1. BUILDING A EUROPEAN
DATA ECONOMY
Francesco Barbato
DG CONNECT, Data Policy and Innovation Unit
Data Driven Innovation Conference
Roma, 24 February 2017

2. Digital Single Market Strategy
Background
Ensuring that Europe's economy, industry
and employment take full advantage of
what digitalisation offers.
Creating a European Digital Economy
and society with growth potential
•Digitising industry
• Cloud
• Inclusive digital economy and society
• e-government
• Standardisation & interoperability
• Digital skills
•Data economy
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Pillar 3
ECONOMY
&
SOCIETY

3. Data Economy
Background
The digital revolution is built on data
6 million
people
employed
7.4 million
people
employed
Most economic activity will depend on data within a decade
Potential of the data-driven economy

4. Data Economy

5. Data Economy
Addressing current barriers
1. Free Flow of Data
The data localisation problem
Around 50 restrictions – legal and administrative rules
identified so far
Restrictions yet to be discovered (e.g. regulatory
practices, public procurement requirements)
Strong perception by businesses and public sector
organisations of the need to localise data in a
particular Member State, including perceived threat of
unfavourable regulatory scrutiny if data is not stored
and processed locally

6. Data Economy
Addressing current barriers
1. Free Flow of Data
Why data is localised ?
• Data localisation is used as a proxy for privacy, security and
availability of data for law enforcement and supervisory
authorities
However
• Data localisation ≠ data security, just as
• Data localisation ≠ better availability of data for authorities
• Certain restrictions may be justified and proportionate in
particular contexts (e.g. public security)

7. Data Economy
Addressing current barriers
1. Free Flow of Data
POSSIBLE ACTIONS
 Structured dialogues with the Member States and other
stakeholders (first dialogue on 23/02)
 Followed by, where needed and appropriate, infringement
proceedings and if necessary, further initiatives on the free flow of
data
OBJECTIVE
Removing data localisation restrictions except if they are required
for national security and similar objectives

8. Data Economy
Addressing current barriers
2. Data access and transfer
Why is it important ?
Machines now generate enormous
amounts of data
This drives innovation, creation of
new products
Market players need access to large
and diverse datasets

9. Data Economy
Addressing current barriers
2. Data access and transfer
• Limited access to data: companies tend to analyse data only
in-house and keep data to themselves, creating data silos
• Lack of comprehensive policy framework for the economic
utilisation, re-use and tradability of machine-generated data
• When contract is king, there is risk of unfair standard
contract terms imposed on weaker parties
• Manufacturers de facto "owners" of machine-generated
data
• Data silos hamper innovation

10. Data Economy
Addressing current barriers
2. Data access and transfer
POSSIBLE ACTIONS
Guidance on data sharing
Foster technical solutions to identify and exchange data
Default contract rules
Access for public interest and scientific purposes
Data producer's right
Access against remuneration
OBJECTIVE
Making machine-generated data more accessible for businesses to
boost innovation and the digital economy

11. Data Economy
Addressing current barriers
3. Data portability, interoperability and standards
• GDPR rules on portability do not apply to non-personal data
• Portability of non-personal data could foster innovation and new
services, and stimulate competition
• Data portability should be made easier and less costly in B2B
contexts
• Importance of interoperability of services, and of appropriate
technical standards
POSSIBLE ACTIONS
 Recommended contract terms to facilitate switching costs of service
providers
 Developing further rights to data portability
 Improving technical interoperability and sector-specific standards

12. Data Economy
Addressing current barriers
4. Liability in the context of IoT
and autonomous systems
POSSIBLE ACTIONS
 Defining responsibilities according to how a risk is generated or how
it is managed
 Considering voluntary or mandatory insurance schemes
• Internet of Things (IoT) and autonomous systems combine
hardware, software & data from many market players, making it
difficult to identify who is responsible
• Legally difficult to qualify as either products or services
• Established concepts & principles possibly not fit for purpose

13. Data Economy
Addressing current barriers
5. Experimentation and testing
• Important part of the
exploration of the emerging
issues
• Dedicated trials should be
organised for testing possible
solutions
EXAMPLES
 Cooperative connected and automated mobility – with trials based on 5G
 Experimenting with geo-spatial data

14. Data Economy
Public consultations and debate with Member States
The way forward
 Free flow of data
 Access to and transfer of data
 Portability
 Liability (IoT and robotics)
• Communication and Staff Working Document to inform the debate
• Launching wide dialogue with Member States / stakeholders,
including public consultation (10/01 to 26/04) on:
• BDVA to consider a joint contribution
• Studies to gather further evidence

15. Data Economy
For more information:
• Commission outlines next steps towards a European data economy
• Communication "Building the European Data Economy”
• Staff Working Document accompanying the Communication
• Public consultation on Building the European data economy
francesco.barbato@ec.europa.eu

16. Horizon 2020
ICT-LEIT
Big Data PPP
Work Programme 2017

17. Big Data PPP: The Challenge
• The main objective is to roll out an industrial strategy
to develop Europe's data driven economy as outlined
in the EC Communication 'Towards a thriving data-
driven economy' COM(2014)442
• The Work Programme 2016-17 implements the Big
Data PPP's Strategic Research and Innovation Agenda
(http://www.bdva.eu)

18. • ICT 14 Big Data PPP: cross-sectorial and cross-lingual data
integration and experimentation (IA) - Budget 27 M€
• ICT 15 Big Data PPP: large scale pilot actions in sectors
best benefitting from data-driven innovation (IA) - Budget
25 M€
• ICT 16 Big Data PPP: research addressing main technology
challenges of the data economy- Budget 33 M€
• ICT 17 Big Data PPP: support, industrial skills,
benchmarking and evaluation (1 RIA) - Budget 2 M€
• Inducement Prize: Big Data technologies (Other Actions 7)
Budget 2 M€
Big Data PPP

19. ICT 14 (aka Innovation Spaces) - IA
To foster the exchange, linking and reuse of data assets.
To integrate data assets from multiple sectors across
languages and formats in a safe environment for
experimentations of innovative services and product
ideas.
a) Innovation Actions addressing cross domain data
integration challenges of EU industries arranged along
data value chains. Wide range of technical issue to be
tackled (i.e. data models, entity identifiers, standards,
multi-lingual support, brokerage schemes, data quality,
privacy, etc…)

20. ICT 14 (continued)
b) Data experimentation incubators addressing big data
industrial challenges in a cross-sectorial, cross-lingual and/or
cross-border set-up. Experimenters: SMEs and start-ups. At
least 50% of experiments to be defined by data providers.
The incubator will offer access to cross-sectorial, cross
language data pools, computing infrastructure and open
software tools in addition to an organizational, legal, IPR
support environment.
Cascading Grants scheme to be implemented.

21. Large Scale Pilot Actions in data intensive sectors involving
key European industrial actors.
Their objective is to demonstrate how industrial sectors will
be transformed by putting big data technologies at their
core.
The Large Scale Pilot actions are meant to serve as best
practice examples to be transferred to other sectors.
Possible industrial sectors for Large Scale Pilot actions
include (but are not limited to) health, energy,
environment, earth observation, geospatial, transport,
manufacturing, finance and media.
ICT 15 (aka Lighthouse Projects) - IICT 15 (aka Lighthouse Projects) - IA

22. The challenge is to fundamentally improve the technology,
methods, standards and processes, building on a solid
scientific basis, and responding to real industrial needs, to
increase the efficiency and competitiveness of EU companies.
Cross-sector and cross-border challenges.
Examples (non-exhaustive): distributed data and process
mining, predictive analytics, visualization, real time complex
event processing, software stacks to take advantage of new
architectures to optimize Big Data tasks…etc…etc..
ICT 16 - RIA

23. Testing in real world scenarios (i.e. usability,
robustness, performance, privacy aware) on real
datasets, by professional/domain experts as opposed to
researchers or software developers.
Proposals must demonstrate that they have access to
appropriately large, complex and realistic data sets.
From day 1 of the project!
ICT 16 (continued)

24. The benchmarking action will identify specific data
management and analytics technologies of European
significance, define benchmarks and organise evaluations
that allow following their certifiable progress on
performance parameters (including energy efficiency) of
industrial significance.
To give European developers the means to continuously
improve their performance (and thus their
competitiveness)
With industrial actors that have expressed interest in the
technology for very specific business reasons.
A sustainable facility after the project end.
ICT 17 b) Benchmarking - RIA

25. The problem: becoming very accurate and efficient in predicting the
future based on past data.
• Extremely large amounts of past data about EU weather,
energy production/consumption will be made available to train
your algorithm(s)
• You submit your fully implemented prediction algorithm to a
platform prepared by H2020 SEE.4C project (as many
submissions as you want)
• The platforms automatically scores the performance of your
submission on unseen data based on a public and verifiable
success metric
• Several prizes available: best score in category (accuracy,
energy consumption, others to be announced…) wins the prize
• Eligibility criteria: The contest will be open to any legal entities
(including single persons) or groups of legal entities, according
to H2020 rules.
Prize on Big Data Technologies

26. The Calls for proposals
• H2020-LEIT-ICT-2017
ICT-14, ICT-15, ICT-16, ICT-17
The Call opens on 8/12/2016
The Call closes on 25/04/2017 at 17:00 CET
• Inducement Prize: Big Data Technologies
Opening of the contest: Q2 2017
Deadline for application: Q4 2017
Award of the Prize: Q2 2018

27. Additional information
• Technical Background Notes will be available at:
https://ec.europa.eu/digital-single-market/events/cf/ict-
proposers-day-2016/item-display.cfm?id=18467
• Contact:
francesco.barbato@ec.europa.eu