Efficient control of a bioprocess relies on the ability to systematically capture and represent the process dynamics of critical process parameters. Multivariate monitoring techniques in biopharmaceuticals has resulted in the generation of large amounts of data comprising real-time measurements of critical quality and performance attributes. If exploited efficiently, these can provide an opportunity for developing better control action. For this, it is important to have a comprehensive view of the critical process parameter landscape, which can only be achieved by integrating both online and offline data into a single data matrix that can then be subjected to standard data analysis protocols. However, owing to the difference in the number of readings available for variables recorded online and offline, there is a need for new methods to achieve condensation capability. This paper introduces a novel methodology for condensing online data into an offline data matrix, which performed better when compared to traditionally employed averaging and helped increase the number of variables available for representing the design space of the process. The method was also used to understand how error propagates through online data, so as to identify an interval of tolerance in online monitoring of bioprocesses.