Data-driven innovation has recently changed the mindset in data sharing from centralized architectures and monolithic data exploitation by data providers (data platforms) to decentralized architectures and different data sharing options among all involved participants (data ecosystems).
Differences between the various data ecosystems are not clear, making it hard to choose the most suitable for each use case, negatively impacting their adoption. Since the domain is growing fast, a review of the state-of-the-art data ecosystem initiatives is needed to analyze what each initiative offers, identify collaboration prospects, and highlight features for improvement and open research topics. In this paper, we review the state-of-the-art data ecosystem initiatives, describe their innovative aspects, compare their technical and business features, and identify open research challenges. We aim to assist practitioners in choosing the most suitable data ecosystem for their use cases and scientists to explore emerging research opportunities.
13 Jul 2026 21:28
Interesting read. I've glanced over sections and read others.
I've been involved in the use of some of these frameworks. They are good reference frameworks but they are most often overly heavy to get started and/or the implementation is most often not supporting what the marketing material suggests.
Good integration projects start from limited partners with limited datasets exchanging with each other knowing they want to exchange with more partners in the future. Limited can be two to seven and they can even be in the same organisation. Perhaps it works with more, I have not been involved in that. They contain only the necessary roles and know how they'll expand. These frameworks are great to get the expansion options in place. However, to get something going we've seen that a cheap and structurally correct solution to data sharing gets the ball running more quickly and for longer, then an expensive and heavy project which needs all sorts of approvals (even if it would be superior).
Projects that start with the weight of all the roles filled in by different actors need substantial funding to get started and I don't think they generally last after funding dried up. Projects faking their way through the extra roles and providing the most limited interpretation of some of these roles with the project having some (even vague) shared benefit seem to survive past the grant stage.
Get some basics right and get going is my advice. Identifiers, shared and extensible data models, and a clear way of sharing data.
I like these frameworks, they help set the stage, but my gosh do they see problems where there are none when a project is just getting started.
13 Jul 2026 22:52
Lemmy
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Steam just keeps winning....
Valve reportedly generated $11.1 billion in the first half of 2026 alone, setting another all-time record.
PC gaming isn't slowing down anytime soon. 🚀
🚀
Would you return to classic LoL? 🤔
