CONTENTS. The Big Data Value PPP in a nutshell Process and timing SRIA: Innovation concepts Implementation aspects, Structure & Governance model

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2 CONTENTS The Big Data Value PPP in a nutshell Process and timing SRIA: Innovation concepts Implementation aspects, Structure & Governance model

3 CONTENTS The Big Data Value PPP in a nutshell Process and timing SRIA: Innovation concepts Implementation aspects, Structure & Governance model

4 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, academia supply & 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)

5 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 )

6 CONTENTS The Big Data Value PPP in a nutshell Process and timing SRIA: Innovation concepts Implementation aspects, Structure & Governance model

7 NESSI Membership > 450 members NESSI Partners

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9 Launch of the BDV PPP

10 Workshops organised on Energy, Manufacturing, Retail, Mobility, Health, Media, Content, Geospatial/Environment, Public Sector/SMEs to feed into SRIA More than 80 organisations Open consultation on the SRIA draft ran between 9 April and 5 May 2014 More than 2700 downloads of the SRIA More than 100 responses to the Public consultation questionnaire Various separate feedback via etc.

11 Consultation/ questionnaire University of Zurich, Binarypark, Hitachi, Hitachi Data Systems, Locali Data, Information technologies Institute (ITI), TNO Innovation for Life, Sintef Petroleum Research, OpenData.sk / Utopia + EEA, OKFN Belgium, My Digital Footprint, GC Venture Consulting (formerly IceStat), SAP Deutschland AG & Co. KG, Technical University of Cluj-Napoca, Statistics Netherlands, Oxford Sustainable Development Enterprise (EEIG), Paradigma Tecnológico, DialogCONNECTION Ltd, Wolters Kluwer Deutschland GmbH, Telefonica, Paluno, Fuhrwerk, Euroalert, ICM, University of Warsaw, Numeral Advance, Karlsruhe Institute of Technology, ELIXIR, Fundacion centro tecnoloxico de telecomunicacio ns de Galicia, TU Delft, Universidad Lyola Andalucia, CGI Nederland B.V., TECNALIA RESEARCH & INNOVATION, Engineering Ingegneria Informatica, Comunidad de Madrid, TU Dortmund, RENAM, Telefonica 2, GISIG, ESTeam AB, CERTH-ITI, Universitat Politècnica de València, inmark, Orange, data2knowledge gmbh, WU Wien, Fraunhofer, Zurko Research, Surveying and mapping authority of the Republic of Slovenia, ELRA, Santer Reply SpA, Athens Information Technology Direct inputs Beuth Hochschule für Technik Berlin, Compass (roadmap), Department of Planning and Design/ Municipality of Neo Iraklio Attikis, ESTeam AB/ LT-Innovate, INSEE, LT-Innovate / Philippe Wacker (input and report), National Technical University of Athens, Reed Elsevier Rethink Big, Rovertshaw Steve, Synergetics, Telefonica, TransLectures project, UC, Vienna University of Technology /Institute for Software Technology and Interactive Systems, Wolters Kluwer Deutschland GmbH, Zabala Innovation Consulting

12 Strength European Aspects o Strong medium-sized sector o EU is a stable environment still (life standards, currency, ) Market and Business o European capacity to start in Niches and then grow Business Potential o Lots of SMEs who are dynamic and flexible, can react quickly to market changes o Cooperation between content providers in several domains o Existing and strong content/data market in Europe Technical o Computer clusters and Clouds resources available o Growing interest in archiving, sensing, in situ data, model data and citizens Big data Data and Content o Wealth of content data o Existing ecosystems and portals (INSPIRE, Copernicus and GEOSS, National...) o Foundations from geospatial and environmental data sets and supporting infrastructures Education and Skills o Broad & detailed domain know-how, process know-how o Some domains with lots of good Technology and People o Capacity and Universities to develop skills o Good education regarding engineering /domain specific Policy, Legal and Security o European background is free/open process Usage o No items Weakness European Aspects o Decentralized Europe o Conservatism and Long Innovation Cycles of some domains o Lack of start-up culture, risk aversion, no failure tolerance, move to US o Almost no European players in the data analytics solution/suit arena o Few larger companies, many with small size o Focus on national market, and then small extension international Market and Business o Access to affordable big data facilities and make data more easily accessible o Visibility of ecosystem service offerings o A lack of business models for how long to preserve the data, what data, etc o EU cloud providers not as reliable and sophisticated as US-players eg Amazon Technical o Processable linked data, aggregate/combine data o Interoperability, Harmonisation/standardisation of content assets formats o Access, interconnectivity -Information in silos, data sharing, longterm data o Technical framework for data sharing across Heterogeneous environments o Migration of data o Amount of data is often so huge that you cannot give the data to the user o From 2D to the handling of 3D and 4D data Data and Content o Missing public data in EU o Multilingualism / Language barrier o Low quality of data in open data portals o Structural Data identifies, ontologies and semantics o English bias to data and content assets o Metadata management o Access to sub-sets of data Education and Skills o Integrated education of data analysts and amount of people Policy, Legal and Security o Legislative restrictions on data sharing o Restricted/Few European initiatives o Non-pragmatic regulation (like USA regulation) o Fragmented rules & regulation across Europe: e.g. Open Gov Data o Security demands and need for more cost/benefit models o IPR, Privacy, Legal issues Usage o Changing consumer behaviour o Citizen science - data from citizens, how to qualify, use o Big Data (Value) for SME use

13 Opportunity European Aspects o Various cultures, various strengths can result in creative thinking if mixed o Co-opetition enablers such as Big Data Value PPP, Industry 4.0 o Strengthen the European Market, e.g. by fusing the emerging start-up nuclei o Lots of SMEs for the low hanging fruits of big data for which agility is required o Investment into the entirety of the innovation chain beyond basic research o Big Data Start-ups have a lot of creativity potential o Useful investment support mechanisms for SMEs (like e.g. European loans). Market and Business o Opening up of private content assets o Increasing use of analytics o Extending the INSPIRE and COPERNICUS at global scale (not only Europe). o Improve creativity, cost effective solutions. An opportunity to change. o Completely new and different business areas and services possible o New applications for the whole Big data ecosystems from acquisition, data extraction, visualization and usage of obtained patterns. Technical o Syndication of data and content o Micropayments o Wearables and Sensors o Device type explosion o Suitable API for access. o Interoperability o Visibility and greater use of Directory services for data sources Data and Content o Greater use of European cultural and data assets o Interconnected content, semantics and data o Navigable data and Data curation o Context and Personalisation o Increased volume of electronic data Education and Skills o New research areas for universities / Training with innovation o Educate the younger generation of students to use big datasets and explore them Policy, Legal and Security o Storing data information on safe grounds (data security) under European legislation o Need for uniform policies for data access Usage o User Generated Content and Crowd sourcing/recommendation o Data(driven) as a service - can enable entry into new markets for Europeans o Opportunity in shift from technology push to end user workflow Threat European Aspects o Europe vs the rest of the world; US / China are running with an open mind o US Companies understand Data and Information has a value and can be traded o Dominance of US players and their bottom-up Ideas o No Big Data and Data-sharing culture in Europe) Market and Business o Dominant large corporations that own important data o Consolidation of different stakeholders and marketplace in sector o Barriers in market for SMEs eg in owning data o Outflow from EU, in order to be nearer to the customers or due to production costs Technical o No items Data and Content o No items Education and Skills o Professional Brain Drain o Lack of skilled professional resources and graduates in field Policy, Legal and Security o Legislation missing / falling behind; responsible too connected to old data world o Full analysis of ethical and privacy issues need o Over regulation, protectionism in Europe; privacy regulations in 3rd countries are less restrictive o Political and legal aspects: eg imagery / high definition is security risk (terrorism). o Expectations/laws: if you provide some data, then you cannot stop providing the data or asked to provide the same data for others o Data availability policies; eg Companies not willing to put data available eg if a branch in US because data linked to data might have to be provided Usage o No one wants to share data but everyone wants to share others data o o Cross border data flows Data-driven services are not tied to a particular location; e.g. maintenance is location-based but the service can be orchestrated from anywhere

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16 Formal documents of the process: BDV Frame, BDV SRIA, Template for BDV PPP Public Consultation

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18 CONTENTS The Big Data Value PPP in a nutshell Process and timing SRIA: Innovation concepts Implementation aspects, Structure & Governance model

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20 The EIS/E Should support the legitimate ownership, privacy and security claims of corporate data owners (and their customers) pre-condition Ethical considerations and legal/regulatory requirements Motivation for data owners: get access to advanced technologies; discover business opportunities thanks to participants interactions Motivation for researchers, entrepreneurs, small and large companies: ease of experimentation and business opportunities Resources to invest, but not unlimited Initial steps (planning): Data assets and technologies will be prioritized based on their economic potential Reporting stage: quantitative evidence on increases in performance for core technologies or reduction in costs for business processes Capitalize on existing environments and initiatives (avoid duplication of resources: incubators, PPPs, EU infrastructures..:); Welcome federation approaches PPP KPIs

21 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 A Smart Data Centre and a Smart Data Community Prompt Co-Working on valuable Problems with valuable Big Data : Requires Structure (SDIL Orga) and Trust (SDIL Contract) Knowledge Transfer from Research to Industry and vice versa Exploration of Smart Data Potential / Innovation Potential Best Practice and Standards Research in Analytics, Systems, Application Domain specific Smart Data Tools, Support, Management, Privacy, Legal and Curation HCI and Usability First focus: short term high impact projects

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24 The Big Data Analysis Support GE offers a solution for both Big Data Batch processing (BD Crunching) and Big Data Streaming; unified set of tools and APIs allowing developers to program the analysis on large amount of data and extract relevant insights in both scenarios using Map&Reduce (ex. Social Networks analysis, real-time recommendations, etc). Technology A true open innovation ecosystem

25 Innovation Spaces serve as hubs for bringing the technology and application developments together and cater for the development of skills, competence, and best practices. Improving understanding of data by deep analytics (e.g. predictive modelling, graph mining,...) Optimized Architectures for analysing data including real-time data (e.g.recommendati on engines,...) Visualization and user experience (e.g. User adaptive systems, search capabilities,...) Lighthouse Projects Large scale demonstrations focusing on certain sectors and domains Data management engineering (e.g. Data integration, data integrity,...) Privacy and anonymisation mechanisms

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

27 Actions for skills Work with higher education institutes and education providers to support the establishment of: New educational programmes with a core focus on data science including interdisciplinary curricula with a focus on specific application domains Novel courses to educate and train the current workforce with the specialized skillsets needed to be Data Engineers. Foundational modules in data science, statistical techniques and data management within related disciplines such as law and humanities. Leverage Innovation Spaces to develop a network between scientists (academia) and industry that foster the exchange of ideas and challenges Encourage industry to enhance the industrial relevance of courses by making datasets and infrastructure resources available Actions for Best Practices Establish Innovation spaces to foster learning, develop competence, and identify best practices for Data Engineers. Leverage the innovation space to: Package best practices in the form of frameworks, toolboxes and integrated models to bring individual solution components into a system perspective Formally define a Big Data Value Body of Knowledge (BDVBoK) Deliver online seminars to highlight the latest in best practice developed in the innovation space and encourage and promote the adoption of best practices across sectors, especially targeting SMEs. 27

28 Business model methodology: cross-enterprise, cross-sector level approach comprising all stakeholders of the Big Data Ecosystem (within Innovation Spaces) Traditional consulting, consultation workshops, employee idea competitions, utilization of social networks to foster information exchange and best practice sharing Secure environments for testing Business models Data service clearing house European and national level support to companies, including SMEs, in terms of legal practice and advice

29 Contribution by the PPP: Bring together participants in an integrated and collaborative way, with the aim of creating a forum for a structured dialogue around data-driven innovation issues Involvement of the citizen as active stakeholder, to address concerns and raise awareness Input to legislative discussions to embrace data-driven innovation. Information to data owners on the benefits of data sharing Develop a cross domain understanding of key issues of the big data value economy Identify crucial elements of future eco-systems that represent a challenge or roadblock to faster take up Topics Identification of issues from data acquisition, ownership of raw data, processed data, augmented data, aggregated data; Liability of data analysis, to deal with potential damage caused by incorrect analysis Measurement the value of data, the succession of data rights and the legacy of stored data for new, merged or bankrupt companies; Delete mechanisms, policies for data processing and data usage. Identifying the barriers with regard to ownership of data and intellectual property rights incl. copyright and access rights

30 CONTENTS The Big Data Value PPP in a nutshell Process and timing SRIA: Innovation concepts Implementation aspects, Structure & Governance model

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32 Big Data Strategies of User Industry, Source: Morgan Stanley

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36 Nuria de Lama Representative to the European Commission Research & Innovation