"What People Analytics Can’t Capture" by Daniel Goleman

1. WHAT PEOPLE ANALYTICS CAN’T
CAPTURE BY DANIEL GOLEMAN

2. INSIGHT 1
Many businesses are reaping rewards from big
data analytics.
But there are also some areas of
disappointment. Experts caution that big data,
like any other, is only as good as the questions
being asked – and that some algorithms can
make unhelpful assumptions.

4. There’s a saying in the sciences, “Statistics
means never having to say you are certain.”
 In any massive data analysis, for instance,
there will be random correlations that look
“significant” but actually are noise, not signal.

6. Big data needs a hard outcome metric for
performance, but the most readily available
metrics may not actually be the most important
variables in organizational flourishing.

8. INSIGHT 2
An outcome metric like an executive’s earnings
performance, while ignoring his role as a boss
and his impact on the morale, loyalty, focus,
and stress levels of his direct reports, may
result in a false indication of who’s really the
best boss.

10. EXAMPLE OF GOOGLE
At Google, the bastion of algorithms emerging
from giant data sets, are refused by engineers
to use as a method to decide on promotions.
As Laszlo Bock, head of hiring at Google
explained, the very fact the company knows so
much about algorithms lets it see their limits.

12. The assumptions built into a test can
themselves be biased against certain traits and
so discriminate unfairly.

13. MANAGERIAL RELEVANCE
A manager – like the demotivating petty
tyrant–can force his people to work hard to
meet quarterly targets
Using an outcome metric like an executive’s
earnings performance, may result in a false
indication of who’s really the best boss.

15. MANAGERIAL RELEVANCE
But the biggest objection comes from the fact
that the strongest predictor of a person’s future
behavior is their past performance itself.
And that performance gets evaluated best by
people who know that person well.