Understanding the volume and complexity of data created throughout the scientific process is key. Biopharmaceutical companies generate a vast amount of high-value process data that is typically stored in paper records, databases, electronic files and in the heads of key personnel. This makes obtaining an holistic view of the processes problematic.
Whether this is biologics experimental data recorded as part of cell line development, upstream, downstream, or analytical processes, or result and raw data generated through instrumentation; extracting, analyzing and reporting on this data in different formats, from disparate sources, can cost businesses results, time, and money.
The majority of biopharmaceutical organizations lack a standardized, comprehensive approach to data management, and exhibit a heavy reliance on paper-based, manual processes using siloed, unconnected systems.