What limits data-sharing in Europe?

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Nurturing Data Skills Dataset-sharing and their use and reuse of data by multiple actors enable the data value extraction and lead to the creation of new services and business models. Data-sharing often represents one of many steps of digital transformation of a company. The diffusion of open platforms for data-sharing and the availability of interoperable datasets is one of the key success factors which may help to drive the European Data Market towards a High Growth scenario by 2020, and push the contribution of the data economy to the EU GDP up to 4%.* However, European industries are far from exploiting the full value of data-sharing. According to IDC’s Digital Transformation Maturityscape Benchmark, the majority of European enterprises (62%) are between the “digital explorer” or “digital player” maturity stages, characterized by an opportunistic approach to digital transformation, while 20% are “digital resisters” refusing to engage in this digital innovation. IDC estimates, therefore, that the majority of European enterprises have not yet fully implemented data sharing but are interested to do so, and are dealing or starting to deal with data sharing issues. There are three main typologies of data sharing barriers emerging from the analysis of the market:
  • cultural/organizational (lack of awareness of potential benefits, lack of trust and fear of competition)
  • legal/regulatory (restrictions of data location and of the free-flow of data, uncertainty about data ownership and data access)
  • technical/operational barriers due to the lack of interoperability between different datasets, lack of standards, high costs of data curation to adapt it for sharing.
Interoperability and technical issues are perceived by stakeholders as an additional cost. These are the problems which could be solved with different solutions depending on the specific situation, based on some combination of technology, standardization and human work. Despite many new technologies, data interoperability problems are likely to become more complex and difficult to solve as the data-driven ecosystem grows in depth and sophistication, for example through the emergence of Industry 4.0 ecosystems and the diffusion of Internet of Things (IoT) networks of sensors. This paper explores data sharing between a number of companies:
  • Between the producer of the machinery (e.g. Siemens) and the manufacturing company running the industrial plant (e.g. BASF, N&W Global Vending);
  • Between the manufacturing company and its clients (e.g. the operators of N&W vending machines);
  • Between the manufacturing company and the big data start-ups (e.g. Yukon Digital);
  • Between the manufacturing company and the data marketplace (e.g. Datary.io).
Get the paper now for real-life stories and final considerations. Download it here. *According to the preliminary results of the European Data Market Study.