Classic Machine Learning Through the Eyes of an SRE — Part 7
Picture a client health metric that has been flat at 2 out of 10 for six months. Ask PCA to compress your client-health data and that metric will contribute almost nothing to the directions PCA decides to keep.
Not because PCA is broken. Because PCA treats variance as importance, and a signal that barely moves contributes almost no variance. Reduce the data far enough and the independent information it carried is simply not there anymore.
But a CSAT frozen at 2/10 is not noise. It is a crisis nobody is escalating. And after compression, it may no longer be available to anything downstream.
That is the bet, and in ops data it is frequently wrong. The critical signals are often the quiet ones.
