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<p>The amount of biomedical data is growing, and managing it is increasingly challenging. While Findable, Accessible, Interoperable and Reusable (FAIR) data principles provide guidance, their adoption has proven difficult, especially in larger enterprises like pharmaceutical companies. In this manuscript, we describe how we leverage an Ontology-Based Data Management (OBDM) strategy for digital transformation in Novo Nordisk Research &amp; Early Development. Here, we include both our technical blueprint and our approach for organizational change management. We further discuss how such an OBDM ecosystem plays a pivotal role in the organization’s digital aspirations for data federation and discovery fuelled by artificial intelligence. Our aim for this paper is to share the lessons learned in order to foster dialogue with parties navigating similar waters while collectively advancing the efforts in the fields of data management, semantics and data driven drug discovery.</p>

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Digital evolution: Novo Nordisk’s shift to ontology-based data management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月22日

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  • Shawn Zheng Kai Tan,
  • Shounak Baksi,
  • Thomas Gade Bjerregaard,
  • Preethi Elangovan,
  • Thrishna Kuttikattu Gopalakrishnan,
  • Darko Hric,
  • Joffrey Joumaa,
  • Beidi Li,
  • Kashif Rabbani,
  • Santhosh Kannan Venkatesan,
  • Joshua Daniel Valdez,
  • Saritha Vettikunnel Kuriakose

摘要

The amount of biomedical data is growing, and managing it is increasingly challenging. While Findable, Accessible, Interoperable and Reusable (FAIR) data principles provide guidance, their adoption has proven difficult, especially in larger enterprises like pharmaceutical companies. In this manuscript, we describe how we leverage an Ontology-Based Data Management (OBDM) strategy for digital transformation in Novo Nordisk Research & Early Development. Here, we include both our technical blueprint and our approach for organizational change management. We further discuss how such an OBDM ecosystem plays a pivotal role in the organization’s digital aspirations for data federation and discovery fuelled by artificial intelligence. Our aim for this paper is to share the lessons learned in order to foster dialogue with parties navigating similar waters while collectively advancing the efforts in the fields of data management, semantics and data driven drug discovery.