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]
I'm having fun watchng OP video after reading this thread
https://www.reddit.com/r/programming/comments/80whkc/why_i_quit_google_to_work_for_myself/