1. Getting Semantic Technologies into the Business- Major Issues and Key Success Factors Martin Romacker, Principal Scientist Data/Information Architecture & Terminologies Roche Innovation Center Basel Semantic Web and Data Integration- New Technologies and Applications to Industry Alan Turing Institute, London 26 th – 27 th May 2016

2. Information and Data Architecture Why Semantic Technologies? Urgent need for a comprehensive strategy to manage our data assets! Impossible to answer complex scientific questions using a single knowledge repository. Need for a flexible and fast federation layer. Impossible to anticipate scientific queries of the future Need for an open architecture supporting flexible data integration Support of external collaborations based on data standards (trend CROs) Scientists have high expectations with regards to data quality (big data - variety, varacity) Terminology/ Ontology management is perfect for precompetitive activities (Standards for Data Sharing and Data Integration)

3. Life Sciences (R&D) Knowledge Space and Information Objects in R&D Medical Science Chemistry Biology

4. Biomedical Ontologies/ Terminologies Established Platforms/ Services Life Sciences have a long tradition in the Semantic Space

5. Semantic Integration Layer Strategic Importance New projects/ intiatives - urgent need for high quality and comprehensive terminologies/ ontologies

6. Practical Usage of Semantic Technologies The Reality Clash Niche Players: poorly adopted in the industry Technology Challenge: justification needed Perception issue: Data Quality strangely neglected Missing ownership: Poor interest in business Data Governance : Works only with pressure Range Issue: Only few people understand topic Clear objective and urgent need vs poor adoption

7. Scientific Data Integration An Example Modest Level for Semantics: Code Lists/ LOVs

8. Key Success Factors Strategic Collaborations eTRIKS (European Translational Information and Knowledge Management Service) – Framework for managing study data supporting other IMI projects – Workpackage 3 on Data standards and terminologies – PostDoc position at University of Oxford funded by Roche (3.5 years) – Deliverable «Starter Package» recommendation for Standards – Deliverable «Curation Guidelines» – eTRIKS Harmonization System Pistoia Alliance and transMART foundation – Ontologies Mapping Project (Guidelines and Best Practices) – Ontology Working Group Leverage workforce, Influence the definition of Data Standards Participate in collaborative curation exercises, align workflows

9. Key Success Factors Overview Tenacity: do not give in too early – sometime it takes years to get buy in Lower the shreshold for adoption and integration – support business with integration tasks *at all levels* – bring knowledge to the point of usage (easy technical access) Communicate, communicate, communicate and be patient – semantic technologies are anything else but self explanatory (eg UG, forum) Master plan for stakeholder management – provide right information to the right people at the right time (increasing mgt level decreasing interest) Support software vendors (ease of integration in application landscape) Support content providers – commercial and public (solve issues at source) Create and foster semantic awareness – Semantics are crucial to business

10. Acknowledgement (basel.terminology-service@roche.com) DIAT/ RTS Core Teambasel.terminology-service@roche.com Joachim Rupp (Team Lead, Content Owner, Curator) Kenny Niedworok (Technical Service Management, Project Support) Christian Blumenröhr (RTS Component Development) Thomas Thies, Werner Gotzeina, Bela Borsos, Fausto Agnetti (RTS Data Backend, ETL Processes) Pascal Kuner, Mathias Leddin, Ralf Jäger (Content Owner, Curator) Nika Rack (Content Owner, Curator) Samuel Croset (Drug/ Product Terminology)

11. Doing now what patients need next