Data Spaces and Data Sharing

A Journey Through the Evolution of Data Solutions

Data has always been at the heart of innovation, driving decisions, strategies, and technologies. And now we stand at the beginning of a yet another new era that is defined by advanced data sharing solutions based on data spaces. So, in order to be able to understand its potential and its challenges, it’s essential to understand how we got here and why these concepts are transformative for businesses and society.

How data-intensive solutions evolved

Data has always been at the heart of software technology. Whether it is about data-processing aimed at transforming data into information we can use or controlling industrial processes using operational data, it always boils down to the use and processing of data.
Therefore, given the longstanding history of information technology and computer science, the solutions that were designed to handle data can be divided into a number of generations:

• The Early Days: Data Silos and Manual Processes

  In the 1970’s and 1980’s, organizations began to digitize their operations, but data remained fragmented across departments. Mainframes and early relational databases like IBM’s DB2 were in use, but data was typically isolated within organizational silos. Although these types of systems enable new levels of parallel usage and scalability, the systems suffered from a lack of integration that resulted in inefficiencies and limited insights, as data could not be easily shared or analyzed collectively.

• The Database Revolution: Centralization and Structured Data

  During the 1990’s the advent of powerful relational database management systems (RDBMS) like Oracle, SQL Server, and MySQL marked a significant shift. Data started to be centralized and was modeled using a standardized approach, making it more accessible within organizations. This resulted amongst other in the rise of Enterprise Resource Planning (ERP) systems, which integrated various functions like finance, HR, and supply chain into a unified database.

• The Era of Big Data: Volume, Variety, and Velocity

  In the 2000’s, the massive adoption of the internet and the rise of social media generated unprecedented amounts of data. Technologies like Hadoop and NoSQL databases (e.g. MongoDB, Cassandra) were developed to handle unstructured and semi-structured data at scale. This was the breeding ground for new businesses and business models, because now vast amounts of data could be processed and analyzed in real-time, leading to more informed decision-making and predictive analytics.

• Cloud Computing: Accessibility and Scalability

  Apart from the change in data storage technology, we saw the rise of virtualization technology, which resulted in Cloud platforms such as AWS, Google Cloud, and Azure in the 2010’s. These public Cloud platforms democratized access to powerful computing resources. Organizations could now store and process data without significant upfront investments in infrastructure. This also introduced a new way of organizing large amounts of data: Data lakes. Data lakes became popular, allowing for the storage of raw data in its native format until it was needed for analysis.

Enter Data Spaces: A New Paradigm

Data spaces represent the next stage in the evolution of data-centric solutions. They build upon the concepts of data lakes and cloud computing but combine this with a federative governance model. This allows organizations to not only share data in a decentralized manner, where data can even remain within the organization’s local environments, but also create a shared set of policies and regulations that guarantee the trust and confidence between these oganizations. This ensures data privacy, security, and compliance with regulatory standards, while promoting interoperability and data reuse.

Data Spaces refer to a federated, virtual environment where data is shared and used in a secure, controlled, and seamless manner. Unlike traditional data storage systems, data spaces enable multiple stakeholders to collaborate and exchange data while maintaining control over their own data assets, so-called data sovereignty. This approach facilitates innovation and supports the creation of new services and products by leveraging shared data.

Key Features of Data Spaces

Building on top of the technological advancements described earlier, the main innovation of data spaces is founded in the combination with the federative governance model.
Essentially, this governance model is split into 3 individual planes, each representing a different type of responsibility:

• Governance Plane: Here the processes, audit control and legal framework for the data space is defined. It is aimed at fostering a federated governance and control of the ecosystem that consists around a data space.
• Control Plane: Here the automated services, that enforce both the legal and control frameworks, reside. They are designed to ensure transparency for the use of assets and the conditions within a data space. Basically, the services in this layer only work with metadata; the actual data is only present in the data layer.
• Data Plane: Here the actual sharing of data is done through the dedicated, generic infrastructure. All interactions with data are logged for later auditting purposes. It is required that this infrastructure is secure-by-design.

The EU Data Strategy: A Driving Force for Data Spaces

One of the main thrusts behind data spaces is the EU. The EU Data Strategy, introduced in 2020, aims to create a unified market for data, where big tech companies cannot pursue their one-sided, data-centric value creation strategies. This level playing field market is conceived to enable the free flow of data across sectors and countries through the use of data spaces. Fundamentally, this strategy is built on:

• Data Sovereignty: access and ownership of data is under control of the data-owner. This ensures that data can be accessed and shared without compromising privacy and security.
• Interoperability: By adhering to standards and frameworks that allow data to be reused across different systems and organizations. This also advances the use of data outside of their original designed context.
• Innovation: By sharing data on a large scale, new business models and market mechanisms will emerge. This will foster the development of new technologies and services through improved data accessibility.
• Trust: The inherent trust of data spaces will also have a societal impact, which will improve the relation between invidual citizens and organizations in general and with public governance specifically.

The EU’s proactive stance on creating a single European data space sets a global example, promoting a data-driven economy that can drive innovation, enhance public services, and empower citizens. By understanding the historical context and the strategic vision behind these concepts, we can better appreciate their potential and work towards a more connected and data-rich future.

Data Sharing: The Lifeblood of Data Spaces

Effective data sharing is crucial for the success of data spaces. It involves not just the technical ability to exchange data, but also the establishment of trust and mutual benefit among stakeholders.

Data spaces and federated data sharing are transforming the way we think about data and collaboration. Supported by the EU data strategy, these concepts are not just theoretical; they are practical solutions built on the lessons learned from decades of data-oriented innovations. As we embrace these new paradigms, we stand on the cusp of a new era where data is not just a resource, but a catalyst for growth and innovation across all sectors of society.

Whether you are a business leader, a data professional, or simply a technology enthusiast, understanding and embracing these concepts will be key to thriving in the data-centric world of tomorrow.

Want to know more about (federated) data sharing? Download our free white paper here.