Data sharing is the key for new value creation. Just imagine if you could easily know where your data is and determine how it is used. Imagine you could set the very same data into a data market just to wait for the right moment for value creation. While intriguing, this is the hardest nut to crack in data business: how value can be created?
Value thinking is extremely important when we want to understand the benefit of data sharing in an organization refitting itself to data economy. The difference from earlier, IT system development within single organizations, is that the value of data depends on how the data ecosystem is organized: who needs data, how data is related to real material flows, or services. A good example is the energy network system and its transformation to small-scale production, where production sites are numerous, even in household granularity. How can one share data between local production site and an end customer, and how to make electricity market flexible and affordable in such a case?
Data sharing technologies have existed for quite some time but tapping into a data space is a totally new aspect. A data space is a construct, which aims to provide trusted and scalable data sharing environment that has data sovereignty built into it. International Data Spaces Association provides a framework for data space development. Such technology is important as an enabler. Besides technology, however, there are key non-technical issues that affect very much how we design data spaces and how we use the technology.
The first key issue is the new era of data value via data sharing, which requires new inventions, if not even new terminology. Extraction of value in a data ecosystem is still the final frontier where no man or woman has gone before. I think understanding value is crucial to start with, or we will not see convergence of ecosystem stakeholders to common approaches.
Second requirement for data sharing is that it must be easy for the users. The point in a standard approach is that interoperability can be taken for granted and it is possible to create a scalable data space. All the complexity of the data space access, however, must be hidden from the user.
Third, although federated approach seems promising in a sense that no central player is needed, the reality will most probably lead us to new governance models. Someone needs to be aware how to embrace quality of the data and the data space, how basic services work, and be ready to respond to any challenges data space members will encounter. Much of this can be resolved with automation or even with AI, but a control room will be necessary in form of data intermediaries, service providers, or trusted data management providers in some co-op model.
I did not raise yet legal framework to the podium here, as forthcoming Data Act will be an object for another analysis. However, it is clear that data spaces address many issues in a new way and we still lack conceptual and methodological tools to work to build on recent development of technologies.
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