Be prepared

Comprehensive support packages for easy adoption to learn, operate and scale

8-tailor made accelerator packages to gain understanding of new data ecosystems and its impact on business and technology. Expert assistance and support for technical readiness, implementation, testing and scaling as per business needs

Investigate & Understand


Implement & Scale


Your journey to unlock true potential of your data...

Investigate & Understand

Assisting users to understand data ecosystems by identifying concrete use cases, preparing datasets and developing detailed strategy & project roadmap based on digital economy fundamentals and evaluate business opportunities and implications.



Carry out design thinking to identify focus area and use cases

0.5 days


Solution Workshop

Refine use case ideas and outline solution

0.5 - 2 days


Strategy & Roadmap

Design data economy strategies and update roadmap for implementations

8 - 12 weeks


Data Exploration

Preparing datasets for cleansing, consolidations, and validation to make data AI-ready

1 - 2 weeks

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    Knowledge building on data sovereignty and dataspace solutions

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    Resolution for customer’s challenges and other value-added perks

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    Identification of concrete use cases and new business opportunities

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    Technical readiness for implementation

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    Improving value of datasets to make it future applications ready

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    Well-prepared consulting packages to lead our customers towards digital sovereignty

Implement & Scale

Planning and building connectivity solutions and demonstrate its benefits to stakeholders via prototypes post which explore scaling strategies as per business needs.


Data Hackathon

Technical planning, preparation and execution


Dataspace onboarding

Onboarding support and backend integrations for trustful data exchange


PoC & Prototype deployment

Architecture planning & iteratively solution development


Production Rollout

Operate and scale as per business requirements with DIH as your managed services

2 - 10 days

Vary per your needs

Your benefits

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    Gaining hands-on experience in implementing dataspaces

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    Technical planning for solution deployment

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    Identification of datasets and sources required for onboarding

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    Start small, scale fast

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    Developing prototype and testing solutions in your own environment

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    Know-how to scale dataspaces environment to make it productive and reliable

Use cases

  • Use case cover image
    Joint process improvement through Collaborative Condition Monitoring

    The value chain of production in German industry is made up of many different individual segments. For example, a component supplier produces a wide variety of goods, which are then sold to many different machine suppliers. These suppliers manufacture machines that are later used in production. If one imagines the production chains in industry, they resemble a puzzle to whose different parts only individual actors have access and which produces diverse types of data.

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  • Use case cover image
    Building management in the cloud

    Room temperature control, access security, maintenance appointments, cleaning work: To manage buildings optimally and keep technical systems in good shape, automated processes are necessary. Secure and easy data exchange is important for facility managers and service technicians. Because different service providers assume different tasks in buildings and use their own information systems and data formats, it is often time-consuming to consolidate the accumulated and required data. Building managers therefore want a broad range of information with uniform interfaces for real estate management and operations. Data sovereignty and ownership play an important role in this.

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    Discover anomalies in production earlier

    In production facilities, all processes are precisely timed, every step must be exact, often in 24/7 operations. If individual machines are down or a production lines fails, then the entire factory gets out of sync, sometimes causing total standstill. The result: production losses, lost sales, repair costs. The goal: to minimize machine downtime – whether through damage or maintenance. This is achieved with artificial intelligence and intelligent evaluation of machine data, in a process that assesses the collected sensor values, control and maintenance data, compares and delivers results to technicians in a targeted manner. Because of the multitude of sensor, control and maintenance data, it is often impossible to consolidate the complex but valuable data and evaluate it in correlation. On top of that, often additional data, e.g. environmental data or data along the supply chain is essential and secure data exchange is required for companies and service technicians.

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    Data analyses to improve breathing air quality

    Something in the air. Municipalities are responsible for making sure there’s clean air on their streets. To do so, air quality is measured at selected locations with the aid of complex and expensive sensors and transmitted to the responsible authorities such as the state, federal government or the EU.

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  • Use case cover image
    What is a data marketplace: Data trading

    The emergence of data marketplaces is accompanied by the growth of data as the number of collected data is steadily increasing by governments, businesses and websites. Besides data is recognized as an asset and that you can trade it. Data can be bought and sold, creating data marketplaces. But what is a data market place actually? What are the characteristic? These questions are clarified in this blog article.

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for your data-driven future