Uni Bern 2022 - Digitale Ökosysteme, Datenökosysteme, Plattformen im Gesundheitswesen

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1. Digitale Ökosysteme, Datenökosysteme, Plattformen im Gesundheitssystem — Dr. Matthias Naab Zertifikatskurs 22.01 (CAS Healthcare Management MiG) 30.03.2022
2. Technology changes Media & Data with Digital Products Technology changes Markets & Industries with Digital Processes Technology changes Economy & Society with Digital Business Models Digitization Digital Transformation Digitalization Digital Disruption
3. New and innovative Services & Products Data-Driven Business Models Cross-Domain Business Models Digital Business Processes Offshore- Development & Operation Business Process Reengineering Radical Innovation Incremental Innovation Efficiency Optimization & Cost Reduction New Value Creation Digitization | Digitalization | Digital Transformation Digital Ecosystems
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6. © Fraunhofer IESE 24 Digital Ecosystems ◼ Socio-technical system (organizations, people, digital systems, relationships) ◼ Independent participants ◼ Collaborating to generate mutual benefit ◼ Collaboration based on ecosystem service, offered by ecosystem initiator, provided through digital platform Participants
7. © Fraunhofer IESE 25 Ecosystem Service ◼ Collaboration based on ecosystem service ◼ Generates core benefit of the ecosystem ◼ Provided by ecosystem initiator ◼ Provided purely digitally Ecosystem Service
8. Airbnb Lodging offers brokering of private accommodations provided by private hosts for travelers
9. Ebay Marketplace offers brokering of any kind of goods provided by sellers for buyers
10. AVIATAR offers brokering of maintenance data provided by aircraft component manufactures for airlines
11. Caruso Dataplace offers brokering of vehicle telematics data provided by OEMs for automotive aftermarket service providers
12. © Fraunhofer IESE 46 <Ecosystem Service Name> offers brokering of <Assets> provided by <Asset Providers> for <Asset Consumers> <Ecosystem Service Provider> Digital Ecosystem Service Description Template
13. © Fraunhofer IESE 48 Digital Platform ◼ Core technical aspect in digital ecosystem ◼ Realize ecosystem service and manifest ecosystem rules ◼ Partners directly use the digital platform ◼ Additional supporting features (e.g. payment, trusteeship, security, management of legal relationships, standardization, rating, …) Digital Platform
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15. © Fraunhofer IESE 50 Technology Platform Digital (Ecosystem) Platform Application / Business Logic Technology / Infrastructure Hardware Software Operated by Developer or Customer Externally Operated / as-a-Service Hardware Platform IaaS Platform Operating System Platform Runtime Environment Platform Runtime Environment Platform with Business Logic PaaS Platform PaaS Platform with Business Logic Marketplace Platform On-Demand Service Platform Communication / Interaction Platform Data Harvesting Platform Content Crowdsourcing Platform Content Distribution Platform Platform Economy Platform “Use the platform to build and run software on top” “Use the platform to consume its service and participate in the ecosystem”
16. © Fraunhofer IESE 51 1 Digital (Ecosystem) Platform Application / Business Logic Technology / Infrastructure Hardware Software Operated by Developer or Customer Externally Operated / as-a-Service Hardware Platform IaaS Platform Operating System Platform Runtime Environment Platform Runtime Environment Platform with Business Logic PaaS Platform PaaS Platform with Business Logic Marketplace Platform On-Demand Service Platform Communication / Interaction Platform Data Harvesting Platform Content Crowdsourcing Platform Content Distribution Platform Platform Economy Platform “Use the platform to build and run software on top” “Use the platform to consume its service and participate in the ecosystem” Software Ecosystem Platform Application (potentially provided as SaaS) Application (potentially provided as SaaS) Technology Platform AWS IaaS, Azure IaaS Intel x64 Windows Java, .NET, Cloud Foundry AWS PaaS, Azure PaaS Salesforce Word Open Source ERP Systems Slack Eclipse, Firefox, iOS, Android AirBnB, Ebay Uber Youtube Wikipedia AdSense Waze Facebook, WhatsApp Examples
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18. © Fraunhofer IESE 62 Different Constellations of Ecosystem Partners Types All partners of same type e.g. Tinder, WhatsApp (Rare constellation) Provider / consumer e.g. AirBnB, Uber (Most frequent constellation) More partner types e.g. Foodora, Schüttflix (Rather rare constellation)
19. © Fraunhofer IESE 63 Digital Ecosystem with more than 1 Ecosystem Service ◼ Typically start with one ecosystem service ◼ Optional: Add services with tight (business) connection later ◼ Without connection: separate digital ecosystems ◼ With only technical connection: separate digital ecosystems Digital Platform
20. © Fraunhofer IESE 64 Digital Ecosystem Multiple Ecosystem Services
21. © Fraunhofer IESE 65 Domain Ecosystem ◼ Established business ecosystem in a business domain ◼ Existing players and relationships
22. © Fraunhofer IESE 66 Domain Ecosystem ◼ Digital ecosystems emerge and address needs ◼ Competing digital ecosystems possible ◼ Various digital ecosystems possible
23. Industrie 4.0 Smart Farming Smart Energy Smart Mobility Smart Health Smart Rural Areas Smart Teams Smart X Smart Ecosystems
25. © Fraunhofer IESE 78 Mobility Internet Giants OEMs Suppliers (1st Tier ) Material & Tool Suppliers User Mobility Workshops Roadside Assistance Diagnostics Parts Trade Ambulance Dealership Gas Stations Leasing Insurance
26. Cross-Domain and Overlapping Digital Ecosystems Car Manufacturers (> 102) Insurance Comp. Car Rental Comp. Smartphone and App Users (> 108) Drivers (> 105) Passengers (> 107) Bus Companies (> 103) Passengers (> 105)
27. Passengers (> 105) Cross-Domain and Overlapping Digital Ecosystems Car Manufacturers (> 102) Drivers (> 105) Bus Companies (> 103) Insurance Comp. Car Rental Comp. Smartphone and App Users (> 108) Passengers (> 107) MOBILITY Domain Ecosystem INSURANCE Domain Ecosystem
28. Passengers (> 105) Cross-Domain and Overlapping Digital Ecosystems Car Manufacturers (> 102) Drivers (> 105) Bus Companies (> 103) Insurance Comp. Car Rental Comp. Smartphone and App Users (> 108) Passengers (> 107) APP Domain Ecosystem
29. New possibilities for business models and services Access to large number of ecosystem participants Simplicity by harmoni- zation and great user experience Openness for many participants and easy access Huge freedom of design Possibility for disruptive repositioning Great potential for growth and high scalability Network effects and participation in business Possibility to invest in growth, UX, attractiveness Chances by Digital Ecosystems
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31. This ecosystem thing can‘t be that difficult … We already built a platform … We just call it „ecosystem“! It is our ecosystem: it has to serve mainly us That‘s what our CDO is doing No one else will be able to initiate such an ecosystem Small innovations lead us in the future, as well We only care about ourselves We will care about the business model later … There is just no choice for our ecosystem participants The ecosystem has to deliver a RoI after 18 months We let a consultant just do this for us
32. … unfortunately it is outside the comfort zone Big Picture End-to-End Long-term thinking Patience Data-driven business models Value networks Balanced Interests Missing overall control Crossing organizations Diversity Uncertainty No clearly prescribed ways and methods
33. Business Technology Legal CORE Vision Strategy Engineering Usage Operation Governance ORGANIZATION Partner Scouting Casting & Matching Pairing & Engagement Technical Scouting Collaboration Evaluation PARTNERS Establishment Exploitation Nurturing COMMUNITY Observation Exploration Analysis Intervention COMPETITORS OTHER KNOWN PLAYERS Platform Economy Fraunhofer Digital Ecosystems Reference Model Google Apple Facebook Amazon Microsoft Samsung
34. TANGIBLE ECOSYSTEM DESIGN SERVICE BLUEPRINT SERVICE MAP MOTIVATION MATRIX
38. Ecosystem Evolution Time Time Network Effects More Services More Benefits More Asset Types Business Volume Volume by Asset Consumers #Asset Consumers Volume by Asset Providers #Asset Providers Platform Status
39. From the Outside, it seems to be just a Website or an App … Time Business Volume ~17.000 ~5.300 ~18 M ~22.000 ~75 M ~7.000 ~167 M ~12.700 ~50 M ~15.000 ~570 M #Employees #Consumers Successful ecosystems have been growing over 5 .. 20 years No quick ROI Patience over years Investment into fast growth
40. Datenökosysteme
41. © Fraunhofer IESE 273 Relevance of Data and AI in Digital Ecosystems Digital Platform Many ecosystem participants → large data sets Data as „assets“ in digital ecosystems Data as foundation for new business models Data as foundation for improvement of services Large data sets → good foundation of usage of AI
42. © Fraunhofer IESE 274 Data Ecosystems – The ASSET is DATA DATA PROVIDER DATA CONSUMER DATA BROKERING DATA BROKERING
43. © Fraunhofer IESE 275 Data – Information - Knowledge https://www.i-scoop.eu/big-data-action-value-context/dikw-model/
44. © Fraunhofer IESE 276 Types of Data – Data can be Classified According to Many Dimensions ◼ Sensitivity of Data ◼ Data related to persons ◼ Data sensitive for an organization ◼ Data open to public ◼ … ◼ Content types of data ◼ Images, Video, Audio ◼ Free text ◼ Structured data ◼ … ◼ Degree of processing ◼ Raw Data ◼ Aggregated data ◼ Analyzed data ◼ Data incorporated in ML models ◼ … ◼ Data according to creators ◼ Data collected via sensors observing machines ◼ Data collected via sensors observing people ◼ Data collected from analyzing data in IT systems ◼ … ◼ Frequency of change of data ◼ Data created once, never updated ◼ Data created once, rare updates ◼ Data frequently updated ◼ … ◼ Data usage ◼ Master data ◼ Transactional data ◼ Configuration data ◼ … ◼ …
45. © Fraunhofer IESE 277 Data – Collection to Usage Data Collection Data … Data Usage Data … … ◼ Data Modeling ◼ Data Collection ◼ Data Transformation ◼ Data Aggregation ◼ Data Analysis ◼ Data Visualization ◼ Data Storage ◼ Data Transportation ◼ Data Cleaning / Preparation ◼ Data Harmonization ◼ Data Learning ◼ Data Management ◼ …
46. © Fraunhofer IESE 278 Perspective and Scope of Data: 1 Organization Many business processes Many applications Many data bases Data silos (Partial) interoperability? (Partial) integration?
47. © Fraunhofer IESE 279 Extended Perspective: Organizations using SaaS of other Company Business processes partially externally defined Software externally hosted Data externally stored Data externally analyzed Provide SaaS
48. © Fraunhofer IESE 280 Extended Perspective: Company-Crossing Business Processes Business Processes across company boundaries Exchange of data according to defined data formats, interfaces, software applications Cooperation & Data Exchange
49. © Fraunhofer IESE 281 Extended Perspective: Data-Driven Business Models Services based on data of a single customer (e.g. analysis of financial situation, offered as SaaS) Services based on data of many customers (e.g. analysis of music suggestions across many users in spotify) Services based on external or public data (e.g. analysis of weather conditions for driving safety) Services based on „selling“ or „brokering“ of data (e.g. data market places) … Data-Driven Services and Business Models
50. © Fraunhofer IESE 282 Data Ecosystems – The ASSET is DATA DATA PROVIDER DATA CONSUMER DATA BROKERING DATA BROKERING
51. © Fraunhofer IESE 283 Boundaries and Roles in Digital Ecosystems Participants, Asset Providers Participants, Asset Consumers Partners Partners Digital Platform Digital Ecosystem Service Ecosystem Initiator Service Provider Platform Operator
52. C A R U S O - D A T A P L A C E . C O M 284 #carusodataplace CARUSO B2B DATAPLACE PROVIDING IN-VEHICLE DATA FOR MOBILITY SERVICES CARUSO DATA PROVIDERS DATA CONSUMERS END USERS Collect Standardize Distribute New services New customers Efficient processes Different providers Different data formats Different quality / price Added Value for B2X Use Cases
53. DATA ECOSYSTEM IN MOBILITY / AUTOMOTIVE OEM INSURANCE B2C Car Driver
54. © Fraunhofer IESE 286 Data Catalogs and Data Harmonization ◼ Huge effort to harmonize data ◼ Requests for data (number of data items, frequency of delivery) can be extreme ◼ Demand for data is strongly expressed ◼ Nevertheless, often no clear usage scenarios for the data
55. © Fraunhofer IESE 287 Datenökosystem im Bereich Finance Banking Data Hub Kunde D Kunde C Kunde B Kunde A Kunde B Bank Z Bank Y Bank X Kunde A Bankkunden Banken Ökosystem- Initiator FinTechs FinTech-Kunden (=Bankkunden) Bonitäts- prüfung Vertrags- optimierung “Energie” Vertrags- optimierung “Versicherung” Kunde C
56. © Fraunhofer IESE 288 Datensouveränität – Betrifft mich das überhaupt?
57. © Fraunhofer IESE 289 Data Sovereignty ◼ Data sovereignty goes beyond personal data protection (GDPR) ◼ Applies also to data not related to persons ◼ Highly demanded or often “assured”, but rarely defined ◼ Definition ◼ „Data sovereignty means the greatest possible control, influence and insight into the use of data by the data provider. The data provider should be entitled and empowered to exercise informational self-determination and receive transparency about data usage.” Transparency Self- Determination Data Sovereignty
58. © Fraunhofer IESE 290 Data Sovereignty – More Detailed Aspects … Still needing Refinement Control the Usage of Data ◼ Data Owner and Data User agree on a Data Usage Contract / Data Owner gives consent about Data Usage ◼ Consent can be revoked ◼ Some Rules need to be enforced @ Data Provider Side (before passing data to the consumer) ◼ Some Rules need to be enforced @ Data Consumer Side (using the data at the consumer) Transparency about the Usage of Data ◼ Information before giving consent ◼ Data Consumer provides a description about the intended data accesses and usages ◼ Information at runtime (Logging, Notifications) ◼ Transparency about the data accesses (whenever the Data Provider provided data to the Data Consumer) ◼ Transparency about the data usages (whenever the Data Consumer uses the Data) Freedom to Use the Data in other Systems ◼ Ability to provide third parties / other systems with access to the Data ◼ Ability to delete all data ◼ Ability to get a copy of the data in a common format
59. © Fraunhofer IESE 291 Lösungsbausteine, um zu Datensouveränität zu kommen Verständliche Datenschutz- informationen Nutzbare Datenschutz- einstellungen Datennutzungs- kontrolle Nachvollzieh- barkeit Sicherheits- anforderungen Qualitäts- modelle Usable Security and Privacy Privacy Dashboards Basis-Sicherheit Sicherheits- konzepte für Digitale Ökosysteme Grundlagen Transparenz Selbst- bestimmung Lösungen für Daten- souveränität
60. © Fraunhofer IESE 292 Digitale Plattform Digitales Ökosystem Support Service Betreiber Plattform Betreiber Cybersecurity in Digitalen Ökosystemen: Eine sichere Digitale Plattform ist bei weitem nicht genug! Asset Konsument Asset Anbieter Digitale Plattform Ökosystem Service Support Service Daten Viele und vielfältige Daten Exponiertes Gesamtsystem mit vielen Angriffspunkten Interessantes Angriffsziel Security der Digitalen Plattform + Security in allen IT-Systemen und Organisationen des Digitalen Ökosystems Ganzheitliche Lösungen bei mangelnder Gesamtkontrolle
61. © Fraunhofer IESE 293 Sicherheitskonzepte für Digitale Ökosysteme ◼ Datensicherheit auf der Plattform ◼ Datensouveränität im Ökosystem ◼ Ökosystemübergreifende Datensouveränität
62. © Fraunhofer IESE 294 Datentreuhänder ◼ Datentreuhänder vermittelt in Konstellationen zwischen Datenanbietern und Datenkonsumenten ◼ Fehlendes direktes Vertrauen → zusätzliche Instanz schafft Vertrauen ◼ Zentraler Hub ◼ Datentreuhänder können Datenökosysteme aufbauen ◼ Unterschiedlichste Ausprägungen und Aufgaben denkbar ◼ Sicherer, nachweisbarer Datenaustausch ◼ Bündelung und / oder Aggregation von Daten ◼ Eventuell auch Anonymisierung / Pseudonymisierung von Daten ◼ Genauer Auftrag des Treuhänders (Bezahlung, Anonymisierung, Auslieferung, …) ◼ Verwaltung der Daten vs. Weiterleitung der Daten ◼ Keine klar akzeptierten Definition; noch in der Findungsphase; individuelle Modelle möglich
63. Plattformen im Gesundheitswesen
64. © Fraunhofer IESE 296 Players in the Medical / Healthcare Domain Ecosystem Internet Giants People Patients Health Insurance Personal Device Manufacturers Pharmacy Material / Drug Provides Doctors Rescue Service Medical Device Manufacturers Medical Software & Platform Providers Therapy Personal Health Trainers Hospitals
65. © Fraunhofer IESE 297 Related Studies ◼ Term „Platform“ used in many different ways ◼ Often, its not about digital ecosystems but SaaS solutions or health- specific PaaS solutions
66. © Fraunhofer IESE 298 © Roland Berger: The rise of healthcare platforms
67. © Fraunhofer IESE 311 Where is Our Place in Ecosystems? ?
68. Digitale Ökosysteme, Datenökosysteme, Plattformen im Gesundheitssystem — Dr. Matthias Naab Zertifikatskurs 22.01 (CAS Healthcare Management MiG) 30.03.2022
69. Digitale Ökosysteme: Welche Herausforderungen stellt der Aufbau und wie gelingt er? https://www.informatik-aktuell.de/management-und-recht/digitalisierung/digitale-oekosysteme-welche-herausforderungen-stellt-der-aufbau-und-wie-gelingt-er.html Digitale Ökosysteme und Plattformökonomie: Was ist das und was sind die Chancen? https://www.informatik-aktuell.de/management-und-recht/digitalisierung/digitale-oekosysteme-und-plattformoekonomie.htm Dr. Marcus Trapp Dr. Matthias Naab Dr. Dominik Rost Claudia Nass Matthias Koch Bernd Rauch
70. https://youtu.be/gVdtVa8Tp1Y
71. https://youtu.be/Bc3FJeFMLAs