There has been a lot of press about Yahoo CEO Marissa Mayer and her 'possibly' having introduced a system of ranking individuals on some kind of bell curve. The implication is that this tool is for the purpose of deciding which employees to get rid of. Putting the veracity of the reports aside (not my place or intent in this blog) I'd like to take a look at what this actually means.
It isn't necessary to understand the specifics of this curvy stuff, except that it just informs an assumption about how many 'awesome', 'average' and 'far from awesome' employees exist in any company or team. This bell curve approach dictates that we have lots of 'average' people in the middle and then smaller numbers of the 'awesome' and 'far from' on either side.
The most important outcome of all this on the ground is that managers are forced to rank at least some people into the 'awesome' and 'far from awesome' categories and push everyone else into the middle.
The assumption here is that teams ranked this way really do look this way. They must have a a distribution that is something like what I just described. So, this means that if you have a team and you think that they are all truly 'awesome' that you are not going to be able to rank them as such.
Taken to a logical conclusion, using the curve is saying such a truly awesome team doesn't exist at all. So, in just the same way, by using a curve, the following assumptions are also made (if not precisely then at least in probable effect):
1. There are no 'awesome' teams.
2. There are no 'average' teams.
3. There are no 'far from average' teams.
4. There are no teams full of 'awesome' people.
5. There are no teams full of 'average' people.
6. There are no teams full of 'far from average' people.
8. That manager rankings are more important than peer rankings.
9. That the occasional 'awesome' or 'average' person being fired by accident will have less damage than the benefit of the overall program.
10. And so on...
To learn how your teams are working together, Culture Amp's 360 feedback surveys are a great start.