Data space strategy for industrial companies

11/25/2025 | Cases

Data is the key to competitiveness in industry. European data space initiatives are creating standards for secure and interoperable data exchange—an opportunity for companies to future-proof their digital platform strategy. But how can these standards be integrated in a meaningful way? Our project shows how a clear strategy provides orientation and paves the way for innovative business models.

Industry & Customer

The project was implemented for a production technology company that offers automation and digitization solutions for industrial applications.

Initial Situation

The customer operates a digital platform for industrial applications and was faced with the strategic decision of how to integrate data space standards into the existing architecture. This presented several key challenges:

Uncertainty about the relevance of European data space standards for the business strategy
Lack of assessment of the need for data space connectors at different architecture levels
Governance gaps in product data, sustainability data, and supply chain information
Technical uncertainty regarding the data integration strategy

Objective of the consulting

The aim of the consulting project was to assess the strategic importance of data space standards for the company's competitive position and to clarify the need for connectors at different system levels. In addition, industry-specific data requirements were to be identified, a data integration strategy for the platform defined, and concrete next steps and areas for action developed.

Consulting services

As part of a structured, eight-hour workshop, impulse talks on data space standards and current developments were given, followed by moderated discussions with impact analyses. The workshop was divided into four modules: overview of standards, analysis of data requirements, development of a connector strategy, and integration into the platform.

Technical implementation

The workshop methodology followed a clear sequence: impulse talk, open discussion, and impact analysis per session. Frameworks used included the IDS Reference Architecture Model, Data Spaces Radar, AAS standards, and Digital Product Passport frameworks. In addition, analysis tools such as maturity analyses for data governance, technology benchmarking, and architecture decision templates were used.

Results

The company now has a clear understanding of its data space requirements and has identified critical governance gaps, particularly in product, sustainability, and supply chain data. Prioritized areas for action were defined and a decision-making framework for the next steps was created.

The quantifiable added value: The structured workshop replaced months of internal discussions, led from uncertainty to a clear roadmap, and prevented costly wrong decisions through early risk identification. In addition, strong alignment was achieved between the departments involved.

Lessons learned

The combination of structured workshop methodology, external expert knowledge, and internal organizational knowledge was particularly successful. The visualization of the multi-layer architecture created a common understanding, and the early gap analysis prevented premature decisions.

The challenge was to strike a balance between strategic overview and technical details, as well as working in a multi-stakeholder environment with varying degrees of maturity.

For similar projects, we recommend a governance assessment in advance, an iterative approach with several focused sessions, and the early integration of technical deep dives.

Next Steps

Recommended consulting services for comparable customers are:

Data Space Readiness Assessment
Data Space Architecture Design
Pilot Implementation Support
Development of a Data Governance Framework

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