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07/03/16 1www.bdva.eu
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
07/03/16 2www.bdva.eu
About	Me	
Vice	President
07/03/16 3www.bdva.eu
OBJECTIVES
•  What is the BDV PPP and BDVA?
•  What role can standards play in technology
adoption?
07/03/16 4www.bdva.eu07/03/16 4www.bdva.eu
WHAT IS THE BDV PPP AND
BDVA?
07/03/16 5www.bdva.eu
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
07/03/16 6www.bdva.eu
Who is behind BDVA?
Over 130 Members
07/03/16 7www.bdva.eu
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
07/03/16 8www.bdva.eu
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
07/03/16 9www.bdva.eu
BIG DATA IS
TRANSFORMING SECTORS
BY BREAKING SILOS AND
DRIVING ECOSYSTEMS
07/03/16 10www.bdva.eu
A Holistic Big Data Value Ecosystem
Big	Data	
Value	Chains	
Skills	
Legal	
Technical	
Applica@on	
Business	
Social
07/03/16 11www.bdva.eu
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
07/03/16 12www.bdva.eu
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
07/03/16 13www.bdva.eu
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
07/03/16 14www.bdva.eu07/03/16 14www.bdva.eu
WHAT ROLE CAN STANDARDS
PLAY IN TECHNOLOGY
ADOPTION?
(MY PERSONAL OPINION)
07/03/16 15www.bdva.eu
Technology Adoption Lifecycle
Rogers,	Evere`	M.	(1962).	Diffusion	of	Innova@ons.	Glencoe:	Free	Press.	ISBN	0-612-62843-4.
07/03/16 16www.bdva.eu
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
07/03/16 17www.bdva.eu
07/03/16 18www.bdva.eu
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.
07/03/16 19www.bdva.eu
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
07/03/16 20www.bdva.eu
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
07/03/16 21www.bdva.eu07/03/16 21www.bdva.eu
THE BIG DATA
…..ERA OF FERMENTATION….
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.
07/03/16 23www.bdva.eu
07/03/16 24www.bdva.eu
07/03/16 25www.bdva.eu
07/03/16 26www.bdva.eu
07/03/16 27www.bdva.eu07/03/16 27www.bdva.eu
BIG DATA
…..TIME TO SELECT THE
DOMINATE DESIGNS ?….
07/03/16 28www.bdva.eu
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.
07/03/16 29www.bdva.eu
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
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)]
07/03/16 31www.bdva.eu
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
07/03/16 32www.bdva.eu
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/
07/03/16 33www.bdva.eu
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

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The Big Data Value PPP: A Standardisation Opportunity for Europe

  • 1. 07/03/16 1www.bdva.eu 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
  • 3. 07/03/16 3www.bdva.eu OBJECTIVES •  What is the BDV PPP and BDVA? •  What role can standards play in technology adoption?
  • 5. 07/03/16 5www.bdva.eu 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. 07/03/16 6www.bdva.eu Who is behind BDVA? Over 130 Members
  • 7. 07/03/16 7www.bdva.eu 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. 07/03/16 8www.bdva.eu 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
  • 9. 07/03/16 9www.bdva.eu BIG DATA IS TRANSFORMING SECTORS BY BREAKING SILOS AND DRIVING ECOSYSTEMS
  • 10. 07/03/16 10www.bdva.eu A Holistic Big Data Value Ecosystem Big Data Value Chains Skills Legal Technical Applica@on Business Social
  • 11. 07/03/16 11www.bdva.eu 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. 07/03/16 12www.bdva.eu 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. 07/03/16 13www.bdva.eu 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
  • 14. 07/03/16 14www.bdva.eu07/03/16 14www.bdva.eu WHAT ROLE CAN STANDARDS PLAY IN TECHNOLOGY ADOPTION? (MY PERSONAL OPINION)
  • 15. 07/03/16 15www.bdva.eu Technology Adoption Lifecycle Rogers, Evere` M. (1962). Diffusion of Innova@ons. Glencoe: Free Press. ISBN 0-612-62843-4.
  • 16. 07/03/16 16www.bdva.eu 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
  • 18. 07/03/16 18www.bdva.eu 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. 07/03/16 19www.bdva.eu 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. 07/03/16 20www.bdva.eu 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. 07/03/16 21www.bdva.eu07/03/16 21www.bdva.eu THE BIG DATA …..ERA OF FERMENTATION….
  • 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.
  • 27. 07/03/16 27www.bdva.eu07/03/16 27www.bdva.eu BIG DATA …..TIME TO SELECT THE DOMINATE DESIGNS ?….
  • 28. 07/03/16 28www.bdva.eu 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. 07/03/16 29www.bdva.eu 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)]
  • 31. 07/03/16 31www.bdva.eu 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. 07/03/16 32www.bdva.eu 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/
  • 33. 07/03/16 33www.bdva.eu 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