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Making data FAIR at Bayer

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A key objective of the healthcare industry is to accelerate translational science, ie. the translation of scientific discovery into products and services for the benefit of our patients. The technological progress and the digital transformation offer many approaches to enhance data driven decision making. Data quality is a must have for AI outcome, and curation of data to improve machine readability is a core activity in order to enable data science.

The data quality problem of health research has been acknowledged by the European Commission and they launched the European Open Science Cloud initiative. Within this context, the FAIR Guiding Principles were published. FAIR stands for Findable, Accessible, Interoperable, and Reusable. FAIR data has become a global movement and also reached the Pharma industry.

At Bayer, we approach the FAIR data topic in three different ways: Bottom up with use case driven projects; top-down strategic initiatives to develop infrastructure, capabilities and mindset change; collaboration, i.e. cross-divisional within Bayer and across pharma in public-private consortia.

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Making data FAIR at Bayer

  1. 1. /////////// Connected data London Making data FAIR at Bayer Oct 3-4 2019 Dr. Alexandra Grebe de Barron IT Business Partner for Real World Evidence
  2. 2. Making data FAIR at Bayer /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 Why we need it What is FAIR How we do it
  3. 3. /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019
  4. 4. A transformational change in R&D productivity is required to reverse declining trends in R&D returns across the biopharma industry /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 Deloitte: Unlocking R&D productivity / Measuring the return from pharmaceutical innovation 2018
  5. 5. Drug development is a highly-regulated, long and costly endeavor with a low probability of success /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 Preclinical testsDrug research Clinical trials Approval 1 2 3 4 After launch 5 1 JAMA Intern. Med. 2015, 175, p. 635-638 Permanent…benefit-risk assessment of products 15 years…from idea to market approval >2 billion €… investment per asset until approval <1%...chance for a project to get approved 12.4 years…average time effect. market exclusivity1 Numbers… 1 JAMA Intern. Med. 2015, 175, p. 635-638
  6. 6. Digital health revolution requires a new mental model and offers new digital business possibilities /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 The past Innovative, effective and safe drugs Market authorization Focus on internal trial data, IP protection R&D process optimization The future Patient centricity Data democracy Real world data Data products / AI personalized medicine, prevention ownership, charge of care open, collaborative partnerships automation, science apprentice, medical assistant EHR
  7. 7. Mission /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 grow the data workforce be data driven not data rich advance the digital agenda
  8. 8. Information asset 360 /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 a data product that has a value greater than the sum of its parts whereby it consists of multiple datasets integrated for a data domain context and unlocks insights for multiple functions for the enterprise / division
  9. 9. for both humans and machines FAIR is the foundation for unlocking the value of Bayer‘s data /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 F A I R Findable means to uniquely and persistently identify existing data assets in a searchable and accessible resource Accessible means that data assets can be easily retrieved upon appropriate authorization does not mean open without constraint Interoperable allows data to be actionable by presenting data assets in a formal, broadly applicable way and linking it to other data Reusable clarifies the context, meaning, trustworthiness and origin of data, and how it can be used with a clear and accessible data usage license
  10. 10. European Open Science Cloud (EOSC) - European Commission What is FAIR and where does it come from? /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 … aims to accelerate and support the current transition to more effective Open Science and Open Innovation in the Digital Single Market. It should enable trusted access to services, systems and the re-use of shared scientific data across disciplinary, social and geographical borders. https://www.nature.com/articles/sdata201618
  11. 11. Collaboration, especially public-private, is the key for successful research output and innovation /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 https://fairplus-project.eu/
  12. 12. Implementing a FAIR ecosystem @Bayer /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 picture from www.slideshare.net implicite digitalization Challenges process vs data centric value focus fragmented investments into data technologies and initiatives complex static policies and regulations incomplete data life cycle coverage AI explicite Costs set up of FAIR ecosystem/expertise make legacy data FAIR make data generation and integration FAIR Create awareness, educate, change mindset, incentivise
  13. 13. Target reference architecture to accelerate data & analytics Implementing a FAIR ecosystem @Bayer /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 picture from www.slideshare.net13 Data @ Scale Visualization, Apps & Systems Internal Data Sources External Data Sources Data Ingestion Pipelines Persistence Layer Science@Scale Exploration Zone Data Robot Commercial R&D Product Supply Commercial Product Supply R&D MAPV MAPV Data Warehouse Data Lake Near Realtime Streaming Automated Batch Processing Data Market Place Metadata & Ontologies DataAccessLayer Others PID Top Braid API SQL Stream FAIR ecosystem components SPARQL
  14. 14. Information asset 360 /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 a data product that has a value greater than the sum of its parts whereby it consists of multiple datasets integrated for a data domain context and unlocks insights for multiple functions for the enterprise / division
  15. 15. Selection and definition of 360 Information Assets is based on aligned design principles /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 Must have broad relevance across the business Reflects an interconnected portfolio of datasets that express the future lens of our business Connects isolated signals across the value chain and enables multiple functions (R&D, PS, M&S) Represents core domain datasets and is "invariant" to normal business process changes Reduces risk, controlling data usage & handling by embedding security / entitlements by design
  16. 16. • Prescriber • Sales contact • Key opinion leader • Research collaboration • Reporter on adverse events • Real world data provider • Principle investigator • Trial site Development Pharmacovigilance MarketingResearch Example „Healthcare professional“ 360 Information Assets connect isolated signals across the value chain and enable multiple functions /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 EHR EHR
  17. 17. Concept for a concerted implementation of information assets /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 PATIENT PRODUCT CUSTOMER information asset XYZ FAIR culture Sharing, governance, security, education, incentives FAIRification process Knowledge engineering, curation, APIs FAIR ecosystem IT architecture, products, services for data integration and consumption
  18. 18. Deliverables of the FAIRification process /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 FAIR digital objects data/metadata software/code/algorithms protocols models licenses other research outputs FAIR components skills and investment policies data mgmt plans (DMPs) persistent identifiers standards FAIR services curation and stewardship data lifecycle management long-term preservation file format transformation data protection / security handover plans for discontinued services
  19. 19. FAIR = Findable, Accessible, Interoperable, Re-usable Building the basic infrastructure for making data FAIR /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 ontologies curation tools services Curate Ontologize Standardize Govern FAIR datathons bring your own data and make them FAIR PID Data Market Place Persistent ID (URI) Resource registry Register Share F A I R
  20. 20. Acknowledgements FAIR ladies, ontologists Drashtti Vasant Melanie Hackl Johanna Völker Olga Streibel Marius Michaelis Data as an asset, 360s Manuela Schwenninger Gökhan Coskun Rolf Grigat Henning Dicke Anja Tilinski Maria Horsch Andy Montgomery Tim Williamson /// Making data FAIR at Bayer /// A. Grebe de Barron /// Connected data London, Oct 2019 MAPV information asset program Michael Heese Karsten Hanff EdiSON program/FAIR data stream Elke Hess Angeli Möller Saskia Schmidt-Riddle Barbara Weidgang Peter Borowski DINOS Nicole Philippi Sebastian Lühr Qiong Lin Jens Scheidtmann

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