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      Data-Driven Enterprise Architecture for Pharmaceutical R&D

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      Digital
      MDPI AG

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          Abstract

          This paper addresses the research gap in the realm of data-driven transformation by leveraging the Resource-Based View (RBV) theory and the dynamic capabilities concept to the contours of a data-driven enterprise. It confronts the limitations of conventional digital and data transformation programs, which often prioritize technological enhancements over crucial organizational and cultural shifts. Proposing a more holistic perspective, the Data-Driven Enterprise Architecture Framework (DDA) is introduced, emphasizing the domain decomposition and productization of an architecture, distributed ownership, and federated governance, while ensuring the continuous harmonization of data, application, and business architecture. A case study featuring a leading pharmaceutical company illustrates the practical implementation of the DDA framework as a pillar of their Digital Transformation Strategy. By integrating scalable and distributed data architecture into the overarching Enterprise Architecture landscape, the company has initiated their data-driven transformation journey, showcased through their initial and very early results. This research not only offers valuable insights for pharmaceutical organizations navigating the complexities of data-driven transformations, but also addresses a research gap in the field.

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          Most cited references64

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance

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              Digital transformation: A multidisciplinary reflection and research agenda

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                DIGIBA
                Digital
                Digital
                MDPI AG
                2673-6470
                June 2024
                April 22 2024
                : 4
                : 2
                : 333-371
                Article
                10.3390/digital4020017
                b168f86f-19bc-4ab4-9706-04f129aba4c9
                © 2024

                https://creativecommons.org/licenses/by/4.0/

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