1. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum THE BIG PROJECT: RESULTS AND IMPACT October 2nd, Heidelberg Edward Curry, Insight @ NUI Galway Nuria de Lama, Representative of Atos Research & Innovation to the EC

2. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 2 BIG DATA: EUROPE NEEDS TO REACT! Big Data is mainstream in North America, but Europe lagged behind due to Size factor: smaller organizations and smaller data sets Expensive, scarce data analytics skills Economic crisis, cautiousness in new investments

3. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 3 BIG DATA IN EUROPE Possibly one of the few last chances for Europes software industry to take a true leadership K-H Streibich, CEO This is a revolution: and I want the EU to be right at the front of it. Neelie Kroes, Vice-President of the European Commission responsible for the Digital Agenda, March 2013

4. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 4 The BIG Project BIG aims to promote a well-developed EU industrial landscape in Big Data: Providing a clear picture of existing technology trends and their maturity Acquiring a sharp understanding of how Big Data can be applied to concrete environments / use cases Pushing European Big Data research and innovation to contribute to European competitiveness (define how the future should look like) Building a self-sustainable, industry-led initiative (implementation) Overall Objective Work at technical, business and policy levels, shaping the future through the positioning of IIM and Big Data specifically in Horizon 2020. Bringing the necessary stakeholders into a self-sustainable industry-led initiative, which will greatly contribute to enhance the EU competitiveness taking full advantage of Big Data technologies.

5. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 5 A PAN-EUROPEAN EFFORT Funding: Euros 2.499.998,00 Duration: 26 months

6. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 6 BIG - THREE LEVEL APPROACH Big Data Public Private Partnership Impact Assessment Sustainability Towards Horizon 2020 Roadmapping activity Individual roadmap elaboration (per sector) Roadmap consolidation (cross-sectorial) Technology state of the art and sector analysis Definition of the proposed application sectors Asses the impact/applicability of the different technologies Big Data Initiative definition

7. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 7 HOW: SECTORIAL FORUMS AND TECHNICAL WORKING GROUPS Health Public Sector Finance & Insurance Telco, Media& Entertainment Manufacturing, Retail, Energy, Transport Needs Offerings Big Data Value Chain Technical Working Groups Industry Driven Sectorial Forums Data Acquisition Data Analysis Data Curation Data Storage Data Usage Structured data Unstructured data Event processing Sensor networks Protocols Real-time Data streams Multimodality Stream mining Semantic analysis Machine learning Information extraction Linked Data Data discovery Whole world semantics Ecosystems Community data analysis Cross-sectorial data analysis Data Quality Trust / Provenance Annotation Data validation Human-Data Interaction Top-down/Bottom-up Community / Crowd Human Computation Curation at scale Incentivisation Automation Interoperability In-Memory DBs NoSQL DBs NewSQL DBs Cloud storage Query Interfaces Scalability and Performance Data Models Consistency, Availability, Partition- tolerance Security and Privacy Standardization Decision support Prediction In-use analytics Simulation Exploration Visualisation Modeling Control Domain-specific usage

8. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 8 RELEVANT SOURCES: SUBJECT MATTER EXPERT INTERVIEWS

9. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 9 AVAILABILITY OF A WIDE SPECTRUM OF RESULTS Interviews, Technical White Papers, Sector's requisites and Roadmaps available on: http://www.big-project.eu Expert Interviews Technical Whitepapers Executive Overview Key Insights Social & Economic Impact Concise State of the Art Future Requirements & Emerging Trends Sector-specific Case Studies

10. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 10 THE DATA LANDSCAPE Big Data technology is mostly evolutionary Old technologies applied in a new context Volume, Variety, Velocity, Value Business processes change must be revolutionary to enable new opportunities Technology Evolution Process Revolution The long tail of data variety is a major shift in the data landscape Variety Reuse Cross-sectorial uses of Big Data will open up new business opportunities

11. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 11 BIGGEST BLOCKERS Lack of Business-driven Big Data strategies Undiscovered & unclaimed business values Data Sharing & Exchange Need for format and technology standards Data Privacy and Security Regulations & markets for data access Legal frameworks for data sharing & communication are needed Human resources Lack of skilled data scientists and data engineers Key Technical Requirements

12. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 12 COMMON REQUIREMENTS Data Privacy and Security Legal frameowrks for data sharing & communication are needed Investment Long-term investments require conjoint engagement of several partners Not-Technology-related Data Digitalization only small percentage of data is documented (lack of time) with low quality Data Enrichment transform unstructured data into structured format Data Sharing & Integration Overcome data silos and inflexible interfaces Business Cases Undiscovered und unclaimed potential business values Regulation & Technology Technology-related Data Quality Reliable insights for health-related decisions require high data quality Openness Leadership to promote common standards for Open Data: APIs, format and schemas, as well as covering licensing and legal aspects Data ownership Fragmentation of data ownership that leads to the data silo and interoperability issues

13. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 13 SECTORIAL ROADMAPS Health Public Sector Finance & Insurance Telco, Media& Entertainment Manufacturing, Retail, Energy, Transport

14. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum PUBLIC PRIVATE PARTNERSHIP

15. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 15 MOTIVATION Needs for everyone to remain competitive Opportunities for only a few?

16. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 16 Europe has to react! A Call for Action a public private partnershipcan be a powerful way to work togetherPublic money is not free money. Before you can unlock it you need a very clear plan, showing how any public investment will work, how it connects to the activities around it, and how it will pay offwe need a Strategic Research and Innovation Agenda from a broad, inclusive and representative basis, pulling together different priorities, so they make sense we need to do all this quickly, and to the highest quality Neelie Kroes Vice-President of the European Commission

17. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 17 DRIVERS NESSI Partners NESSI Membership > 450 members http://www.nessi-europe.eu/ http://www.big-project.eu/ http://www.bigdatavalue.eu/

18. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 18 THE BIG DATA VALUE PPP (PUBLIC-PRIVATE PARTNERSHIP) The goal of the Big Data Public Private partnership is to increase the amount of productive European economic activities and the number of European jobs that depend on the availability of high quality data assets and the technologies needed to derive value from them. European cross-organizational and cross-sector environments Meeting point for different stakeholders (small, big companies, academiasupply & demand) to discover economic opportunities based on data integration and analysis Resources to develop working prototypes to test the viability of actual business development Availablity of data assets (secure environments to enhance data sharing; i.e. not only open data) Technologies to derive value from them (this could entail bringing analytics close to data)

19. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 19 MUTIDISCIPLINARITY

20. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 20 I-SPACES: THE RELEVANCE OF THE ECOSYSTEM Technology A true open innovation ecosystem

21. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 21 BUILDING UPON EXISTING INFRASTRUCTURES AND TECHNOLOGIES: DATA INCUBATORS TeraLab (FR): digital services platform that provides both the research community and businesses, with an environment conducive to research and experimentation focused on innovative applications and industrial prototypes in the field of Big Data Physical resources (including a substantial processing capacity with several teraoctets of RAM), huge databases and various cutting-edge applications and tools (through SAAS/PAAS model) Facilitating batch or real-time processing and storage of huge amounts of data Data assets: anonymous, publicly-available information (e.g. OpenStreetMap, Common Crawl), and open data, but also data which has been processed to render it anonymous, provided by professional sources Access via secure and ultra-secure systems using technology provided by the CASD (Centre for Secure Remote Access). SDIL (DE): Similar approach in the domains of Industry 4.0 Energy Smart Cities Health

22. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 22 RESEARCH PRIORITIES Privacy and anonymisation mechanisms Improving understanding of data by deep analytics (e.g. predictive modelling, graph mining, ...) Optimized Architectures for analysing data including real-time data (e.g.recommendation engines, ...) Visualization and user experience (e.g. User adaptive systems, search capabilities, ...) Data management engineering (e.g. Data integration, data integrity, ...) Innovation Spaces serve as hubs for bringing the technology and application developments together and cater for the development of skills, competence, and best practices. Lighthouse Projects Large scale demonstrations focusing on certain sectors and domains Research Priorities Instruments

23. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 23 ESTIMATED EXPECTED R&D OUTCOMES Year 1 Year 2 Year 3 Year 4 Year 5 Priority : privacy and anonymisation Generalisation of Secure Remote Data Access Centre techniques. Method for deletion of data and data minimization. Robust anonymisation algorithms Priority : deep analysis Improved statistical models by enabling fast non-linear approximations in very large datasets Predictive modeling Graph mining techniques applied on extremely large graphs Real-time analytics Semantic analysis in near-real-time Algorithms for multimedia data mining Deep learning techniques Descriptive language for deep analytics. Contextualisation. Priority : architectures for analytics of data at rest and in motion Optimized tools for the integration of existing components to new types of platforms with both data at rest and in motion. Synergies between massively parallel architectures (MPP) and batch processing/stream processing architectures Priority : advance visualisation New data search solutions / paradigms Semantic driven data visualisation stronger links between visualization and analytics User adaptation Collaborative real- time, dynamic 3D solutions Priority : engineering data management APIs for improving the process of data transformation Collaborative Tools and techniques for Data Quality (including integrity and veracity check) Harmonized description format for meta-data Methodology, models and tools for data lifecycle management Data management as a service

24. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 24 BIG DATA CHALLENGES EXPAND TECHNOLOGY Privacy & Regulation Incentives and awareness to foster adoption Traditional industries New industries Business models and commercialization Skills

25. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 25 PROCESS & TIMING Launch of the BDV PPP Investment of approximately 1068M for [2016-2020]

26. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 26 STAKEHOLDER INVOLVEMENT Big Data Strategies of User Industry, Source: Morgan Stanley

27. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 27 VALUE PROPOSITION Technology and data assets development Means for benchmarking and testing performance of some core technologies (querying, indexing, feature extraction, predictive analytics, visualization) business applications evaluated according to different criteria (ex. usability) Development of business models Optimizing existing industries New business models along new value chains Improvement of the skills of data scientists and domain practitioners (enrich educational offering) Dissemination of best practices showcases to stimulate big data adoption and transfer of solutions across sectors Analysis of societal impact transfer of data management practices to domains of societal interest (health, environment)

28. ISC BIG DATA October 2nd, 2014 - Heidelberg BIG Big Data Public Private Forum 28 FORMAL SIGNING EVENT OF THE PPP Date: 13th October 2014 Place: Brussels Contact: CNECT-G3-EVENTS@ec.europa.eu, mentioning "Event for the Big Data PPP Signature" in the subject field