1 Inherent Issues with Ecosystems

Digitalization, including data and digital infrastructures, is substantially transforming industry landscapes, in particular affecting the production, logistics, and health sectors, to name a few. In the current global framework, German and European firms at large are challenged to embrace digitalization to secure their competitiveness. In this respect, International Data Spaces (IDS) [1] provide an IT architecture with the aim of safeguarding data sovereignty. In order to understand how IDS helps, it is important to firstly understand, and classify, the nature and inherent issues with ecosystems, and secondly the concerted actions of regulators inside and outside Europe.

1.1 Functions and Natural Characteristics of Ecosystems

Firms need to interact with one another within ecosystems for mutual benefit to deliver their offerings to a market. The level at which this occurs can often depend on a host of factors. Two really important factors are the level of modularity and the level of coordination needed [2].

Level of modularity is the degree to which components of a system can be produced by different producers. For example, vehicle components tend to be produced by just a few suppliers, whereas construction materials tend to be produced by many firms and can be used to create any blueprint.

Level of coordination is the degree to which firms need to cooperate with one another to bring an offering to the market; again, vehicle component manufactures need to work closely with car manufacturers to create vehicles, while construction material firms and home builders largely don’t.

These factors influence how industries are organized and their characteristics. Figure 27.1 shows a representation of industrial ecosystems with respect to varying levels (low to high) of modularity and coordination.

Fig. 27.1
figure 1

Representation of the levels of modularity and coordination [2]

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In the so-called vertically integrated ecosystem, the hub firm (buying firm) takes ownership of the supply chain because the level of coordination and control it can bring improves the value proposition, e.g., Tesla™ vs Nissan™ electric car charging or Apple™ vs Google™ phones.

In the hierarchical supply chain ecosystem, the hub maintains hierarchical control—not by owning its suppliers but by fully determining what is supplied and at what cost.

In business ecosystems, members retain control and claim over their assets. No one party can unilaterally set the terms, for example of, pricing and quantities. Standards and decision-making processes are mostly distributed, even though standards, rules, and interfaces are often set by hub firm(s).

In open market ecosystems, firms build offerings from generic modular components and require little coordination. Often components have multi-purpose utility, e.g., nut and bolts and sheet metal.

Within the technology sector, business ecosystems are becoming the most dominant; firms use business ecosystems for a number of reasons [2]:

  • They face an unpredictable but highly malleable business environment that requires them to collaborate with others in order to shape or reshape the industry for the common good.

  • The scope of required capabilities are too broad to be kept in-house.

  • The individual components of the end-user’s solution are modular; however, requirements for coordination are high, for example, firms need to identify the required third-party partners, specify their roles, and align their activities.

  • They can benefit from access to external capabilities and data that enable fast scaling of the ecosystem.

Technology business ecosystems tend to exist in three forms, often revolving around two-sided or multi-sided platforms [2, 3].

Fixed Core Ecosystem

Orchestrator offers the core thereby allowing the participants to tailor the rest of the offering based on a predefined list of functions or by allowing partners to define new use cases, e.g., Nespresso™ and Apple™.

Transactional Ecosystem

Orchestrator serves as an intermediary (offering a multi-sided platform) for standardized transactions between participants of an ecosystem, e.g., Uber™ and eBay™.

Innovation Toolkit Ecosystem

Communities of people or organizations that work toward the development of solutions that draw on common standards, e.g., Linux.

When functioning well, platforms can take fragmented and inefficient ecosystems and transform them through acceleration of industry standardization and orchestration of value creation, to the benefit of most stakeholders. This has been seen in many industries from cloud computing to short-term rentals. It is imperative for platform orchestrators to maintain control, to maximize returns, but this eventually comes at a high cost to complementors, as the business ecosystem life cycle matures.

Technology platform-based ecosystems tend to follow a life cycle shown in Fig. 27.2.

Fig. 27.2
figure 2

Life cycle, market share as a function of time, cit. Martin Reeeves et al. (2019), MIT Sloan

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  1. (A)

    Seize the Opportunity

    • Be the first or radically disrupt, scale fast investing persistently and sufficiently.

    • (Cumulative success rate 50%)

  2. (B)

    Evolve the Model

    • Broaden ecosystem scope and increase engagement.

    • (Cumulative success rate 25%)

  3. (C)

    Lock in Leadership

    • Manage vested interests, maintain differentiation, and renew the platform.

    • (Cumulative success rate 15%)

Creating a platform-based business ecosystem often depends on good timing and rapid investment (A) once winner-takes-all and network affects take hold and the market has “tipped” (B) those few that succeed can generate deeply entrenched advantages over all challengers.

As business ecosystems evolve, business ecosystem orchestrators tend to consolidate control of their ecosystems; (C) this is often where control starts to shift from a distributed control to centralized control; this pattern has replayed in many industries from mobility to operating systems [4].

At their core, business ecosystem platform orchestrators want to maintain control over their complementors by determining who can access the platform and under what terms. They become the global regulators controlling competition, price, licensing, and enforcement [5]. Platform orchestrators tend to seek to own the control points on the platforms that determine who can and can’t access the ecosystem and under what terms. Common control points include ownership of key platform APIs, access to key data, and ownership of key platform services or IPR.

Platforms have to maintain uneasy paradoxical balances; the biggest danger to platforms can come from the complementors they orchestrate, which are well characterized by the platform paradoxes [6]:

  • The Control Paradox—Too much control drives partners away, too little hurts standardization and orchestration of value creation.

  • Weak Partner Paradox—Strong partners view you as a threat; your future depends on weak partners that can be controlled.

When platforms become very large, controls imposed in the lock-in phase can become overbearing, and this can ultimately stifle innovation.

Platform-based ecosystems are a massive part of the current and future economy, and regulators around the world are becoming aware of their negative externalities and are taking actions to address this.

In this context, the European Strategy for Data primarily aims to create a single market for data that will ensure Europe’s global competitiveness and data sovereignty and to create a strong legal framework—in terms of data protection, fundamental rights, safety, and cybersecurity—in its internal market with competitive companies of all sizes and varied industrial base. The challenge is highlighted by a document published by the European Commission in February 2020 states [7] “A small number of Big Tech firms hold a large part of the world’s data. This could reduce the incentives for data-driven businesses to emerge, grow and innovate in the EU.”

International Data Spaces along with regulatory oversights are key to addressing governance challenges that platform economics bring to the data economy.

2 Concerns and Actions of Regulators

The market size of global platforms were measured [3] at $7 trillion this exceeds the combined GDP of Germany and France. A survey by the Centre for Global Enterprise [3] showed that North American and Asian platforms are currently dominating the digital economy. Europe’s digital competitiveness has been a concern for some time. Surprisingly, while Europe has emerged as an important consumer of platform services, it has generated relatively few platform companies [3] (Fig. 27.3).

Fig. 27.3
figure 3

Platform companies by size [3]. Source: Global Platform Survey, The Center for Global Enterprise, 2015

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The failure of European companies to seize opportunities and “take-it-all” has been attributed to high initial scaling costs in the European digital market which is fragmented in terms of language, consumer preference, and rules and regulations compared to more uniform markets like the USA and China. The risk-averse nature of European investment culture and the lack of supports from governments is another. Europe’s Strategy for Data is the plan to address these and other systemic problems. It has a number of aims that are outlined in Fig. 27.4.

Fig. 27.4
figure 4

Europe’s Strategy for Data [7]

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Backing up this policy, the EU has prepared legislative actions designed to regulate digital business such as the Digital Services Act (DSA) [8, 9], Digital Markets Act [10] (DMA) and the Data Governance Act [8] (Fig. 27.5).

Fig. 27.5
figure 5

Synoptic table of the EU legislation actions for DSA, DMA, and data governance [8]

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3 Issues with the Data Ecosystems and How Europe Intends to Address It

The EU in its Strategy for Data [7] sees several issues holding Europe back from realizing its potential in the data economy.

Availability of Data

It aims to increase the use of public sector information by business (G2B—data sharing), and the use of privately held data by other companies (B2B, data sharing).

Imbalances in Market Power

Small numbers of players may accumulate large amounts of data, gathering important insights and competitive advantages from the richness and variety of the data they hold. The “data advantage” can enable large players to set the rules on the platform and unilaterally impose conditions for access and use of data.

Data Interoperability and Quality

Application of standard and shared compatible formats and protocols for gathering and processing data in an interoperable manner across sectors and vertical markets should be encouraged through the rolling plan for ICT standardization.

Data Governance

Enforcement of data governance in society and the economy.

Skills and Data Literacy

General data literacy in the workforce and across the population is relatively low, and participation gaps exist.

Data Infrastructures and Technologies

The EU believes it needs to reduce its technological dependencies in these strategic infrastructures; it sees difficulties in the supply and demand side.

On the Supply Side

  • EU-based cloud providers have only a small share of the cloud market. The Hyperscalers represent more than 70% of cloud provisioned in Europe; figures from Synergy [11] show that since 2017, the European cloud market has grown more than threefold to €5,9 billion in the third quarter of 2020, but the European market share has declined from 26% to 16%.

  • Subjectivity to legislation of third countries and compliance of cloud service providers with important EU rules and standards.

  • Sector-specific data spaces, lack of ontologies, and widely accessible application programming interfaces (APIs) limit the applicability of comprehensive and EU-wide solutions.

On the Demand Side

  • There is a low cloud uptake in Europe (1 company in 4, only 1 in 5 SMEs).

  • European businesses often experience problems with multi-cloud interoperability, in particular data portability.

  • Currently digital service users cannot make self-determined decisions on data use, due to a lack of control and transparency over stored or processed data.

Empowering Individuals to Exercise Their Rights

Potential of Article 20 of GDPR to enable novel data flows; there are calls to give individuals the tools and means to decide at a granular level what is done with their data.

Europe hopes to accomplish these aims through:

  1. A.

    A cross-sectoral governance framework for data access and use

  2. B.

    Enablers: Investments in data and strengthening Europe’s capabilities and infrastructures for hosting, processing, and using data.

  3. C.

    Investment in a High Impact Project centered on European data spaces and federated cloud infrastructures

  4. D.

    Competences: Empowering individuals, investing in skills and in SMEs

  5. E.

    Creating common European data spaces in strategic sectors and domains of public interest

  6. F.

    An open, but proactive international approach

In this context, the GAIA-X project [12, 13], initiated by various European countries’ representatives (22 founding members from France and Germany, comprehensive of industry and other organizations), was kicked off in 2019, and it was given a structure in 2020, with a non-profit association called GAIA-X AISBL. The aim of GAIA-X is to establish a framework for collaboration and enable, or accelerate, the use of secure and trusted data services, with emphasis on SMEs, leveraging existing open standards. Great attention has been given to data processing and storage (EU/EEA area), transparency (incl. applicable jurisdiction), cybersecurity and portability (incl. practices to facilitate the switching between providers).

4 Role of IDS in Helping to Address these Concerns

Clearly International Data Spaces capabilities together with GAIA-X will play a key role in helping Europe achieve its stated goals of establishing an attractive secure and dynamic digital economy by providing the standards and trust infrastructure required to enable the establishment of European data spaces.

However, IDS and GAIA-X are not enough on their own to address imbalances in market power as well as data interoperability and governance issues.

To address this, the EU is preparing ex ante regulatory proposals aimed at large cloud “gatekeeper” platforms which will address this issue.

Articles 5 and 6 of the proposed Digital Markets Act [10] will compel cloud gatekeepers to:

5(e) refrain from requiring business users to use, offer or interoperate with an identification service of the gatekeeper

6(f) allow business users and providers of ancillary services access to and interoperability with the same operating system, hardware or software features that are available or used in the provision by the gatekeeper of any ancillary services.

6(h) provide effective portability of data generated through the activity of a business user or end user and shall, in particular, provide tools for end users to facilitate the exercise of data portability, in line with Regulation E 2016/679, including by the provision of continuous and real-time access.

6(i) provide business users, or third parties authorized by a business user, free of charge, with effective, high-quality, continuous and real-time access and use of aggregated or non-aggregated data […].

These measures and investments will allow data to flow more freely and facilitate the establishment of data spaces through IDS and GAIA-X which in turn will increase contestability and innovation.

5 Laws, Regulations, and National Standards in China on Data Protection

Around the world, at a governmental and societal level, there has been a push towards a more explicit set of rules to protect consumer data and privacy as technology services continue to fulfil an increasingly more important role in economics and everyday life. China is no exception in this respect; the cadence of laws, regulations, and national standards in China on data protection has been especially intense in recent years (Fig. 27.6).

Fig. 27.6
figure 6

KPMG: Overview of Draft Personal Information Protection Law in China [14]

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On April 26, 2021, following public consultation, the second draft of the Personal Information Protection Law (PIPL) was submitted to the Standing Committee of the National People’s Congress (NPC) of China. Similarly to Europe’s GDPR, it lays down a comprehensive set of rules around data collection and protection [14].

Liu Junchen, deputy director of the Legal Affairs Committee of the Standing Committee of the NPC, noted the importance of the protection of personal information for the development of the digital economy in China:

… the formulation of a personal information protection law is an objective requirement to further strengthen the legal protection of personal information protection; is a practical requirement for maintaining a healthy cyberspace; and is an important step in promoting the healthy development of the digital economy [14].

China’s PIPL applies to the country’s citizens and to companies and individuals handling their data. It contains 70 articles and shares many of the same principles and values of GDPR including transparency, fairness, purpose limitation, data minimization, limited retention, data accuracy, and accountability [14,15,16,17].

In particular, the key parts of the law:

  • Clarify the role and liabilities of the data processor.

  • Set out the lawful basis for the processing of data.

  • Specify that consent must be informed, specific, freely given, and indicative of the wishes of the data subject; it also details the additional consent required for the processing of sensitive personal information.

  • Define the data subject’s rights: right to information on data processing, right to access and request copy of personal data, right to correction, right to object to processing, right to withdrawal of consent, and right to deletion.

  • Set out data localization requirements and clearer rules on cross-border transfer of personal data.

  • Define the protective obligations for data processors that hold personal data, such as regular compliance audits, risk assessments, data breach reporting, and remedial measures in response to data breaches.

Serious violations of the PIPL, such as illegal processing of personal data or failure to adopt necessary measures to protect personal data, can result in fines of up to $7,4 million or up to 5% of the preceding year’s revenue. There are also terms for personal liability in the context of a violation of the PIPL. Like GDPR, PIPL will apply extraterritorially to protect the interests of Chinese data subjects.

Global Data Security Initiative

In order to address data security risks and enhance dialogue and cooperation in cyberspace governance, China has issued the Global Data Security Initiative [18], which proposes three principles and eight initiatives for global data security governance. These principles include agreeing global data security rules through consultation and co-operation, balancing security and development, and adherence to the principles of fairness and justice. These principles provide a feasible path for the healthy development of data protection and cyberspace. As a leading global information and communications infrastructure service provider, Huawei will maintain consistent cooperation, transparency, and openness in the field of data security, actively participate in global industry collaboration, and contribute to building a fair, secure, trustworthy, and stable cyberspace.

Huawei’s Data Lake Governance Center (DGC) [19] can help build an enterprise-class data governance platform and is used by both public and private sector enterprises. With DGC, organizations can manage data ingestion, data governance, data development, data services, and data visualization from end to end. DGC is focused on the elimination of data silos, compliance with data standards such as IDS and GAIA-X, acceleration of data monetization, and facilitation of digital transformation [19, 20].

6 Why International Data Spaces Are Important

IDS will help unleash innovation in the data economy.

Platform orchestraters can enable considerable value creation that can be difficult for complementors to resist, but they pay a price; the platform orchestrator becomes their regulator, and it often controls aspects such as access, standards setting, competition, price, licensing, and enforcement [5].

There are also considerable business risks as the platform life cycle matures: the risk of the platform assimilating core product functions, platform lock-in and lock-out, security and sovereignty issues, loss of control of emerging industry standards, data sovereignty, and decision-making. Many industries are rightly unwilling to accept these risks or hand over control.

There are many reasons why firms want to maintain control and sovereignty over their data, and these were outlined by Prof. Achim Wambach at the GAIA-X Summit on November 18, 2020 [12]: To protect user’s privacy (there are severe penalties for failure here), to protect sensitive data and trade secrets, and to gain a return on investment.

Many sectors of the economy handle sensitive data that depends on trust and security such as manufacturing, health, energy, telecoms, and government services, to name a few. Often, in these sectors, data is also fragmented and in heterogeneous formats.

Trust and interoperability are key, and the data economy in these sectors will not reach its full potential unless: a trust infrastructure is in place that ensures data can be shared accurately and securely, there is interoperability and data portability, and parties act in good faith and are verified. IDS and GAIA-X can provide this trust infrastructure and be the anchor for data ecosystems.

IDS and GAIA-X can be the trusted regulator in the data economy [12] orchestrating value creation, setting standards, offering federated platform services, and incentivizing and encouraging the exchange of data through data portability and interoperability. This will lower switching costs and also reduce co-dependencies.

The benefits are numerous [12]:

  • Direct benefits from the data, for example, the ability to create sophisticated AI models used on a host of applications through data partnerships.

  • Reduced ecosystem transaction costs between business ecosystem participants of all types leading to more efficient supply chains.

  • Prevention of captured and siloed data markets.

7 Specific Examples on How IDS and GAIA-X Can Be Used from Huawei Perspective

The International Data Space Association (IDSA) concepts have been successfully prototyped by Huawei’s internal IT department and applied to a real business scenario with one of its supply-chain ecosystem partners which requires daily bidirectional data exchange of sensitive production configuration and pricing data as well as confidential design and development documents.

Also for the early validation of the emerging GAIA-X concepts, Huawei started several activities to prototype pilot use cases together with partners in joint R&D projects.

  1. 1.

    Networked Production

    Under the umbrella of a GAIA-X project co-funded by the German government, a use case that had been tested as a German-Dutch demonstrator for a 3D production environment for individualized USB sticks is being extended to validate the feasibility of the concept of trusted and GAIA-X-compliant networked production. Conceptually, the use case is an evolved implementation of a state-of-the-art production process that builds upon Industry 4.0 standards. It demonstrates two physically distributed factories, third-party ISVs and associated cloud services cooperating in the production of a USB stick. Partners have the option to personalize the USB stick by pre-installing a software package, and they also have the option to change the individual form factor and color based on the request of a field-test customer. The integration of production is carried out across company boundaries, and the offering requirements of the value-added partners are instantiated according to the individual production order demands. Cloud services are also used to monitor the production and ensure quality control.

Furthermore, 5G connectivity is incorporated into the trusted networked production example to enable the integration of remote cloud-based services for real-time feedback, as illustrated in Fig. 27.7. Retrofitting of legacy machines, e.g., by deploying smart AI-services from the edge, will also be demonstrated as part of the project.As a secondary use case, remote servicing will also be implemented; this will enable the ad hoc provisioning of maintenance based on the concepts of a common application catalogue, identity services, and federation services.

  1. 2.

    Federated Trustworthy AI

    With a view to the increasing use of AI across all market verticals and distributed market players along multidimensional value chains, there is another important aspect to embrace which goes well beyond sovereign data infrastructures and common data spaces, but is equally important for its adoption: the trustworthiness of AI itself.

    In this context, Huawei Cloud is starting to explore the use of GAIA-X-compliant federated artificial intelligence for: control and (predictive) maintenance, match-making of demand and supply actors, as well as auction and contract provision of wind power plants operated by different utility providers. The idea is that the manufacturer of the wind turbines will offer an AI service that allows monitoring, control, and predictive maintenance of the turbines. A potential barrier is that while the power plant operator may be very much interested in using intelligent ready-made value-added services which are tailored to the turbines it bought, it may not be willing to share sensitive data about the actual operation and utilization of its wind farm, e.g., providing sensitive data may comprise future equipment tender negotiations or violate local cybersecurity regulations. To overcome this, the wind turbine manufacturer bundles the AI with the physical machinery. The master AI model(s) is (are) initially trained and hosted in a public cloud environment and operated “as is” at the wind power plant sites. Due to the implicit nature of AI as a learning system, the model continues to be trained on the operational data without that data leaving the wind farm. Instead of sharing the operations data with the manufacturer, the updated AI model trained onsite is fed back to the cloud and synthesized, resulting in an improved master AI model. This can then be redistributed to the customers, i.e., wind power plant operators. Without going into the details, there are variations in the approach to the federation with regard to central or decentral orchestration and homogeneous and heterogeneous data sources to be integrated. The selection will depend on the specific setting and can be implemented in combination, i.e., decentralized within the wind farms and centralized as seen from wind farms of different providers to the cloud backend of the turbine manufacturer. In any case, privacy by design is ensured with regard to data sharing. However, there are still several research questions to be solved with regard to federated learning such as optimizing the required communication among participating nodes and in particular on the characteristics of the robustness and integrity.Footnote 1

Fig. 27.7
figure 7

5G connectivity is added to networked production-enabled cloud-based services

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For both use cases, Huawei and partners are also addressing questions related to the federation of data and services as active assets within a networked production ecosystem related to the concept of digital twins and the required extension of the asset administration shell (AAS).Footnote 2

While the past focus of the AAS definition could be placed more on hardware, firmware, and connectivity-related aspects within the shop floor or physical factories and their physical environment, now and in the future, virtual factories, digital twins, and the increased integration of intelligent services require us to put more emphasis on the software dimension and cloudification aspects. Huawei is working with the ZVEI working group “IT in Automation” on a corresponding white paper for the “software type plate: as part of the AAS.

Moreover, considering the cloud as an embedded part of the (eco-)system of production-as-a-service requires new approaches to the integration of enterprise data across the information technology (IT) and operation technology (OT) domains. The cloud will have to integrate all steps of the manufacturing life cycle and offer holistic trustworthy data management. The cloud enriches the picture with business-focused data logic and offers the required platform services for the shop floor. Maintenance or value-added services can make use of AI and analytics cloud features, and product development and product twins will benefit from dedicated high-performance compute clusters. The increasing abstraction at scale at the manufacturing level, e.g., for production design based on production twins can be supported through an integrated cloud-edge IoT platform with rich and powerful data ingress, data lake, data storage, and advanced analytics capabilities.

In the aforementioned projects, Huawei considers digital twins for the manufacturing sector that are truly enabled through the cloud-edge continuum are a crucial component of an open, trustworthy, and sovereign data infrastructure. The various activities in that space driven by the EU and national governments in close interaction with industry and academic institutions put Europe at the forefront of the future of production.

8 Conclusions

In the current global framework, German and European firms at large are challenged to embrace digitalization to secure their competitiveness. International Data Spaces (IDS) and GAIX-X provide an IT architecture and a collaboration framework with the aim of safeguarding data sovereignty. These capabilities are extremely relevant for Huawei as its goal is to foster a fertile business environment in Europe and globally, governed by trusted services and a high level of interoperability, fully complying with regional digital sovereignty.

In the first part, we highlighted the current inherent issues and limitations in the data spaces ecosystem. Platform-based ecosystems are a relevant part of the current and future digital economy. However, it has been observed that business ecosystem platform orchestrators tend to maintain control over their complementors by determining who can access the platform and under what terms, and this reflects into offers, pricing models, and access to key data and IPR, thus creating unbalanced data-driven businesses, affecting SMEs in particular.

Moreover, various sectors deal with sensitive data that depends on trust and security, e.g., in production, health, and telecommunications. However, there is a widespread lack of interoperability and high heterogeneity of data, often fragmented.

Regulators have become increasingly aware of the problem, and they are introducing key policies, in particular a legal framework encompassing data protection, fundamental rights, safety, and cybersecurity (European Strategy for Data). Measures have also been taken to facilitate competitiveness while achieving a high level of trust and interoperability, for all stakeholders of all sizes (SMEs to Big Tech). Here is also where the IDS and GAIA-X initiatives will come into play.

The “cloudification” of the main industrial processes with leverage of artificial intelligence (AI)-based solutions and approaches (e.g., basic AI, machine learning, neuronal networks, and deep learning) gradually leads to the dynamic and flexible establishment of multiple interwoven industrial ecosystems; it is paving the way for the creation of various business models and richer offerings which will ultimately benefit efficiency, increase throughput, and reduce CAPEX. They also allow different levels of business relationships among the stakeholders. This creates an unprecedented level of opportunity, growth, and relative complexity, but it also makes it more apparent that aspects like interoperability, legislation, digital sovereignty of the data flows verifiability, and trustworthiness of services need to be considered carefully.

In this context, Huawei has started to look at contributing suitable solution architecture designs and open software building blocks to the open-source community, to support the ability to govern dataspaces and enhance existing governance frameworks with additional recommendations and insights. For Huawei, it is, imperative that the bidirectional data exchange of structured and non-structured data (like product configuration, pricing, design, feedbacks, and deliverables) with other ecosystem partners can occur frequently and efficiently in a trusted, safe, and controllable environment. Implementing the IDS concepts in data flows can lead to more scalable services with our partners and ultimately allow augmentation of the overall ecosystem.

We then considered a use case in manufacturing. The use case is an evolved implementation of Industry 4.0 in a factory. The integration is carried out across company boundaries, with value-added partners that are constantly being integrated in order to fulfil a particular production order. 5G connectivity is integrated into this example of trusted networked production to further enable the deterministic integration of remote cloud-based services. Retrofitting of legacy machines, e.g., by deploying smart AI-services from the edge is also demonstrated in the use case.

Finally, we mentioned how Huawei Cloud is also starting to explore the use of GAIA-X-compliant federated artificial intelligence for control and (predictive) maintenance as well as match-making of demand and supply actors.

With its engagement, Huawei hopes to contribute to the emerging European data economy in strict compliance with the related regulations.