Big Data Strategies in an Open Innovation Context

Thursday, August 9, 2018

Big data can provide immense economic, scientific and social value. New value and information can be derived from big data by linking up existing data sets. This has pushed organisations to pursue open data initiatives. These initiatives can be found among government, industry and academia.
While organisational benefits are clear to pursue open innovation in the field of big data, individual researchers’ motivation for opening their big data sets have not been addressed to make open science realise its true potential.
Scientists’ data sharing behaviour is largely driven by perceived career benefits and risks, effort needed to share data and the availability of data repositories. These are factors that need to considered when pushing open data initiatives in academia.
Opportunities for open big data through open innovation collaboration include organisational transparency, accelerated and reproducible research and new businesses. These opportunities are hindered by key stakeholder’s unwillingness to participate in collaboration due to perceived risks, privacy and ethical issues and technical issues related to the complexity of big data.
Commercialisation of big research data can be realised through patenting, licencing and spin-outs. There exist data-driven business models that can potentially applied to the research big data of scientists. Most of these business models rely heavily on external data sources. Virtual research environments and boundary organisations are two examples of potential big data ecosystems fostering open innovation collaboration.

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