Today, metrology toolsets report out more information than ever. This information applies not only to process performance but also metrology toolset and recipe performance through various diagnostic metrics. This is most evident on the Critical Dimension Scanning Electron Microscope (CD-SEM). Today state of the art CD-SEMs report out over 250 individual data points and several images per measurement. It is typical for a state of the art fab with numerous part numbers to generate at least 20TB of information over the course of a year on the CD-SEM fleet alone pushing metrology toolsets into the big data regime. Most of this comes from improvements in throughput, increased sampling and new data outputs relative to previous generations of tools. Oftentimes, these new data outputs are useful for helping to determine if the process, metrology recipe or tool is deviating from an ideal state. Many issues could be missed by singularly looking at the key process control metric like the bottom critical dimension (CD) or a small subset of this available information. By leveraging the entire data set the mean time to detect and finding the root cause of issues can be significantly reduced. In this paper a new data mining system is presented that achieves this goal. Examples are shown with a focus on the benefits realized using this new system which helps speed up development cycles of learning and reducing manufacturing cycle-time. This paper concludes discussing future directions to make this capability more effective.