BUILDING A EUROPEAN DATA ECONOMY

Francesco Barbato

DG CONNECT, Data Policy and Innovation Unit

Data Driven Innovation Conference

Roma, 24 February 2017

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

Pillar 3

ECONOMY &

SOCIETY

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

Data Economy

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

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)

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

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

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

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

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

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

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

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

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

Horizon 2020 ICT-LEIT

Big Data PPP

Work Programme 2017

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)

• 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

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…)

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.

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) - I ICT 15 (aka Lighthouse Projects) - IA

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

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)

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

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

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

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