In recent years, awareness for the automation of laboratory workflows has increased dramatically. As a result, researchers have worked tirelessly to make the improvement of lab digitalization and sensor technologies a main priority.
Being able to collect large quantities of data from R&D and Quality Control (QC) in a short amount of time, has the potential to speed up time-to-market, as well as develop more robust processes.
A variety of experiments, including in silico or in vitro studies, generate extensive data such as time series, quality measurements or pictures. To take full advantage of the experimental work, companies and laboratories need an efficient data management and data analytics strategy, which will enable them to prepare and analyze data properly.
The value of the data analytics strategy
As one of the most valuable concepts in bioprocess R&D, a data analytics strategy includes three main actions: It provides guidelines on how to efficiently analyze the relationship between various process parameters and product quality, as well as process performance.