Designing Sustainable Business Models for Data Spaces: Insights from the Manufacturing Sector

Received: Revised: Accepted: Published: Citation: . Designing Sustainable Business Models for Data Spaces: Insights from the Manufacturing Sector. Journal Not Specified 2025,1, 0. https://doi.org/ Copyright: © 2025 by the authors. Submitted to Journal Not Specified for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. org/licenses/by/4.0/). Article Designing Sustainable Business Models for Data Spaces: Insights from the Manufacturing Sector Elena Politi 1* , Axel Weissenfeld 2, Aristotlelis Ntafalias 3, , Giorgos Chrysokentis4, , Victoria Katsarou5, George Fragiadakis1, George Dimitrakopoulos1, Christos Papaleonidas4, Sotiris Tsakanikas3, and Panagiotis Papadopoulos3, 1 Department of Informatics and Telematics, Harokopio University of Athens 1; ( [email protected], [email protected], [email protected]) 2Austrian Institute of Technology, Vienna, Austria; (Axel.W[email protected]) 3Motor Oil Hellas; ([email protected], [email protected], [email protected]) 4Watermelon Consulting; ([email protected], [email protected]) 5SPACE Hellas; ([email protected]) *Correspondence: [email protected]; Abstract 1 Secure, sovereign data sharing is essential for the digital transformation of industrial sec2 tors, yet designing sustainable Business Models (BMs) for multi-stakeholder Data Spaces 3 remains a significant challenge. This study situates BM innovation for Data Spaces in the 4 context of European manufacturing, addressing regulatory, technological, and economic 5 constraints. The methods involve mapping stakeholder needs to platform capabilities, 6 and systematically developing and validating revenue and value-capture models using 7 established frameworks and real-world use cases. Results show that collaborative, modu8 lar BMs—built on principles of data sovereignty, interoperability, and trust—enable the 9 creation and distribution of economic and strategic value for operators, data providers, and 10 consumers. Application in predictive maintenance use cases for refineries and wind farms 11 validates these models, demonstrating cost reductions, improved operational efficiency, 12 and compliance with current European data governance standards. The findings suggest 13 that viable BMs for industrial Data Spaces hinge on inclusive stakeholder participation, 14 flexible monetization strategies, and continuous adaptation to regulatory environments. 15 These insights provide practical guidance for stakeholders seeking to realize the full value 16 of data-driven manufacturing ecosystems. 17 Keywords: Data Spaces; Business Models; Manufacturing; Asset Management; Predictive 18 Maintenance 19 1. Introduction 20 Data sharing is the cornerstone of the digital economy, serving as a catalyst for in21 novation, efficiency, and collaboration across various sectors. Within the context of the 22 European Data Economy, the seamless availability and accessibility of high-quality data are 23 fundamental for driving technological progress, reinforcing Europe’s digital sovereignty, 24 and fostering the development of trustworthy, human-centric AI systems that embody 25 European values and ethical standards [ 1 ]. In addition, with data volumes projected to 26 grow by 120 zettabytes annually [ 2 ], data is rapidly becoming a critical asset for generating 27 new knowledge and driving value for individuals, businesses, and society. The economic 28 potential of data is underscored by estimates placing its business value at 1.75 trillion 29 Version October 22, 2025 submitted to Journal Not Specified https://doi.org/10.3390/1010000 Version October 22, 2025 submitted to Journal Not Specified 2 of 18 dollars by 2030, highlighting the central role that data sharing and utilization will continue 30 to play across all sectors in the years to come [3]. 31 As digital transformation accelerates across industrial sectors in the context of Industry 32 4.0, the demand for trusted platforms that enable secure and efficient data sharing has 33 become increasingly critical. Data Spaces have emerged as a key response to this need, offer34 ing structured environments that support collaborative ecosystems and enable the seamless 35 exchange of data among stakeholders. By fostering data sovereignty, interoperability, and 36 trust, Data Spaces unlock new revenue streams and facilitate innovation [ 4 ]. Positioned 37 at the core of this evolving landscape, they provide the foundational infrastructure for a 38 data-driven industrial economy that is both competitive and resilient [ 5 ]. Moreover, The 39 European regulatory landscape, comprising the Data Governance Act (DGA), and the Data 40 Act (DA), is shaping the foundation for compliant and sovereign data exchange that aligns 41 with European values and principles [6]. 42 Ensuring trust and sovereignty in data sharing is paramount, particularly in industrial 43 domains where stakeholders rely on the integrity, security, and reliability of exchanged 44 data. Data Spaces are designed to support transparent governance models, enabling 45 organizations to define and enforce data usage policies, while adhering to regulatory 46 and ethical requirements. The International Data Spaces (IDS) Reference Architecture 47 Model (RAM)3 [ 7 ], Gaia-X [ 8 ], and iSHARE [ 9 ] serve as established frameworks that 48 provide technical, legal, and organizational mechanisms to uphold trust. These frameworks 49 integrate certified IDS connectors, identity management solutions, auditability measures, 50 and smart contracts, ensuring that data remains controlled, traceable, and compliant across 51 the entire data-sharing lifecycle. At the core of this evolving niche lies the question of viable 52 BMs: how can stakeholders create, deliver, and capture value in ecosystems designed to 53 prioritize data sovereignty over centralization. 54 As Data Spaces gain prominence as enablers of secure, sovereign, and interoperable 55 data sharing across organizational and sectoral boundaries, understanding the BMs that 56 underpin their development and sustainability becomes increasingly critical. These BMs 57 define how value is created, delivered, and captured across various use case, facilitating 58 trusted data sharing and ensuring long term sustainability. Creating a sustainable BM for a 59 data space presents unique challenges due to its multi-stakeholder nature and the need to 60 balance value creation, participation, and economic viability. Unlike traditional BMs, a data 61 space requires collaboration between multiple organizations—including data providers, 62 data users, and infrastructure service providers—to ensure mutual benefits and long-term 63 sustainability. Therefore, a data space BM is collaborative and aligned with the different 64 BMs of the different involved participants [ 9 ]. Finally, the BM of the data space should 65 be monitored, reviewed, and adapted regularly to maximise its potential for growth and 66 sustainable expansion. It is important to leverage the strengths and synergies of the existing 67 and potential data space participants to identify growth opportunities while addressing 68 potential challenges. 69 Traditional BMs often fail to encompass the complex requirements of decentralized 70 governance, data sovereignty, and legal compliance inherent to the structure and oper71 ation of Data Spaces. To address this gap, this paper provides a structured framework 72 for designing BMs that align with the unique characteristics of Data Spaces, emphasizing 73 collaborative value creation, trust-based data sharing, and sustainability. Additionally, 74 this work provides an in-depth investigation of the current landscape of BM development 75 for Data Spaces, identifying existing approaches, underlying challenges, and emerging 76 design patterns that support sustainable and trustworthy data sharing. Drawing insights 77 from the manufacturing sector, we develop a taxonomy of monetization and value creation 78 approaches that are aligned with core principles of transparency, interoperability, and 79 Version October 22, 2025 submitted to Journal Not Specified 3 of 18 regulatory alignment. Building on this foundation, we propose a BM specifically designed 80 for the unique requirements of a manufacturing Data Space, showcasing how theoretical 81 principles can be translated into practical, sector-specific applications. The proposed frame82 work is validated through real-world use cases, which confirm its practical applicability 83 and effectiveness in fostering value creation and supporting transparent value-sharing 84 mechanisms within Data Space ecosystems. The findings further reveal that the frame85 work promotes the development of sustainable, interoperable, and resilient Data Space 86 environments capable of driving innovation and supporting long-term economic growth. 87 This rest of work is organized as follows. Section 2presents the state of the art in BMs 88 for Data Spaces, outlining trends in data-driven ecosystems and industrial digital platforms. 89 In Section 3we outline the foundational concepts for building sustainable and viable BMs 90 for Data Spaces. Section 4presents a BM development approach tailored for delivering 91 effective BMs within a manufacturing Data Space. Section 5presents the validation of the 92 proposed BMs through use cases grounded in real-world industrial challenges that enable 93 building trusted and sustainable data ecosystems. Finally, Section 6provides a summary of 94 the key findings and concludes with overarching reflections and final remarks. 95 2. Related Work 96 Nowadays, advancements in the global economy have altered the traditional balance 97 between customer and supplier relationships. The proliferation of new communications 98 and computing technology, coupled with the establishment of reasonably open global 99 trading regimes, have opened the way for new opportunities in the business landscape. 100 Businesses can now leverage digital platforms and software tools, data analytics, and 101 global supply chains to create new products, streamline processes, and enhance customer 102 experiences, ultimately reshaping industries and fostering greater competition on a global 103 scale [ 10 ]. In such a context, current businesses are required to explore more customer104 centric modes of operation, especially since technology has evolved to allow the lower cost 105 provision of information and customer solutions. In addition, these developments have 106 amplified the need to consider not only how to address customer needs more accurately, 107 but also how to capture value from providing new products and services [11]. 108 This evolving environment has leveraged the necessity to address customer needs in a 109 less fragmented manner providing more transparent alternative solutions. Consequently, 110 without a well-developed BM, innovators will fail to either deliver – or to capture – value 111 from their innovations. Originally, the term BM stands for a conceptual tool that contains a 112 set of elements and their relationships and allows to express the business logic of a specific 113 firm [ 12 ]. A BM reflects the strategic framework used to create and deliver the value that 114 a company creates through its products, along with its operations and customer engage115 ments. It articulates the logic and provides data and other evidence that demonstrates 116 how a business creates and delivers value to customers. It also outlines the architecture of 117 revenues, costs, and profits associated with the business enterprise delivering that value. 118 In that sense, a viable BM leverages on balancing profitability with customer satisfaction, 119 ensuring that the price charged reflects the perceived value of the product or service while 120 meeting quality expectations of the customer. Thus, by effectively aligning their offerings 121 with market demands, businesses can sustain operations and foster long-term growth [ 13 ]. 122 As mentioned already, Data Spaces are digital, federated infrastructures that enable 123 participants to share and access data based on a common governance framework linked to 124 principles such as sovereignty and trust. Data Spaces are operating without a dominant 125 intermediary, enabling decentralized value creation. Hence, the BMs in this context should 126 account its unique characteristics namely, data sovereignty, federated governance and trust. 127 Over the past few years, data space initiatives have moved from theoretical constructs to op128 Version October 22, 2025 submitted to Journal Not Specified 4 of 18 erational pilots and, in some cases, early market deployments [ 5 ]. While their architectures 129 are still evolving, the BMs underpinning these initiatives reveal several trends that reflect 130 the shift toward a more federated and trust-centric data economy. Frameworks like Gaia-x 131 and IDSA propose a federated model that introduce some neutral intermediaries -such as 132 brokersto ensure compliance, proper usage policy and identity verification. Initiatives 133 like Catena-X (in the automotive sector) and the Mobility Data Space are integrating with 134 energy, health, and manufacturing Data Spaces to create interconnected ecosystems [ 14 ]. 135 This cross-sectoral trend is also being supported by Horizon Europe calls and EU digital 136 strategy document. Moreover some data space operators do not directly monetize data 137 but they prioritize enabling services such as traceability, benchmarking, or compliance 138 tools that drive indirect value creation [ 15 ]. This includes cost savings in supply chains or 139 reduced regulatory burden, as seen in Catena-X or the European Health Data Space [16]. 140 However, transforming Data Spaces into economically sustainable ecosystems in141 volves a complex set of challenges that span governance, economic, and technological 142 domains. On the governance front, the absence of standardized frameworks creates un143 certainty, making it difficult to establish clear rules, responsibilities, and accountability 144 mechanisms. This is further complicated by the need to balance public-interest goals -like 145 openness and societal benefitwith the commercial interests of private companies, leading 146 to misaligned incentives and reluctance to share data openly [ 17 ]. From an economic 147 perspective, sustainability is hard to achieve due to the substantial upfront investments re148 quired to build and maintain the necessary infrastructure, including secure data platforms, 149 interoperability layers, and coordination mechanisms [ 17 ]. These investments are often 150 made without immediate or clearly defined value propositions for participants, especially 151 smaller organizations or public entities that may lack the resources to absorb initial costs. 152 Moreover, monetization models for data sharing remain underdeveloped, creating concerns 153 about value concentration among major actors and the distribution of benefits across the 154 ecosystem [ 18 ]. Technical and interoperability challenges in Data Spaces are both complex 155 and foundational. One of the key issues is ensuring consistent data quality and standard156 ization across different sources. Without common formats and reliable metadata, data 157 integration becomes inefficient [ 19 ]. It is also important the need for infrastructures that 158 can scale securely to accommodate a wide range of participants, from small organizations 159 and SMEs, to large enterprises, without compromising performance or data protection. 160 However, the most challenging issue is the integration of heterogeneous systems. Data 161 Spaces often involve legacy systems, proprietary platforms, and varying data models, 162 making seamless interoperability difficult to achieve. 163 A plethora of BMing approaches has been proposed in recent literature addressing 164 the specific needs of secure and sovereign data sharing for value creation. Among these 165 models, Osterwalder’s process of BM innovation is based on the participation of a range of 166 stakeholders, and his BM canvas has become immensely popular in the business world 167 [ 12 ]. Building on such foundational frameworks, more recent research has focused on data168 centric and platform-oriented BMs that support data sovereignty, interoperability, and trust 169 frameworks, particularly within the context of European Data Spaces. For example, the 170 International Data Spaces Association (IDSA) and the GAIA-X initiative have introduced 171 reference architectures and governance models to support sovereign data exchange while 172 enabling sustainable value creation among ecosystem participants [ 20 , 21 ]. Similarly, other 173 recent studies have proposed extensions to incorporate data governance mechanisms, 174 sharing incentives, and regulatory compliance, reflecting the increasing complexity of 175 data-driven value networks [ 22 ]. Collectively, these developments underscore a shift 176 from traditional BM frameworks toward dynamic, data-driven approaches that integrate 177 Version October 22, 2025 submitted to Journal Not Specified 5 of 18 technological, regulatory, and collaborative dimensions to enable secure and sovereign 178 value creation in modern data ecosystems. 179 3. BMs in Data Spaces 180 The conceptualization and design of BMs for Data Spaces presents distinctive chal181 lenges that differ fundamentally from those observed in traditional or even hybrid digital 182 business environments. Traditional BMs are typically organization-centric, focusing on 183 how a single entity creates, delivers, and captures value through a well-defined value 184 proposition and direct relationships with customers. In such models, data and resources are 185 controlled internally, and value generation occurs largely within the boundaries of one firm. 186 Hybrid BMs, which are increasingly prevalent in digital ecosystems and platform-based 187 economies, extend this perspective by involving multiple stakeholders who contribute 188 complementary resources or services. Despite their collaborative nature, these models 189 usually maintain a clear hierarchical structure, with a central orchestrator or platform 190 owner governing the value creation and distribution mechanisms [11]. 191 In contrast, BMs for Data Spaces embody a decentralized and federated paradigm. 192 They are inherently multi-stakeholder and collaborative, encompassing data providers, 193 consumers, intermediaries, and governance entities—each with unique objectives, re194 quirements, and revenue expectations. These must be aligned within a digitally enabled, 195 trust-based collaboration framework that ensures data sovereignty, interoperability, and reg196 ulatory compliance. Such configurations create a complex environment where individual 197 value capture is often less direct and more diffuse, potentially discouraging participa198 tion without clear incentive alignment. Consequently, Data Space BMs emphasize the 199 co-creation of value through the design and implementation of use case–driven collabora200 tions, ensuring that benefits are distributed equitably across all participants. This section 201 examines these distinctions in depth, providing a conceptual foundation for the subsequent 202 development of a structured framework for Data Space BM design [23]. 203 BMs of Data Spaces are inherently dynamic and tend to evolve over time as the 204 ecosystem matures. Often, hybrid models emerge that combine elements of various revenue 205 and value-capture strategies to better meet the diverse needs of participants and to balance 206 economic sustainability with collaborative governance. Real-world initiatives demonstrate 207 such evolution as Data Spaces move from pilot stages to operational ecosystems [21]. 208 3.1. Data Spaces Business Objectives 209 Typically, the key purpose of establishing a Data Space is to enable a trustworthy 210 environment for secure and controlled, in terms of sovereignty, data exchange between 211 participants for value creation, abiding to a certain set of rules. However, Data Spaces can 212 be enabled among ecosystem members with the purpose of achieving customer innovation 213 (Joint Innovation), fulfilling common objectives such as operational efficiency, regulatory 214 compliance etc. (Cost Sharing), preventing the emergence of limited dominant players in 215 the market (Combined Forces Ecosystem), providing quality-assured, easy access to data 216 of a domain of common interest such as open data, business partner data, etc. (Shared 217 Marketplace) and finally delivering societal impact through a public and private sector 218 collaboration (Greater Common Good) [ 24 ]. The evolution and hybridization of BMs 219 are evident as traditional frameworks increasingly integrate with data-centric, platform220 oriented, and governance-driven approaches, complementing the growing recognition 221 of non-monetary value forms—such as data-driven innovation, regulatory compliance 222 facilitation, and sustainability objectives—alongside traditional economic incentives in 223 manufacturing ecosystems [ 21 ]. Additionally, BMs in Data Spaces increasingly recognize 224 non-monetary value forms such as data-driven innovation, regulatory compliance facilita225 Version October 22, 2025 submitted to Journal Not Specified 6 of 18 tion, and sustainability objectives, which complement traditional economic incentives in 226 manufacturing ecosystems [25]. 227 In essence, these patterns can be summarized in three key strategic motivations for 228 Data Space establishment: 229 • Commercially driven, where participants are usually charged for using the Data Space, 230 which remains well-maintained and valuable, professional services are offered. 231 • Cooperative Initiatives, where participants take part in decision-making, have equal 232 stakes and share in the benefits. 233 • (Non) Governmental or NGO-Driven, usually initiated by public bodies or NGOs and 234 prioritize societal impact, while still remaining sustainable in the long-term. 235 Therefore, newly established Data Spaces are typically driven by either of the three 236 key strategic motivations, adopting one of the presented business patterns among the 237 participants. 238 3.2. Data Spaces Main Actors and Roles 239 As collaborative ecosystems, Data Spaces enable value creation among participants. 240 To fully capture this value, it is essential to identify the role each participant plays within a 241 data space or a specific co-creation use case. While slightly different or expanded terms 242 appear in the literature, they generally converge on the following core roles, which are 243 described in Table 1.244 Table 1. Examples of Ecosystem Actor Roles in Industrial Data Spaces Role Description Example Data Provider Entity providing data to other Data Space participants and defining access conditions. Oil refineries, wind farms, manufacturers Data Consumer Entity consuming data available in the Data Space. Manufacturers, researchers Service Provider Entity providing services and functionalities to the Data Space. Companies supplying AI algorithms, IoT devices, cloud storage, security infrastructure Governance Authority or Data Space Operator Legal entity or consortium defining the rules of the data space and overseeing its operation to meet business objectives. Consortium managing a cross-industry data sharing platform Trust Provider Entity that verifies claims related to trust. Certificate authority These roles collaborate to enable a framework for the execution of Data Space Use 245 Cases to deliver value to all involved participants. Note that not all roles appear in every 246 data space, and the specific governance and trust provider arrangements vary depending 247 on the domain, size, and maturity of the ecosystem. 248 3.3. Data Spaces Revenue Models 249 To capture this value delivery in economic perspective, we must recognize the different 250 data monetization pathways that are pertinent to Data Spaces. These pathways refer to 251 the process of transforming data into financial value, allowing organizations to leverage 252 their or other parties’ data assets for revenue, value creation or innovation stimulation. As 253 such, the monetisation strategies for Data Spaces focus on leveraging data as a strategic 254 asset that drives value across various touch points within the ecosystem. Table 2outlines 255 distinct monetization models uniquely identified for Data Spaces, highlighting the different 256 strategies that can be employed to generate revenue from shared data assets. 257 These monetization models and participant roles reflect those observed in leading 258 data space deployments such as Catena-X [ 16 ] in automotive manufacturing and the Smart 259 Connected Suppliers Network (SCSN) [ 26 ] in high-tech equipment supply chains. These 260 Version October 22, 2025 submitted to Journal Not Specified 7 of 18 Table 2. Key Monetization Strategies Employed Within Data Spaces Data Monetization Model Description Pay-per-Use Model Participants are charged according to the volume and type of data they consume. Subscription-Based Model Participants pay a regular fee to access the data space and/or premium features. Data-as-a-Service (DaaS) Data providers package their data as services that participants can access on demand. Freemium Model Basic data access is provided for free, while advanced features or premium services are subject to fees. Revenue Sharing Model Earnings are distributed among participants (data providers, intermediaries, and consumers) based on usage or predefined agreements. Tiered Pricing Model Users choose from different service levels or data quality tiers, tailored to diverse needs and budgets. initiatives demonstrate how adaptable BM configurations, combined with collaborative 261 governance, enable trusted and economically sustainable industrial data ecosystems. 262 Data Spaces adopt either of these monetization pathways or even a combination of 263 those, depending on the needs, to drive economic benefits. The appropriate revenue model 264 for a Data Space must be determined considering both the strategic drivers and business 265 objectives of the data space, the type of participating actors, and -most importantlythe 266 domain (or cross-sector domains) in which it operates. 267 4. Business Model Innovation for Manufacturing and Industrial Assets 268 In industrial settings, data-sharing ecosystems must support interoperability and 269 sector-specific requirements, ensuring that participants derive both economic and strate270 gic value. This section presents the BM development approach and offers a structured 271 overview of the key strategic components necessary for designing and delivering effective 272 BMs focusing on Data Spaces for the Manufacturing Sector and Assets (DS-MSA). Empha273 sizing long-term viability, the BMs of a DS-MSA are designed to adapt to evolving market 274 dynamics and regulatory environments. Importantly, the BM for a manufacturing data 275 space is not static; it consists of multiple models for different actors and often evolves over 276 time to meet the changing needs and opportunities within the ecosystem. A key objective is 277 to demonstrate how a trusted data exchange can unlock greater revenue opportunities for 278 participants, particularly when operating with digital assets in highly regulated or rapidly 279 shifting sectors where traditional scaling approaches may fall short. Effective BMs facilitate 280 collaborative innovation, cost-sharing structures, and new market opportunities, making 281 Data Spaces a foundational element for the future of data-driven manufacturing. 282 4.1. Mapping User Needs to Business Capabilities 283 In most Data Spaces customers require a secure, transparent, and compliant infras284 tructure for exchanging data across organizations and borders. This infrastructure should 285 enable trustworthy collaboration through strong mechanisms for identity management, 286 access control, and data protection in line with relevant regulatory frameworks. They 287 also need a modular and scalable environment that allows seamless integration of diverse 288 tools and services, including user management, secure data connectors, and automated 289 rule enforcement. Data usage policies must be clearly defined and automatically verified 290 before any exchange occurs, ensuring that only authorized participants can access or share 291 information for approved purposes. Moreover, to maintain data sovereignty and opera292 Version October 22, 2025 submitted to Journal Not Specified 8 of 18 tional control, customers expect a decentralized, peer-to-peer data exchange model where 293 data remains under the ownership and jurisdiction of its providers. Transparency and 294 accountability are also essential—requiring detailed activity records, auditable transactions, 295 and rule-based governance to guarantee compliance and traceability. 296 To address the complex requirements of secure, sovereign, and interoperable data 297 exchange in DS-MSA, the appropriate infrastructure provides a modular architecture that 298 aligns with both user needs and regulatory expectations. Table 3summarizes how key 299 functional capabilities of the Data Space map to core user requirements and the corre300 sponding value they deliver. The focus is on enabling trusted data transactions, enforcing 301 granular control policies, and ensuring legal compliance within a federated, decentralized 302 ecosystem—particularly relevant for manufacturing and other data-intensive sectors. 303 Table 3. Data Spaces capabilities mapped to user requirements and value outcomes User Requirements Manufacturing Data Space Capabilities Value Delivered Secure and trusted data exchange Federated connectors and blockchain-backed authentication ensure secure interactions, governed via a distributed Authority Portal. Enhances trust and transparency by ensuring secure, transparent, and governed data transactions. Legal and regulatory compliance Integration of identity and access management (IAM), encrypted channels, and policy enforcement aligned with EU frameworks (e.g., DGA). Peer-to-peer data transaction, adhering to EU principles of data sovereignty while enabling participants to retain jurisdictional and operational control over their assets. Granular access control Policy-driven access mechanisms using standardized rights languages (e.g., ODRL) allow fine-grained permissioning. Guarantees that data usage is strictly aligned with the provider’s terms and conditions. Data sovereignty Peer-to-peer data transactions and verifiable participant authentication. Preserves full ownership and control for data holders, in line with European data sovereignty principles. Data quality Integrated services enable a data quality assessment of time-series data, returning a report with comprehensive statistics. Ensures datasets can be immediately used without costly rework or delays. Dataset interoperability Semantic technologies such as vocabulary hubs and ontology-based data access enable interoperability and data discovery. Facilitates quick dataset discovery and combination from multiple sources, supporting model training on larger, richer datasets. 4.2. Value Proposition 304 The value proposition of the DS-MSA defines the distinct benefits, conditions, and 305 costs for all participants, ensuring mutual value creation. Operating an evolving Data Space 306 platform offers a compelling value proposition marked by both strategic advantage and 307 prestige. As the orchestrator of a trusted digital ecosystem, the operator gains monetization 308 opportunities through platform fees, and value-added services such as analytics and AI 309 Version October 22, 2025 submitted to Journal Not Specified 9 of 18 tools. The operator acts as a neutral facilitator, providing the technical infrastructure and 310 ensuring compliance with collectively agreed rules. This role does not undermine the 311 federated nature of the data space; rather, it enables practical operation and scalability The 312 Operator delivers a modular, scalable, and regulation-compliant infrastructure that enables 313 industrial and other relevant stakeholders to share data securely, transparently, and on 314 their own terms. 315 Beyond this, the operator plays an important role in identifying, attracting, and on316 boarding high-impact use cases, while also supporting their continuous development and 317 evolution, providing significant value to various users. For data providers such as refineries 318 and wind farms, the primary motivation for collecting and analysing operational data is 319 to optimize their own performance by anticipating failures, minimizing downtime, and 320 extending asset life. By leveraging data internally, providers gain immediate, tangible 321 benefits in efficiency, safety, and cost reduction. For data consumers, the value proposition 322 includes the availability of high-quality, curated data and AI-driven insights to help them 323 make informed decisions. In addition to financial incentives, successful DS-MSA deploy324 ments deliver critical non-monetary value, including compliance with environmental and 325 data regulations, increased transparency, and the ability to innovate rapidly in response to 326 emergent supply chain or sustainability challenges. 327 Value proposition for DS-MSA Operator: 328 • Revenue Generation: The Operator can generate recurring revenue from participants 329 through subscription fees for access to the platform, premium features, and data 330 hosting options. 331 • Ecosystem Growth and Network Effects: As more participants (data providers, 332 consumers, and service providers) join the data space, the Operator benefits from 333 network effects. Each new participant adds value to the ecosystem, increasing the 334 utility and attractiveness of the platform for others. 335 • Scalability and Market Outreach: The flexibility and customizability of DS-MSA 336 enables the Operator to cater to diverse industries and sectors, further expanding their 337 reach and customer base. 338 • Data Sovereignty & Trust: The Operator can create additional value through data 339 sovereignty guarantees, offering assurance to participants that their data is securely 340 hosted and only shared under strict access policies. 341 Value proposition for Data Providers: 342 • Ease of Use: A user-friendly platform that simplifies the complexities of data discovery 343 and exchange. It offers secure, diverse data hosting services that reduce integration 344 costs and support platform adoption. 345 • Broader Market Reach: Data providers can gain greater visibility to a wider pool of 346 potential buyers, opening new revenue opportunities and lowering acquisition costs. 347 Data assets are described and searchable on a granular level. 348 • Trust, Security and Data Sovereignty: Ensures trust in the data exchange mechanism, 349 giving control over how data is accessed and used. 350 • Usage of analytics services: Seamless integration of third-party analytics services; e.g. 351 for predictive maintenance. 352 •Monetary Benefits: Opportunity to monetize data and unlock financial value. 353 •Data Reusability: Discovery of new purposes and use cases for data assets. 354 • Compliance with EU Regulations: Adherence to legal frameworks such as the Data 355 Act and Data Governance Act. 356 Version October 22, 2025 submitted to Journal Not Specified 16 of 18 value despite the associated costs as outlined in Tab. 6. Some functionalities (e.g. trustful 547 and secure data exchange) offered by the data space are mandatory to realize the predictive 548 maintenance service. Otherwise, a different proprietary solution would need to be used, 549 which could be even more expensive. Data space costs are significantly reduced as the 550 number of participants increases, since certain fixed costs can be shared among them. The 551 main benefit of using a data space is the scalability of available datasets and services with 552 more participants, which in turn lowers costs and creates new business opportunities. 553 The cases introduced also demonstrate that the same Data Space infrastructure is 554 not only scalable across multiple participants, but also across different industrial sectors 555 (refinery, wind farms), further bringing down the costs as more stakeholders of the broader 556 ecosystem become relevant. Both use cases set the ground for a predictive maintenance 557 service which can be enhanced with the combination of complementary data assets and 558 the participation of external parties, contributing to analytics services and AI models’ 559 provision. These parties can enjoy the trust environment of the Data Space, backed by 560 the digital sovereignty principles adopted, as well as its compliance with relevant EU 561 data policies and standards, while integrating seamlessly those services with their existing 562 operations. Lifting the interoperability and ownership barriers for data exchange, the 563 data space enables service provision and generates operational benefits such as reduced 564 downtime or lower costs. In doing so, it acts as a sustainability driver for all participants. 565 While the benefits are clear, ongoing challenges remain—including managing data 566 quality, interoperability across legacy systems, and coordinating participation among 567 heterogeneous stakeholders. Addressing these hurdles is essential for maximizing the 568 impact of future data space deployments under the Digital Europe Programme. 569 6. Conclusions 570 Data sharing stands as the fundamental pillar of the digital economy, driving in571 novation, efficiency, and collaboration across diverse sectors. As data volumes grow 572 exponentially and its economic value continues to surge, effective data sharing mechanisms 573 become essential for generating value across individuals, businesses, and society at large. 574 Within this evolving landscape, Data Spaces represent a key enabler for establishing trust 575 frameworks that provide transparency, accountability, and governance in data-sharing 576 ecosystems, while the BMs that underpin their development and sustainability becomes 577 increasingly critical. Yet, the sustainability of Data Spaces hinges on viable, collaborative 578 BMs that balance the interests and contributions of multiple stakeholders, including data 579 providers, users, and infrastructure operators. 580 Our work introduces a structured framework for BM development, emphasizing 581 collaborative value creation, interoperability, trust, and long-term sustainability. Through a 582 comprehensive analysis of existing approaches and emerging design patterns, particularly 583 within the manufacturing sector, we define a taxonomy of monetization and value creation 584 strategies aligned with principles of transparency and collaborative value creation. Building 585 on this foundation, we validate the proposed BM framework through the UNDERPIN use 586 cases, which demonstrate its practical applicability and effectiveness in real-world Data 587 Space implementations. The findings confirm that the framework enables value creation 588 through flexible governance structures, harmonized data standards, and transparent value589 sharing mechanisms, supporting the sustainable growth of Data Space ecosystems. Future 590 enhancements will focus on expanding and refining the validation of the proposed BM 591 framework through the UNDERPIN use cases, This will generate broader empirical insights 592 into the framework’s flexibility and robustness, demonstrating its capacity to adapt to 593 diverse operational environments, regulatory landscapes, and market conditions across 594 multiple sectors. 595 Version October 22, 2025 submitted to Journal Not Specified 17 of 18 Author Contributions: “Conceptualization, Elena Politi, Axel Weissenfeld, Aristotlelis Ntafalias, 596 Giorgos Chrysokentis and Victoria Katsarou; methodology, Elena Politi, Axel Weissenfeld, Aristotlelis 597 Ntafalias, Giorgos Chrysokentis, Victoria Katsarou, George Fragiadakis, George Dimitrakopoulos, 598 Christos Papaleonidas, Sotiris Tsakanikas and Panagiotis Papadopoulos; validation, Elena Politi, 599 Axel Weissenfeld, Aristotlelis Ntafalias, Giorgos Chrysokentis and Victoria Katsarou; formal analysis, 600 Elena Politi, Axel Weissenfeld, Aristotlelis Ntafalias, Giorgos Chrysokentis and Victoria Katsarou; 601 investigation, Elena Politi, Axel Weissenfeld, Aristotlelis Ntafalias, Giorgos Chrysokentis and Victoria 602 Katsarou; resources, Elena Politi, Axel Weissenfeld, Aristotlelis Ntafalias, Giorgos Chrysokentis, 603 Victoria Katsarou, George Fragiadakis, George Dimitrakopoulos, Christos Papaleonidas, Sotiris 604 Tsakanikas and Panagiotis Papadopoulos; data curation, Elena Politi, Axel Weissenfeld, Aristotlelis 605 Ntafalias, Giorgos Chrysokentis, Victoria Katsarou and George Fragiadakis; writing—original draft 606 preparation, Elena Politi, Axel Weissenfeld, Aristotlelis Ntafalias, Giorgos Chrysokentis, Victoria 607 Katsarou and George Fragiadakis; writing—review and editing, Elena Politi, Axel Weissenfeld, 608 Aristotlelis Ntafalias, Giorgos Chrysokentis, Victoria Katsarou, George Fragiadakis, George Dimi609 trakopoulos, Christos Papaleonidas, Sotiris Tsakanikas and Panagiotis Papadopoulos; supervision, 610 George Dimitrakopoulos, Christos Papaleonidas, Sotiris Tsakanikas and Panagiotis Papadopoulos; 611 funding acquisition, Elena Politi, Axel Weissenfeld, Aristotlelis Ntafalias, Giorgos Chrysokentis, 612 Victoria Katsarou, George Fragiadakis, George Dimitrakopoulos, Christos Papaleonidas, Sotiris 613 Tsakanikas and Panagiotis Papadopoulos. 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