I started down this path, looking at things like:
- number of people who Emailed an individual asking about a specific topic
-percent of time spent in Outlook vs Visual Studio vs WoW (RescueTime-esque stuff)
- number of IMs initiated from someone at least one level higher than them
But at the end of the day, can that really tell you quantitatively that Bob is better than Suzie? Even the example above about being stressed - I know people who are super chill and excel at their job, and others who are stressed all the time and also excel (and vice versa).
Maybe the answer is to pull in hundreds of these variables and run a neural net against actual performance rankings to see if any statistically significant correlations pop out?
I think you are right about pulling in hundreds of these variables to correlate with both intuitive and quantitative tests that we have to define 'productive' employees. Then you can send an electric shock whenever someone's theta waves get too low or something..
Seriously though, our first level of productivity is dictated by things biological, and if you can create models to paint a strong enough picture of a productive worker then it gives you a feedback loop. And don't forget, not everyone is a knowledge worker like us, people still drive trucks for a living, pick fruit, do construction, etc. That is a huge market.
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That books looks sweet, thanks for pointing it out. Reminds me of a good post by Seth Godin I read the other day: http://sethgodin.typepad.com/seths_blog/2008/07/let-me-see.h...
I started down this path, looking at things like: - number of people who Emailed an individual asking about a specific topic -percent of time spent in Outlook vs Visual Studio vs WoW (RescueTime-esque stuff) - number of IMs initiated from someone at least one level higher than them
But at the end of the day, can that really tell you quantitatively that Bob is better than Suzie? Even the example above about being stressed - I know people who are super chill and excel at their job, and others who are stressed all the time and also excel (and vice versa).
Maybe the answer is to pull in hundreds of these variables and run a neural net against actual performance rankings to see if any statistically significant correlations pop out?
Hell yea its sweet! :) I initially was pointed there by this great presentation: http://www.tomtaylor.co.uk/talks/delighting-with-data
I think you are right about pulling in hundreds of these variables to correlate with both intuitive and quantitative tests that we have to define 'productive' employees. Then you can send an electric shock whenever someone's theta waves get too low or something..
Seriously though, our first level of productivity is dictated by things biological, and if you can create models to paint a strong enough picture of a productive worker then it gives you a feedback loop. And don't forget, not everyone is a knowledge worker like us, people still drive trucks for a living, pick fruit, do construction, etc. That is a huge market.