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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

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About Me
Vice President

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OBJECTIVES
• What is the BDV PPP and BDVA?
• What role can standards play in technology
adoption?

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WHAT IS THE BDV PPP AND
BDVA?

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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

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Who is behind BDVA?
Over 130 Members

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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

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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

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BIG DATA IS
TRANSFORMING SECTORS
BY BREAKING SILOS AND
DRIVING ECOSYSTEMS

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A Holistic Big Data Value Ecosystem
Big Data
Value Chains
Skills
Legal
Technical
Applica@on
Business
Social

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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

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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

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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

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WHAT ROLE CAN STANDARDS
PLAY IN TECHNOLOGY
ADOPTION?
(MY PERSONAL OPINION)

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Technology Adoption Lifecycle
Rogers, Evere` M. (1962). Diffusion of Innova@ons. Glencoe: Free Press. ISBN 0-612-62843-4.

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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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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.

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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

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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

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THE BIG DATA
…..ERA OF FERMENTATION….

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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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BIG DATA
…..TIME TO SELECT THE
DOMINATE DESIGNS ?….

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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.

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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. 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)]

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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

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THANK YOU
Further Information:
Edward Curry: edward.curry@insight-centre.org
(Vice-President BDVA)
BDVA: http://www.bdva.eu/
info@core.bdva.eu
Insight: http://www.insight-centre.org/

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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://@ny.cc/NewHorizons