In the process industry data-driven and hybrid modeling approaches are increasingly popular in regards to process monitoring, optimization and control. The major problem with process data is that the data collected in process plants during operation, even though available in vast amounts, might generally be low in information content. The collected data usually represents certain operating points while anomalies, ramp-up and shut-down are rare occurrences and therefore only seldom covered. Due to its possibly low quality, the use of such data might lead to an inadequate model coverage and overall low model performance. Data quality assessment prior to modeling is crucial to allow an estimation of model quality prior to the model development. Therefore, the following paper discusses prescriptive and descriptive assessment metrics for the quality assessment of process data and their potential application in the quality assurance of data-driven and hybrid models. This approach will in later application support the user in their choice of modeling approach.