Bioprocess data creates value for biotech companies. No doubt. Accelerated process development. Investigation & troubleshooting. Operations excellence. Assess optimization potential. Just to name a few use cases. Industry 4.0. Digitalization of the processing environment. New sensor technologies and data integrity requirements disrupt how process data is managed and analyzed. As a result, requirements to data management and bioprocess data analytics changes dramatically. Today, data sources are very complex and multidimensional. Spectra from spectroscopic sensors, such as NIR and Raman, images from flow cytometry and in-line microscopes, mass spectrometry chromatograms, time series sensor data, quality data, software sensors. Upstream. Downstream. Just to name a few. New solutions capable of managing and analyzing all process relevant data types are necessary.
Process data is now multidimensional and of multiple origins
Industry 4.0 and process digitalization brought birth to new process analytical technologies. Processes are better monitored than ever before. The technological possibilities to mine chemical-, physical- and biological data from manufacturing are cheaper and more accurate than ever before. A great quantity of big process data is collected on a routine basis. But data size is not the issue. Data sources are now multidimensional and need to be align holistically, contextualized for doing value added bioprocess data analytics. This is an issue. For integrated bioprocess data analytics, we need to wrangle all relevant data sources. The images below give a brief overview of data types you encounter in bioprocessing.