's Multi-Stakeholder Governance Scheme | Guidelines to set up multi-stakeholder governance, crucial for trust and collaboration in data spaces and LDTs. | DS4SSCC | Data Spaces |
| Guidance for the Integration of Digital Twins in Data Spaces | This document focuses on the integration of digital twins in data spaces: it provides a context on data spaces, digital twins, IoT and edge computing and standardisation; it provides an analysis on the integration of digital twins in data spaces taking an architecture approach; it describes a large number of digital twin use cases in domains such as agriculture, connected vehicles, smart cities, energy, smart manufacturing. The document can be used to provide insights and sources for future standardisation work related to the integration of digital twins in data spaces. | AIOTI | Digital Twins |
| Policy processes and building blocks for Digital Twins | GEONOVUM | Digital Twins | |
| IDSA - Rulebook | The IDSA Rulebook serves several purposes regarding the development and operation of data spaces. The aim is to describe clearly which rules are mandatory and which are optional guidelines. This governance framework includes functional, technical, operational, and legal dimensions. It also presents a PESTEL analysis of the overarching considerations of data spaces which is interesting regarding the impact assessment. | IDSA | Data Spaces |
| Catalogue of Tools | A curated set of ready-to-use resources (tools, frameworks, guidelines) to support local authorities in digital transformation: includes procurement templates, interoperability standards (e.g. MIMs), capacity-building tools. Pilots of LDT4SSC can use these tools to procure and build Local Digital Twins (LDTs) in line with EU best practices. | Living-in.EU | Cities Network and Supporting Actions |
| Knowledge Base | A knowledge repository of documents, studies, guidelines, webinars, and working-group outputs. For LDT4SSC pilots, this Knowledge Base provides evidence, legal and technical guidance, and community-shared lessons relevant to digital twin implementation. | Living-in.EU | Cities Network and Supporting Actions |
| Procurement Support Materials | Procurement guidelines, templates, and a glossary designed specifically to help EU cities and communities acquire digital infrastructures and digital twins. For LDT4SSC pilots, these materials simplify tender processes, ensure alignment with EU standards, and embed interoperability, sustainability, and social criteria. | Living-in.EU | Cities Network and Supporting Actions |
| Digital Twin Business Maturity Model | A maturity model from the Digital Twin Consortium defining five maturity stages (Passive → Master) across dimensions such as Strategy, Culture, Technology. LDT4SSC pilots can use it to assess their current maturity in digital twin adoption, plan progression, and benchmark against peer organizations. | Digital Twin Consortium | Digital Twin |
| Digital twins for cities and municipalities DIN SPEC – Build a digital twin for smart cities (Germany) | Gives a holistic overview of urban digital twins. At the heart of the document are the use cases, the description of urban digital twins (including capabilities) and a maturity model. As part of the development of this DIN SPEC, around 100 different municipal usage scenarios were identified by the participating municipalities and assigned to municipal spheres of activity. | DIN | Digital Twin |
| DTC Digital Twin Capabilities Periodic Table | The Digital Twin Capabilities Periodic Table (CPT) is an architecture and technology agnostic requirements definition framework for digital engineering projects. It is aimed at organizations who want to design, develop, deploy and operate digital twins based on use case capability requirements versus the features of technology solutions. | DIGITAL TWIN CONSORTIUM | Digital Twin |
| UNRAVELLING THE USE OF DIGITAL TWINS TO ASSIST DECISION- AND POLICY-MAKING IN SMART CITIES | "The main objective of this ongoing research is to review the existing literature on the intersection of digital twins and smart cities with a focus on decision and policy-making support and to answer the research question: "What are the existing applications of digital twins for smart cities for aiding decision-and policy-making?"" | ARXIV | Digital Twin |
| Trusted Data Transaction | Provides terminology, concepts and mechanisms in the field of data exchange focusing on trusted data transactions. | CEN / CENELEC | Legal terminologies |
| High-Level Forum on European Standardisation Final report of work-stream 14 | The report is the result of Workstream 14 on Data Interoperability of the High-Level Forum on European Standardisation (HLF). The workstream was initiated to help establish a coordinated approach to (European) standardisation in support of the interoperability requirements established in the horizontal data legislation, implementing the European data strategy (2020). The clarity, transparency, and reliability resulting from a well-designed overall set of standards will significantly lower entry barriers for the application of data spaces and thus support the emergence of a vibrant data ecosystem in Europe. | EUROPEAN COMMISSION | Data Spaces |
| New European Interoperability Framework | Promoting seamless services and data flows for European public administrations | EUROPEAN COMMISSION | Data interoperability |