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UPDATE ON ARCHITECTURE AND ECOSYSTEM DESIGN
PROF. DR. BORIS OTTO  FRANKFURT  22 MARCH 2018
INDUSTRIAL DATA SPACE
www.industrialdataspace.org // 2
Mobility
• Autonomous driving
• Mobility services
• Smart traffic
management
Service Innovation
Manufacturing
• Smart factory
• Adaptive
manufacturing
• “Industrie 4.0”
Organizational
Innovation
Healthcare
• Personalized medicine
• Translational
medicine
• Smart healthcare
devices
Product Innovation
Retail
• Supply chain visibility
• Goods and data
traceability
• Sustainability
Process Innovation
DATA IS A KEY RESOURCE FOR BUSINESS MODEL
INNOVATION
Image source: sildeshare.com (2018); SmartFace project (2016).
www.industrialdataspace.org // 3
HOWEVER, THIS RESOURCE HAS TO BE SHARED IN
ECOSYSTEMS TO SUCCEED IN THE DIGITAL WORLD
Image sources: Johns Hopkins University (2016), Umweltbundesamt (2016), Smellgard, Schneider & Farkas (2016), urbanmanagement.nl (2017).
Data Sharing
Energy
Healthcare
Material Sciences
Manufacturing and
Logistics
“Smart Cities”
Sharing of material information along the entire
product life cycle
Shared use of process data for predictive asset
maintenance
Exchange of master and event data along the
entire supply chain
Anonymized, shared data pool for better drug
development
Shared use of data for end-to-end consumer
services
Interoperability
Data Exchange
„Sharing Economy“
Data Centric
Services
Data Ownership
Data Security
Data Value
DATA SOVEREIGNTY IS A KEY CAPABILITY
FOR TRUST BETWEEN ECOSYSTEM PARTNERS
is the capability of a natural
person or legal entity for
exclusive self-determination
with regard to their data
goods.
DATA SOVEREIGNTY
www.industrialdataspace.org // 5
SUPPLY CHAIN RESILIENCE AND EFFICIENCY
LOGISTICS DATA SPACE
OEM»Tier 1« Supplier
Risk
Management
Supplier
Management
• Contact
person
• Risk type
• Risk location
• Affected parts
• Affected sub-
suppliers
• Capacities and
inventory
levels
• Contact
person
• Parts demand
• Inventory
levels
Use context
Risk
management
Condition
Deletion after 3
days
Use context
Supplier
management
Condition
Deletion after 14
days
www.industrialdataspace.org // 6
BUSINESS INNOVATION IN HEALTHCARE
MEDICAL DATA SPACE
Pharma Company
Usage context
Clinical research
Anonymization
Data record must
consists of at least
150 individual
anonymized data
sets
University Hospital
Patient
Management
Smart Drug
Development
• Health data
• Medication plan
• Electronic case
records
www.industrialdataspace.org // 7
FLEXIBLE AND DYNAMIC PRODUCTION NETWORKS
INDUSTRIAL DATA SPACE
Image source: ingenieur.de (2018)
“Production as a
Service” Provider
OEM
Production
Planning and
Control
• CAD data
• Configuration
parameters
• Production
volume
• Usage time
• Temperature
data
• Certificates
Usage context
Maintenance, no
forwarding
Condition
Operator
anonymous
Maintenance
Usage context
Machine type
Condition
Delete CAD data
after first use
www.industrialdataspace.org // 8
USAGE CONDITIONS FOR DATA ARE MULTIFOLD
SELECTED EXAMPLES
Dimension Specification Example
Geo-information
Coordinates 51.493773, 7.407025, radius 1km
Geo polygon
ZIP code 44227
Country code DE
Expiration date Absolute date December 24, 2017
Anonymization
Role, function
Usage purpose
Positive list Use for machine configuration
Negative list Not for marketing use
Propagation
Allow, deny
Allow on a fee Yes, with 20 percent surplus charge
Number of uses Absolute figure Once
Deletion
System constraints
THE INDUSTRIAL DATA SPACE ADDRESSES THE
MOST IMPORTANT ISSUES IN DATA SHARING
© PwC-Study on "Industrial Data Space"
57%
worry about revealing
valuable data and
business secrets.
59%
fear the loss of
control over their
data.
55%
feel inconsistent
processes and
systems as a (very)
big obstacle.
32%
fear that platforms do
not reach the critical
mass, so that data
exchange will be
interesting.
InteroperabilityData SovereigntyTrust and Security Join us!
Today
Industrial
Data Space
Approach
www.industrialdataspace.org // 10
Central Architectures
(e.g. Data Lakes)
Federated Architectures
(e.g. Industrial Data Space)
Distributed Architectures
(e.g. pure Blockchain)
Data Ownership Central or distributed Distributed Distributed
Data Stewardship Central or distributed Distributed Distributed
Data Capture and Creation Distributed Distributed Distributed
Data Storage Central Distributed Distributed, redundant
Data Enrichment and Data
Preprocessing
Central Distributed Distributed
Data Integration and Fusion Central Central (e.g. through Linked Data and Data
Space approaches)
Distributed
Data Sovereignty Central (if any) Distributed Distributed
Data Provenance Central (if any) Central Distributed
Data Brokering, Clearing,
Billing
Central Central Distributed
DIFFERENT ARCHITECTURE PATTERNS EXIST WHEN
IT COMES TO DATA EXCHANGE AND SHARING
www.industrialdataspace.org // 11
THE INDUSTRIAL DATA SPACE ARCHITECTURE EMBRACES
THE IDEA OF “ARCHITECTURAL PLURALITY”
Business Data
Use Case
Architecture
Pattern
Knowledge Generation
Data Integration
Trusted Data Exchange
Data Sovereignty
Data Consistency
Data Transparency
Data Lake Industrial Data Space Blockchain
www.industrialdataspace.org // 12
THE INDUSTRIAL DATA SPACE CONNECTS VARIOUS
CLOUD PLATFORMS
Industrial Data
Cloud
IoT Cloud
Enterprise
Cloud
Data
Marketplace
Company 1 Company 2 Company n + 2Company n + 1Company n
Open Data
Source
IDS
IDS IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
Legend: IDS Connector; Data usage constraints; Non-IDS communication; NB: Viewgraph w/o broker and clearing house.
www.industrialdataspace.org // 13
THE INDUSTRIAL DATA SPACE FORMS A NETWORK
OF TRUSTED DATA
FOR ALL INDUSTRIES TO LINK DATA
www.industrialdataspace.org // 14
Verticalization Communities
SUCCESS OF THE INDUSTRIAL DATA SPACE FOLLOWS
THE DIFFUSION OF INNOVATION PRINCIPLES
Time
Use
(= Success)
R&D Standardization/Roll-out Market Penetration
Industrial Data Space Association
Standardization
User Companies
Software and Technology Providers
Innovative Business Models
Research Projects
Legend: Research; Association; Market.
www.industrialdataspace.org // 15
THE DEVELOPMENT PATH FOLLOWS SIX STAGES
TOWARDS THE DATA ECONOMY
Everything needs to be secure
• Authentication & Authorization
• Usage Policies & Usage Enforcement
• Trustworthy Communication
• Security by Design
• Technical certification
SECURITY & SOVEREIGNTY
Connection of every data endpoint
• Integration of existing vocabularies
• Using different data formats
• Connection of clouds and platforms
STANDARDIZED
INTEROPERABILITY Data is being traded as an asset
• Clearing & Billing
• Domain specific Broker and
Marketplaces
• Use Restrictions and Legal
Aspects (Contract Templates,
etc.)
DATA MARKETS
Being able to explain, find and
understand data
• Data source description
• Brokering
• Vocabulary
ECOSYSTEM OF DATA
Typical tasks can be solved
easier with apps
• Processing of Data
• Remote Execution
VALUE ADDING APPS
Trust is the basis of the IDS
• Identity management
• User-certification
TRUST
1 2 3
4 5 6
// 16
JOIN US
!PROF. DR. BORIS OTTO
MANAGING DIRECTOR
FRAUNHOFER ISST
DEPUTY CHAIRMAN OF THE BOARD
INDUSTRIAL DATA SPACE ASSOCIATION
EMIL-FIGGE-STR. 91
44227 DORTMUND | GERMANY
+49 231-97677-200
@ids_association
#industrialdataspace
www.industrialdataspace.org
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IDS: Update on Reference Architecture and Ecosystem Design

  • 1. UPDATE ON ARCHITECTURE AND ECOSYSTEM DESIGN PROF. DR. BORIS OTTO  FRANKFURT  22 MARCH 2018 INDUSTRIAL DATA SPACE
  • 2. www.industrialdataspace.org // 2 Mobility • Autonomous driving • Mobility services • Smart traffic management Service Innovation Manufacturing • Smart factory • Adaptive manufacturing • “Industrie 4.0” Organizational Innovation Healthcare • Personalized medicine • Translational medicine • Smart healthcare devices Product Innovation Retail • Supply chain visibility • Goods and data traceability • Sustainability Process Innovation DATA IS A KEY RESOURCE FOR BUSINESS MODEL INNOVATION Image source: sildeshare.com (2018); SmartFace project (2016).
  • 3. www.industrialdataspace.org // 3 HOWEVER, THIS RESOURCE HAS TO BE SHARED IN ECOSYSTEMS TO SUCCEED IN THE DIGITAL WORLD Image sources: Johns Hopkins University (2016), Umweltbundesamt (2016), Smellgard, Schneider & Farkas (2016), urbanmanagement.nl (2017). Data Sharing Energy Healthcare Material Sciences Manufacturing and Logistics “Smart Cities” Sharing of material information along the entire product life cycle Shared use of process data for predictive asset maintenance Exchange of master and event data along the entire supply chain Anonymized, shared data pool for better drug development Shared use of data for end-to-end consumer services
  • 4. Interoperability Data Exchange „Sharing Economy“ Data Centric Services Data Ownership Data Security Data Value DATA SOVEREIGNTY IS A KEY CAPABILITY FOR TRUST BETWEEN ECOSYSTEM PARTNERS is the capability of a natural person or legal entity for exclusive self-determination with regard to their data goods. DATA SOVEREIGNTY
  • 5. www.industrialdataspace.org // 5 SUPPLY CHAIN RESILIENCE AND EFFICIENCY LOGISTICS DATA SPACE OEM»Tier 1« Supplier Risk Management Supplier Management • Contact person • Risk type • Risk location • Affected parts • Affected sub- suppliers • Capacities and inventory levels • Contact person • Parts demand • Inventory levels Use context Risk management Condition Deletion after 3 days Use context Supplier management Condition Deletion after 14 days
  • 6. www.industrialdataspace.org // 6 BUSINESS INNOVATION IN HEALTHCARE MEDICAL DATA SPACE Pharma Company Usage context Clinical research Anonymization Data record must consists of at least 150 individual anonymized data sets University Hospital Patient Management Smart Drug Development • Health data • Medication plan • Electronic case records
  • 7. www.industrialdataspace.org // 7 FLEXIBLE AND DYNAMIC PRODUCTION NETWORKS INDUSTRIAL DATA SPACE Image source: ingenieur.de (2018) “Production as a Service” Provider OEM Production Planning and Control • CAD data • Configuration parameters • Production volume • Usage time • Temperature data • Certificates Usage context Maintenance, no forwarding Condition Operator anonymous Maintenance Usage context Machine type Condition Delete CAD data after first use
  • 8. www.industrialdataspace.org // 8 USAGE CONDITIONS FOR DATA ARE MULTIFOLD SELECTED EXAMPLES Dimension Specification Example Geo-information Coordinates 51.493773, 7.407025, radius 1km Geo polygon ZIP code 44227 Country code DE Expiration date Absolute date December 24, 2017 Anonymization Role, function Usage purpose Positive list Use for machine configuration Negative list Not for marketing use Propagation Allow, deny Allow on a fee Yes, with 20 percent surplus charge Number of uses Absolute figure Once Deletion System constraints
  • 9. THE INDUSTRIAL DATA SPACE ADDRESSES THE MOST IMPORTANT ISSUES IN DATA SHARING © PwC-Study on "Industrial Data Space" 57% worry about revealing valuable data and business secrets. 59% fear the loss of control over their data. 55% feel inconsistent processes and systems as a (very) big obstacle. 32% fear that platforms do not reach the critical mass, so that data exchange will be interesting. InteroperabilityData SovereigntyTrust and Security Join us! Today Industrial Data Space Approach
  • 10. www.industrialdataspace.org // 10 Central Architectures (e.g. Data Lakes) Federated Architectures (e.g. Industrial Data Space) Distributed Architectures (e.g. pure Blockchain) Data Ownership Central or distributed Distributed Distributed Data Stewardship Central or distributed Distributed Distributed Data Capture and Creation Distributed Distributed Distributed Data Storage Central Distributed Distributed, redundant Data Enrichment and Data Preprocessing Central Distributed Distributed Data Integration and Fusion Central Central (e.g. through Linked Data and Data Space approaches) Distributed Data Sovereignty Central (if any) Distributed Distributed Data Provenance Central (if any) Central Distributed Data Brokering, Clearing, Billing Central Central Distributed DIFFERENT ARCHITECTURE PATTERNS EXIST WHEN IT COMES TO DATA EXCHANGE AND SHARING
  • 11. www.industrialdataspace.org // 11 THE INDUSTRIAL DATA SPACE ARCHITECTURE EMBRACES THE IDEA OF “ARCHITECTURAL PLURALITY” Business Data Use Case Architecture Pattern Knowledge Generation Data Integration Trusted Data Exchange Data Sovereignty Data Consistency Data Transparency Data Lake Industrial Data Space Blockchain
  • 12. www.industrialdataspace.org // 12 THE INDUSTRIAL DATA SPACE CONNECTS VARIOUS CLOUD PLATFORMS Industrial Data Cloud IoT Cloud Enterprise Cloud Data Marketplace Company 1 Company 2 Company n + 2Company n + 1Company n Open Data Source IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS Legend: IDS Connector; Data usage constraints; Non-IDS communication; NB: Viewgraph w/o broker and clearing house.
  • 13. www.industrialdataspace.org // 13 THE INDUSTRIAL DATA SPACE FORMS A NETWORK OF TRUSTED DATA FOR ALL INDUSTRIES TO LINK DATA
  • 14. www.industrialdataspace.org // 14 Verticalization Communities SUCCESS OF THE INDUSTRIAL DATA SPACE FOLLOWS THE DIFFUSION OF INNOVATION PRINCIPLES Time Use (= Success) R&D Standardization/Roll-out Market Penetration Industrial Data Space Association Standardization User Companies Software and Technology Providers Innovative Business Models Research Projects Legend: Research; Association; Market.
  • 15. www.industrialdataspace.org // 15 THE DEVELOPMENT PATH FOLLOWS SIX STAGES TOWARDS THE DATA ECONOMY Everything needs to be secure • Authentication & Authorization • Usage Policies & Usage Enforcement • Trustworthy Communication • Security by Design • Technical certification SECURITY & SOVEREIGNTY Connection of every data endpoint • Integration of existing vocabularies • Using different data formats • Connection of clouds and platforms STANDARDIZED INTEROPERABILITY Data is being traded as an asset • Clearing & Billing • Domain specific Broker and Marketplaces • Use Restrictions and Legal Aspects (Contract Templates, etc.) DATA MARKETS Being able to explain, find and understand data • Data source description • Brokering • Vocabulary ECOSYSTEM OF DATA Typical tasks can be solved easier with apps • Processing of Data • Remote Execution VALUE ADDING APPS Trust is the basis of the IDS • Identity management • User-certification TRUST 1 2 3 4 5 6
  • 16. // 16 JOIN US !PROF. DR. BORIS OTTO MANAGING DIRECTOR FRAUNHOFER ISST DEPUTY CHAIRMAN OF THE BOARD INDUSTRIAL DATA SPACE ASSOCIATION EMIL-FIGGE-STR. 91 44227 DORTMUND | GERMANY +49 231-97677-200 @ids_association #industrialdataspace www.industrialdataspace.org Ressource Hub – Press Area – Blog