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our guests today are Adam gr and Kade
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Massie who lead Barton's people
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analytics initiative gentlemen welcome
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to knowledge at Wharton thanks thanks
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for having us so Kate we when we spoke
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last year about people analytics that
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was just before your conference and you
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have another conference coming up uh and
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it seems to me that during this past
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year that interest in people analytics
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has really gone up why is people
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analytics so hot well I agree with you
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that it does seem to be blowing up a
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little bit um it started I feel like it
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started in technology more than any
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other industry and then Finance picked
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it up and now we see it kind of
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everywhere um my sense is that people
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appreciate that this is a very important
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function and yet hasn't been approached
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very in a very sophisticated way in the
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past and all of a sudden they realize
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you can use all these tools that we're
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accustomed to using in marketing or
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Finance we can use them for hiring
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people and compensating people and
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what's better than that given how how um
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important those things are to an
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organization so it's the it's the
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potential people seeing the potential of
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this that's making it popular Adam what
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are your thoughts on why it's becoming
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so popular yeah I I didn't even know it
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existed until Kate and I ended up
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working with Google about five years ago
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and they had built this whole people
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analytics team um that was a mix of
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traditional HR folks Consultants
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engineers and and folks like us who
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study organizational behavior and it was
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amazing that they were able to take
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questions that used to be answered based
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on intuition and actually run
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experiments and gather data to figure
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out what were the right choices to make
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and you know I think that Google's
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gotten a ton of press for all the great
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work they've done in this area and other
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leaders have started thinking why aren't
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we doing this shouldn't we be making all
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of our important decisions based on
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evidence too yeah should we just be
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giving glasa Bach credit here because
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he's been like an evangelist for this
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surprisingly he's been out there he's
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the head of HR at Google and he's always
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been very willing to say hey this is
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what we're doing this is what you should
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be doing kind of uncharacteristic for
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someone to say that essentially to his
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competitors so one of the things I find
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very interesting about people analytics
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is as as you said Alam how a lot of
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decisions HR decisions that used to be
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based on intuition are now sort of
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migrating over into datadriven decisions
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can you give me some examples of say if
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you take hiring as the first uh point of
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Engagement between a company and an
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employee how is hiring changing because
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of people
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analytics so uh people are very
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interested now and can we can we
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identify from objective measures who's
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going to work well in our firm so rather
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than having to bring them in here and
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talk to them in person can we grab you
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know their their GPA in college and
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where they went to school and who they
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worked for and predict somehow from
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these inputs how they're going to be if
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you could that'd be great right because
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you save a lot of time you can process
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all these information all these
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applications real efficiently so that's
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pretty promising at least that has a lot
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of appeal now there's more appeal to to
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it than substance right now because it's
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really hard to do but there that would
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be great if you can pull it off and it
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would it would it would be great
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um any investment you can make in making
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that happen is going to have high
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returns but it's still new so there's no
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there there's no silver bullets um but
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people are drawn to it because it could
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be great if it happens So based on the
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evidence does it matter where you went
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to
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school wow this is like a question that
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people with high school age kids are
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forever asking right they're like does
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it matter if they go to Princeton versus
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the state school what do is there good
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evidence of that I don't know there's
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good evidence on that right now I mean I
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I think it's a debate that continues to
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rage um but my my favorite research on
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this uh is Caroline Hawkes and if I
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remember correctly what she shows is
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that it's mostly selection effect so
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that if you you know if you come out of
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an Ivy League school um you typically
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will end up with a higher income and
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more job opportunities uh but all of the
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characteristics that led you there were
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visible before University chose you and
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it was essentially you know bringing in
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an ambitious and talented group of
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students to begin with as opposed to
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something magical that happened in your
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four years um that being said um we like
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to think that we're doing something of
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use ex magical happens here at Wharton
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NBA magical happens um but you know that
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the what I what I like to say is
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that probably Great Schools provide an
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advantage through the Network that
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people get to know maybe through the
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training but there's it's kind of like
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stereotypes there there may be true
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differences in social categories between
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people but people believe these
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differences are bigger than they
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actually are and there's huge variation
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within the category so there's huge
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variation in outcomes from people who
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come and go to pen and there's huge
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variations and outcomes from people who
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go to pen State and there's a lot more
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overlap between these two things than
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people think got so so so let's say the
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hiring step is over and now you on to
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this uh next step of onboarding people
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what can uh people analytics do to
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improve the onboarding process so you
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know we need to be careful not just you
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know uh law Google at every turn but
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they are an organization that has looked
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at this explicitly and they did it in
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the best possible way they ran
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experiments on let's on board in this
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way and then manipulate in a different
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group in a different way to see what
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actually makes a difference so it's not
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just telling stories we're actually
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doing science here to figure out what
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makes the most difference and I I don't
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I don't know all the details the study
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this is something they've been doing for
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the last couple of years but they
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recognized the onboarding process as
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kind of really important and completely
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underexplored so they went out and ran
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and and uh and experiment do you
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remember the details of this there
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there's one finding that jumps out at me
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which is um when they they looked at all
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the things that make a difference in the
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first few days or first few weeks of
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your time at Google um probably the most
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critical thing to happen is just that
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you meet your manager on day one and
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people are busy right um You may have a
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lot of direct reports um and maybe also
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as a new hire that you're being sent to
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a lot of different places um but based
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on that evidence they they said look one
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of one of our rules for onboarding is
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you've got to meet your manager on day
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one um and that's such a critical part
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of building a bond between an employee
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and an employer um it's not to say that
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we didn't know it was important for you
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to meet your manager but I think all of
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us right myself included really
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underestimated how much of an effect
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that would have on day one and so I
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think that's that's an example of the
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kind of thing we've learned from their
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work something else I talk about a
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little bit that seems wise is that a
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person's success at a company often
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depends heavily on who they work for and
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yet who they work for is essentially
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completely outside their control and so
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this isn't exactly on boarding but it is
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early career early stage consideration
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if things aren't working out you need to
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be wise about it and not just blame that
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person and you probably want to see that
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person in more than one circumstance
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before you draw a too strong a
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conclusion about this is just they're
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both examples of being more systematic
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and scientific about evaluating your
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employees or trying to train your
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employees as opposed to the old school
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kind of would do this because this is
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what we've always done got it so what
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once you start working in the company
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increasingly we find that uh more and
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more of us are working in teams uh and
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teams are very often geographically
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dispersed very often across different
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countries uh but also across Generations
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so you have Baby Boomers and Millennials
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sort of having to figure out how to work
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together what can we learn through
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people analytics about the creation and
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construction of high performance teams
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MH mhm so um an infinite number of
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things and this is such a rich area and
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Adam I'm sure has his favorite examples
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one of the ones that I first think about
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is recent work by Chris shabri and his
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colleagues on um Team IQ essentially and
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they do some really interesting stuff
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that looks at the productivity of teams
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um as a function of their individual
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characteristics versus what they do
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collectively and he finds that there is
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something they seem to find something
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like team IQ that is different from the
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some of the parts it's not that if you
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put all the smart people together that
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they produce the smartest work it's that
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they need to be people who understand
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how to work with each other and work as
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a part of a team there's something
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unique about Team level intelligence
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that's different from the sum of the
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individual level
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intelligence and I I think to build on
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that there are probably ways that you
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can compose a team to enhance the
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likelihood right that that they'll be
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intelligent as a group so one of the
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things I hear a lot is that diversity is
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good and there's no question that we get
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a lot of value from diversity in terms
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of people bringing unique perspectives
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thoughts skills to the table but when
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you look at personality research there
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are some characteristics on which it's
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actually helpful to have similarity as
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opposed to variety so um if you look at
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the the data extroversion introversion
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is the clearest trait where variety is
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useful um your team of whole extroverts
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essentially never starts working on the
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task team of all introverts um often
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forget to bond and the data say that the
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most effective teams have a mix of the
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two but don't necessarily stretch that
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into every other personality trait if
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you look at a personality trait like
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agreeableness for example people who
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love social harmony one of the worst
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things you can do is put them on a team
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with people who are extremely critical
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and skeptical um because the
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disagreeable people are feeling like
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they have to walk on eggshells
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constantly meanwhile the poor agreeable
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people they have this this catch22 of I
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can be really agreeable and act
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disagreeable like the disagreeable
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people and then hate myself afterward or
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you know I can be really agreeable and
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then you know it doesn't doesn't quite
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gel and so there it's actually helpful
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to have either similarity in personality
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or a consistent Norm of how we're going
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to interact and so I think we have to be
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really more thoughtful about composition
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than we have been have been in the past
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probably and one of the things that
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people analytics brings to that task in
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general is just the inclination to study
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it precisely and ideally to run
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experiments around it so again we're not
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just going to take conventional wisdom
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we're not going to take you know um
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something that written by someone who
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used to run some teams we're going to
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actually collect some data and run some
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experiments and figure ask these
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questions and figure them out
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let's push a little bit further on the
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diversity uh aspect uh as you know
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there's been a lot of some controversial
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news as well about uh say women and high
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technology companies for example uh has
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people analytics come come up with any
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evidence that shows uh you know how
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gender roles and uh even racial roles
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are are related to Performance I just
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wish we had someone who' done some
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writing on some gender issues if only
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somebody
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don't look at me um sh Sandberg is the
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brains behind that operation um I uh I
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I've learned a ton from working with
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with Cheryl on gender issues and um she
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is a a wonderful researcher Maryann
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Cooper at Stanford who's um who's
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collaborated with us on on looking
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through what did what did the data
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really show and I think the probably the
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the thing that I would say right now is
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um there's a lot of academic research
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that hasn't been leveraged um so we we
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know a lot about how to design for
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example Performance evaluations that
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actually lead people to judge
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contributions as opposed to the person
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behind them um we know a lot about how
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to attract more female applicants in the
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high-tech world for example um turns out
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the recruiter that comes and shows up
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matters a lot um our own Matthew Bidwell
00:11:14
here at Wharton has shown this in the
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finance realm that one of the reasons
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there are so few women in finance is
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that they don't apply um at very high
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rates um they actually have a slightly
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higher odds of getting hired because um
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Financial Services organizations are
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really trying to solve this gender
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problem and bring in more women but
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where they they start to get discouraged
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is when a bunch of Partners show up who
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are all men to recruit they say well I'm
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never going to get this job why bother
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trying and I I just think there's a
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there's a pretty big gap between what
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the social science shows on gender and
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what most organizations are actually
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doing um it's great to see the Facebooks
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and Googles of the world really trying
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to make Headway on this um we've also
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seen I think a growing number of
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consulting firms um make gender a big
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priority and their people Analytics work
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so I know this is a big topic McKenzie
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is working on right now um Mercer has a
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whole initiative about how to create a
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gender balanced Workforce uh that's
00:12:07
completely data driven and I think
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that's only going to grow in the next
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few years broadly how about um
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performance evaluations and compensation
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issues what does uh people analytics
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have to say about these issues more
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broadly so I mean one we should be a
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little careful about realize we're
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saying people land LS this and people
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land LS this I mean it's it's it's you
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know it's not a terrifically well-
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defined space it's mainly just bringing
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data where data hasn't been used in
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before and in kind of a Moneyball spirit
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it's not taking conventional wisdom
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we're going to be evidence-based here
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kind of regardless so um there there is
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there is an intersection though with
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some Fields like psychology and and the
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biases that people bring so one of the
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one of the real motivating factors for
00:12:51
for us because of the worlds we come out
00:12:53
of is we can actually improve decision-
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making by using these tools and
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performance evaluation is a classic
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example because there have been so many
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biases that show up in performance
00:13:02
evaluation so um PE it's it's giving
00:13:05
people analytics maybe too much credit
00:13:07
because it's really just a vehicle to
00:13:08
bring in some psychology we've known
00:13:10
about for decades but because we're
00:13:12
being more rigorous and trying to and
00:13:14
focusing more on how we can improve this
00:13:16
process um we're able to far it out some
00:13:18
of those biases so um in in many many
00:13:21
many different ways so um trying to keep
00:13:24
opinions for example independent so you
00:13:26
don't want people to judge I don't want
00:13:28
to judge judge Adam after having had
00:13:30
heard you judge Adam we try to get the
00:13:32
opinions independent like that we want
00:13:34
to judge as much as possible blind so we
00:13:36
don't bring um information about a
00:13:38
person to the table that isn't relevant
00:13:40
because we know for a fact from Decades
00:13:42
of research that if we know things about
00:13:43
them it's impossible for us to to
00:13:45
separate that so I think people in has
00:13:48
just kind of help that cause we can't
00:13:49
give them too much credit because we've
00:13:51
kind of known it but um it's been a
00:13:53
great vehicle to to ride and I think um
00:13:56
Kate actually just touched on something
00:13:57
that brings us full circle which is we
00:13:58
we talked about the important role
00:14:00
Google has played and and really
00:14:01
stimulating interest in this field but I
00:14:03
think Moneyball and the way that that
00:14:04
Sports analytics has has taken off was
00:14:06
probably the other Catalyst and and Kade
00:14:08
was on the the front lines of that at
00:14:10
least what over a decade ago before I
00:14:12
even knew it existed yeah we we sport
00:14:15
the sports professional sports has been
00:14:16
ahead of um almost the entire non-sports
00:14:19
world in using these tools because their
00:14:22
whole existence is the performance of
00:14:24
these individuals and they've got better
00:14:26
data um they can see inputs they can see
00:14:29
outputs precisely and so you can you can
00:14:32
often look at what's going on in sports
00:14:35
around analytics now and know that 10
00:14:37
years from now those tools are going to
00:14:38
trickle down and even just the rigor and
00:14:41
the scientific orientation of the people
00:14:43
who use the numbers are better literally
00:14:46
because the guys who crunched numbers
00:14:48
for baseball um 20 years ago were
00:14:50
figuring things out so Google has come
00:14:53
up a couple of times in this
00:14:55
conversation are there any other
00:14:57
companies uh or organiz ganizations in
00:15:00
uh that that you have been impressed by
00:15:02
the work they're doing in this area and
00:15:04
what other companies can learn from
00:15:06
their experience well as soon as we open
00:15:08
that box we got a lot a long list to go
00:15:10
down um you know across Industries and
00:15:12
AD Adam has been very active lately with
00:15:14
a lot of these firms but I want to start
00:15:15
out by saying there have been some firms
00:15:17
who have focused on this for a long time
00:15:19
so like Delo and touch has had an
00:15:20
analytics a Workforce analytics practice
00:15:22
for a long time um some of the new folks
00:15:25
in uh in the financial services have
00:15:28
been Goldman Sachs has take undertaken a
00:15:29
big initiative over the last couple of
00:15:31
years um crit credit Swiss has been very
00:15:33
interested with some full-time people
00:15:35
but it now it's gotten now we have to
00:15:36
like really talked about everybody
00:15:37
Johnson and Johnson is involved Adam's
00:15:39
got a longer list I'm sure no I I'll
00:15:41
just add a few you know from from other
00:15:42
Industries um Teach for America
00:15:44
particularly on their their selection uh
00:15:46
or what they call Admissions um domain I
00:15:48
mean they they've been tracking for
00:15:49
years what do they need to assess in the
00:15:51
hiring process to figure out who's going
00:15:53
to be a star teacher and who's going to
00:15:54
stick with us um Jet Blue has actually
00:15:57
made a lot of Headway in this area as
00:15:58
well um they're doing work in several
00:16:00
different domains one of my favorites is
00:16:02
recognition so how do you actually build
00:16:04
a science of of recognition so that you
00:16:06
know when it's important to give people
00:16:08
a sense of gratitude and appreciation um
00:16:10
do you make recognition public do you
00:16:12
make it individual um there are all
00:16:14
sorts of questions that the managers
00:16:15
have have basically answered on
00:16:17
intuition for years that now you know
00:16:19
the airline industry when there's so
00:16:21
many customer appreciation events that
00:16:23
happen every day um is really sort of
00:16:25
opening our eyes to how do you do this
00:16:27
more effectively the Teach for America
00:16:30
uh is such a great example because you
00:16:32
you know as we're talking about Google
00:16:33
and Goldman you wouldn't think
00:16:34
necessarily go to the not for-profit
00:16:36
world but they are the best we know on
00:16:39
the hiring I think I I was just talking
00:16:40
about this at lunch actually that if if
00:16:42
I could name one firm that knows the
00:16:44
most about their hiring practices it'd
00:16:45
be those guys now they're kind of
00:16:47
ideally suited for it because they see
00:16:49
50 or 60,000 applications for kind of
00:16:51
the same job and so it's this perfect
00:16:52
stream to get good at but we have
00:16:55
learned I can tell you the Wharton um
00:16:57
NBA admissions group has learned and
00:17:00
improved their processes because of the
00:17:02
way Teach for America hires people
00:17:03
absolutely they they've got a lot to
00:17:05
teach a lot of people so what's the
00:17:06
secret ingredient of how do you how do
00:17:08
you choose the best people to hire you
00:17:10
know you know what it is to recognize
00:17:12
that you're never right and that you're
00:17:14
never going to be done those are the two
00:17:16
unbelievable what Teach for America does
00:17:18
they say they say you know we say all
00:17:19
the time we're never going to be done
00:17:21
this is not a one-time project it's not
00:17:22
a one-year project we're never going to
00:17:24
be done and the other thing they say
00:17:26
which is amazing is they say you know
00:17:28
these are our metrics these are our
00:17:29
objectives we know they're not right we
00:17:32
know they're wrong and that leads to an
00:17:35
a continuous conversation about okay how
00:17:37
can we refine them what exactly in what
00:17:38
way are they wrong and how can we how
00:17:40
can we tweak them but they say we know
00:17:42
we know we're wrong that kind of
00:17:44
humility is a great um counterbalance to
00:17:48
analytics because you can get pretty uh
00:17:50
pretty confident about your model you
00:17:51
can get kind of in love with your model
00:17:53
you need the humility that says we're
00:17:54
wrong we know we're wrong humility is a
00:17:56
good thing overall it is and I I think
00:17:58
actually it's part of the founding of of
00:18:00
the evidence-based Management Field that
00:18:01
I think people analytics probably
00:18:03
belongs to um Jeff Feer and Bob Sutton
00:18:05
have have long said that if you want to
00:18:07
do analytics right you need an attitude
00:18:09
of wisdom which is in their definition
00:18:11
basically the willingness to act on the
00:18:13
best information you have while
00:18:15
constantly doubting what you know and
00:18:17
it's it's easy as Kade points out to
00:18:19
lose sight of of the doubt part um I
00:18:21
would say though there's there's one
00:18:23
other thing from the hiring um
00:18:24
perspective that that's been very eye-
00:18:25
openening to me which is there a bunch
00:18:27
of data by Rick Jacob and his colleagues
00:18:29
suggesting that the costs of a bad hire
00:18:31
are usually about triple the benefits of
00:18:33
a good hire and I think a lot of
00:18:35
selection is actually more about
00:18:36
screening out um than it is screening in
00:18:39
um you're always going to have um you
00:18:41
know false positives and false negatives
00:18:43
um but it's it's much more risky to to
00:18:45
bring in somebody that than you have to
00:18:47
go and replace or do a bunch of cultural
00:18:49
damage repair um and that's I think
00:18:51
where probably you want to put more of
00:18:53
the emphasis now as if I were to sort of
00:18:56
switch gears and turn to the two of you
00:18:59
as researchers uh what are some of the
00:19:02
big questions that you are trying to
00:19:03
answer and what has surprised you most
00:19:06
about what you learned so
00:19:07
far um one of the things that I'm
00:19:09
working on right now is how to is
00:19:12
exactly on this stuff is how to get
00:19:14
people to be more open to um analytics
00:19:18
so explicitly so in some in some task we
00:19:21
need to forecast what's going to happen
00:19:23
it's a market or Price or the
00:19:25
performance of an employee and you might
00:19:27
have some algorithm and you might have a
00:19:29
good algorithm and in most cases you
00:19:32
need to blend that algorithm with some
00:19:33
expert judgment it's not person or
00:19:35
computer it's best if you can blend
00:19:37
these things and yet people are
00:19:39
reluctant to take input from computers
00:19:41
especially the more expert you are in
00:19:42
the field the more like you know no I
00:19:44
need to use my you know my head as
00:19:46
opposed to that so we're trying to
00:19:48
understand the psychology about what
00:19:50
what leads people to resist those um
00:19:53
inputs and what can we do to help break
00:19:55
that down one of the things that that
00:19:58
I've gotten increasingly interested in
00:20:00
is um the the problem of of
00:20:02
collaboration creep so to speak where
00:20:04
we're constantly having to go to
00:20:06
meetings and answer emails and there's
00:20:08
just massive explosion in
00:20:10
interdependence and nobody knows how to
00:20:11
handle it everybody thinks collaboration
00:20:13
is great uh but everybody is overwhelmed
00:20:15
with the amount of collaboration that
00:20:16
they do um so I'm working on a project
00:20:18
with Rob cross and Reb Reb right now
00:20:20
that that looks at uh the following
00:20:22
question if you do a network analysis of
00:20:25
an organization and you ask people who
00:20:27
do you depend on for critical knowledge
00:20:30
and advice and expertise um what Rob
00:20:32
finds is there's a certain number of
00:20:35
people that can write your name down um
00:20:37
and after that number you're at serious
00:20:39
risk for Burnout and overload and I
00:20:41
think that of course the unanswered
00:20:43
question is exactly where does that
00:20:44
number fall um but it it turns out to
00:20:46
actually be quite deadly uh to have
00:20:48
everyone depending upon you for
00:20:50
expertise um and I'm interested in how
00:20:52
do you redistribute um you know the help
00:20:55
um the Insight the connections uh so
00:20:57
that it's not all bottleneck in one or
00:20:59
two people right that's that's really
00:21:01
interesting and one final question if
00:21:03
you were to look at people analytics and
00:21:06
the state of knowledge of the field
00:21:07
today where are the biggest knowledge
00:21:10
gaps and and what should be done to fill
00:21:14
them um this I would say in in in in
00:21:20
being effective not just running better
00:21:22
numbers but actually making change in
00:21:23
organization so it's it's one thing to
00:21:25
have a fancy regression model or to
00:21:27
really have some insight numerically
00:21:29
it's a very different thing to actually
00:21:31
translate that into action and um it
00:21:34
kind of doesn't matter how good your
00:21:35
model is until you get good at that
00:21:37
translation and um it's kind of natural
00:21:39
that's going to come later but right now
00:21:41
everyone's enamored with the models and
00:21:43
the data and the analysis and it's not
00:21:45
going to matter unless they can actually
00:21:47
persuade and change an
00:21:48
organization I I think from my
00:21:50
perspective the probably the biggest
00:21:53
unanswered question for people analytics
00:21:55
uh is what Cade's working on right now
00:21:57
which is why don't more organizations do
00:21:59
this and how can you get senior leaders
00:22:01
to realize that just because sometimes
00:22:04
you know these these variables are hard
00:22:05
to measure doesn't mean you shouldn't
00:22:07
bring better science to them and what
00:22:10
does it take to to open the minds of
00:22:11
leaders to to recognizing that if we had
00:22:13
more data it won't replace our jobs will
00:22:15
actually give us the tools we need to
00:22:17
make better
00:22:18
judgments great okay Adam thanks so much
00:22:21
for speaking with knowledge at Wharton
00:22:23
and good luck with the conference thank
00:22:25
you appreciate
00:22:27
it
00:22:32
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