The relationship runs in both directions, and that bidirectionality is what makes it worth examining carefully.

The governed data foundation AI needs

Most valuable data sits inside organizations that will not share it without legal clarity about permitted uses, liability and the boundaries of data sovereignty. Web scraping and generic platform terms of service cannot reliably reach this data. A data space can. It lets providers and consumers negotiate access terms directly, for each dataset and each intended use, under machine-readable usage policies that make those terms explicit and enforceable.

For AI development, this means access to curated, domain-specific datasets with verifiable provenance. Shared catalogues make data findable across organizational boundaries. Federated identity mechanisms extend trust between parties that have no prior relationship. Usage policies expressed in open standards such as the Open Digital Rights Language (ODRL) specify what data may be used for and under which conditions. These are the governance properties a general AI stack assumes are already settled. In most cross-organizational settings, they are not. Data spaces provide them.

The automation data spaces need to scale

Building a data space means integrating its components into the existing systems of many different participating organizations. That integration work is expensive. AI substantially reduces the cost by generating metadata, aligning schemas across heterogeneous backends, translating human-readable legal terms into machine-readable policies and monitoring policy compliance across participants. These are tasks that would otherwise require sustained manual effort for every new participant onboarded and every new dataset described.

The FAIR principles — Findability, Accessibility, Interoperability and Reusability — provide a practical way to map out where each direction of the relationship applies. On the findability dimension, AI supports metadata generation and semantic search, making catalogues more useful. On the accessibility dimension, the Model Context Protocol (MCP) provides a standardized interface through which AI systems can connect to heterogeneous data services. On interoperability, AI assists in aligning vocabularies and bridging terminology differences without requiring full upfront standardization. On reusability, AI can help translate legal terms into enforceable policies and assess whether datasets are fit for a given purpose.

Three patterns of collaboration

Research by Fujitsu and Fraunhofer ISST describes three patterns by which AI workloads draw on a data space. In collaborative model development, organizations train a shared model by exchanging data or model parameters, with the data space providing the usage policies and provenance records that govern what enters training. In inter-organizational model inference, one organization enriches its own model at inference time with data held by others, with the data space providing discovery, access control and usage conditions. In inter-organizational agent collaboration, autonomous agents from different organizations accomplish a task together, with the data space providing participant identity, contract negotiation and an auditable record.

These patterns recur across the pilots documented in the paper, from AI-enabled robotics in Germany to data marketplace negotiation in Japan. They are not theoretical. They are working in practice.

Implications for organizations

For AI practitioners, the paper is a caution against treating API connections as automatically trustworthy. A model-connected interface is not automatically compliant. A retrieval-augmented generation pipeline is not automatically authorized. Data spaces supply the missing layer: identity, contract, usage control, semantic interoperability and provenance.

For data space practitioners, the paper is a preparation guide. The familiar primitives — participants, credentials, catalogues, data products, Connectors, usage policies — are sufficient. What changes is that some participants will now be autonomous agents. Handling that requires making agent identity, scope, tool access and audit explicit. The foundations are already in place.

The next post in this series looks at how data spaces make AI trustworthy across organizational boundaries — covering the trust framework, verifiable identity and what the regulatory landscape requires.

The post Sharing data better makes AI work better and vice versa appeared first on International Data Spaces.

Company Certificate Management in Catena-X addresses exactly this: a certificate is uploaded once and shared securely with all business partners, with auditable access. It is the entry use case of the Data Space Accelerator and a concrete way to experience what productive data exchange looks like in day-to-day operations.

What the Data Space Accelerator is

The Data Space Accelerator is a study investigating how companies move from initial readiness to productive data exchange in Catena-X. The potential of data spaces is widely recognized, while there is still limited empirical evidence on which factors explain successful onboarding and where the gaps behind slower adoption lie.

The study addresses this with a before-and-after survey design: participating companies are assessed at the start of onboarding and again after completing their first productive data exchange. This makes it possible to diagnose typical readiness gaps along the data value chain, identify company profiles and use cases with a higher likelihood of successful adoption, and derive targeted levers to accelerate onboarding. The reference data space is Catena-X, and the use cases are standardized Catena-X use cases documented in the open-source repository Tractus-X.

For participating companies, the study is designed to deliver:

• empirical evidence on the economic and organizational benefits of data sharing,
• actionable recommendations to strengthen digital readiness,
• case-based insights from multiple use cases.

What participation involves

Participation focuses on one or more concrete use cases implemented in real operations, supported by certified partners who guide companies through each step. The path follows six stages:

1. Submit an offer and choose the program path.
2. Review and acceptance against the tender documents.
3. Select the use case (entry: Company Certificate Management).
4. Guided onboarding into the Catena-X data space.
5. Complete the first productive data exchange.
6. Receive the milestone-based payout.

Two paths are available. The Basic Path covers Company Certificate Management. The Advanced Path adds one further Catena-X use case of the company’s choice — such as Product Carbon Footprint, Digital Product Passport, Traceability, Data Driven Quality, Demand and Capacity, Short-Term Supply (PURIS), or Circular Economy. Prior experience with data spaces is not required, and internal effort is designed to stay limited and predictable.

Remuneration based on verified results

Remuneration is milestone-based and tied to verified results: successful onboarding, implementation of the selected use case, and the first productive data exchange along that use case. The Basic Path provides €15,000 (net), and the Advanced Path adds a further €15,000 (net), up to €30,000 (net) in total. The amount reflects the milestone-based structure of the program rather than a reimbursement of a company’s actual implementation costs. For participants based outside Germany, it is recalculated using a purchasing-power index, as set out in the tender documents.

Who can take part

The study primarily addresses SMEs and Mid-Caps in automotive and adjacent industries, including machine building, that are not yet active in Catena-X. Companies of all sizes may apply, provided the program requirements are met. Eligibility extends to EU member states and to countries that have signed a Government Procurement Agreement with Germany, listed in the DSA Remuneration PPP Index.

Timeline and how to apply

All program activities and milestones are completed within 2026, and applications are open until the end of September 2026. Onboarding and the first productive data exchange need lead time, so an early start gives teams room to work through the steps with their onboarding partner.

Full details, participation requirements, and the application are available on the official program website. Questions on participation, eligibility, onboarding, remuneration, timelines, or study requirements can be directed to the program team.

Learn more and apply: data-space-accelerator.com

Contact: dsa@internationaldataspaces.org

The post Inside the Data Space Accelerator: a study on the value of industrial data sharing appeared first on International Data Spaces.

One participant captured the spirit of the discussion in a simple yet powerful statement:

If data spaces did not exist today, we would have to invent them.

The remark resonated because it reflected a broader realization shared across the roundtable. The conversation was not primarily about technology. Technical interoperability, standards, and federated architectures continue to advance at an impressive pace around the world. Rather, the discussion focused on a complementary challenge: How to create the governance, trust, and economic incentives necessary to make large-scale data sharing work in practice.

This challenge has become particularly urgent in the age of AI.

Artificial intelligence is dramatically increasing demand for high-quality, trustworthy, and context-rich data. At the same time, organizations are becoming more cautious about how their data is accessed, combined, used, and monetized. As AI systems become more powerful, questions about accountability, transparency, provenance, and control are moving from the margins to the center of the debate.

The rise of AI is therefore exposing a critical governance gap. While the world has made significant progress in developing technologies for data exchange, the governance mechanisms required for a functioning global data economy remain unresolved.

Who determines the conditions under which data can be used? How can compliance be ensured? How can data contributors retain meaningful control? And how can value generated from data be distributed fairly?

These questions are no longer theoretical – they are central to whether large-scale data sharing can work in practice.

This is where data spaces increasingly come into focus. Rather than serving merely as technical infrastructures for data sharing, they emerge as governance frameworks that combine interoperability, trust mechanisms, and shared rules for participation. Sovereignty is not an obstacle to data exchange – it is a prerequisite. Organizations are willing to share valuable data only when they can rely on clear rules, transparent governance, and enforceable usage conditions. Data spaces establish exactly these conditions, enabling responsible and scaled data sharing while preserving the control and trust that participants require.

The discussion also highlighted the importance of ensuring that the future data economy is truly global.

Participants stressed that global interoperability cannot be achieved through technology alone. It also requires governance approaches capable of bridging different regulatory environments, economic realities, and societal expectations.

This perspective became particularly visible in discussions about equitable participation in data-driven value creation. Several participants raised concerns about scenarios in which data from less economically mature regions contributes to global innovation without corresponding participation in the value generated. The challenge, therefore, is not only to enable global data flows, but also to ensure that trust, data sovereignty, and value creation can be shared more broadly across regions and communities.

Another area of strong consensus concerned business value.

Participants repeatedly emphasized that the long-term success of data spaces will depend on their ability to create tangible economic benefits. Organizations will not participate because a technology is elegant or because a standard exists. They will participate when data spaces help them solve real problems, unlock new opportunities, reduce costs, improve compliance, or create new forms of collaboration.

The question is therefore shifting from “How do we build data spaces?” to “What value do data spaces create?”

The Global Data Spaces Roundtable pointed to a broader conclusion: the future of the data economy will not be defined solely by our ability to move data across boundaries. It will be defined by our ability to establish trusted governance for how data is shared, used, and transformed into value.

As AI accelerates this transformation, data spaces are increasingly being viewed not merely as a technological concept, but as a governance framework for the next generation of the global data economy – one that can integrate emerging markets, respect sovereignty, and create equitable value for all participants.

The post Global Data Spaces Roundtable appeared first on International Data Spaces.

Horizontal data space that connects sector-specific data spaces

The strategic relevance lies in how this base layer is designed to support domain-specific extensions. The Geo Data Space is conceived as a horizontal data space: a spatial “ground layer” onto which sector-specific data spaces can be connected. Energy data, mobility data, or health-related aggregates are added as domain layers, all aligned through a shared geospatial reference model. This layered approach reduces fragmentation and enables interoperability across domains that traditionally operate in silos.

The initiative reflects a key principle of data spaces: sovereignty through structure. Data is not pooled indiscriminately. Instead, it is shared under clear licensing terms, aggregated where necessary, and governed through data space connectors. This approach highlights a strong focus on aggregation and depersonalization, particularly for sensitive domains such as mobility and health. Spatial aggregation is used as a practical mechanism to enable data sharing while respecting legal and ethical boundaries.

A multi-dimensional trust model

Another notable element is the planned integration of trust and data quality indicators into the connector layer. The initiative foresees a multi-dimensional trust model that allows users to assess data quality, reliability, and uncertainty. This responds directly to a growing challenge in data spaces: decision-makers and AI systems increasingly depend on understanding not just data access conditions, but also the quality and limitations of the data itself.

The Geo Data Space Germany is explicitly framed as a collaborative effort. While the initial focus is national, the ambition extends to Austria, Switzerland, and other European countries. The need for a common data model is considered a prerequisite for cross-border geospatial data spaces. Without shared semantics and structure, technical interoperability alone remains insufficient.

Lowering entry barriers for sector-specific ecosystems

Foundational data spaces such as the Geo Data Space can lower entry barriers for sector-specific ecosystems by providing trusted base data and governance structures upfront. They also illustrate how public-sector data, private expertise, and data space standards can converge in a bottom-up, scalable way. As Europe advances its data strategy, such horizontal data spaces may become critical enablers for federated, cross-domain data sharing.

The post Geo Data Space Germany: Building a sovereign geospatial foundation for cross-domain data spaces appeared first on International Data Spaces.

Background: why an update was necessary

Several external developments motivated the revision. International standardization has progressed, most notably through ISO/IEC 20151 on data space concepts, providing a stable terminology and reference model. In parallel, open specifications and reference implementations have matured within the Eclipse Foundation, moving core building blocks from experimental to operational quality. Finally, real deployments have generated empirical feedback on governance models that scale and those that do not. The earlier Rulebook increasingly reflected an earlier phase of the ecosystem. Release 2026-1 updates this baseline and reconnects the document more explicitly to the IDSA Manifesto principles of participant autonomy, decentralization, and trust.

Decentralization as a default system property

One of the most consequential changes is the explicit treatment of decentralization as the default architectural assumption. The Rulebook now frames participant autonomy as a primary requirement, rather than an optional design preference. While alternative patterns are acknowledged, decentralized interaction models are positioned as the reference case against which deviations must be justified.

This clarification matters because decentralization affects identity management, policy enforcement, catalogues, and governance responsibilities. By making this assumption explicit, the Rulebook reduces interpretive flexibility that previously allowed centralized platforms to be labelled as data spaces without meeting the underlying intent.

Trust as a continuous, runtime concern

Another structural refinement concerns trust. Earlier interpretations often treated trust as a static onboarding decision. The new Rulebook reframes trust as a continuous, context-dependent process that must be evaluated and maintained during runtime. This includes clearer guidance on claims, policies, and mechanisms for reconciling them in concrete interactions. Trust is no longer implied by membership alone but is operationalized through verifiable behavior and enforceable agreements. For implementers, this shifts attention from certification events to ongoing monitoring and policy evaluation, with direct implications for connector design and governance processes.

Clear separation of mandatory and recommended requirements

Release 2026-1 introduces a more disciplined distinction between mandatory requirements and recommended practices. This separation provides governance bodies and implementers with unambiguous guidance on what is required for conformance and what remains discretionary. The absence of such clarity in earlier versions often led to over-engineering or inconsistent interpretations across data spaces. The revised structure supports proportional implementation, allowing organizations to priorities compliance efforts while still benefiting from best-practice guidance where appropriate.

Release 2026-1 maps more directly to ISO/IEC 20151, the Dataspace Protocol, the Decentralized Claims Protocol, and Eclipse Dataspace Components. This alignment reduces conceptual translation work for architects and developers and makes the Rulebook easier to integrate into existing design and compliance processes.

Functional requirements, not architecture

A deliberate clarification concerns scope. The Rulebook defines functional requirements for governance and participation, not architectural prescriptions. Architectural guidance remains the responsibility of the Reference Architecture Model (RAM). This distinction is reinforced in the 2026-1 release to avoid conflating governance obligations with technical design choices. For practitioners, this means the Rulebook should be used to frame decisions about roles, trust models, and obligations, while concrete system architectures are derived elsewhere. The separation preserves architectural flexibility without weakening normative consistency.

Forward-looking extensions: AI agents in data spaces

The inclusion of a dedicated chapter on AI agents signals an expansion of the Rulebook’s conceptual horizon. The chapter establishes initial requirements and considerations for autonomous or semi-autonomous actors participating in data spaces. This acknowledges emerging usage patterns where AI systems negotiate access, evaluate policies, or act on behalf of participants. By addressing this early, the Rulebook provides a foundation for future refinement without delaying current implementations.

Key takeaways for the data spaces community

The IDSA Rulebook 2026-1 delivers clarity of direction. Decentralization is treated as a default condition, trust as an operational process, and requirements as explicitly tiered. The document aligns more tightly with standards and tooling that organizations already use, while preserving a clear separation between governance requirements and architectural design. For working groups, implementers, and decision-makers, the Rulebook is best approached as a basis for concrete governance and participation decisions, supported by technical specifications elsewhere in the IDSA Knowledge Base. Used in this way, it provides a stable foundation for interoperable and trustworthy data spaces.

The post IDSA Rulebook 2026-1: Structural clarifications for operational data spaces appeared first on International Data Spaces.

These questions are now the real bottleneck for AI at scale. And they are precisely where data spaces come into focus.

What data spaces already provide

Long before AI became a board‑level priority, data spaces were designed to address precisely these governance challenges. Through shared standards and specifications, they establish identity and participation rules, define machine‑readable usage policies, enable enforceable data contracts, and ensure traceable data provenance across organizations.

Data spaces operate as an operational trust layer, one that allows data to flow while remaining under the control of its providers.

Complementary, not competing, layers

At the same time, new AI‑specific protocols are emerging to solve technical problems. Protocols such as the Model Context Protocol describe how AI systems access tools, retrieve context, or interact with resources. They focus on interaction mechanics.

Data spaces address a complementary question: under which rules these interactions take place across organizational boundaries, and how accountability is maintained when systems act autonomously. Seen together, these layers do not compete. They complete each other.

The relationship is simple but often misunderstood. AI protocols explain how systems interact. Data spaces define the conditions under which those interactions are allowed, governed, and trusted. Better together is not a slogan, but an accurate description of this division of responsibility.

Making the connection visible

Within IDSA, this convergence of AI and data spaces has led to focused work. A dedicated Task Force on AI and Data Spaces has been established to document how existing data space standards apply to concrete AI scenarios already being deployed. These scenarios range from retrieval and inference to federated learning and agent‑based workflows.

The task force is not developing new protocols or architectures. Its mandate is to translate what already exists into guidance that is accessible to AI practitioners beyond the traditional data space community. Work is carried out transparently, with a public scoping paper planned for release by the end of June 2026.

Trust as infrastructure

Trust and data sovereignty are not features that AI systems can simply add at the end. They are infrastructure. And in the case of data spaces, that infrastructure is already in place.

“As AI continues to move deeper into operational environments, this perspective reframes the discussion away from future promises and toward present capabilities. It highlights that many of the governance challenges facing AI today have already been addressed elsewhere, quietly, through years of standardization and deployment.”

Reinhold Achatz, Chairman of the Board of IDSA

AI and data spaces are not separate conversations. They are part of the same system. And increasingly, they are better understood together.

The post Better together: Data spaces ground AI in trust and accountability appeared first on International Data Spaces.

When data becomes unavoidable

Manufacturers today depend on information that extends far beyond their own systems. Sustainability reporting, emissions accounting, circularity requirements, due‑diligence obligations, and digital product documentation all require data that spans the entire product lifecycle across companies, sectors, and borders.

At the same time, that data is sensitive. It reflects process know‑how, supplier relationships, and competitive advantage. Sharing it casually is not an option. This tension – needing data you do not control, while needing to stay in control – sits at the core of manufacturing’s digital transformation. And it is exactly the tension explored in the IDSA Executive Brief on Manufacturing Data Spaces.

Why data spaces fit manufacturing logic

The paper makes one point unmistakably clear: manufacturing does not need another platform. Instead, it needs governed environments that allow organizations to share data under transparent and enforceable rules, without centralizing ownership or dissolving responsibility.

Data spaces follow this logic. They enable collaboration while preserving data sovereignty. They are federated rather than centralized, interoperable rather than uniform, governed rather than ad‑hoc. This approach aligns with how manufacturing already works: distributed, specialized, and deeply dependent on trust.

A global movement with local realities

The brief situates manufacturing data spaces in a global context. Across Europe, Asia, and the Americas, industrial initiatives are converging on similar principles. Secure data flows, lifecycle transparency, and compatibility across multi‑tier value chains.

Yet the paper is careful not to present a single blueprint. Manufacturing evolves differently across regions and sectors. What matters is not identical solutions, but shared foundations that prevent fragmentation into incompatible ecosystems.

From funding programs to operational structures

One of the most valuable contributions of the paper is its sober view on Manufacturing‑X. The brief describes Manufacturing‑X as a public–private funding framework from which multiple independent project families emerged, each shaped by its domain, partners, and priorities.

Factory‑X, Semiconductor‑X, Chem‑X and other initiatives illustrate how manufacturing data spaces are forming around real industrial challenges. Diversity is not a weakness here, it reflects the sector itself. The risk lies elsewhere: in allowing this diversity to harden into incompatible islands.

The importance of shared building blocks

This is where the paper goes deeper than most discussions. It highlights the growing reuse of shared governance and technical building blocks across manufacturing data spaces: trust and identity mechanisms, usage‑control principles, semantic alignment practices, and operational functions such as onboarding, verification, and certification

These building blocks are what make scaling possible. Without them, data spaces remain pilots. With them, they become infrastructure.

IDSA’s role: alignment, not control

International Data Spaces Association is a partner in this multi‑actor environment. IDSA contributes mature concepts such as the IDS Reference Architecture Model, the IDSA Rulebook, and the Dataspace Protocol to help align diverse initiatives without overriding domain‑specific needs.

This is an important distinction. Manufacturing data spaces will not be built by one organization or one standard alone. They emerge through coordination, compatibility, and shared responsibility.

If you want to understand how manufacturing is organizing data on its own terms, this paper is the place to start.

Read the full IDSA Executive Brief: Manufacturing data spaces and the role of IDSA

The post Manufacturing data spaces: What’s actually taking shape appeared first on International Data Spaces.

The question is no longer if data will be shared, but how.

Industrial data spaces are no longer a future discussion. Across global value chains, companies face mounting pressure: transparency requirements, sustainability reporting, cross‑border collaboration, and digital interoperability. All without sacrificing control over their data.

Both Brazilian demonstrators showcased at Hannover Messe offer a clear answer: data sharing without centralization, without loss of sovereignty, and with governance built in by design.

ELDORADO’s BrasAuto Demonstrator: automotive coordination without data exposure

At the ELDORADO stand, visitors are invited into a simulated automotive logistics data space, one that feels strikingly close to real industrial operations.

The BrasAuto Demonstrator connects three fictional but realistic roles: a vehicle manufacturer, a parts supplier, and a logistics service provider. Each operates its own IDS Connector. Each decides which operational data is shared and under which usage policies.

What flows through the data space are confirmed plans, inventory levels, consumption data, and logistics execution events. What stays strictly local are forecasts, optimization logic, and analytical models.

For visitors, the value lies in seeing how an IDSA‑compliant data space can reduce coordination friction in complex supply chains while preserving competitive integrity. It is a clear, accessible learning environment designed to make data sovereignty understandable, not abstract.

ABINC’s Federated Industrial Data Space: trust across borders

Where ELDORADO zooms into automotive logistics, ABINC broadens the lens. Its Federated Industrial Data Space Demonstrator, developed in a Brazil–European collaboration, shows how industrial data can travel across companies, sectors, and even continents.

The demonstrator focuses on low‑risk, high‑value industrial indicators: machine states, operating hours, downtime reasons, and energy consumption. These indicators enable benchmarking, efficiency analysis, ESG reporting, and operational learning, while sensitive production data remains protected.

There is no central database. No platform lock‑in. Instead, governance rules, purpose limitation, and consent are embedded directly into the technical architecture.

At Hannover Messe, this demonstrator tells a compelling story for international visitors. Cross‑border industrial cooperation is possible today, if trust is engineered and not assumed.

Two paths, one shared principle

While the two demonstrators address different industrial contexts, they converge on the same core idea. Data spaces are not about sharing more data but about sharing the right data, under the right conditions.

Both were built primarily as educational environments to help Brazilian companies, institutions, and policymakers understand what data spaces mean in practice. At Hannover Messe, they now invite the global community to step inside functioning, IDSA‑aligned ecosystems.

These demonstrators allow visitors to:

• see IDSA principles applied end‑to‑end
• understand governance not as theory, but as a running system
• explore how trust can be embedded into technology—not negotiated case by case

Continue the conversation at Hannover Messe 2026

If you want to explore how industrial data spaces are moving from strategic vision to measurable business value, join IDSA at Hannover Messe in Hall 27, Booth F60 – partner pod at the OPC Foundation.

On Tuesday, April 21, 2026, IDSA hosts Industrial Data Spaces in Action – Simple, Scalable, Profitable, a dedicated event for industry leaders and IT decision‑makers who want to understand how sovereign data sharing moves from strategy to execution — and how it delivers measurable business value.

The event will take place on April 21, 13:00 to 16:30, in the Hannover Messe Convention Center, room 18.

Register free for the Journey & Event: forms.office.com

The post IDSA members from Brazil bring data spaces to life at Hannover Messe appeared first on International Data Spaces.

Data spaces as industrial infrastructure

The challenge is no longer whether data should be shared, but how. Companies need to exchange information without losing control over sensitive knowledge, without building a custom integration for every partner, and without navigating a different trust model each time. Traditional approaches struggle to scale in this environment.

Data spaces respond to this structural challenge. They are not platforms, and they do not centralize data. Instead, data spaces create governed environments in which organizations can share data under transparent and enforceable rules, while retaining sovereignty over how that data is used.

As regulatory expectations grow and supply chains become more interdependent, data spaces increasingly function as industrial infrastructure. Much like logistics networks or energy systems, they enable distributed production systems to operate reliably and transparently at scale.

Manufacturing data across boundaries

This transformation is not confined to Europe or to a single initiative. Across the world, industrial data spaces are emerging to solve similar problems: secure data flows, lifecycle transparency, and interoperability across complex value chains.

Different regions pursue different governance models, but the direction is shared. Manufacturing data must move across organizational and national boundaries in ways that align with real supply chains, not political borders. Forums such as the International Manufacturing-X Council reflect the growing recognition that compatibility across regions will be essential for long-term success.

In this landscape, IDSA contributes experience built over more than a decade of research and implementation. Through its work, such as the IDS Reference Architecture Model, the IDSA Rulebook, and the Dataspace Protocol, IDSA supports trusted, interoperable data sharing across diverse technical environments. This role is collaborative rather than centralizing. Manufacturing data spaces can only succeed if they remain interoperable across sectors while respecting domain-specific needs.

Continue the conversation at Hannover Messe 2026

Hannover Messe brings together the physical and digital realities of manufacturing. It is where strategic questions meet operational constraints, and where infrastructure thinking moves from concept to implementation.

On Tuesday, April 21, 2026, IDSA hosts Industrial Data Spaces in Action – Simple, Scalable, Profitable, a dedicated event for industry leaders and IT decision-makers who want to understand how sovereign data sharing moves from strategy to execution — and how it delivers measurable business value.

The event will take place on April 21, 13:00 to 16:30, in the Hannover Messe Convention Center, room 18.

Register free for the Journey & Event: forms.office.com

The post The factory is no longer enough appeared first on International Data Spaces.

From one book to many focused articles

The most visible change sits in the structure. The current chapter-based format will give way to a collection of individual articles. Think of it less as a single manual and more as a growing library.

This shift allows content to evolve faster. Therefore, updates no longer require reshaping an entire document. Instead, specific articles can change as requirements, practices, and standards move forward.

Meanwhile, content that no longer supports today’s work will quietly leave the stage. The result should feel leaner and easier to navigate.

Sharpening the focus on what matters now

Functional requirements take center stage in the upcoming version. Many of these topics have changed in recent months, and the IDSA Rulebook will reflect that reality more clearly. As a consequence, outdated sections will be removed. Readers should spend less time filtering information and more time applying it.

One topic surfaced quickly as both important and overdue: a dedicated IDSA glossary.

At the moment, the IDSA Rulebook relies on external glossaries, including those from other initiatives. While useful, this creates friction. Terms drift. Interpretations vary. Discussions slow down.

Therefore, several working groups will now collaborate on a shared IDSA glossary. This work cuts across domains, which makes coordination essential. However, it also offers a chance to align language across technical, legal, and organizational perspectives.

Over time, this glossary should reduce misunderstandings and speed up collaboration.

A living and growing rulebook

Taken together, these changes signal a shift in how the IDSA Rulebook lives and grows. It becomes more modular. It stays closer to current practice. It speaks a clearer language.

Most importantly, it invites participation. The IDSA Rulebook is no longer something that updates occasionally in the background. It becomes a shared working space that reflects how data spaces actually operate.

The post A new chapter for the IDSA Rulebook appeared first on International Data Spaces.