A4.1 Perspectives on how leadership, statistics and new insights can bring data management closer to data science

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The increase in collection of complex clinical data requires that data management collaborates closely across the data value chain from initial collection via analyses to reporting. The perception of data management from “the outside” is often described “how challenging can it be”? After 15 years within biostatistics in senior roles my reflections are that data management is much more technical and it-system focused than anticipated. Looking into a future with increased digitalization and constantly new data sources provides data management organizations a unique opportunity to embark on a leading role in the data science world by utilizing data availability, data standards, and data quality.

I will share reflections and examples from our data management organization on how we have embarked on new possibilities and methodologies inspired from across data science e.g., to detect abnormal data points based on machine learning and historical data and increased use of advanced visualizations in data cleaning processes. We have a focus on improving use of analytics methods and clinical understanding to obtain smarter ways of working and increased data understanding. In a transformation of data management to become closer to clinical data science we have some unique opportunities to increase the voice of data management in bringing the scientific hypothesis closer to successful regulatory submissions moving towards a stronger role in the “outskirts” of the global project teams by discussing new type of clinical data and their feasibility into current systems and how drug development programs can be orchestrated to allow operational challenging trials not being on the critical path.