See Whether a Change Caused Problems

Root cause analysis looks at each sensor and tries to correlate whether there were changes in the environment that caused that sensor to trip. The second grid shows what changes occurred at the same time across the enterprise. The higher the correlation, the more likely it is that the change had something to do with the sensor.

If I wanted to know more about a particular sensor, I can select it and see which percentage of users have also had this change. If the correlation is a lower number, it is unlikely that this particular change caused the sensor to trip, but if the correlation percentage was in the seventies or greater, the likelihood that it’s related gets higher.

Now let’s flip that—did the change that I just made break anything adverse? Impact of change looks at each change first and shows which sensors were tripped at the same time the change occurred. For example, I added a new piece of software—did that have anything to do with system issues? So we’re basically looking at change analysis.

Another new feature in Prevent is change performance. If we made a change to the environment and want to see what impacts might have occurred, we can check here. For example, we had an update to Microsoft Edge that I want to check out, so I’ll filter for that here.

Notice that there are colors in the grid: green means there was a significant improvement after the update, and red means there was a significant impact after the update. If we click on the value, we can see a graphic view of that same information.