The BYTE Project
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Presentation: The BYTE Project - by Rachel Finn, Trilateral Research & Consulting (UK), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
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The BYTE Project
- 1. BYTE: Big Data Externalities – the BYTE Case Studies Rachel Finn Trilateral Research & Consulting, LLP Big data roadmap and cross-disciplinarY community for addressing socieTal Externalities European Data Economy Workshop 15 September 2015
- 2. @BYTE_EU www.byte-project.eu Project details: BYTE •Big data roadmap and cross-disciplinarY community for addressing socieTal Externalities (BYTE) project •March 2014 – Feb 2017; 36 months • Funded by DG-CNCT: €2.25 million (Grant agreement no: 619551) • 11 Partners • 10 Countries
- 3. @BYTE_EU www.byte-project.eu Objectives The BYTE project has three main objectives: 1. To produce a research and policy roadmap and recommendations to support European stakeholders in increasing their share of the big data market by 2020 and in capturing and addressing the positive and negative societal externalities associated with use of big data. 2. To involve all of the European actors relevant to big data in order to identify concrete current and emerging problems to be addressed in the BYTE roadmap. The stakeholder engagement activities will lead to the creation of the Big Data Community, a sustainable platform from which to measure progress in meeting the challenges posed by societal externalities and identify new and emerging challenges. 3. To disseminate the BYTE findings, recommendations and the existence of the BYTE Big Data Community to a larger population of stakeholders in order to encourage them to implement the BYTE guidelines and participate in the Big Data Community.
- 4. @BYTE_EU www.byte-project.eu Case studies: big data practitioners assist to identify externalities Environmental data Energy Utilities / Smart Cities Cultural Data Health Crisis informatics Transport
- 5. @BYTE_EU www.byte-project.eu Understanding ‘externalities’ In BYTE we consider the externalities or impacts of big data Positive effects or benefits realised by a third party Negative costs (or harm) that affects a third party Externalities relate to social processes linked to big data, as well as the opportunities & risks that may arise as a result of the existence of the data. Some effects may be unexpected or unintentional IMPACT ECONOMIC SOCIAL LEGALETHICAL POLITICAL
- 6. @BYTE_EU www.byte-project.eu Big data concerns: externalities Economic • Boost to the economy • Innovation • Increase efficiency • Smaller actors left behind • Shrink economies Legal • Privacy • Data protection • Data ownership • Copyright • Risks associated with inclusion & exclusion Social & Ethical • Transparency • Discrimination • Methodological difficulties • Spurious relationships • Consumer manipulation Political • Reliance on US services • Services have become utilities • Legal issues become trade issues Economic • Boost to the economy • Innovation ✔ • Increase efficiency ✔ • Smaller actors left behind • Shrink economies Legal • Privacy ✔ • Data protection ✔ • Data ownership ✔ • Copyright • Risks associated with inclusion & exclusion Social & Ethical • Transparency ✔ • Discrimination • Methodological difficulties • Spurious relationships • Consumer manipulation • Improved services ✔ Political • Reliance on US services ✔ • Services have become utilities ✔ • Legal issues become trade issues • Dependent on public funding ✔
- 7. @BYTE_EU www.byte-project.eu Select horizontal findings Positive externalities • Efficiencies • Product and service innovation • New business models • Societal benefits (improved decision- making in healthcare, crisis management, commercial organisations; personalised services) Negative externalities • Dependence on public funding to create the environment in which big data business models can flourish • Privacy concerns • Fear of losing proprietary information • Outdated legislation • Difficulty in adapting business models
- 8. @BYTE_EU www.byte-project.eu Case study-specific findings: health •Big data in healthcare is quite well developed and widespread across a number of health areas. •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. •However, there tends to be a reluctance by public sector initiatives to share data due to legal and ethical constraints. “So in our own consent we never say that data will be fully anonymous. We do everything in our power so that it is deposited in a anonymous fashion and […] when we consent we are very careful in saying look it’s very unlikely that anyone is going to actively identify information about you” (Program head, Clinical geneticist )
- 9. @BYTE_EU www.byte-project.eu Case study-specific findings: crisis informatics •Crisis informatics is in the early stages of integrating big data. •Currently, its primary focus is on integrating social media and geographical data. •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 combination of human computing and machine 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. •Some evidence of reliance on US cloud and technology services. “And I have seen this on multiply occasions from […] big private companies in this, they’ll deal with their own huge amount of data and response to crisis and so on. But [then] become very unpredictable unsustainable outside of an emergency, do a good job of talking about what they do during a crisis but then sort of disappear in-between.” (Programme manager, International Governmental Organisation)
- 10. @BYTE_EU www.byte-project.eu BYTE project key outputs • Define research efforts and policy measures necessary for responsible participation in the big data economy • Vision for Big Data for Europe for 2020, incorporating externalities • Amplify positive externalities • Diminish negative ones • Roadmap • Research Roadmap • Policy Roadmap • Formation of a Big Data community • Implement the roadmap • Sustainability plan
- 11. @BYTE_EU www.byte-project.eu Next event Validating case study externalities Dublin 14th October 2015, 9am-5pm Presentations by: Sonja Zillner, SIEMENS Big Data in a Digital City Knut Sebastian Tungland, Statoil Big data in the energy sector
- 12. @BYTE_EU www.byte-project.eu THANK YOU Any questions? Key contacts: ◦ Rachel Finn – rachel.finn@trilateralresearch.com ◦ Kush Wadhwa – kush.wadhwa@trilateralresearch.com
Notas del editor
- 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.
- 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.
- 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).
- 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.
- 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