Identfying spikes in data I am a little new at this - I am used to just querying data and not so much analysis of the data so any help would be greatly appreciated.

I have some data that is trending month over month. Some months with an increase in volume and others with a decrease. The goal is to identify a "spike" in the volume. Basically, is the increase abnormally large. I was trying to figure out the best way to do this. I thought the best thing to do was take the standard deviation and anything outside of two deviations - BAM!, that's a spike. Then I thought about it some and I got concerned about what happens next month. That spike is still in that data, it happened, but doesn't it shift the standard deviation so that the next month may not show a "spike" when without the prior "spike" it could have? For continuous data maybe this is less an issue but my data is rolling, I only have the last 6 months of data. If there is a "spike" this month and every one is put on notice, and the level never changes, then in six months wouldn't it seem like there is no spike?

I guess my question is, how would you quantify a measure for a "spike" given this limitation?
Your intuition about the algorithm is about right and is a known technique from six-sigma.

As you may expect from six-sigma, take 3 times deviation. You could also take the running average and deviation to compare the new value to if you expect trends.

You need to inspect the spike but can consider removing it, which will reduce variations again.