Since StreamInsight can process raw/snapshot data (I mean data that has not been compressed yet), StreamInsight (SI) could be used to help tune (or perhaps auto-tune) PI tags' compression (CompDev) parameters. The importance of compression is clear to everybody and it was the main topic of the very first talk at the 2008 PI Dev Conf. This is well explained here:
The compression methods in process information management systems (PIMS) are needed to store the masses of data measured in large-scale production processes. In practice, the quality of the compressed data is often unsatisfactory due to inadequate compression settings. This can lead to significant delays and high cost when production problems occur, because the compression settings must be corrected, and more data must be collected before the problem can be analysed and solved.
The reason for inadequate compression settings is the high cost associated with the PIMS configuration, as it is mostly done manually. The piecewise linear compression methods in use today, like the swinging door algorithm or the modified Boxcar-Backslope algorithm must be parameterized for each process variable separately. As a consequence, the parameters are often left at their default values, typically 1% of the maximum value. This is often larger than the actual noise variance in the data, resulting in loss of information.
The paper from which I copied these explanations describes a method for adjusting PI compression parameters. With some knowledge of Wavelet analysis, it should not be too hard to implement in SI.