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Exploring cutting-edge data ecosystems: A comprehensive analysis

delete2025-12-02
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PRE
AI
I
Ioannis Chrysakis *
D
David Chaves-Fraga
G
Giorgos Flouris
E
Erik Mannens
A
Anastasia Dimou
DOI:10.1016/j.datak.2025.102539delete
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Abstract

Abstract

En 中文
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). Data sharing is further strengthened through the establishment of several legal frameworks (e.g., European Strategy for Data, Data Act, Data Governance Act) and the emerging initiatives that provide the means to build data ecosystems, which is evident in the formulated communities, established use cases, and the technical solutions. However, the data ecosystems have not been thoroughly studied so far. The 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. Furthermore, we will provide a framework that outlines the key criteria for evaluating these initiatives, ensuring that stakeholders can make informed decisions based on their specific needs and objectives. By synthesizing our findings, we hope to foster a deeper understanding of the evolving landscape of data ecosystems and encourage further advancements in this critical field.

Journal

D
Data and Knowledge Engineering
IF:
2.6
Papers:
116
Citations:
1.7K

Organization

D
department of electronics and information systems
Scholars:
13
Papers: 8
Citations: 0
D
Department of Computer Science
Scholars:
1.7K
Papers: 998
Citations: 8
I
Institute of Computer Science
Scholars:
119
Papers: 62
Citations: 266
U
Universidade de Santiago de Compostela
Scholars:
1.5W
Papers: 1.3W
Citations: 1.4W
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Cited Papers

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