Reference Data

How building a Reference and Master Data hub based on Standards and Semantics improves Data Quality

Bayer shows how building a reference and master data hub helped the company establish FAIR data adherence and pursue a long-term data strategy.

Building a Reference and Master Data Hub based on Standards and Semantics - Learnings and Success Factors

Bayer CropScience utilises digital technologies to support sustainable agriculture around the world. Especially in R&D this means that data from many different departments needs to be aligned to enable effective cross-organizational collaboration from early research to regulatory submissions. As part of this, the Data Sciences and Digital Transformation team provides shared references for data objects like locations, organisms, substances, products, etc. and drives their adoption with business stakeholders. In this talk we explain the key building blocks of an in-production Reference and Master Data Hub built on public and internal standards and semantic technologies meeting the popular FAIR Data Management principles. This hub allows to jointly govern internal master data with publicly available reference data and ontologies. We illustrate value drivers along use-cases from bio-research, regulatory and controlling and demonstrate how the ACCURIDS Lookup Service is used to drive wide adoption across user-groups and business functions.

With the deployment of Accurids, Bayer can perform a fast search on millions of entities, get an improved visualisation of trees, and the option of Ontology modularisation. 

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