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.
Our experience so far shows that using Linked Data can give you many apps that work together on parts of the data.
Depend on semantic data models (vocabularies of URIs such as foaf), use triples in your applications, and share these same triples whilst looking at the serialisation (such as xml, jsonld, ttl) as an implementation detail, and you’re a long way towards interoperable apps and information sharing.
If you have many interoperable apps and they can share data, then you’ve went a good distance towards and open market where software can compete. If the data is also openy available to you (as it should) then you took large steps towards sovereignty too. You’ll have a choice of applications to use and you can easily move the data between the applications.
Feel free to ask questions should you have any.