
Google’s Quest to Build a Better Boss - px
http://www.nytimes.com/2011/03/13/business/13hire.html?ref=technology
======
gjm11
So, apparently Google found that of their 8 characteristics-of-good-bosses,
technical excellence was the least importance. Fair enough. But note that this
is looking at a sample of _technically excellent bosses_! So, e.g., Bock is
quoted as saying: "In the Google context, we’d always believed that to be a
manager, particularly on the engineering side, you need to be as deep or
deeper a technical expert than the people who work for you."

It doesn't _at all_ follow that technical excellence isn't extremely important
for bosses who haven't been chosen for technical excellence. (Perhaps it
isn't; but Bock's work doesn't seem to provide any evidence.)

~~~
nostrademons
The article actually said that technical excellence was important, it just
said that it wasn't as important as some of the people skills.

I can't really see Google dropping technical competence as a qualification for
managers. I've heard that from 2005-2007 (before my time), Google brought on a
bunch of non-technical managers from other companies, and the result was a
pretty uniform disaster. Most of those managers are no longer with the
company.

~~~
kragen
Reverse-engineering from the article, the original statistical result was
probably something like, "Variations among managers in technical skill account
for very little of the variations among managers in performance reviews."

One way to get that result would be for technical skill to be unimportant.
Another way would be for the variations in technical skill within the sample
to be much smaller than other relevant variations. Given that ( _según_ the
article) the managers were selected for technical skill, but not for the other
qualities, it seems almost certain that this was the case.

This might sound like a bug that could be avoided by better statistical
analysis techniques, but it's really not. You really want to know what causes
the _actual_ variations in performance in your sample, not what _could
conceivably_ cause variations in performance. Surely there are hundreds of
things that would cause even bigger variations than what's in your actual
sample — you could have managers who kill all their employees, or managers who
are interned incommunicado in Guantanamo, or managers who obsessively turn
every project into an investigation of fluoride contamination of their
precious bodily fluids, or managers who are actually undercover headhunters
from a competitor trying to steal away all of your best talent.

Cosma Shalizi has an excellent post about the pitfalls of multivariate
statistical analysis at
<http://www.cscs.umich.edu/~crshalizi/weblog/520.html>; although he's talking
about genetics, it's very likely that what he's saying applies to Project
Oxygen as well:

 _To see why gene-environment interactions matter, consider one of the best-
established links between genetic variations and intelligence,
phenylketonuria. This is a recessive genetic disease which interferes with the
normal metabolism of the amino acid phenylalanine. If someone with one of the
defective forms of the gene for phenylalanine hydroxylase consumes too much
dietary phenylalanine, it leads, among other problems, to serious mental
retardation. Under suitable diets low in phenylalanine, however, they grow up
mentally normal. Assigning shares of this effect to the genes and to the
environment is exactly as sensible as trying to say how much of the fact that
a car can go is due to its having an engine and how much is due to their being
fuel in the tank. The best the usual biometric model could do here would be to
predict that having the gene_ always _reduced intelligence, as did consuming
phenylalanine (which would be bad news for makers of artificial sweeteners);
the fact that it's the combination, and only the combination, which is a
problem would be missed, and the predicted size of the effect would be badly
wrong. … So while everyone piously says that genes and environments interact
in development, they typically use models which assume that they do so only in
trivial ways, and hope that any actual interactions are small enough to be
treated as noise._

(So if these simple linear models are so bad, why does everybody use them?
Because they have fewer parameters than more complicated models, which means
that they're not as prone to overfitting. It's easy to construct a nonlinear
multivariate statistical model with more parameters than you have managers in
your company, which will discover correlations no more meaningful than the
fact that people whose initials are "BHO" are far more likely to currently be
the president of the United States than people with other sets of initials.)

~~~
nostrademons
Right, I'm saying that the managers in charge of interpreting this data are
undoubtedly aware of this effect, and so they're looking for those effects
_besides_ technical skill. It's like every Google manager already _has_ an
engine (they took care of that problem several years ago), now what can they
do to make sure that there's always gas in the tank?

~~~
kragen
I was disagreeing with your comment, "it just said that it wasn't as important
as some of the people skills." The article did say that, but the statistics
almost certainly did not. I agree with your new comment.

------
sriramk
Microsoft learned this lesson a long time ago. In the old days of MSFT, the
saying was that BillG believed that everyone's manager needed to be a better
coder than him/her. The company learned eventually that the best engineers do
not necessarily make the best managers and engineered the IC/manager split-
career path that exists today.

P.S I like how Google is finding out they're just the same as pretty much any
other large organization :).

~~~
reeses
Being there in the old days, I really liked two things.

1) Any question I had, someone could either give me the answer, direct me to
the book (yeah, that long ago) that had the answer, or do the back and forth
so I could figure out the answer with the knowledge I already had.

2) Rip the sh*t out of my code in a way that, while humiliating, made me a
much better programmer.

You went in thinking that you're the smartest kid in the room, but while I was
there, the mean IQ was 132. It was a place with a bunch of smart _adults_ who
also had a significant amount of knowledge and experience, and very few
hoarders of that information. After all, how can you show how smart you are if
you keep quiet when someone is wrong? My niches were that I had K&R memorized
(that's really embarrassing now, but "actually, free(NULL) is legal," beats a
full house) and my proof of concept client code made its way into SDKs.

Joel Spolsky talks a lot about the same time era, basically Microsoft pre-
Internet. Many of us ex-MSFTies idealize it to a certain degree, but that's
fine–take and keep the good and drop the bad. Many of us are engineering
leads/CTOs/flounders elsewhere, and the rigor present in the hierarchy of
ability-to-execute helps to excel in other companies or industries.

But most of us secretly would like to go back to a few thousand nerds in a
rainy forest and hack products all day. :-)

~~~
kragen
> but while I was there, the mean IQ was 132

Only 132? Are you serious? How did the IQ-105 people get hired, and what were
they doing — opening envelopes in the mail room?

~~~
reeses
This was also the time when the legendary "Microsoft interview" started coming
together. I.e., you'd have an all-day interview with everyone on your
prospective team (if you were targeting a particular position), and each
interviewer would have one or two IQ-test questions and at least one
whiteboard coding exercise.

People really went after the lateral-thinking questions and the estimation
questions to find "smart" people. "How would you calculate how much rain falls
in Washington in a day,"[1] "How many gas stations are there in
(IYFStateOrCity)," and "How would you make a desk calendar with two cubes,"[2]
were all fun ones.

Now, by making your questions similar to those on IQ tests, you select people
who tend to do well on IQ tests. Funny, that.

[1] I got a boost on this one by spitting out,"weigh the clouds on their way
in and out of the state," right away before building a slightly more feasible
estimate. :-)

[2] The trick is just like the Jimi Hendrix song, if six was nine.

~~~
kragen
So you're saying the mean IQ at the time was so low because they hadn't
started selecting for IQ yet?

~~~
reeses
Well, you also run into a practical testing ceiling. Most of the general
purpose IQ tests (WAIS, Stanford-Binet) don't measure very accurately over
160. 132 as the mean of a set systemically limited to a maximum of 167 paints
a different picture, not that I have a good handle on how many of us exceeded
160, especially across the entire campus.

------
tokenadult
"Once they had some working theories, they figured out a system for
interviewing managers to gather more data, and to look for evidence that
supported their notions."

Oops. That's called confirmation bias.

<http://www.sciencedaily.com/articles/c/confirmation_bias.htm>

<http://www.skepdic.com/confirmbias.html>

Did they also look for evidence that cast doubt on their notions? Looking for
both kinds of evidence, and evaluating them fairly, would lead to a more
robust conclusion.

~~~
arctangent
I don't want to disparage your comment because I do agree to some extent.

However, I think you might be getting voted up because you gave links to some
interesting content instead of because people think you are necessarily
correct.

I'd like to provide a little balance.

It's routine practice in statistical analysis of situations to form a
hypothesis which goes against the grain of accepted belief and then to test it
using analytical techniques [1]. In fact, this is how the scientific process
works.

[1] <http://en.wikipedia.org/wiki/Null_hypothesis>

------
Groxx
I feel this is particularly relevant: <http://www.bobbemer.com/DAVINCI.HTM>

> _One of the brighter students (by the name of L. da Vinci) was immediately
> promoted to the manager of the project, putting him in charge of procuring
> paints, canvases, and brushes for the rest of the organization._

Everyone, except businesses (it seems), knows that technical skill !=
management skill, and the best workers will not necessarily be even good
managers. But such knowledge is so rarely _followed_.

~~~
athom
If the guy outta Venice knew his stuff, he'd have immediately delegated those
tasks to others and got back to his own business, rather than do them all
himself.

Such is the art of management.

------
DanielBMarkham
I get to see companies of all sizes grapple with various problems and the most
interesting thing I've noticed is that each company has a "culture" that
influences everything it does. So if it's an insurance company, they're going
to think in terms of risk and coverage. If it's a manufacturing company
they're going to make decisions based on flow models and statistical process
control. If it's an quasi-military organization, they're going to think in
terms of hierarchical structures.

It's neat to see Google continuing this pattern by applying tagged data
collection and statistical inference to their quest for organizational
optimization.

I can only imagine what it's like to work in a large-scale IT operation in the
porn industry ;)

~~~
nostrademons
"I can only imagine what it's like to work in a large-scale IT operation in
the porn industry ;)"

I've heard it's about the most unsexy thing ever. The technical aspects are
all about shipping massive quantities of data cheaply and reliably. The, erm,
video-production aspects are about presenting a picture that will satisfy
customers and leave them coming back for more.

In other words, it's pretty much like Google or any other big-data startup
(and there're employees at Google who used to work in the porn industry). Not
having been a part of it, I don't know what the culture is like, but I've
heard it's pretty much all business, and they have significantly less fun than
we do at Google.

~~~
narag
_The technical aspects are all about shipping massive quantities of data
cheaply and reliably._

That rang a bell.

------
zach
So, typically there's a split between creative, project-based leadership and
productive, people-based management. In games these creative leaders are (in
the west) lead designers, Microsoft calls them "program managers," movies call
them directors, and so on. Sometimes it's called "matrix management" and
rigidly structured. Whereas at Blizzard, project management is just clearly
set up so that the design staff are in complete creative control of the
project's direction.

Anyway, I don't really see that separation of roles in evidence within the
scope of this article. How does it work at Google? Also, is there some good
analysis on this general subject someone can point me to?

------
apedley
Wow, they only just figured out that managers don't need to have a greater
technical skill than those they are managing? I thought that was management
101?

Managing and engineering are 2 different skills. While managers benefit from
technical knowledge it certainly doesn't need to be greater.

And with Marissa Mayers comments on Google wants to connect the real world to
the digital world, where have they been. In case they weren't aware, internet
connected appliances, location based apps, RFID, QR codes, mobile phones have
been around for a little while now.

Google's search engine is great though slowly declining in quality but that is
all they have been great at. Android is a decent mobile OS but it only
received market pentration due to it's zero cost. Their social attempts have
all failed. They only seem to get wide market adoption from free software or
services, which I suppose they use to keep their search engine traffic high so
they can maintain their advertising revenues.

They seem like a company unable to create another profitable software line.

------
sduralde
Google’s approach optimizes a sub-optimal model. A poor performer is less bad
after a year of coaching. Our data show that talent occurs in predictable
combinations, each combo in predictable frequency. Some combos DO NOT exist at
all. The talents of great leaders (envision future, inspire followers) does
not exist in the same person as a great manager (sees unique talents in
others, applies and develops the talent, fosters purposeful collaboration).
Google should have asked: What talents do our best managers posses? How do we
find and develop more, faster? See [http://methodteaming.com/did-google-miss-
the-forest-for-the-...](http://methodteaming.com/did-google-miss-the-forest-
for-the-trees-googles-quest-to-build-a-better-manager)

------
codex
Does anyone have the actual stack ranked list of important qualities?

~~~
piotrSikora
[http://www.nytimes.com/imagepages/2011/03/11/business/201103...](http://www.nytimes.com/imagepages/2011/03/11/business/20110313_sbn_GOOGLE-
HIRES-graphic.html?ref=business)

~~~
dmoney
I notice they have no bullet points listed under "Help your employees with
career development." I wonder if this means they feel obligated to mention it
but don't really want to encourage it (because developing your career will
often mean leaving) or if it just means they don't know how.

Or maybe it's supposed to be so obvious that it doesn't need explanation.

~~~
jsnell
Probably the opposite, making sure that the employees can figure out what they
want to do with their career, and know how to get there without leaving the
company is a good retention tool.

------
cploonker
It is very difficult to measure the quality of each manager on these eight
effective habits in an objective manner. What is the opinion of this group,
about, google people rank algorithm(ratings from good performers carry higher
weight and vice versa) to rate employees within the company.

------
redsymbol
The story links to the 8 behaviors in the form of a JPG image, but I
transcribed it to put in the HR section of our wiki. Thought someone else
might want the plaintext version:

EIGHT GOOD BEHAVIORS

1\. Be a good coach

\- Provide specific, constructive feedback, balancing the negative and the
positive.

\- Have regular one-on-ones, presenting solutions to problems tailored to your
employee's specific strengths.

2\. Empower your team and don't micromanage

\- Balance giving freedom to your employees, while still being available for
advice. Make "stretch" assignments to help the team tackle big problems.

3\. Express interest in team member's success and personal well-being

\- Get to know your employees as people, with lives outside of work.

\- Make new members of your team feel welcome and help ease their transition.

4\. Don't be a sissy: Be productive and results-oriented

\- Focus on what employees want the team to achieve and how they can help
achieve it.

\- Help the team prioritize work and use seniority to remove roadblocks.

5\. Be a good communicator and listen to your team

\- Communication is two-way: you both listen and share information.

\- Hold all-hands meetings and be straightforward about the messages and goals
of the team. Help the team connect the dots.

\- Encourage open dialogue and listen to the issues and concerns of your
employees.

6\. Help your employees with career development

7\. Have a clear vision and strategy for the team

\- Even in the midst of turmoil, keep the team focused on goals and strategy.

\- Involve the team in setting and evolving the team's vision and making
progress toward it.

8\. Have key technical skills so you can help advise the team

\- Roll up your sleeves and conduct work side by side with the team, when
needed.

\- Understand the specific challenges of the work.

THREE PITFALLS OF MANAGERS

1\. Have trouble making a transition to the team

\- Sometimes, fantastic individual contributors are promoted to managers
without the necessary skills to lead people.

\- People hired from outside the organization don't always understand the
unique aspects of managing at Google.

2\. Lack a consistent approach to performance management and career
development

\- Don't help employees understand how these work at Google and doesn't coach
them on their options to develop and stretch.

\- Not proactive, waits for the employee to come to them.

3\. Spend too little time managing and communicating

