“The confusion between effectiveness and efficiency that stands between doing the right things and doing things right. There is surely nothing quite so useless as doing with great efficiency what should not be done at all”  —  Peter F. Drucker (1963)

Digital Transformation Done Right: Unleash Better Data with Cross-Organizational Dataspaces

Welcome to the future of digital transformation! Data ecosystems and dataspaces have emerged as game-changing infrastructure for secure, trustful data sharing across organizations at scale. They are enablers to provide the critical fuel for digital transformation—not just ‘Big Data,’ but better data—so you can do the right thing first by obtaining data (or training data) with the relevant and accurate information content. This README is an executive-level introduction, grounded in science, and a guide to additional, in-depth resources.

Benefits
Timing is everything: As data fuels digital transformation, new dataspace technology has emerged to provide better, more reliable data. Distributed by design—like the Internet—it is, therefore, inherently more flexible and resilient. Often associated with Web3 (see McKinsey 2023), dataspace infrastructure goes beyond operational efficiency, unlocking opportunities of strategic importance.
Strategically, make your organization more readily adaptive in an era of relentless, rapid disruption—natural disasters, trade upheavals, even wars, and more—by forming ecosystems with more resilient supply chains. Ecosystems have long looked good on paper, but now technology has caught up with the theory. Dataspaces for trusted cross-organizational data sharing serve as a key enabler for actual resource exchange across an ecosystem’s interconnected organizations. See Data 1: Ecosystems 2.0 – Built on Data
Operationally, accelerate to ‘China speed’ and cut costs by adopting digital twins. Why destroy your product to test safety or reliability when a digital replica can do it faster, cheaper, and repeatedly? Until recently, accessing primary data across multiple supply chain tiers for accurate digital twins was a bottleneck. Now, dataspace technology can eliminate that hurdle. It can provide the critical primary scope-3 supply chain data so you can trace quality problems and faulty parts for targeted recalls, and reduce CO2 emissions with Product Carbon Footprint (PCF) tracking. With AI, achieve fast returns by fine-tuning existing global Large Language Models (LLMs) with local training data that reflects your unique context, amplifying your competitive edge. And yes, use dataspace technology to bridge internal data silos and extract relevant training data quickly.

Simplified system
Figure 1 illustrates how a data ecosystem integrates seamlessly with your existing IT landscape, operating atop cloud or on-premise hardware and communication networks to support the automation of your business use case. It simplifies complexity by organizing information system capabilities into layers (see Turban et al. 2022). The figure further conceptualizes the software stack of a data ecosystem, such as Catena-X* with its dozen-plus components from the open-source Tractus-X repository, into three distinct layers:
(a) dataspace network (see Data 2: Dataspaces 101)
(b) data products (see Data 3: Data products, digital twins)
(c) super-apps (see Data 4: Dataspace super-apps)
This structure aligns with the seven abstraction layers of the Open Systems Interconnection (OSI) model, a reference framework by the International Organization for Standardization (ISO) that provides a unified foundation for coordinating standards to enable system interconnection (ISO/IEC 7498-1: 1994). Similar to the Internet, the dataspace network layer (dark grey) is industry-agnostic, while Catena-X data products and super-apps are tailored to the automotive sector (see What is Catena-X).

Implementation: Easy with SaaS and open-source core
While nothing about this is easy at the code level, first data ecosystems like Catena-X have emerged with solutions that shield complexity and simplify adoption. Backed by government seed funding—similar to DARPA’s role in developing core Internet technologies—the foundational software is open-source and designed to serve not only global corporations but also small and medium-sized businesses. Delivered as a fully managed, browser-based SaaS solution, there’s no need for complex systems integration. Onboarding is as simple as ordering mobile phone service: complete online forms, make a credit card payment, and you’re ready to go. No more tinkering under the hood or soldering wires—just open your browser and start doing the right thing first by ensuring better data for your use cases and AI. Our two case studies provide insights into first best practices and lessons learned:
• Auto 6: Sustainability with Catena-X
• Auto 5: Mobility Super-app Disruption

References
Drucker, P. 1963. Managing for Business Effectiveness. Harvard Business Review (May), link

ISO/IEC 7498-1:1994. Information technology – Open Systems Interconnection – Basic Reference Model: The Basic Model (Ed. 1, 1994; last reviewed and confirmed in 2000), link

McKinsey. 2023. What is Web3. Explainers (2023-10-10), McKinsey & Company, New York City, New York, link

Schlueter Langdon, C., and K. Schweichhart. 2022. Dataspaces: First Applications in Mobility and Industry. In: Otto, B. et al. (eds.). Dataspaces – Part IV Solutions & Applications. Springer Nature, Switzerland: 493-511, link

Telekom DIH. 2024. RoX: Habeck kickoff, lauch@Duerr. Insights story (2024-12-05), T-Systems International, Frankfurt, link

Turban, E., L. Volonino, G. R. Wood, R. D. Watson. 2022. Information technology for management. Wiley 12th ed. Hoboken, NJ

* The Catena-X Consortium served as the tech incubator for today’s Catena-X data ecosystem, a $250 million, 3-year (2021–2024) initiative with automotive use cases, uniting 28 partners, including BMW, Mercedes-Benz, Volkswagen, and tier-1s from Bosch to ZF, and software vendors like SAP. This author wrote Deutsche Telekom into it from the initial 15-page government proposal to finalizing the 1,700-page agreement while serving on the 3-member agile SAFe-based Product Management team responsible for creating and delivering the software release, now available as an open-source reference implementation in the Eclipse Foundation’s Tractus-X project. As a scientist-manager, the author is grateful for successfully transfering research innovation into industrial applications through close collaboration with leading scientists such as Boris Otto (Fraunhofer), often regarded as the academic “godfather” of dataspaces, and Frank Koester (DLR).