#online event: Follow-up Event to ENDORSE 2021: Data FAIRification at Scale – Semantic Core Capabilities and Implementation

Date(s) - 31/05/2022
11:00 - 12:30

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The next follow-up event of ENDORSE will take place on 31 May 2022 (10.00-11.30 CET) as an online webinar on Data FAIRification at Scale – Semantic Core Capabilities and Implementation.

Martin Romacker (Senior Principal Scientist, Data & Analytics, F. Hoffmann-La Roche, Basel Switzerland)

Join via Webex: https://ecconf.webex.com/ecconf/j.php?MTID=mb6376647ea4da6c1004dca904245bebf

Join by meeting number:

Meeting number (access code): 2740 618 9202

Meeting password: PYtpJiZ@286 (79875491 from phones)


The pharmaceutical industry has started to massively invest in digital transformation to become data-driven and, thereby, to increase its productivity. The FAIR principles have been adopted as a blueprint to drive our data strategy and to make this transformation successful. However, Data FAIRification at scale has deep implications on the ways we work along our data management value chain affecting people, culture, processes and information technology. Consequently, semantic technologies are almost mandatory to build and represent the necessary core capabilities: terminology management, (meta)data element registries and conceptual modelling. But just building these capabilities is not sufficient, they also have to be adopted in the organization both in terms of data integration and data governance which necessitates a long term commitment and strategy.
At Roche, we have successfully established a semantic backbone to support our digitalization efforts with a high acceptance in the business as we can fully customize and contextualize the reference data (terminologies, metadata models and conceptual models) whilst keeping them fully governed and FAIR at the same time.
The presentation will cover business related aspects and give an introduction to our high level architectural approach. We will then focus on FAIR by design by walking through our capability stack and show we apply our semantic reference data in the various application contexts.

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