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Hi Ann,
Good post. It is fantastic to hear that their managers want to own their decisions and not hide behind a “black box” or HR. I don’t know all the underlying data that Google has to measure Engineer performance but in my experience it is a difficult function to measure completely objectively. You can measure how much code is written and the efficiency of that code but how do you objectively measure innovation? I’m sure Google is better than most at quantifying performance. It sounds like their model was successful in predicting who would be promoted but I did not read if it predicted the performance of those promoted. There is a big difference between the two. I wonder if they could predict future performance if it would have some role in the process.

In my experience there are areas of a business that can be more objective in the measurement of performance than others. Data analytics is not a perfect science but can play a role if balanced appropriately. In the right context it can be used as a data input but not the only input. It is important that the approach fits the culture and workforce strategy.

All human decisions are B.A.D. decisions, at best relying on the Best Available Data known at the time.

Yes good (and timely), as I was part of working group today (Friday), including some of our senior managers - discussing primarily promotion budget allocations, but also the actual, individual promotion decisions, what they should be based on, and who should make (and own) them.

And before we attribute data-based decision making infallibility too unequivocally to our friends in Mountain View . . . a funny story. About five years ago when Google announced its "astounding" 10 percent across-the-board increase for its entire workforce, I asked a top-level Google HR manager where they came up with 10 percent as the "right" amount. And strangely, he couldn't trace back to the basis for that decision. When I suggested the psycho-physics construct of Just Noticeable Differences (the sensory threshold were a "difference" is detectable to people), he acknowledged that could have been the (subconscious) basis. Et tu, data?


All good points - we don't know (and aren't told) what kinds of measures go into the "Avrg Perf" element of the algorithm. And it sounds as though the Google People Analytics team has reached the same conclusion that you advocate - that analytics are better used for context and input to decision-making, rather than to spit out automatic decisions with no human intervention.


B.A.D. is a very fitting acronym!


Great Google story. Et tu, data indeed!

Thanks all for the comments and observations!

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