The BYTE Project

Editor's Notes

• #6: Positive externalities occur when a product, activity or decision by an actor causes positive effects or benefits realised by a third party resulting from a transaction in which they had no direct involvement. Negative externalities occur when a product, activity or decision by an actor causes costs (or harm) that is not entirely born by that actor but that affects a third party, e.g., citizens (Business Dictionary, 2014). externalities are related to processes (i.e., production, service, use) and not to the product itself. That is, it is not big data per se that causes a particular externality, but rather, it is the social processes employed via big data that can produce externalities. Furthermore, these externalities may result from the direct collection or processing of data (e.g., privacy infringements), as well as the opportunities and risks that may arise as a result of the existence of the data (e.g., linking data sets). In addition, as externalities may have unexpected effects on third parties, a central task in BYTE is the identification of the involved processes, their effects as well as the potential affected parties.
• #7: Bullet one – how we define an externality – as an “impact” Public opinion surveys reveal that citizens are concerned about many of these issues, especially privacy and data protection.
• #9: Generally, data utilisation in the healthcare sector is developed and widespread across a number of health areas, especially in terms of medical research and diagnostic testing that translates into improved, more specialised care for patients. Genetic data use is maturing and focused on high-grade analytics and the discovery of rare genes and genetic disorders. The key improvements include timely and more accurate diagnosis, the development of personalised medicines, and drug and other treatments/ therapy development, which can save lives Key innovations include the development of privacy protecting and secure databases for genetic data samples, which is vital given the highly sensitive nature of the personal data utilised; and new business models focused on big genetic data sequencing However, there tends to be a reluctance by public sector initiatives to share data on open databases or in collaborations with private organisations (big pharma etc.) due to legal/ ethical constraints (e.g. consent/ privacy), and public sector ethos (public good v. profit generation).
• #10: Crisis informatics is in the early stages of integrating big data into standard operations and is primarily focussed on integrating social media and geographical data (There has not yet been much progress integrating other data types – e.g., environmental measurements, meteorological data, etc) The key improvement is that the analysis of this data improves situational awareness more quickly after an event has occurred. A key innovation is the use of human computing, primarily through digital volunteers, to validate the data collected and determine how trustworthy it is. Stakeholders in this area are making progress in addressing privacy and data protection issues, which are significant and complex, given their focus on data from social media sources.
• #11: Production of a roadmap outlining a plan of action to enable European scientists and industry to capture a proportionate share of the big data market. Provision of assistance to industry in capturing positive externalities (efficiencies, new business models, etc.) and addressing potential negative externalities before beginning a project, initiative or programme. A series of clear and precise future research needs and policy steps