How Big Data Ecosystems Work

#bdvasummit
#bdvasummit
How Big Data
Ecosystems Work
Dr. Edward Curry
Vice-President, BDVA
Insight Centre for Data Analytics

30/11/16 2#bdvasummit www.bdva.eu30/11/16 2www.bdva.eu
THE NEED FOR A BIG DATA
ECOSYSTEM

30/11/16 3#bdvasummit www.bdva.eu

Siemens

Connected care and health informatics
PreventionHealthy living Diagnosis Treatment Home care
Connected personalized care

Aggregating different data silos
Healthcare
providers
Health
Tech
sector
Payers Pharma
sector
Hospitals, GPs,
Health Systems
Consumer
Integrated data
Insights

WHAT MAKES AN
ECOSYSTEM WORK….

Technology
Providers
Data Value Chain
Core Value Chain
Extended Value Chain
Big Data Ecosystem
Suppliers of Complementary
Data Products and Services
End-Users of
my End-Users
Direct Data
End-Users
Direct Data
Suppliers
Data Value
Distribution
Channels
Suppliers of
my Data
Suppliers
Co-opetitors
(Competitors and cooperation)
Other Stakeholders and Peripheral Actors
Government Organisations
Regulators
Investors, Venture Capitalist & Incubators
Industry Associations
Data
Marketplace
Standardisation
Bodies
Start-ups and
Entrepreneurs
Researchers
& Academics
Stakeholders in a Big Data Value Ecosystem

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)]

BDV PPP: A Holistic Big Data Value Ecosystem
Big Data
Value Chains
Skills
Legal
Technical
ApplicaKon
Business
Social

BDV Ecosystem Principles
Focus BDV Ecosystem Principle
Openness The Ecosystem should embrace and contribute to openness in
terms of data, technology standards, interoperability, best
pracKce, educaKon, and innovaKon.
Connectedness The ecosystem should prioriKse connectedness and synergies
between actors, industrial sectors, languages, and across
boarders.
Cross-Sectorial The ecosystem should involve all relevant sectors of industry,
society, and scienKﬁc disciplines.
Sustainable The ecosystem should strive for self-sustainability in structure
and funcKon, in order to maintain long-term ecosystem
services.
Co-evoluAon The ecosystem should support compeKKon, cooperaKon and
co-evoluKon between actors.
IncubaAon The ecosystem should incubate start-ups and entrepreneurs

2016 2017 2018 2019 2020
Horizon 2020 Work Programme 16/17 18/ 19 2020 Beyond PPP
Lighthouse Projects
European InnovaKon Spaces
R&I and InnovaKon Projects
Ecosystem Enablers
3 Stages
1st Gen. High-Impact Sectors
2nd Gen. High-Impact Sectors
Establish Federation
Governance
Innovation Acceleration
Phase 1
Establish InnovaKon
Ecosystem
Phase 2
Pioneer DisrupKve
SoluKons
Phase 3
Ecosystem
Sustainability

3 KEY IMPACTS TO SCALE
THE DATA ECOSYSTEM

Impact #1: Industrial Adoption of
Big Data
Barrier: Europe is behind other regions
in the adoption of Big Data
BDV PPP Action: Drive the adoption of
big data to transform industrial sectors

Lighthouse Projects (ICT-15)
• Demonstrate Big Data Value
in a target industrial sector
• Large-scale data-driven
demonstraKons
• Propose replicable soluAons
by using exisKng technologies
or very near to market
technologies
• Create high level impact and
broadcast visibility

  Demonstrate Relative Advantage:
Demonstrate increase of productivity/
competitiveness of the target sector
  Provide proof points: Availability of
evidence and practice efficacy for the
target sector to justify investment
  Risk: Understanding of the level of risk
associated with the implementation and
adoption
  Develop Ecosystem: Connect key
stakeholders within the sector across
the value chain with active participation
(including SMEs).
  Sustainability: Enable large scale
replication for sectorial transformation
Lighthouses: Driving Adoption
DataBio: Data-Driven Bioeconomy
ICT-15-2016-2017
TT: Transforming Transport IA
ICT-15-2016-2017

Impact # 2: Promote Secure Data Sharing for
Innovation
Barrier: Availability of data is paramount, but
data sharing is an uncommon
BDV PPP Action: Facilitate Cross-domain
Data sharing & Foster Data Experimentation

European Innovation Spaces (ICT-14)
(i-Spaces for short)
Hubs to bring together…
Data Owners
+
Data Innovators
...in a secure, trusted,
and controlled
environment
From “proof of concept” to
“proof of ROI”

I-Spaces will deliver:
Support Cross-Domain Data
Integration
•  Simplify data analytics carried
out over datasets from different
companies and sectors
•  Shorten time to market for new
data products and services
Incubate and Support Data
Experimentation
•  Encourage participation of SMEs
and web entrepreneurs
•  Support testing and
benchmarking of technologies,
applications, and business
models.
ICT-14-2016-2017 - Projects
•  BigDataOcean: Exploiting Ocean's of
Data for Maritime Applications
•  SLIPO: Scalable Linking and
Integration of Big POI data
•  Data Pitch: Accelerating data to
market
•  AEGIS: Advanced Big Data Value
Chain for Public Safety and Personal
Security
•  euBusinessGraph: Enabling the
European Business Graph for
Innovative Data Product and Services
•  QROWD: Because Big Data
Integration is Humanly Possible
•  FashionBrain: Understanding
Europe’s Fashion Data Universe
•  EW-Shopp: Supporting Event and
Weather-based Data Analytics and
Marketing along the Shopper Journey

Impact # 3: Technical Innovation
Barrier: New techniques are need to create
new opportunities and develop or sustain
competitive advantages
BDV PPP Action: Overcome Technical
Barriers to delivering Big Data Value
Solutions

Philips Healthcare Transformation
Interoperability
Decision Support
User Experience
Real-Time Analysis
Data-in-Motion Privacy and TrustData-in-Motion and
Data at Rest

Data Protection
Privacy Preserving Technology
•  Substantial improvement of
technologies for data access,
processing and analysis to protect
consumer and personal data
•  Creating secure environments for
data access, process and analysis
•  With ICT-14 (i-Spaces)
Ethics and Awareness
•  Ethically sound approach to big
data processing with involvement
of relevant actors and stakeholders
•  Improve awareness and
confidence in Big Data
communities (industry, research,
policy makers, regulators)
ICT-18-2016 - Projects
•  SODA: Scalable Oblivious
Data Analytics
•  MH-MD: My Health - My Data
•  SPECIAL - Scalable Policy-
awarE linked data arChitecture
for prIvacy, trAnsparency and
compliance
•  e-Sides: Ethical and Societal
Implications of Data Sciences

BDVe: Big Data Value ecosystem
IMPACT
Framing conditions
for data economy
Big Data landscape
Business
opportunities
Innovation Booster
Industrial Impact &
investment
ECOSYSTEM
Communities
SMEs and startups
National, regional
and local
dimension
Relevant EU
initiatives
Collaborative
platform
Governance of
PPP portfolio
SKILLS
Network of Big Data
CoE
Big Data Value
Education Hub
Certification of
curricula & Training
Programmes
Data Scientist
Mobility Programme
MARKETING
Strategy &
Operational
marketing plans
Branding and
marketing material
Channels in action:
Online, Media,
Events
ICT-17-2016-2017

THANK YOU
Further Information:
Edward Curry edward.curry@insight-centre.org
Vice-President, BDVA
Insight Centre for Data Analytics
BDVA: http://www.bdva.eu/
info@bigdatavalue.eu

How Big Data Ecosystems Work

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à How Big Data Ecosystems Work

Similaire à How Big Data Ecosystems Work (20)

Dernier

Dernier (20)

How Big Data Ecosystems Work