DAVEY NICKELS: Please join me
in welcoming Cole Nussbaumer
Knaflic, "Storytelling
with Data" for Authors At.
[APPLAUSE]
Cole was on our team, people
analytics and people operations
here at Google many,
many years ago,
and she led our
relationship with sales.
And she was known
throughout the team
for her incredible expertise
with presenting data,
visualizing data, and
making it actually speak
to clients and to users.
And lots of funny stories about
Cole, but one that stands out
is her feelings on pie charts.
I put together an analysis
once that showed a distribution
of a specific population
by level or something,
put it in a beautiful pie chart
that I thought was amazing.
And Cole gave me very
brutally honest feedback
about how ineffective it
was in actually showing
the differences in the group.
So Cole, I'm very
curious to hear today
if you still have those
views on pie charts,
or if maybe they're a little
bit more acceptable now.
COLE NUSSBAUMER KNAFLIC: I
still have a pretty strong view
when it comes to pie charts.
DAVEY NICKELS: OK.
Well, we look forward to hearing
a little more about that.
So the way that this
is going to work
is we're going to have 30
minutes of a lesson, actually,
from Cole from her book.
And then second, we're
going to have Q&A, moderated
by Tina Malm, from
people analytics and me,
Davey Nickels, from
people analytics as well.
So please, again, join
me in welcoming Cole
to her first stop on her
book tour here at Google.
[APPLAUSE]
COLE NUSSBAUMER
KNAFLIC: Awesome.
So it seems so fitting
for me for Google
to be the first stop after
the official publication
of my book, since this is
where so much of it began.
I started at Google back in 2007
on the people analytics team.
And people analytics
is an analytical team
in the people ops
organization here,
where the goal is
to try to ensure
that all the decisions
about people--
employees, future
employees-- are data-driven.
Now, I had the
opportunity of joining
when the team was
relatively small,
which meant that I got to
work on a ton of cool stuff
over the years, learning
about things like what
makes a manager effective, how
do you build productive teams,
what drives attrition.
Now, in 2010, we developed
a program called Base Camp.
It was an internal
MBA-like training program
within people operations.
And I was asked to build content
on data visualization, which
was an awesome opportunity,
because I'd always been really
interested in this space.
But this meant I could pause
and research and understand
why some of the
things I'd arrived
at through trial and error
over time had been effective.
So as a side note, I can
honestly say that Google for me
was life-changing.
I didn't know it at the
time, but the very first time
I delivered the Data
Visualization course, which
was at a people ops off-site
in Monterrey, in the very first
row sat my future husband.
So you can really say without
the Data Visualization course
here at Google, I
wouldn't have these
or the third that's on the
way-- super life-changing.
But back on Google, there
was broader interest,
we were finding, in the
Data Visualization course.
So we actually ended
up rolling it out
across Google, which meant
I got the opportunity
to travel to offices in London
and Dublin and Zurich and train
trainers and teach courses.
I also got a chance
to teach courses
across a number of US
offices, including many right
here in Mountain View.
One of the things that
was interesting for me
was to see salespeople
and engineers sitting side
by side in those courses.
I came to realize that the
skills needed in this area
were fundamental.
They weren't specific
to any given role.
They also weren't
specific to Google.
Over time, other organizations
started reaching out to me,
wanting me to go teach their
teams and their organizations
how to communicate
effectively with data.
So over the course of
the past couple of years,
I've had the opportunity
to work with hundreds
of different teams across
many different organizations.
And usually this takes
the form of workshops,
where I'll spend half a
day or a full day teaching
foundational lessons on
communicating with data.
And oftentimes what I'll
do is solicit examples
from the group ahead
of time, and we'll
go through the
lessons, and then we'll
spend time talking about that
group's specific examples.
And I'll go through my
makeovers as one potential path
that leverages the lessons
that we've covered.
So I thought it might
be cool to take you
through a couple of these.
We won't go through
them in a lot of detail.
We'll just give you a
visual sense of what
you can learn in the book.
So this first example is one
from the philanthropic sector.
So it's a foundation that
wanted to start a conversation
on shifting spending from
non-initiative, which
is the big, cream-colored
segment at the top left,
to higher education,
that tiny, blue segment.
So in this case, we change
from the pie chart--
we've already talked about my
views there-- to this, right?
If we want to shift
spending, let's
say we want to shift
spending and start
a conversation about that.
Use visual cues to draw our
audience's attention to where
we want them to pay it.
Let's look another example.
This is one from an IT group who
couldn't believe that people,
after looking at this graph,
weren't doing anything
with this information.
Why weren't they acting
upon this information?
Here, it gets totally
lost in the graph, right?
There's no story to
bring it to life.
Now, the backstory was-- if
we stare at this long enough,
we can see there's a
gap starting to form out
on the right-hand side, where
the number of tickets coming in
are exceeding the number
that are being processed.
Now, the backstory was they'd
lost a couple of resources.
They were understaffed.
And they really wanted to
hire a couple more people.
And they couldn't understand
why nobody was doing that
based on this graph.
So in this case, we
changed from that to this,
again making that
call to action clear,
annotating the context
directly on the graph,
drawing attention to
that gap that's forming
on the right-hand side.
So I've got one more of these.
This is an example from
a small organization who
was recognizing that their
regional sale composition had
shifted over time
and wanting to have
a conversation about what some
of the implications of that
were.
Now, this is an interesting one.
I've used this a number
of times in workshops.
And when people
flip to this graph,
there's often a sort of
immediate, negative, visceral
reaction, which is
something we want to try
to avoid in our audiences.
And now, I can't
imagine all of them
were Packers fans
like my husband.
These are sort of
Broncos colors, right?
But rather that this graph
was unnecessarily confusing.
Now, the beautiful thing here is
there are some clear take-aways
articulated at the bottom.
It's just almost
impossible to know
where to look in the data for
evidence of those take-aways.
So in this case, changed
from that to this,
making the focus on the
change, tying the text
directly to the data,
both through proximity
and similarity of color.
Now, one thing to
note is these examples
cross many different industries.
I mentioned before that these
skills are not role-specific.
They're also not really
industry-specific.
Rather, they're foundational.
And over time, through
all of the workshops
that I've taught over
the past few years,
I've codified these lessons.
And that's what ultimately
led to this, my book.
And I'm super excited to be
able to share with you today
a couple quick lessons
from my new book,
"Storytelling with Data."
So we're going to talk
today about two key lessons,
first off, focusing attention,
and secondly, telling a story.
We want to draw one
important distinction
at this point, which
is the distinction
between exploratory analysis
and explanatory analysis.
So exploratory analysis,
you perhaps start off
with a question or
hypothesis, or you're just
digging through
your data, trying
to understand
what's interesting,
what can I learn about this
data that somebody else
might care about?
Once you've identified
that interesting thing,
then we move into
explanatory space.
That is where you have
something specific
you want to communicate
to somebody specific.
And it's this latter space we
want to keep in mind today.
And when it comes to
explanatory analysis,
these lessons become more
important than perhaps
any others-- first off,
thinking about where
you want your audience
to pay attention
and doing things on purpose
to make that happen,
and then secondly, never
simply showing data, but rather
making data a pivotal point
in an overarching story.
So we'll talk briefly
through each of these.
First off, focusing
attention-- I
can recall a time at
Google where I was working
on the Project Oxygen study.
Quick show of hands--
how many people
are familiar with
Project Oxygen?
Most people in the room.
So Project Oxygen was a study
led by my colleague Neil Patel.
And the goal really was
to try to understand,
on a mathematical,
statistical level,
what makes managers effective.
One of the challenges
that we encountered
was, after the study was done,
communicating the results of it
to a very mixed audience, where
we had both engineers, who
had a great desire for detail--
they wanted to understand
the methodology,
they wanted to be
convinced of the
robustness of the analysis.
At the same time,
we were also wanting
to communicate to
sales managers,
for example, who were mostly
less concerned with methodology
and more concerned about
what's in it for me.
How should I act
differently based on this?
And so what we found was by
really being careful about how
we focused attention, we could
preserve a lot of that detail
but push it to the background
and make the meta-point pop
out so that it was clear.
Let's talk a little
bit about how
people see to get into more of
this how we focus attention.
So here's a super-simplified
picture of that process.
On the left-hand side, you
have light refracting off
a stimulus.
This gets captured by our eyes.
We don't fully
see with our eyes.
Rather, most of what we
think of as visual processing
takes place in our brains.
Now, in the brains there
are a few types of memory
that are important to
understand as we're designing
visual communications.
We'll talk about one of them
today, which is iconic memory.
Iconic memory is
super short-term.
It's shorter than
short-term memory,
and information stays there
for fractions of a second
before it gets forwarded on
to our short-term memory.
The really cool thing
about iconic memory
is that it's tuned to a
specific set of what we
call pre-attentive attributes.
So let's actually pause here
and do a quick exercise.
So in a moment, I'm going
to put a bunch of numbers
up on the screen.
What I'd like you all
to do as fast as you can
is count the number
of 3s that you see.
We got it?
We're going to count 3s.
When you know the
answer, shout it out.
It is a race.
You would like to win.
Ready, set, go!
All right, six is
the correct answer.
This took a bit of
time, though, right?
You had to physically
read through these four
lines of text,
look for a 3, which
is kind of a complicated shape.
Watch what a
different exercise it
becomes when I make
one tiny change.
Don't have time to think.
Don't have time to blink.
Suddenly there are six
3s in front of you.
This is so apparent
so quickly because I'm
leveraging your iconic memory.
I'm using the
pre-attentive attribute
of intensity of
color, in this case,
to make the 3s the one
thing that stand out as
different from the rest.
Now, this is hugely critical,
because what this means is
our pre-attentive attributes,
if we use them strategically,
can help enable us
to get our audience
to see what we want them
to see before they even
know they're seeing it.
Here are the attributes.
I won't read through
all of these,
but notice as your eyes
scan across the screen
how they're just
drawn to the one
within each group that's
different from the rest.
You don't really have to
consciously look for it.
Now, one thing to know
about the attributes
is people tend to associate
quantitative values
with some but not others.
For example, most people
will consider a long line
to represent a greater
value than a short line.
It's one of the
reasons bar charts are
intuitive for us to read.
But we don't think of hue,
for example, in the same way.
If I ask you which is
greater, red or blue,
it's not really a
meaningful question.
And this is important
because it tells us
which of the
attributes can be used
in code quantitative information
and which should be used
as categorical differentiators.
Now as you can perhaps imagine,
pre-attentive attributes
become huge tools for focusing
our audience's attention when
it comes to visualizing data.
So here's some sort of generic
data from our annual customer
survey.
We can see how we've fared
across a number of dimensions.
Notice how without other visual
cues, this becomes very much
like the count-the-3s
example again.
We have to look at this
data, read through it,
figure out what might be
important to pay attention to.
Whereas if I'm the one
communicating this data,
I should have already done
that for you, in which case
I can use some pre-attentive
attributes, perhaps paired
with some explanatory text,
to draw your attention very
quickly to one part
of the story, right?
Price and convenience--
we're doing awesome here.
Let's pause and
celebrate our success.
Or I can use this
same broad strategy
to draw your attention to
a totally different place
in the data.
But we're struggling
when it comes
to relationship and brand.
How can we positively
impact these areas?
Now, there's a test I like to
employ in trying to figure out
whether you're using your
pre-attentive attributes
strategically.
And that is the Where
Are Your Eyes Drawn Test,
where you look away from your
visual and look back at it,
or close your eyes
and look back at it,
and just notice where
your eyes land first.
Because it's probably
where your audience's eyes
will land as well.
So I thought we'd do this
with a series of pictures
and just talk about
the implications
for our visual designs.
So I'm going to put a series
of different pictures up there.
When I put the picture
up, just shout out
where your eyes go first.
Ready?
Where do your eyes
go first here?
AUDIENCE: Stop.
COLE NUSSBAUMER KNAFLIC:
Stop sign, right?
You almost can't look
anywhere else at the onset,
because it's bright, it's red.
It's got these big,
bold, capital letters.
It's outlined in white,
which sets it apart
from the background.
We want to think about how
you can use some of those cues
when you're visualizing data
to draw attention as well.
Let's do another one.
Where your eyes go here?
AUDIENCE: The sun.
COLE NUSSBAUMER KNAFLIC: Yeah,
if you're like most people,
they go to the sun.
But if you're like
me, when you're
trying to look at the sun,
you get this plane sort
of tugging on your
peripheral vision.
Or if I try to
look at the plane,
I can see the sun sort of
wanting to pull me that way.
So just be aware
that when you're
emphasizing multiple things
in a graph or on a page,
this tension can be
created in your audience.
How about where do
your eyes go here?
It depends a little
bit, perhaps,
on where you're
sitting in the room.
A lot of people will be
drawn to that Perennial Sale
sign in bright pink,
because it's bright,
because of the black,
bold lettering on it.
And then most people
from there will
continue down and rightward.
And that's because,
without other visual cues,
we typically start at the top
left of our page or our screen
and do zigzagging Zs across.
So in this case, that draw to
the Perennial Sale was strong.
We started there and then
continued on that Z downward
and to the right.
Notice that means
we missed whatever
was happening in the top
left quadrant, and maybe
that second and third
quadrant as well.
So to be thoughtful about
the overarching designs
of the pages on which your
data visualization sits
and take that into account.
Just a couple more of these--
where do your eyes go here?
Everywhere and nowhere
all at the same time.
Colorful is an awesome
goal for a birthday party.
Color is not such
an awesome goal
when it comes to
visualizing data.
When we make so many
things different,
we have a lot of stuff competing
for our attention, which
actually makes it really hard
to look at any one thing.
Check out the difference in how
your attention is focused here
versus here, right?
With the red balloon,
the one thing
that's different
on the whole page
we almost can't not look at it.
That is the power of
color specifically,
used strategically.
Let's take a look
now at an example
from that Project Oxygen study
that I mentioned at the onset.
This is what one of our
original slides looked like.
It's been genericized a bit.
We can see our main
takeaway at the top--
some elements of
job satisfaction
are more sensitive to
manager quality than others.
We've got some categorizations
here and then our data
at the bottom.
Here we're not using
color so strategically.
Here, color is used as a
categorical differentiator.
There originally, we've
taken them off here
but were categories
along the bottom.
You can think of those like
Googlegeist categories,
things like career development
and performance management
and culture, which is
not necessarily how
we want to be using our color.
So in this case, our
redesign looked like this.
The graph is mostly the same.
The contents of the page are
pretty much exactly the same.
We've just rearranged
things a bit
and used our pre-attentive
attributes, color specifically,
more thoughtfully to really
draw our audience's attention
to where we want them to pay it.
While we draw
attention, we also want
to think about embedding
our data in story.
By way of a Google anecdote,
I can recall a time
when I was working with
one of the junior analysts
on our team.
And she had just finished
analyzing Googlegeist results,
results from the annual
employee survey [INAUDIBLE] part
of the organization
and was getting
ready to communicate
those results
to the leader of that team.
And this particular
team had been
struggling in a lot of places.
The scores weren't great.
So there was some sensitivity
around how that message should
be delivered.
And the deck at
that point was page
after page after page
of the standard report--
no story and little narrative
to tie it all together.
It would have been very easy
for the leader of that group
to say, well,
that's interesting,
and move on to the next thing.
That would have been a failure.
So what I had the analyst do was
set the deck aside and tell me
the story.
Tell me what you learned when
you were analyzing this data.
And when we did that, the
articulation of the story
was super powerful.
There were clear
areas for improvement,
and she knew exactly
where to focus action
to achieve that improvement.
This we could use
to light a fire
under the leader for that team.
So it's a good example
of how data without story
isn't always so meaningful.
But the story can help
bring the data to life.
So we want to think about how
we can leverage that power when
we are communicating with data
every time we're doing it.
Here are some facts on a slide.
Go ahead and read through these.
Anybody recognize what
we're looking at here?
What story is this?
Red Riding Hood, right?
But facts on a slide are not
so compelling or memorable.
If I ask you a day
or two from now,
what distance was it
from Red Riding Hood's
house to grandma's, or what time
did Red Riding Hood get there,
these aren't likely
facts that you will
have committed to your memory.
Stories, on the other
hand, are memorable.
How many people--
quick show of hands--
know the story of
Red Riding Hood?
Pretty much everybody
in the room.
We'll do a quick
thought exercise here.
We'll just take
about 15 seconds.
Close your eyes, or
stare up at this screen,
and I'd like you to recall
for yourself the story of Reg
Riding Hood,
thinking specifically
about the plot, the twists, and
the ending-- 15 seconds here.
Quick show of hands--
how many people
were able to get to
the high level story?
People are always a little
afraid to raise their hands
at this point for fear of
what might happen next.
Bear with me.
I'll tell you the story
that resides in my head.
So Red Riding Hood sets off.
She has a basket of goodies.
She's going to Grandma's.
Grandma's not feeling well.
And on her way, she
encounters a wolf.
The wold is able to
extract from her where
she's going and realizes
that if he's patient,
he can have not only
one dinner, but two.
So he races ahead to Grandma's,
eats Grandma, and dress up
in her clothes,
get into her bed.
Red Riding Hood arrives
and senses something
is awry and goes
through a series
of questioning with the
wolf posing as Grandma.
Oh, Grandma, how
big your eyes are.
Oh, Grandma, how
big your ears are.
Oh, Grandma, how big your teeth
are, to which the wold replies?
All the better to eat you with.
So the wolf actually eats
Red Riding Hood as well.
But then guy with an ax shows
up, cuts open the wolf's belly,
and the wolf had eaten
Grandma and Red in such haste
that they're fine.
They come out.
And interestingly, if you go
back to the Grimm's original,
the wolf doesn't die then.
They actually fill
his stomach with rocks
and sew him up so that when
he wakes up, he drops dead.
I think it's a warning
story-- go straight where
you're intended to go,
don't talk to strangers,
and so forth.
But what does this
tell us about what
we're here to talk about today?
So for me, stories
like Red Riding Hood
are evidence of a
couple of things.
First off is the
power of repetition,
when you consider it's probably
been some amount of time
since you've given much thought
to the story of Red Riding
Hood.
And yet over the course
of time, you've perhaps
heard that story
a number of times,
read the story a number of
times, maybe told the story
a number of times.
There's something that happens
with that repetition of use,
of hearing and saying and
reading things multiple times,
that helps form a bridge
from our short-term memory
to our long-term memory.
The other cool thing that
stories like Red Riding Hood
illustrate for us is this
magical combination of plot
and twists and
ending that enable
things to stick with us in a
way that we can later recall
and retell to somebody else.
So we want to think
about how we can leverage
these powerful concepts
when it comes to the stories
that we want to
tell with our data
to get those to be something our
audience will remember in a way
that they can later recall
and retell to somebody else.
So when we think about the
components of the story,
we want to think back
to those same things
that we talked about with
Red Riding Hood-- the plot,
the twists, the ending.
The plot becomes what context
is essential for your audience.
What do they need
to know in order
to be in the right
frame of mind for what
you're going to tell them?
Then the twists-- what's
interesting about the data
and what it shows?
By the way, if
there isn't anything
interesting about the
data, don't show the data.
You run the risk of losing your
audience's attention for when
you do have something
important to say with it.
And then finally, the
ending, the call to action--
what do you want
your audience to do?
My view is we should always want
our audience to do something.
And we should be working
to make that something as
clear as possible.
Because if we simply show
data, as we saw in that case
with the Googlegeist deck,
it's easy for our audience
to say, oh, that's interesting,
and move on to the next thing.
But if we ask for
action, our audience
has to respond to that.
And even if they disagree,
it starts a conversation.
And it's a
conversation may never
happen if we simply show data.
Let's take a look at an example.
So in this scenario,
imagine that you just
wrapped up a summer
learning program on science.
The goal was to get kids
excited about science.
We have some survey
data from a survey
we gave before the
program on the left
and after the program on the
right, where children could
classify their interest
as Bored, Not Great, OK,
Kind of Interested, and Excited.
I'll give you moment
to take this in,
and then we'll talk about it.
How's it feel comparing
segments across two pies?
Not so great, right?
The only thing worse than
a pie, for me personally?
Two pies, especially when you're
trying to compare across them.
Because if anything changed in
the data, which it should have
if there's something interesting
you're trying to say,
the pieces are all in an
entirely different place
over there on the right.
So you always want
to think about what
do you want to allow
your audience to compare.
How do you align those
things to a common baseline
and put them as close
together as possible.
But check out what happens if I
talk you through the narrative.
So going into the program,
the biggest segment
of students is 40% in green
felt just OK about science,
maybe hadn't made up their
minds one way or the other.
Whereas after the program, a
really cool thing happened.
That great big 40%
shrunk down to only 14%.
Now, there was a
little bit of movement
in the negative direction.
Bored and Not Great went
up a percentage point each.
But most of the movement was in
the positive direction, wherein
after the program, nearly 70%
of kids, if we add together
that purple and teal
segments, expressed interest
towards science.
This is a successful program.
We should continue to offer it.
Notice how with a
strong narrative,
I can actually get away with
a kind of crummy visual.
The alternative
does not hold true.
I can have the most
beautiful data visualization
in the world, and without a
compelling story to go with it,
to make my audience
care about it,
to make it something that
resonates with them, that
sticks with them, I run the
risk of that beautiful data
visualization falling flat.
So that's not to say
we shouldn't spend time
perfecting our
data visualization,
but rather to underscore
the importance of story.
And now, nirvana
in this stuff is
reached when both
are strong-- you
have a powerful story and an
effective visual to back it up.
So in this case, we could end up
somewhere like this-- Exposure
to science excites kids.
A bit of background,
our call to action,
let's keep offering this.
Then we get down to the data.
How do you feel about science?
Beforehand, most kids
tied through both color
and proximity to the data point
that is evidence of that point.
Most kids felt OK.
Whereas after the
program, we get
this pull to the
right-hand side,
where kids are
feeling interested.
They're feeling
excited about science.
That's the kind of
story that we want
to create for our audience.
That's the way we want to
be able to focus attention
for our audience.
So those are the
quick lessons I have
to cover here with you today.
I wanted to give
you a quick sense
of how they fit in with the
rest of the content in the book.
So I've listed out
the chapters here.
Chapter 1 starts off
with a lesson on context,
having a really
clear understanding
of who your audience is and
what you want them to know or do
before you really
spend a lot of time
creating visuals or content.
In chapter 2, I talk
about different types
of common displays used to
communicate business analytics
and go through some use
cases and examples of each.
The third chapter is
all about clutter,
getting comfortable
identifying the stuff that's
there that isn't adding
information to our visuals
and stripping those
unnecessary elements away.
Fourth chapter is on
focusing attention.
What we looked at today is just
a small subset of much broader
content that's covered there.
Fifth lesson is on
thinking like a designer.
I talk about how
you can leverage
some concepts of traditional
design, things like affordances
and accessibility and building
acceptance with your audience
when it comes to
visualizing data.
Chapter six looks at a number of
what I consider model visuals,
and I talk about
the design thought
process used to create those.
Chapter 7 is focused on story.
And again, what
we looked at today
is just a small piece of that.
Chapter 8 pulls all of
these lessons together.
It goes through a single
example from start to finish,
showing all of these
in coordination.
Chapter 9 covers
a number of case
studies on common challenges
faced when visualizing data.
And then the final
chapter is a wrap-up,
a recap of what's
learned, talk about where
to go next, and discuss
building storytelling with data
competency in your team
and in your organization.
So before I turn
us over to Q&A, I
wanted to say a
quick word on Google.
So when I joined
Google in 2007, I
was the envy of all my friends.
And one thing that's
been really cool to see
as I've talked to
so many people at so
many different organizations
is that this fascination
with Google still exists today.
So one word of advice
from a former Googler
to current Googlers is to really
just appreciate everything
that Google has to offer,
take full advantage of all
of the opportunities
that you have here.
I like to think that
I did, and it got me
to a really fantastic place.
So with that, I say
a very big thank you.
[APPLAUSE]
TINA MALM: Cool.
Thank you so much.
This was so fantastic.
I've been exposed to your
ideas for such a long time
now, since 2009 or since 2010.
I attended your training.
Can I not put my feet up there?
Oh.
[LAUGHTER]
I attended your trainings
and I read your book.
And I just listened to
this fantastic introduction
to your book, and I still
learned something new.
And something different
sticks with me every time.
It's like one of
those good movies
that you keep watching
over and over again.
So we want to open up in the
next 15 minutes with Q&A.
So I have the
microphone, so I'm going
to be walking
throughout the room
to see if there are any
questions in the room.
If you have any questions
for Cole [INAUDIBLE],
please email [INAUDIBLE].
I did already
receive one question
while you were talking.
And it is not about the book.
But the question is
from San Francisco.
So the question was, what
is life outside of Google?
COLE NUSSBAUMER KNAFLIC:
Oh, life outside of Google--
so it's rough, right?
You have to do things like
make your food yourself, go
to the grocery store.
No, life outside
of Google is good.
But it's sad a little
at the same time.
I think what I miss most
about working at Google,
hands down, were the
fantastic colleagues who
I had sort of right there
when I was working here.
And when you're
working on your own,
you miss that sort of
sense of camaraderie
that you get when you
have a team around you.
So I work with other
people, but it's always
sort of a person here
or a person there.
There's not the
same sort of energy
that you get by being in
an office on a daily basis
with the people
with whom you work.
So that's one thing
to keep in mind when
you venture to the outside if
you're going out on your own.
TINA MALM: What inspired you
to actually write a book?
You've been giving
so many workshops.
COLE NUSSBAUMER KNAFLIC:
Yeah great question.
So I think for me,
it was about being
able to bring some of these
lessons to a broader audience.
I love teaching on this.
I get really excited about it.
And I like to see
the excitement that
sort of builds in other people.
But I can only teach
so many people.
It's just me.
So being able to
write the book means
it can sort of be
out there for anybody
to pick up and especially for
people who may not otherwise
have an opportunity to go
to one of the workshops.
It's nice to be able to
put the lessons out there.
AUDIENCE: Hey, Cole.
So my question is about
interactive visuals.
I would guess that in 2008 or
2009 when you were starting,
mostly visualization
meant a static thing
on a piece of paper.
And now, like "New York Times"
interactive visuals, D3,
there's so much interactive.
I'm curious about how you
think about interactive visuals
in general and
particularly when you think
they're most appropriate
or any lessons
about interactive visuals.
COLE NUSSBAUMER KNAFLIC:
Yeah, great question, David.
So I tend to focus
in the examples
that we saw here and in
the book on static visuals.
When you have a specific
story you're trying to tell,
how do you get that
across your audience
in a way that's going
to be effective?
That said, there's
also certainly
a place for interactive visuals.
One thing I would caution
with interactive visuals
is just questioning
that assumption
that your audience wants to dig.
I think sometimes we
think our audience wants
to dig more than they do.
Or sometimes they
even tell us they
want to dig more than
they may actually do.
And so one way I've
seen done of marrying
the two is to have that
meta-story and to call that out
and to highlight it
and to put it in words,
but then also allow that
interactivity for the audience
who's going to be inclined
to dig to be able to do that.
And "New York Times" is a
great example of that, right?
Because they'll have
a couple headlines
that they pull
out-- here are some
of the meta interesting points.
Are you looking
to rent or to buy?
Or some of these different
interactive visuals
that they've put
out over the years.
But then they also
have all of the data
there for you to be able
to play around with as,
well, which can create a
different sort of engagement
with the audience
as well, which is
one of the really
powerful things
about interactive
visualizations.
Yeah, great questions.
AUDIENCE: Hey, Cole.
So kind of building off of
David's question, I think
another common way that
people look at data that's
different from
interactive, or I guess
it's a little more interactive
but different from a static
is dashboards.
So we create these
big dashboards,
and they're sometimes useful,
sometimes they're not.
But they have a
lot of data there.
And how do you
think about it when
it's I guess a little bit more
challenging because you don't
necessarily know ahead of time
what the story is going to be?
And so how do you still
capture that story aspect
when you just don't know
what it will end up being?
COLE NUSSBAUMER KNAFLIC:
Yeah, dashboards
are sort of a specific,
different use case as well.
And when it comes to
dashboards, if you really
are wanting to allow your
audience to dig and come up
with their own stories,
then you actually
want to stay away
from some of the stuff
that we talked about here today.
Because as soon as you
use color, especially,
to draw your audience's
attention to one story,
it actually makes any other
potential stories much harder
to see.
So dashboards, you want to think
about designing in grays when
you can or using color only as
a categorical differentiator,
not as a visual cue that
says, draw attention here.
Dashboards for me fit-- I
talked about this distinction
between exploratory
and explanatory.
And for me, dashboards fit
more in the exploratory
but I think often get
sort of tried to be
used for the explanatory.
Where a dashboard is sort of,
I've got all of these metrics
on a single page,
on a single screen.
I can scan through them.
I can look for where
things are in line
with what I expect, where
are they not in line
with what I expect,
and then pick out, hey,
something might be
interesting there.
And then dig in on that.
And when you can find the
interesting thing, then instead
of using the dashboard
to communicate that,
my view is that you should do
the stuff we talked about today
and take that interesting
thing and make
that the focus and
not necessarily
confine yourself to
the dashboard for that.
Because the challenge
in trying to communicate
to an audience with a dashboard
is by showing them so much,
it's hard to draw attention
to one particular place.
AUDIENCE: First of
all, thanks for such
a compelling presentation.
It was incredible.
COLE NUSSBAUMER
KNAFLIC: Awesome.
Thank you.
AUDIENCE: My question is,
in addition to reading
your book, what are some
resources that you could
recommend for us to
explore data and create
those effective presentations?
And on the flip side, what
are your favorite tools?
Like do you use Tableau or
Sheets, of course, yourself?
COLE NUSSBAUMER
KNAFLIC: Great question.
So yeah, when it comes
to getting inspiration
for visualizing data, there's
a massive amount of content
out there on the web.
You Google it,
and you'll come up
with some great things,
some really fantastic blogs,
a lot of great work.
There's also a lot
of not great work.
And so you sort of want
to have a lens on of,
what is effective?
Why is it effective?
But then also, when
do you see things
that aren't effective, right?
Just because it's put out
by a recognized publication
doesn't necessarily mean that
they're visualizing data well.
But some sources of
consistently good work
are places like-- we talked
about the "New York Times,"
the "Wall Street Journal."
"National Geographic"
does some really great
data visualizations.
When it comes to
tools in particular,
everything we
looked at today was
Excel, which is what I
find myself using primarily
because it's what most
of my clients use.
Tableau is certainly
increasingly popular.
My view is you should
find a tool, pick a tool,
and get to know
it as best you can
so that it doesn't
become a limiting factor
in applying some of the things
that we talked about today.
Any tool can be used well, and
any tool can be used poorly.
And the cool things
about the lessons
we went through today and
the lessons in the book
is they're not specific
to any given tool.
They're tool agnostic.
They are foundational
principles that you can apply
in varying extent in any tool.
AUDIENCE: I had question just
around what you have seen,
or if you've seen, any
particular learnings as
regards to localization?
Or when you talked
about iconography
towards the beginning
of your presentation,
I know that's so different
that we see in street signs
depending on what
country you're in.
Curious if you've seen that at
all with the data visualization
side of things, including
maybe with color.
COLE NUSSBAUMER KNAFLIC: Yeah.
And color is the place
that comes to my mind
immediately with that question.
Because one thing to be
aware of, color in particular
has this unique ability
to impart tone and sort
of incite emotions.
And so you always want
to think about how you're
using color and
what sort of tone
you want to set,
whether it's in a graph
or in the broader communication
that contains that graph,
and use color to reinforce that.
But on that note, one
thing to keep in mind
is that different cultures
associate different meanings
with different colors.
So depending on who
your audience is,
who you're
communicating to, that's
something to take into account.
David McCandless has its
beautiful sort of visualization
that's at the same time
an interesting tool
for visualizing data.
It's called Colours and Culture.
And it's this big color wheel.
And "Colours" in
that case is British.
He's British.
C-O-L-O-U-R-S.
But it shows you
the connotations
that different colors have
in different cultures.
So it can be a very useful
tool if you are communicating
to an international audience.
His site is
informationisbeautiful.net.
Great question.
AUDIENCE: My question is,
if we have a lot of data
that we want to show, like
there are lots of key insights
that we want to pull,
would you suggest
that we try to parse them
out into different pieces?
Or do you have a
recommendation for something
that we could use
to kind of point
out all the different insights?
COLE NUSSBAUMER
KNAFLIC: Yeah, I have
a couple of thoughts on that.
So one thought is when you
have a lot of different things
you want to say about
the same data set is
to step back and figure out,
is there an overarching story
that you can use to weave
all of those disparate pieces
together?
So as we talked about, that's
one way of really making
it memorable for your audience.
A specific strategy you can
use, depending on the situation,
is something similar to what we
looked at with that generic bar
graph from the
customer survey, where
if you're showing
a bunch of data
and you want to be able to
talk your audience through it
but then focus on one
specific thing at a time,
you can start off
with just the data,
or even just a blank
graph sometimes
that has the axes labeled and
titled but no actual data.
Explain to your audience what
they're going to be looking at.
Then you layer on the data.
And then you maybe use
color or another visual cue
to draw your audience's
attention to one part
and talk about that.
Then draw attention to another
part, and then talk about that.
It's a nice way of it being
able to build familiarity
with the data with your
audience as you talk through it
and then also focus
attention really
specifically within
that broader data set
when you have specific things
you want to say about it.
Great question.
TINA MALM: Let me
ask you one more.
Your opinions about
green-- I noticed
that you didn't use any
green on the slides.
COLE NUSSBAUMER KNAFLIC:
Oh, interesting point.
So that was by accident
probably more than intentional.
Although back on
the topic of color,
one thing you want
to be sensitive to
is color blindness.
So roughly 8% of men and
half a percent of women
experience some form
of color blindness.
Most typically that
manifests itself
as difficulty in
distinguishing between shades
of red and shades
of green, which
means you want to, in general,
avoid using shades of red
and shades of green together.
Or if you want to leverage
that connotation, green, it
went up-- that's good--
red, it went down--
that's bad-- you can do so.
Just make sure you have some
additional visual cues there.
Make the numbers also bold.
Put the plus or minus
sign in front of them.
Do something else to
set them apart visually
so you're not inadvertently
disenfranchising
part of your audience.
Personally, I tend to do
my designs mostly in gray
and then use blue really
sparingly to draw tension.
I like blue because you
avoid the color blind issues.
It also prints well in black
and white if that's a concern.
But that said, blue is
certainly not your only option.
So we talked about the
tones of different colors.
You want to think about brand
colors and all of these things
and how those can fold into
how you visualize your data.
DAVEY NICKELS: Cole, I
just want to point out
that even though
you haven't been
at Google for a number of years,
you're still super influential,
and I see a lot of great
slides that are blue or gray.
And I, for one, have a delight
in cutting all the clutter.
And I think it's because of you.
So thank you so much
for your contributions.
COLE NUSSBAUMER
KNAFLIC: Awesome.
DAVEY NICKELS: One question
that we had for you,
we've talked a lot about
the "what" in the book.
I'm also curious
about the "how."
What was the most difficult
part of writing this book?
I mean, was it a
bigger challenge
than you had predicted?
Like what was super
difficult about it?
COLE NUSSBAUMER KNAFLIC:
Yeah, that's a tough question.
So I tend to be very
organized and very structured.
So once I decided I was
going to do this, I set out,
and I made the plan of here's
what each chapter is going
to be, here's the
timeline, set some sort
of aggressive timelines
with my publisher.
I think really the
hardest part was time.
Because actually physically
writing and creating
all of the visuals
takes a lot of time.
As you saw, there are
a couple small people
who live at my house.
Yeah, so time was
precious, so trying
to fit in between all
of life's other things
was challenging at times but I
think overall worked out really
well.
TINA MALM: What's
one thing you see
people doing consistency wrong?
If there's one message you
want the audience to take away,
what would it be?
COLE NUSSBAUMER
KNAFLIC: Well, I'm
going to parlay that into two
things, because I can do that.
So we talked about color
a bit, but the lowest
hanging fruit,
typically, when I'm
working with different
organizations
is being thoughtful
in their use of color.
I think when it comes to
communicating with data,
you never want to use color
to make something colorful.
But rather color, when used
sparingly and strategically,
can be one of your
most powerful tools
for drawing your audience's
attention to where
you want them to pay it.
So being intentional in your use
of color would be one big tip.
The other, and we
talked about this,
would be to never
just show data.
Always have a story and
articulate that story
in words, either
through your voiceover
or, if it's on a slide or on
a graph, through physical text
on that graph so that
your audience isn't
left guessing what they're
meant to get out of it,
but rather you've put
that work there for them.
DAVEY NICKELS: Cole,
you gave us a preview
on focusing attention
and telling a story,
but I'm curious of the
other eight chapters.
Of all the 10 in the book,
what was your favorite and why?
COLE NUSSBAUMER KNAFLIC:
Interesting question.
I think for me, my
favorite was actually
the chapter on storytelling.
Because for me, that
was the one that
was harder than the others.
The book goes much more
in depth on storytelling
than I've historically
gone in the workshops.
So for some of
the chapters, they
were pretty to write, because
it was mostly just writing
the words that I say
in the workshops.
But the storytelling chapter
was not like that at all.
I paused, and I did
a lot of research,
and I tried to
organize it one way
and then realized that wasn't
working, tried to organize it
another way.
So it was a lot of going
back to the drawing board
and trying to figure out, how
do the pieces fit together?
How can I weave it
together in a way that's
going to be compelling
for people reading it
and be understandable
for people reading it?
But I actually am really happy
with how that one turned out.
So I think that's probably
my favorite chapter.
TINA MALM: Your
target audience--
who do you think your
target audience is?
COLE NUSSBAUMER
KNAFLIC: It's really
anybody who has a need
to communicate with data,
so whether that's working with
data on a daily basis or less
frequently.
And the concepts
that we talk about,
or that I talk about in
the book, the examples
are specific to data,
for the most part,
but really it's any time you
need to communicate visually
to an audience.
And a lot of it goes
back to really thinking
about who your
audience is and how
you want them to use the
information that you're putting
in front of them and then
just designing thoughtfully
with that in mind.
DAVEY NICKELS:
Cole, one question
that kind of came up
in the audience is
like what are other
resources that are available?
What are other
experts out there?
So expanding on that question,
somebody like an Edward Tufte,
do you talk to people
that are known for being
data visualization experts?
And I'm really
curious to hear if you
disagree with them on anything.
Or have you ever had like a
data viz like battle-it-out
or something?
COLE NUSSBAUMER KNAFLIC:
[LAUGHING] Great question.
And actually, we'll come back
to pie charts on this question.
Yeah, absolutely.
Data viz is a really
fun community.
It's relatively close-knit.
The main players all
sort of know each other
or know of each other, and
we have some correspondence.
And one of the things
that's really cool
is that there's really a
lot of open sharing, right?
Because the goal is to
make everybody better,
everybody more
effective at this stuff.
But one particular
disagreement-- so Robert Kosara
is one of the main data
visualization researcher guys
at Tableau.
And he was actually one of
the reviewers on my book.
And he disagrees that pie
charts are inherently evil.
And so he and I have had
some decent debate on this.
My view is relatively
strong, that pie charts,
you can say some
general things, right?
This segment is big,
this segment is small.
But you can't really say how
much bigger, how much smaller,
answer some of the more
specific questions.
Whereas his view is a pie
chart absolutely has its place.
It is the most effective
visual for communicating
a part of a whole.
But people often
misuse them and use
them to try to do other
things outside of that.
So his view is, rather than
banish them completely,
let's teach people how
to use them smartly.
I disagree.
But we've agreed to disagree,
and we're on good terms.
[LAUGHTER]
DAVEY NICKELS: That's awesome.
TINA MALM: Cole, you've
conducted so many workshops
on this topic.
Have you noticed any differences
between people attending
these workshops between
various industries, people
from the tech sector versus
academics versus people
from the banking industry?
COLE NUSSBAUMER KNAFLIC: Yeah,
I think there are absolutely
differences when it
comes to just culturally
how do different
organizations deal with data,
communicate with data?
But one thing that's been cool
to see is that these lessons,
they stay the same
irrespective of industry.
They sort of cut
across all industries,
which is interesting.
And for me, being able to
see the organization's data
and see some of how
they've communicated
with data before
going in gives me
such an interesting perspective
and lens on the organization.
But I think there
are differences
in how organizations use
data, but the concepts
that we talk about in the
book really span everything.
DAVEY NICKELS:
Cole, one question--
you mentioned that you did a
lot of research for the book.
You mentioned
some, I'm guessing,
neuroscience-type concepts.
What sort of
disciplines did you draw
on when you were
researching or composing
as you've become more
and more of an expert?
COLE NUSSBAUMER KNAFLIC: Yeah,
one of the interdisciplinary
places that I've drawn a
lot of inspiration from
is just the area
of physical design.
When you think about if
you're designing a chair,
how do you make that
work for your audience?
And now data is different
because it's not
sort of a tangible thing.
So the things you
have at your disposal
to show how to use
something is not tangible.
It's visual.
So then it's thinking about,
how do you visualize this?
How do people see, bringing in
some of those sorts of things.
One example that I like-- are
people familiar with the OXO
brand of kitchen gadgets?
So there's things like
vegetable peelers or spatulas
or like a garlic press.
And if you just sort of
lay them out on a counter,
it's intuitive how
to pick them up.
And you don't even realize that
when you're picking them up,
because they're
formed in a way that's
going to make you pick
them up in the way
they're intended
to be used, which
is brilliant from a
design standpoint.
And we want to
think about how we
can leverage those same sort of
cues when it comes to our data.
How do you make it so
obvious to the audience
how they're supposed to use
that data that they can't use
it any other way, that
they can't help but see
what you want them to see?
So design is probably
one of the big places
that I drew from when
it came to researching
some of the stuff for the book.
TINA MALM: In regards
to tools, earlier you
mentioned Excel and PowerPoint.
But are there any other
tools I need to be fluent in
to apply your lessons?
COLE NUSSBAUMER KNAFLIC:
No, and not necessarily
be Excel and
PowerPoint, either-- we
talked about Tableau, Sheets.
There are many different
tools out there.
And again, my view is that
any tool can be used well
and any tool can be
used not so well.
But pick a tool, get to know it
as best you can so it doesn't
become a limiting factor when
it comes to applying some
of the lessons that we've talked
about and some of the lessons
covered in the book.
DAVEY NICKELS: Cole,
if you were to do
"Storytelling with Data v.
2" in 10 years or 20 years--
I know it takes a lot of
time to write these books--
what would it be on?
Like would anything
change dramatically?
Or what do you think?
COLE NUSSBAUMER KNAFLIC: Yeah,
that's an interesting question.
I used to always get
that question at Google
as well of like, what's
data visualization 2.01?
When is that coming?
What does that look like?
And for me, there isn't an
obvious sort of next one,
because the concepts
that we talk about,
they're fundamental.
They should be used always.
And as you get more
experience visualizing data,
it's not that the way
you visualize it changes.
I think it just becomes
more nuanced in how
you apply some of the things
that we talk about here.
So for me, there isn't an
obvious next iteration.
But who knows?
That may change after the
next 100 workshops or so.
AUDIENCE: Hi, Cole.
I teach a wonderful course
of data visualization here
at Google.
And so I teach Data Viz
1, which is pretty much
focus on the first part of
your presentation today.
I really loved the second
part of your presentation
with the storytelling.
I think it made perfect sense.
And I think a lot of us could
benefit from learning these two
things together, because
storytelling is obviously
such an important part of
the overall lesson here.
So it is this something that
we can steal with pride?
I used to work with the
people of that team,
so I would love to share
anything that's available.
And I just think
a lot of Googlers
will benefit with the second
part of this presentation
today.
COLE NUSSBAUMER KNAFLIC:
Yeah, absolutely.
I mean, definitely
check out the book.
Like I said, storytelling is
covered in much more depth
there.
Because the storytelling
piece really
came in after the original
course here was developed.
So there's not a lot
of that content there.
But yeah, this is a space
where when you see something
good or effective, steal it.
Use it for your own use.
There's no shame in that at all.
And it's by practicing
these sorts of things
that we all get better.
So yeah, absolutely.
AUDIENCE: Cole, it is
so great to see you back
at Google, as someone
who took the very
early version of the course.
And I knew this was
something special, which
I wish the rest of
the world gets to see.
And I know how passionate
you are about this topic.
I'm so happy to see this coming.
Thank you for taking the
time to come and talk to us.
My question was
around-- this is just
as much from the
organizational perspective.
It's a skill, but it's
also the culture of just
having so much focus on it.
So as you speak with clients
from a variety of industries,
what other type of
thing do you think we
as employees or leaders
can do to kind of get
a culture of having
focus on this aspect
just as much as anything
else, like dad infrastructure
or anything else?
What can we do to get this
message out in the world?
COLE NUSSBAUMER
KNAFLIC: Yeah, so Google
is already taking steps, right?
The fact that there is
a data viz course here
and that it's made
widely available sort of
proves that there is an appetite
for this and the resources
for it, which is awesome.
I think when it comes to
propelling that even further
and embedding it
throughout the culture,
it's about recognizing
when it's done well
and promoting that
when it's done well,
when there are good
examples, really highlighting
those to other people
and making it a goal.
It's always been
interesting to me,
because if you think of the
entire analytical process,
you start off with a
question or a hypothesis.
Then you collect the data.
Then you clean the data.
Then you analyze the data.
And at that point,
you can get away
with just throwing it in
a graph and being done.
Where the graph is the only
part of that entire process
that your audience ever sees.
So my view has always been
it deserves at least as
much time and attention
as the other parts.
So I think as you have more
examples internally of people
doing that well, that you
can sort of hold up and say,
here, this is what
we should emulate,
it starts building a culture
around that over time.
And investing in people when
it comes to the training,
developing internal experts
to whom others can turn,
all of these things
can help sort
of continue that
positive momentum.
TINA MALM: And with
that, thank you so much.
COLE NUSSBAUMER
KNAFLIC: Thank you.
[APPLAUSE]
