One of the themes that I cover in talks is the need for the analytics community to move beyond "insight" and shift attention to "impact". This requires a shift in mentality from "what I can compute" to "what I should compute".
I was looking at this analysis done by the Linkedin team:
They processed their user profiles and found the top 10 words or phrases people use to describe their work experience. Nice insights. Not something you and I could guess at with any accuracy without the data processing capability. However....
What should they be analyzing? Unfortunately, I don't care about "overuse"; in fact, this is an instance of "story time" as overuse is highest frequency with a negative value judgment, and I'm not sure from where the latter arises.
What should they be analyzing? What LinkedIn users care about, I suspect, is whether the use of particular words is correlated with a higher hit rate, more people looking up your profile. If they can show us this correlation, then they have successfully moved from insight to impact. And they are tantalizing close to this analysis, as they already have the word frequency data, and based on the statistics available on their website, they also track the frequency of profile views.