The Future of the Data Economy: Four Building Blocks to Maximize Value From Data Spaces

Data Economy

By Svenja Falk, Surya Mukherjee and Laura Wright

Data has massive potential to create both social and business value. Yet, most companies are yet to establish how to best share, analyse or extract value from their own data. A new study by Accenture Research reveals the common barriers to data-based value creation, and identifies how organizations can accelerate ecosystem engagements to realize the full potential of collective data. If data is the fuel of the new economy, then data spaces are its catalyst. With the right business models, infrastructure and governance, data value – and business – can thrive.

Common Knowledge: Value of data increases when shared

We know data has immense value – and that combining and analysing data leads to richer insights and better outcomes. But with the exception of large platform companies, it is difficult for many organizations to derive substantial value from data. In fact, sharing data among multiple stakeholders can open a pandora’s box of concerns. In a recent open public consultation on the European strategy for data, more than two-thirds (69%) of stakeholders cited technical challenges such as formats and lack of standards as the main obstacle to data-sharing. The costs of entry and maintenance are also a common barrier, with 42% reporting this as off-putting.1  

This is particularly prominent in the B2B sector. A 2022 global Accenture study of more than 4,000 CXOs in 23 countries found the majority (88%) of companies share their data with ease internally, but report higher difficulty sharing externally. And just one in 10 companies find it easy to combine data with external partners within their industry (Figure 1).

N= 4053 CXOs, 50% IT and 50% Business, from 19 industries and 23 countries (Feb-Apr 2022)

figure 1

Consequently, shared data innovation opportunities remain largely untapped and much of the data generated and held by organizations sits in internal, static siloes gathering (virtual) dust. 

From Places to Spaces – A Data Renaissance

A data space is a “federated data ecosystem within a certain application domain and based on shared policies and rules”.2 The three technological requirements are connectivity, digital infrastructure to access and combine data from different sources as well as a software layer to create, manage, and share data.3 What makes data spaces unique is they provide small data (low volume, but very high quality) versus traditional big data (high volume, low quality). 

We analysed strategies and operating and business models of a wide variety of data spaces models and identified four primary archetypes:

  1. Data Exchange: A public/private consortium/society creating a safe space and infrastructure for participants to exchange data among themselves. Supports static as well as real-time data. Co-funded by users, pay per use or licensing. Examples include: Financial Data Exchange. 
  2. Data Marketplace: Public/private commercial organization offering a marketplace to sell and buy data, often aggregating from other data sources. Monetized through data sale and/or analytics and storage offerings. Examples include: Streamr; OpenPrise; BDEX; Shanghai Data Exchange. 
  3. Horizontal Dataspace: Society/Public-Private multistakeholder partnership through which its members provide data, applications, platforms and infrastructure. Wider in scope, aimed at uniting entire industries or companies in a trusted environment around a set of use case. Publicly funded and/or co-funded by users. Examples include: Catena-X; Mobility Data Space; Space Data Space; MELLODDY.
  4. Vertical Dataspace: Private commercial organizations which are orchestrated by one company or a consortium to pursue a business objective, funded by services offered. Examples include: Amadeus; Bosch Home Connect Plus; Mobility in Harmony.

Standards/Governance Providers play an important role in providing the “soft infrastructure” for dataspaces by setting standards and governance principles to ensure data sovereignty, interoperability and trust in a multistakeholder arrangement. Typically, they are membership fee funded and are mostly non-profit organizations or associations. Examples include Gaia -X; International Data Space Association; OPC Foundation; CEN-CENELEC; ETSI; ISO; IEC; ITU as well as the various government led organizations such as the IDS. 

Gaia-X is perhaps the most prominent example of a government-led initiative to provide a governance framework. Launched by the German Federal Government and the Plattform Industrie 4.0 in 2019, this open data infrastructure implements a common set of policies and rules, the Gaia-X standard, which can be applied to any cloud platforms. Data from different sources can be merged in the federated, secure infrastructure, allowing users and providers to trust each other on an objective technological basis, and safely share and exchange data.

Organizations Realizing the Full Potential of Data

  • Horizontal dataspace, MELLODDY, aims to enhance predictive Machine Learning models on decentralised data of 10 pharmaceutical companies, without exposing proprietary information. MELLODDY aims to create new ways of working that can reduce the current average time and cost of 13 years and €1.9 million of investment to bring any new drug to market.5  
  • Mobility in Harmony is a vertical dataspace launched by Foxconn in 2020 which brings together more than 2,000 members in over 60 countries in its MIH Open EV Platform with the ambition to significantly reduce time to market for electric vehicle design and production. The Platform comprises key technologies and reference designs and standards to build connections between members resulting in a lower barrier to entry, shorter development cycles and more innovation.6 
  • Catena-X is a horizontal multistakeholder alliance for the automotive industry established in May 2021. It brings together automotive manufacturers, suppliers, and providers of applications, platforms and infrastructure, including BMW AG, Deutsche Telekom and Robert Bosch GmbH and others alongside small and medium enterprises (SMEs) in a cloud-based platform.7 Catena-X focussed on eliminating barriers for partners and establishing a standardised data and information flow along the whole value chain whilst ensuring data sovereignty according to standards set by GAIA-X. An open network with “SME-ready” solutions, it gave SMEs opportunity to quickly enter the dataspace with minimal IT infrastructure investments, and their active participation is viewed as key to the success and value creation for the entire network.8  

Building blocks for creating data spaces

What a successful data space looks like can differ by region, industry, objective and regulation – there is no one-size-fits-all approach. So creating and operating a data space requires a nuanced strategy that accounts for these variations.

Through our analysis, Accenture Research has identified four essential building blocks to accelerate data ecosystem engagements and maximise their business value: 

1. Digital Business Models

A data space is first and foremost a network. The more companies participate in a data space, the more valuable the outcome becomes for all of them.

Perhaps the most compelling opportunity afforded by data spaces is their potential to facilitate innovative data-based business models and create new value. A data space is first and foremost a network. The more companies participate in a data space, the more valuable the outcome becomes for all of them. Therefore, data spaces need to create incentives for companies to participate. We surveyed more than 4,000 global CXOs about the top factors which would make them consider participating in data spaces. One of the top answers was ‘market demand from clients’, i.e. the need to create profitable offerings in the context of data spaces. By showcasing broad-reaching and innovative use cases, data space creators can quickly gain interest and buy-in. 

2. Technology Enablers

Technology is critical to the establishment of a successful data space. Our research revealed that 40% of CXOs felt new technologies such as blockchain and federated learning would make data sharing easier and more secure. 

CTOs/IT Managers looking to set up data spaces must be able to manage access, identity, and governance on a shared technology infrastructure such as a cloud platform. Typically, the platform which hosts data must contain storage infrastructure and a web frontend available as a service. Beyond this basic framework, data spaces need to contain additional technological elements depending on current and planned use-cases. In terms of technology infrastructure, this may mean augmentation with compute, integration, and development platform infrastructure. 

3. Governance Framework 

A coordinated approach requires solid governance structures to balance the stakes from all actors in the dataspace and establish a standardised ‘soft-infrastructure’ for data.10 Ensuring data quality starts with the Data Owner but flows down the data value chain should become a joint responsibility of all stakeholders. Service Level Agreements (SLAs) are helpful instruments to define service standards alongside accounting, billing, data valuation and smart contracting tools.11

The creation of widely approved data-sharing standards was cited as a key motivating factor for getting involved in data spaces by more than a third of CXOs. This is already happening. For example, consider the EU’s Data Governance Act which will be applicable from the second half of 2023 will introduce conditions on the re-use of data, demand neutrality of data service providers, create a register of recognised data altruism organizations and establish a European Data Innovation Board. This is all with the aim of establishing trust and data sovereignty whilst facilitating the emergence of best practice and a consistent application of the governance framework. Together with the Data Act currently under discussion, this policy will set the standard for all EU data spaces.12  

data economy

4. Data-driven Culture

Organizational culture cannot be underestimated for its influence over the successful participation to a dataspace and the transformation to a data-driven business model. 

  • Culture: Mindsets need to be shifted away from viewing data as a source of proprietary competitive advantage and towards being open to sharing data beyond the boundaries of the organisation. By connecting and communicating with employees about the opportunities, organizations can get employees’ buy-in and commitment. 
  • Leadership: Senior leaders in connected enterprises are pivotal. Assigning a C-level executive as data strategy lead can ensure it is given due focus and investment, and they can balance the organisation’s core business whilst maintaining one eye on the future data opportunities. A top-down approach helps kickstart a data-driven mindset across the entire enterprise. If leaders lead by example, this becomes an inseparable part of organizational culture.
  • People Management: Organisations should prioritise upskilling their employees to be able to better engage with the data space model, and offer them opportunities to engage with the ecosystem and explore use cases for data exchange, analysis and value creation. By building data analytics capabilities and knowledge, enthusiasm and results will follow.

CXOs told us that they would participate in data ecosystems more if they created ‘opportunities to expand business and ecosystem relationships’ and helped them ‘contribute to society and quality of life’. These reinforce the idea that CXOs need data spaces to be much more than static exchanges; they aspire to join thriving communities built around data, where innovation and ideas are born. 


The value of data increases exponentially when it is shared – yet so many organizations are still hesitant to relinquish their grasp on their own private stash of information. Meanwhile, those spearheading the innovation of new ecosystem models, and deriving demonstrable value as a result, pave the way forward. 

It is currently estimated that 1.7 MB of data are created each second for every person globally13, and we expect the rate of data generation to continue to rapidly increase. To be a leader in the future data economy it is crucial to join and contribute to multiple data ecosystems. By putting in place the four building blocks for creating data spaces, you can share the collective value of data and best harness its power. 

About the Authors

Svenja Falk

Svenja Falk is Managing Director and leads Accenture Research Europe. She also is deputy chairwoman on the Council on Digital Sovereignty in Germany and honorary professor at the Justus Liebig University Giessen.

Surya Mukherjee

Surya Mukherjee is a Senior Principal at Accenture and Head of Technology Research in Europe who explores the transformative impact of technologies on industries, companies, and brands.

Laura Wright

Laura Wright is a Research Manager and leads Europe Thought Leadership at Accenture Research.

The authors would like to thank Laetitia Cailleteau, Mattia Dalle Vedove, Samira Azam, Kathleen Trickey, and Gargi Chakrabarty for their support.


  1. Public Consultation on the Data Act: Summary Report
  2. OPENDEI: Design Principles for Data Spaces, April 2021, pg 7
  3. OPENDEI: Design Principles for Data Spaces, April 2021, pg24
  9. Gaia-X, a multistakeholder-led open data infrastructure: 



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