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IDS: Update on Reference Architecture and Ecosystem Design
The document discusses the Industrial Data Space and its significance in facilitating data sharing across various sectors such as mobility, healthcare, and manufacturing to foster business model innovation. It addresses concerns around data security, ownership, and interoperability while outlining architectural patterns for data exchange. Emphasizing the importance of trust among ecosystem partners, the document advocates for a structured approach to enable a thriving data economy.
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IDS: Update on Reference Architecture and Ecosystem Design
- 1. UPDATE ON ARCHITECTUREAND 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“ DataCentric 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 SUPPLYCHAIN 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 BUSINESSINNOVATION 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 FLEXIBLEAND 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 USAGECONDITIONS 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 DATASPACE 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 CentralArchitectures (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 THEINDUSTRIAL 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 THEINDUSTRIAL 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 THEINDUSTRIAL DATA SPACE FORMS A NETWORK OF TRUSTED DATA FOR ALL INDUSTRIES TO LINK DATA
- 14. www.industrialdataspace.org // 14 VerticalizationCommunities 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 THEDEVELOPMENT 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