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International Data Spaces: Data Sovereignty and Interoperability for Business Ecosystems


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This presentation was held in a workshop session on IoT Business Models and Data Interoperability at the Max Planck Institute for Innovation and Competition in Munich on 8 October 2018. The presenation introduces the concept of business ecosystems and the role of data within the latter, then outlines the state of the art in terms of interoperability and sovereignty and finally sketches the IDS contribution.

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International Data Spaces: Data Sovereignty and Interoperability for Business Ecosystems

  2. 2. © Fraunhofer ISST CONTENT  Data in Business Ecosystems  Data Sovereignty and Interoperability  International Data Spaces Approach public· 2
  3. 3. © Fraunhofer ISST Business ecosystems are emerging in various sectors Image source: (2018), (2018); (2018); SmartFace project (2016). NB: green – value proposition; blue – ecosystem partners; orange – required data. Mobility  Autonomous driving  Mobility services  Smart traffic management Manufacturing  Smart factory  Adaptive manufacturing  Industry 4.0 Healthcare  Personalized medicine  Translational medicine  Smart healthcare devices Retail  Supply chain visibility  Goods and data traceability  Sustainability public  Automobile manufacturers  Traffic management agencies  Car sharing providers  Municipalities  Data providers  Original equipment manufacturers  Logistics services providers  Suppliers  Visibility data providers  Pharmaceutical research companies  University hospitals  Patients  Insurance companies  Retail companies  Consumer goods manufacturers  Consumers  Logistics service providers  Location, destination  Traffic data  Car control data  Product, parts data  Production orders  Planning data  Health records  Treatment plans  Clinical study data  Supply chain event data  Transport data  Context data (weather, traffic etc.) · 3
  4. 4. © Fraunhofer ISST In a business ecosystem, companies develop their capabilities around a joint customer value proposition in a coopetition mode Source: Moore (1993). Core Business Suppliers Distribution Channels Extended Enterprise Suppliers of Suppliers Customers Customers of Customers Standards Organizations Complementors Business Ecosystem Investors Other Stakeholders CompetitorsUnions Research Organizations Universities Trade Organizations Core Contributors public· 4
  5. 5. © Fraunhofer ISST In business ecosystems, the role of ecosystem partners and the flow of data are changing fundamentally Source: Beiersdorf, University of St. Gallen (2012). NB: Viewgraphs show betweenness centrality, i.e. the number of edges between two nodes that use a certain node. Analysis shows flow of ingredient data for selected products. 2007 Media 2012 public· 5
  6. 6. © Fraunhofer ISST In the extended enterprise, data must be managed in a consistent way Legend: Information flow; Material flow. Public Data Value Chain Data Commercial Services Industrial Services Lot-Size 1 End-to-End Customer Process Business Ecosystem Hybrid Offerings Data Management Interoperability Human-Machine- Collaboration Autonomous Systems Internet of Things Customer Production Networks Logistics Networks Digitalized Value PropositionDataDigitalized Value Creation public· 6
  7. 7. © Fraunhofer ISST IoT devices represent a rich data source of core business activities for knowledge generation and business insights NB:RIOTANA® by Fraunhofer ISST is a flexible, standards-based easy-to-use IoT analytics framework. Legend: IoT – Internet of Things. public ISO/IEC 30141:2016 RIOTANA IoT Users Physical Entity IoT Device Sensors  Actuators  Tags IoT Gateway Local Services  Data Resource & Interchange System Application Service System Operation & Mngmt System Beacons & Gateway Bosch XDK Smart Sensor Individual Smart Sensor RIOTANA Digital Infrastructure and Analytics · 7
  8. 8. © Fraunhofer ISST CONTENT  Data in Business Ecosystems  Data Sovereignty and Interoperability  International Data Spaces Approach public· 8
  9. 9. © Fraunhofer ISST Data sharing is a key prerequisite for successful business ecosystems Image sources: Johns Hopkins University (2016), Umweltbundesamt (2016), Smellgard, Schneider & Farkas (2016), (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 Sharing 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 public· 9
  10. 10. © Fraunhofer ISST Interoperability is key – both on a business ecosystem and in particular on a data level Source: BMWi (2016). Reference Architecture Model Industry 4.0 Administration Shell Concept The Administration Shell stores all data of a hardware or software component in production scenarios. It makes data and services related to that component available for Industry 4.0 scenarios in a standardized way. public· 10
  11. 11. © Fraunhofer ISST Typically, when shared, data increases in value Source: Moody & Walsh (1999). public Number of users Share of value 100% Data Tangible Goods Tangible Goods Value Data Usage Time Potential value Data Data quality Value 100% Data Integration Value Data Volume Value Data · 11
  12. 12. © Fraunhofer ISST As a consequence, enterprises are confronted with the need of »squaring the data circle« … Source: Otto (2016). Interoperability Data Exchange »Sharing Economy« Data-centric Services Data Ownership Data Privacy and Security Data Value Data sovereignty is the capability of a natural person or corporate entity for exclusive self-determination with regard to its economic data goods public· 12
  13. 13. © Fraunhofer ISST CONTENT  Data in Business Ecosystems  Data Sovereignty and Interoperability  International Data Spaces Approach public· 13
  14. 14. © Fraunhofer ISST The IDS Reference Architecture Model responds to the most important data issues in business ecosystems Source: PwC (2017). The International Data Spaces (IDS) Association publishes the IDS Reference Architecture Model (IDS-RAM). The Industrial Data Space is a vertical application of the IDS-RAM. 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 IDS Approach public· 14
  15. 15. © Fraunhofer ISST The IDS architecture aims at sovereign sharing and smooth exchange of data in business ecosystems Legend: IDS Connector; Usage Constraints; Non-IDS Communication. public 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 · 15
  16. 16. © Fraunhofer ISST »Production as a Service« Provider Use Context Machine Type Constraint Delete CAD data after first use OEM Production Planning and Control Data sovereignty is achieved by attaching usage conditions to shared data – as the additive manufacturing scenario shows  Contact information  CAD data  Configuration parameters  Output  Use duration  Temperature  Certificates Use Context Maintenance, no forwarding Constraint Operator anonymous Maintenance Service public· 16
  17. 17. © Fraunhofer ISST Interoperability is fostered by the use of domain-specific vocabularies and the IDS Information Model Administration Shell Real-World Object Configuration Parameter Product Sheet … IDS Information Model Shop Floor Data Material Master Data eCl@ss ID Data Endpoint Usage Policy Data Ownership … public· 17
  18. 18. © Fraunhofer ISST The IDS itself forms a business ecosystem for data sharing Source: Otto et al. (2017); extended representation of the reference architecture model content. public Runtime EnvironmentRuntime Environment authorize publish app transfer data data flow metadata flow software flow identification useIDSsoftware useIDSsoftware useIDSsoftware identify Data Owner App Provider Vocabulary Provider Clearing House App Store Provider Identity Provider Data Consumer Broker Service Provider Service Provider Software Provider Data Provider Certification mandatory Membership in the IDSA mandatory Certification Authority · 18
  19. 19. © Fraunhofer ISST Example Automobile Logistics The IDS aims at closing a semantic standardization gap Legend: RFID – Radio-Frequency Identification; IoT – Internet of Things; EDI – Electronic Data Interchange. Release orders · Dispatch notifications · Invoices … Parts master data · Change requests · Product information sheets … Supply chain event data … Data usage constraints  Data resource information EDI Electronic Business RFID · IoT IDS Time Value-added, sensitivity of shared data 1990 2000 2010 today public· 19
  20. 20. © Fraunhofer ISST The IDS initiative makes various contributions to business ecosystem interoperability – mainly through semantic standardization public Object of Standardization IDS Scope Remark Data Ecosystem IDS Reference Architecture Model Tangible objects (e.g. machinery, parts) Using domain-specific standards such as eCl@ss Virtual objects (e.g. supply chain events, orders) Using domain-specific standards such as GS1 EPCIS Data objects Contributing to existing standards development organizations such as the W3C Usage constraints/conditions Inventory of policies and rules based on ODRL Communication interface Using von REST, MQTT as well as IDS Communication Protocol · 20
  21. 21. © Fraunhofer ISST Prof. Dr.-Ing. Boris Otto Fraunhofer ISST · Executive Director TU Dortmund · Faculty of Mechanical Engineering · Please get in touch! public· 21