Big Data Externalities – the BYTE Case Studies
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This document discusses the BYTE project which aims to address societal externalities associated with big data use. The project will produce a research roadmap and recommendations, involve relevant stakeholders to identify challenges through case studies, and disseminate findings. Case studies will explore externalities in various domains including environmental, energy, and health data. Both positive externalities like efficiencies and innovation, and negative externalities like privacy concerns and outdated legislation are discussed. Health case studies found improvements in diagnosis and treatment but also reluctance to share data due to legal and ethical constraints.
![@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.
• Gene0c
data
use
is
maturing
and
focused
on
high-‐grade
analy9cs
and
the
discovery
of
rare
genes
and
gene9c
disorders.
• The
key
improvements
include
9mely
and
more
accurate
diagnosis,
the
development
of
personalised
medicines,
and
drug
and
other
treatments/
therapy
development,
which
can
save
lives.
• Key
innova9ons
include
the
development
of
privacy
protec9ng
and
secure
databases
for
gene9c
data
samples.
• However,
there
tends
to
be
a
reluctance
by
public
sector
ini0a0ves
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
ac9vely
iden9fy
informa9on
about
you”
(Program
head,
Clinical
gene9cist
)](https://image.slidesharecdn.com/bigdataeconomyworkshop-150922121457-lva1-app6892/85/Big-Data-Externalities-the-BYTE-Case-Studies-8-320.jpg)
![@BYTE_EU www.byte-project.eu
Case study-‐specific findings: crisis
informa2cs
• Crisis
informa9cs
is
in
the
early
stages
of
integra9ng
big
data.
• Currently,
its
primary
focus
is
on
integra9ng
social
media
and
geographical
data.
• The
key
improvement
is
that
the
analysis
of
this
data
improves
situa0onal
awareness
more
quickly
afer
an
event
has
occurred.
• A
key
innova9on
is
the
combina0on
of
human
compu0ng
and
machine
compu0ng,
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
protec0on
issues.
• There
is
evidence
of
a
reliance
on
US
cloud
and
compu9ng
services.
“And
I
have
seen
this
on
mul9ply
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,
Interna9onal
Governmental
Organisa9on)](https://image.slidesharecdn.com/bigdataeconomyworkshop-150922121457-lva1-app6892/85/Big-Data-Externalities-the-BYTE-Case-Studies-9-320.jpg)
Big Data Externalities – the BYTE Case Studies
- 1. BYTE: Big Data Externali2es – the BYTE Case Studies Rachel Finn Trilateral Research & Consul2ng, 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 Externali9es (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 Objec2ves The BYTE project has three main objec9ves: 1. To produce a research and policy roadmap and recommenda0ons to support European stakeholders in increasing their share of the big data market by 2020 and in capturing and addressing the posi9ve and nega9ve societal externali9es associated with use of big data. 2. To involve all of the European actors relevant to big data in order to iden9fy concrete current and emerging problems to be addressed in the BYTE roadmap. The stakeholder engagement ac9vi9es will lead to the crea9on of the Big Data Community, a sustainable plaXorm from which to measure progress in mee9ng the challenges posed by societal externali9es and iden9fy new and emerging challenges. 3. To disseminate the BYTE findings, recommenda0ons and the existence of the BYTE Big Data Community to a larger popula9on of stakeholders in order to encourage them to implement the BYTE guidelines and par9cipate in the Big Data Community.
- 4. @BYTE_EU www.byte-project.eu Case studies: big data prac22oners assist to iden2fy externali2es Environmental data Energy U9li9es / Smart Ci9es Cultural Data Health Crisis informa9cs Transport
- 5. @BYTE_EU www.byte-project.eu Understanding ‘externali2es’ § In BYTE we consider the externali0es or impacts of big data § Posi0ve effects or benefits realised by a third party § Nega0ve costs (or harm) that affects a third party § Externali9es relate to social processes linked to big data, as well as the opportuni9es & risks that may arise as a result of the existence of the data. § Some effects may be unexpected or uninten0onal IMPACT ECONOMIC SOCIAL LEGAL ETHICAL POLITICAL
- 6. @BYTE_EU www.byte-project.eu Big data concerns: externali2es Economic • Boost to the economy • Innova9on • Increase efficiency • Smaller actors lef behind • Shrink economies Legal • Privacy • Data protec9on • Data ownership • Copyright • Risks associated with inclusion & exclusion Social & Ethical • Transparency • Discrimina9on • Methodological difficul9es • Spurious rela9onships • Consumer manipula9on Poli9cal • Reliance on US services • Services have become u9li9es • Legal issues become trade issues Economic • Boost to the economy • Innova0on ✔ • Increase efficiency ✔ • Smaller actors lef behind • Shrink economies Legal • Privacy ✔ • Data protec0on ✔ • Data ownership ✔ • Copyright • Risks associated with inclusion & exclusion Social & Ethical • Transparency ✔ • Discrimina9on • Methodological difficul9es • Spurious rela9onships • Consumer manipula9on • Improved services ✔ Poli9cal • Reliance on US services ✔ • Services have become u0li0es ✔ • Legal issues become trade issues • Dependent on public funding ✔
- 7. @BYTE_EU www.byte-project.eu Select horizontal findings Posi9ve externali9es • Efficiencies • Product and service innova9on • New business models • Societal benefits (improved decision-‐ making in healthcare, crisis management, commercial organisa9ons; personalised services) Nega9ve externali9es • Dependence on public funding to create the environment in which big data business models can flourish • Privacy concerns • Fear of losing proprietary informa9on • Outdated legisla9on • Difficulty in adap9ng 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. • Gene0c data use is maturing and focused on high-‐grade analy9cs and the discovery of rare genes and gene9c disorders. • The key improvements include 9mely and more accurate diagnosis, the development of personalised medicines, and drug and other treatments/ therapy development, which can save lives. • Key innova9ons include the development of privacy protec9ng and secure databases for gene9c data samples. • However, there tends to be a reluctance by public sector ini0a0ves 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 ac9vely iden9fy informa9on about you” (Program head, Clinical gene9cist )
- 9. @BYTE_EU www.byte-project.eu Case study-‐specific findings: crisis informa2cs • Crisis informa9cs is in the early stages of integra9ng big data. • Currently, its primary focus is on integra9ng social media and geographical data. • The key improvement is that the analysis of this data improves situa0onal awareness more quickly afer an event has occurred. • A key innova9on is the combina0on of human compu0ng and machine compu0ng, 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 protec0on issues. • There is evidence of a reliance on US cloud and compu9ng services. “And I have seen this on mul9ply 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, Interna9onal Governmental Organisa9on)
- 10. @BYTE_EU www.byte-project.eu BYTE project key outputs • Define research efforts and policy measures necessary for responsible par9cipa9on in the big data economy • Vision for Big Data for Europe for 2020, incorpora9ng externali9es • Amplify posi9ve externali9es • Diminish nega9ve ones • Roadmap • Research Roadmap • Policy Roadmap • Forma9on of a Big Data community • Implement the roadmap • Sustainability plan
- 11. @BYTE_EU www.byte-project.eu Next event Valida0ng case study externali0es Dublin 14th October 2015, 9am-‐5pm Presenta9ons by: Sonja Zillner, SIEMENS Big Data in a Digital City Knut Sebas9an Tungland, Statoil Big data in the energy sector
- 12. @BYTE_EU www.byte-project.eu THANK YOU Any ques0ons? Key contacts: ◦ Rachel Finn – rachel.finn@trilateralresearch.com ◦ Kush Wadhwa – kush.wadhwa@trilateralresearch.com