Big Data Socio-Economic Externalities – the BYTE Case Studies

BYTE: Big Data Socio-Economic Externalities – the BYTE Case StudiesAnna DonovanTrilateral Research & Consulting, LLPBYTE project coordinator

Big data roadmap and cross-disciplinarY community for addressing socieTal Externalities

BDVA Summit 17-19 June 2015

@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.


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


Case studies: big data practitioners assist to identify externalities

Environmental data

Energy

Utilities / Smart Cities

Cultural Data

Health

Crisis informatics

Transport


Case studies•QUESTION(S):• Which positive and negative societal externalities are associated

with the use of big data in each sector example?• Who are the (positively and negatively) affected parties? • How might potential positive impacts be captured, and how

might challenges associated with negative impacts be addressed/ diminished?

•METHODS: • Desk-based research;• Semi-structured interviews with high-level industry big data

practitioners; and• Expert focus groups to test and validate research findings.


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

Common externalities across case studies

Examples of Externalities Positive Negative

Economic • Boost to the economy• Innovation• Increase efficiency• Smaller actors left behind• Shrink economies

New business models with social and economic considerations, and increased innovation through open data and source material and by infrastructure and technology improvements

Private companies gaining revenue from organisations that can least afford to pay a premium, humanitarian organisations providing access to data during crises

Legal • Privacy• Data protection• Data ownership• Copyright• Risks associated with inclusion & exclusion

Organisations implementing measures to support data protection, data security and other legal issues, i.e. licensing frameworks for cultural data

Access to proprietary data restricted outside of organisations

Social & ethical

• Transparency• Discrimination• Methodological difficulties• Spurious relationships• Consumer manipulation

Improved services across the sectors, e.g health services enhanced by improved diagnostic testing; / e.g. increased awareness of the need for socially responsible and ethical data practices, i.e. importance of verifiable social media data in crises

Continued issues raised by the use of personal data, data accompanied by intellectual property rights. Data sharing etc

Political • Reliance on US services• Services have become utilities• Legal issues become trade issues

International cooperation through data sharing

Cross national flows of datatensions between for-profit and non-profit organisations


Case study example key findings: big data and health•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).


Economic • Boost to the economy• Innovation• Increase efficiency

“one of the things that we’ve been working on here is trying to develop a database of possible deletions or duplications because the software and the data doesn’t allow that […] as soon as we are confident that we have found something that would be helpful, we would publish it and make it available definitely.” (Translational medicine specialist)

“One area for development as a potential business opportunity is deal with the challenge of interoperability of big health data.” (FG)

Legal• Data protection• Privacy• Data ownership

“Big data demands the development of new legal frameworks in order to […] enhance and formalise how to share data among countries for improving research and healthcare.” (FG)


Case study example key findings: big data and crisis informatics•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.


Social & ethical • Transparency• Discrimination• Methodological

difficulties• Spurious relationships• Consumer manipulation

“I worked on a project called the ethics of data conference where we brought in one hundred people from different areas of knowledge to talk about data ethics. And to infuse our projects and understand and build road maps. There is something called responsible data forum which is working on templates in projects, to be able to help people incorporate those kind of personal data. My colleague has been working on something called ethical data checklists as part of the code of conducts for the communities that he has cofounded. So these code of conducts I have written one for humanitarian open street map about how we manage data.” (Program Manager, RICC)

Political • Reliance on US services• Services have become

utilities• Legal issues become

trade issues

“Humanitarian organisations and others are very worried about creating technology dependence one particular vendor, so they find that our platforms are open source make them more comfortable with adopting our process and our technology because they know that we don’t hold a leverage over their activity.” (SS, RICC)

“Difficulty of potential reliance on US based infrastructure services.” (D, RICC)


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


THANK YOU Any questions?

Key contacts:◦ Anna Donovan – [email protected] ◦ Kush Wadhwa – [email protected] ◦ Rachel Finn – [email protected]

mailto:[email protected]



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Big Data Socio-Economic Externalities – the BYTE Case Studies