Roche, as a leading biopharmaceutical company and member of the Pistoia Alliance, has a diverse and distributed ecosystem of platforms to manage reference data standards used at different parts of the organization. These diverse reference data standards include ontologies and vocabularies to capture specifics of the research environment and also to describe how clinical trial data are collected, tabulated, analyzed, and finally submitted to regulatory authorities. In the context of the EDIS program, Roche has bridged these parts to improve reverse translation from studies into research and also embraced FAIR to emphasize machine-actionability and data-driven processes.
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Organizations that operate worldwide typically need to manage data both locally and globally. Local business units and subsidiaries must address region-specific data and accounting standards, regulations, customer requirements, and market drivers. At the same time, corporate headquarters must share data broadly and maintain a complete view of performance for the entire enterprise. For many global firms, data is the business. They need state-of-the-art data management just to remain innovative and competitive. Hence, multinational businesses face a long list of new business and technical requirements for modern data management.
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Staying competitive in today’s economy requires that organizations derive intelligent insights from data and make their processes automated, intelligent, adaptive, and self-optimizing. This is where artificial intelligence (AI) and machine learning (ML) play a central role in business transformation.
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Data modernization, especially through cloud migration, is critical to realizing value from today’s fast, diverse, and high-volume data. Modernization is about overhauling legacy data management and practices that hold organizations back from achieving business goals, improving resilience, and reducing risk. However, often overlooked in the rush to develop new applications and migrate data is modernizing data governance. Poor attention to data governance will bake problems into overall modernization efforts that become hard to correct, increase risks, and ultimately reduce the value of data.
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