Predictive models used in drug discovery require a viable level of data quality. A faulty model can lead to completely off-the-mark predictions and sunk project costs.In sharp contrast, much of the available biomedical data is unstructured and prone to errors due to varying experimental protocols (incomplete metadata information, missing annotations, inconsistent file formats). To ensure their datasets are ML-Ready, R&D teams must set up a system that continuously assesses and iterates on the data and metadata quality. This session will demonstrate Elucidata’s data quality assessment approach, which ensures an input dataset is standardized and has accurate, complete, and a breadth of metadata information before it is considered model quality.