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The Big Data Value PPP: A Standardisation Opportunity for Europe



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The Big Data Value PPP: A Standardisation Opportunity for Europe

  1. 1. 07/03/16 The Big Data Value PPP: A Standardisation Opportunity for Europe International Workshop on Big Data Standards organized in conjunction with the ISO/IEC JTC 1 WG 9 Big Data Standards Dublin, Ireland, March 8th-11th Edward Curry Vice-President BDVA Research Leader Insight
  2. 2. 07/03/16 About Me Vice President
  3. 3. 07/03/16 OBJECTIVES •  What is the BDV PPP and BDVA? •  What role can standards play in technology adoption?
  4. 4. 07/03/16 4www.bdva.eu07/03/16 WHAT IS THE BDV PPP AND BDVA?
  5. 5. 07/03/16 The EU and Industry launched the Contractual Public Private Partnership on Big Data Value in October 2014 The Big Data Value Association represents ‘Private’ side “Big Data is possibly one of the few last chances for Europe‘s so<ware industry to take a true leadership“ CEO So'ware AG, Karl-Heinz Streibich “… EU ac@on should provide the right framework condi@ons for a single market for Big Data …” European Council Conclusion – 24/25 October 2013 “In the Commission's view, strategic coopera@on through a contractual Public-Private Partnership (cPPP) can play an important role in developing a data community and encouraging exchange of best prac@ces. In line with the principles set out in H2020, the Commission considers that a sufficiently well-defined cPPP would be the most effec@ve way to implement H2020 in this field,…” Commission CommunicaFon "Towards a thriving data-driven economy" - 2 July 2014
  6. 6. 07/03/16 Who is behind BDVA? Over 130 Members
  7. 7. 07/03/16 1st BDVA General Assembly President : Juergen Mueller, SAP VP: Edward Curry, Insight VP Jose-Maria Cavanillas, ATOS VP Milan Petković, Philips Secretary General: Stuart Campbell, ICE DSG: Nuria De Lama, ATOS DSG: Andreas Metzger, Paluno BDVA Summit
  8. 8. 07/03/16 BDVA Activities   TF1: Programme: Contributing to the H2020 Programme content of the BDV PPP   TF2: Impact: Maintain the various KPIs defining the expected Impact of BDV PPP   TF3: Community: Big data community engagement and participation   TF4: Communication: Communication plan for creating awareness around the BDVA   TF5: Legal: Bridge Big Data technology with legal and olicy matters   TF6: Technical: Identifying and refining the technical challenges of the programme – eg Data Management   TF7: Application: Domain usage group which can influence others – eg Telecoms   TF8: Business: Examining the business and economic influences and business areas   TF9: Societal: Examining the societal impact on business, citizens   TF10: Skills and Education: What skills are needed for the next knowledge workers   TF0: Administrative and strategic activities requested by BDVA GA/BOD
  10. 10. 07/03/16 A Holistic Big Data Value Ecosystem Big Data Value Chains Skills Legal Technical Applica@on Business Social
  11. 11. 07/03/16 The main BDV cPPP Elements are: Innovation Spaces: Cross-sector interdisciplinary Data Innovation hubs Lighthouse projects: Demonstrate Big Data Value R & I Projects: addressing technical priorities defined BDV SRIA Ecosystem Enablers: Non-technical including business models, standards, etc. Business Models
  12. 12. 07/03/16 Enablers An Agile Innovation Network Governance •  Monitoring, Advisory Board, Technical Committees Societal Acceptance SkillsBusiness Models Legal Environment Lighthouse Lighthouse Lighthouse R & I Project R & I Project R & I Project R & I Project BDV MOU BDV MOU BDV MOU BDV MOU
  13. 13. 07/03/16 What is the BDV cPPP about The Objective of the PPP is:   The cPPP shall create results that have IMPACT on members, participants, industry, economy and society… The Strategy needs to be:   The main focus is the transfer of technology and application (new from the PPP and state of the art) via the “instruments” designed for the PPP (i- Spaces/Lighthouse projects) Specific Objective on standards:   to enable research and innovation work, including activities related to interoperability and standardisation, for the future basis of big data value creation in Europe   Leverage the cPPP investments through sector investments of 4 times   Open, transparent and inclusive definition   Update Strategic Research & Innovation Agenda (SRIA);   Ensure 20% SME participating organisations;   Support to the ex-post assessment of the implemented projects;   Leverage the achieved results in the market   Develop skills and competences in Big Data Value   Actively involve all relevant sector players,   Work with others for alignment of goals and ensure synergies;   Governance model, which supports openness and efficiency   Monitoring Impact
  15. 15. 07/03/16 Technology Adoption Lifecycle Rogers, Evere` M. (1962). Diffusion of Innova@ons. Glencoe: Free Press. ISBN 0-612-62843-4.
  16. 16. 07/03/16 Technology Adoption Lifecycle 16 Innovators Late majority Laggards Early majorityEarly adopters Central interest Pleasure of exploring the new device properties Buy new product concept very early Not technologists First to get the new stuff Strong sense of practicality Wait un@l something has become an established standard Not comfortable with technology Don’t want anything to do with new technology Technology enthusiast Pragmatists ConservativesVisionaries
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  18. 18. 07/03/16 Characteristics Successful Adoption of Innovation   Relative Advantage: enabling better functioning.   Compatibility: degree to which a technology is consistent with existing stakeholder values, interests, and context   Complexity: the degree of difficulty involved in implementing the initiative and communicating benefits to stakeholders. Trialability: degree to which experimentation is possible in initiative   Cost Efficiency and Feasibility: with respect to existing comparable practice   Evidence: availability of research evidence and practice efficacy   Risk: level of risk associated with the implementation and adoption J. P. Wisdom, K. H. B. Chor, K. E. Hoagwood, and S. M. Horwitz, “Innovation Adoption: A Review of Theories and Constructs.,” Adm. Policy Ment. Health, Apr. 2013.
  19. 19. 07/03/16 19 Technology Cycles   Anderson and Tushman found that technological change proceeded cyclically   Each technology discon@nuity inaugurates a period of turbulence and uncertainty (era of ferment) un@l a dominant design is selected (era of incremental change) Slide Credit: Schilling, “Strategic Management of Technological Innova@on”, 2005
  20. 20. 07/03/16 20 Technology Cycles   Dominant design always rose to command majority of market   unless the next discon@nuity arrived too early   Dominant design was:   Never in same form as original discon@nuity   Not on the leading edge of technology   Bundled features that would meet needs of majority of market   During the era of incremental change, firms o<en cease to invest in learning about alterna@ve designs and instead focus on developing competencies related to the dominant design   This explains in part why incumbent firms may have difficulty recognizing and reac@ng to a discon@nuous technology Slide Credit: Schilling, “Strategic Management of Technological Innova@on”, 2005
  21. 21. 07/03/16 21www.bdva.eu07/03/16 THE BIG DATA …..ERA OF FERMENTATION….
  22. 22. 22 BIG 318062 BIG Big Data Public Private Forum THE DATA VALUE CHAIN 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 •  Predictions •  In-use analytics •  Simulation •  Exploration •  Modeling •  Control •  Domain-specific usage Big Data Value Chain Cavanillas, J. M., Curry, E., & Wahlster, W. (Eds.). (2016). New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe. Springer International Publishing.
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  27. 27. 07/03/16 27www.bdva.eu07/03/16 BIG DATA …..TIME TO SELECT THE DOMINATE DESIGNS ?….
  28. 28. 07/03/16 Technology and Data Standardisation Standardisation is essential to the creation of a Data Economy and the PPP will support establishing and augmenting both formal and de facto standards. The PPP will achieve this by: •  Leveraging existing common standards as the basis for an open and successful Big Data market. •  Integrating national efforts on an international (European) level as early as possible. •  Ensuring availability of experts for all aspects of Big Data in the standardisation process. •  Providing education and educational material to promote developing standards.
  29. 29. 07/03/16 BDV SRIA Technical Priorities Data Management Engineering the management of data Data Processing Architectures Optimized architectures for analytics both data at rest and in motion with low latency delivering real-time analytics Deep Analytics Deep analytics to improve data understanding, deep learning, meaningfulness of data Data Protection and Preservation Mechanism To make data owners comfortable about sharing data in an experimental setting Data Visualization and User Experience Enable intelligent visualization of complex information relying on enhanced user experience and usability
  30. 30. Legal Social EconomicTechnology Application Data & Skills Big Data Value Ecosystem Ownership Copyright Liability Insolvency Privacy User Behaviour Societal Impact Collaboration Business Models Benchmarking Open Source Deployment Models Information Pricing Data-Driven Decision Making Risk Management Competitive Intelligence Digital Humanities Internet of Things Verticals Industry 4.0 Scalable Data Processing Real-Time Statistics/ML Linguistics HCI/Visualisation The Dimensions of a Big Data Value Ecosystem [adapted from Cavanillas et al. (2014)]
  31. 31. 07/03/16 Conclusion   Standardisation is essential to the creation of a Data Economy   Standards can play a key role in improving the adoption of Big Data   I think we now need to select the dominant designs for Big Data technology   The Big Data Value PPP will support establishing and augmenting both formal and de facto standards in collaboration with stakeholder community   Technology Standards   Data Standards
  32. 32. 07/03/16 THANK YOU Further Information: Edward Curry: (Vice-President BDVA) BDVA: Insight:
  33. 33. 07/03/16 Background Reading: New Horizons for a Data-Driven Economy A Roadmap for Usage and Exploitation of Big Data in Europe •  Provides big picture on how to exploit big data, including technological, economic, political and societal issues •  Details complete lifecycle of big data value chain, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation •  Illustrates potential of big data value within different sectors, including industry, healthcare, finance, energy, media and public services •  Summarizes more than two years of research with wide stakeholder consultation Overview Open Access PDF h`p://