Life science companies manage large document repositories. The volume, the demands on the correctness of the information contained therein and the complexity of embedding this information in business processes represent a major challenge. At this point, the use of artificial intelligence offers considerable potential both for increasing data quality and for using the information it contains for automatic evaluation.

AI/ML technologies enable automatic digitization, categorization, and tagging of large document collections to transform unstructured data silos into intelligent data repositories. Automatic extraction of structured information such as names, active ingredients, and quantity information to create databases and quality assurance allows IDMP-compliant data to be made available to regulatory agencies. Document similarity analysis allows relationships to be established and similarity and redundancy to be detected.

A data strategy defines the long-term goals for creating value from corporate data as well as the activities and resources required to achieve them. Planning a data strategy begins with an inventory and an analysis of potential. This is followed by the identification of use cases and an assessment of their feasibility. The creation of a roadmap with identified use cases concludes the data strategy and forms the basis for the subsequent implementation phase. In this phase, the use cases are initially implemented as a proof of concept and, after positive evaluation, as a productive application.