January 6, 2025 • 34 mins
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Cole Nussbaumer Knaflic, author of 'Storytelling with Data' and 'Daphne Draws Data,' shares her journey from studying mathematics to becoming a leading figure in data visualization. Cole discusses her career path, the importance of clear communication in data visualization, and tips on how to make complex data understandable.
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⌚ TIMESTAMPS
00:51 Cole's Background and Career
06:25 The Importance of Effective Data Communication
13:07 Tailoring Data Presentations to Different Audiences
16:06 Practical Tips for Data Visualization
20:23 Advice for Aspiring Data Professionals
26:36 Introducing Her New Book (Daphne Draws Data)
🔗 CONNECT WITH COLE KNAFLIC
🤝 LinkedIn: https://www.linkedin.com/in/colenussbaumer
📕 Storytelling with Data by Cole Knafflic: https://amzn.to/3ZYHhsG
📒 Daphne Draws Data: https://amzn.to/4fJkIOt
📖 Books: https://www.storytellingwithdata.com/books
🔗 CONNECT WITH AVERY
🎵 TikTok
💻 Website
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Cole (00:00):
You can have the most beautiful graph in the world, and if you can't
(00:04):
subsequently talk about that in a waythat makes other people want to listen
and pay attention and do somethingwith it, the beautiful graph fails.
Avery (00:14):
Okay.
Cole, welcome to the Data Career Podcast.
So glad to have you.
Hi, Avery.
Great
Cole (00:18):
to be here.
Thank you.
Avery (00:19):
Yes.
So if you guys haven'theard of Cole before.
Uh, she is the author of thebook Storytelling with Data.
It is one of the, uh, best books onstorytelling with data, but specifically
like data visualization and how topresent and convince people at your
workplace, uh, of your findings.
She's also the, the author of the newbook, Daphne Draws Data, which we'll
(00:40):
talk about in this episode as well, whichis, which is more for kids, right, Cole?
Cole (00:43):
It is, yeah.
Younger audience, but interestingly, it'sa lot of the same lessons that apply.
Avery (00:48):
Okay.
And let's, let's get intosome of those, those lessons.
Um, I want to start off withactually a little bit about, about
your career because you studiedmathematics in college, right?
Cole (00:58):
Yeah.
Math.
I have an undergrad in math, uh,or applied math and, uh, an MBA.
Avery (01:04):
Okay.
And when you graduated, did you ever seeyourself becoming like the author of a
storytelling with data book and, and kindof this whole career that you have now?
Yeah.
Cole (01:12):
No, it didn't exist as as a career.
I don't think at that point I, as Imentioned, I majored in math and I, I
remember getting into my senior year incollege and still trying to figure out
what do I want to be when I grow up?
And I remember going to a series ofsessions that were, you know, like,
What profession to have as a math major.
(01:34):
And so I listened to the actuariesand the, the finance people, and
I had this moment of, or longerthan a moment, you know, the, the
crisis of like, Ooh, none of thesecareers sound like what I want to do.
Uh, and I remember then getting some ofthe best advice that I have received,
I think, as I look back from mymother, which was finish the degree.
(01:56):
And so, so.
Finished my math degree andthen got a job in banking.
Uh, not in finance though, incredit risk management, where I was
building statistical models, uh,forecasting loss, try to understand
how we should reserve for the bank.
And I loved, I loved the technicalside of it, but also being able to
Bringing creativity in and where Ibrought creativity and was in how I
(02:20):
was visualizing the data, simple thingslike colors and some inadvisable things.
As I look back like shadowsor cram as many graphs on a
slide as you can get on there.
But interestingly, what I foundover time was when I spent.
Time and thought on the design of thevisuals, people ended up spending more
(02:41):
time with my work, and so that becamea self reinforcing thing where other
people would come to me, and I becamethe sort of internal expert when it comes
to how do you show data fast forwardthrough a few career changes, and I.
Was it Google still using a lot ofthe same statistical methods, but
(03:01):
now in an analytics role in HR.
So people analytics forecastingthings like who's likely to leave the
organization and when, and what sort ofthings can we test out to change that?
And I still spent a lot of timeon the visuals and the team I was
on, we were doing a lot of reallycomplicated things that we needed to
(03:22):
communicate to the engineers at theorganization and the sales people at the
organization and everybody in between.
So audiences with widely varying.
Needs, technical skills,familiarity with data.
And so that was really interestingto see how do you change how you
show things depending on who you'reshowing it to and where, where is that?
(03:46):
How can that be more successful whenyou think about it from that standpoint?
So also, while I was at Google, I part ofa training program or part of developing
a training program where I was creatingcoursework on data visualization,
which was fantastic because it gaveme a chance to pause and research and
read everything I could get my handson at that point, which was not a lot.
It was like, Tufti, Stephen Few, I thinkhis first book was out at that point,
(04:11):
but really start to get an understandingof why some of the things I'd arrived at
through trial and error over time, youknow, why they work and why some things
work better or worse, and really turnthat around to be able to teach others.
And so I did that at Google, uh, taughtcourses across the organization for a
number of years and around the world.
(04:32):
And then realized that it's not just.
People in technical roles or at atechnology company who need to learn how
to communicate effectively with data.
These aren't skills that we naturallyhave, even though a lot of the things
and we can get into this, a lot ofthe lessons are really Practical and
(04:52):
maybe even obvious once you say them,but until somebody points them out
and shares them, we are sometimesour own worst enemy when it comes to
trying to communicate effectively.
Uh, and so it was, let's see,back in 2012 when I left Google
and started storytelling withdata, uh, which is what I've.
Poured the last decade plus intoreally with the goal of helping people
(05:18):
create graphs that make sense, butalso going beyond the graph to, you
know, you don't want to just show data.
We want to take the data that wework with and learn something new
from it and help communicate thatnew thing to other people so that
we can help drive smarter decisions.
Uh, reinforce that we're doing thingsthe right way or that we should change
(05:39):
how we're doing things and really havesmarter conversations, not about the
data, but using the data to have smarterconversations about the business.
And so we do that mainlythrough workshops.
Uh, there's the book that you mentioned,um, a couple more after that as well.
One focused on practicing anotheron you as the person who is
(06:00):
creating and communicating the data.
And then the latest onefor kids, as you mentioned,
Avery (06:06):
that's such a wild and cool story.
Congratulations on all the success.
I actually attended a, uh,storytelling with data workshop at
my company at ExxonMobil in 2020.
And it was, it was awesome.
And, and obviously I've, I've read thebook and, uh, I actually have multiple
copies, one of all the success in this,this really cool career that you've had.
If you go back to that first job, youknow, one of the things that you said
(06:28):
was that if you designed your charts.
Well, and you use best practices fordata visualization, your boss and your
boss's boss would care about them moreand pay more attention to your work.
And actually I was, I was rereadingyour book and I pulled this quote
and you said, I quickly learned thatspending time on the aesthetic piece,
something my colleagues didn't typicallydo met my work garnered more attention
(06:50):
from my boss and my boss's boss.
And I just want to kind of talkabout that for a second, because.
It's not necessarily that you weredoing better work or that your analysis
was better or it was more meaningful.
It was just easier for them to understand.
And because it was easier forthem to understand, they valued
it more and they valued you more.
Is that true in your career?
Cole (07:10):
I think, yeah, I think it's exactly that, that it became When the
graphs made sense and the messages madesense, it was more of a direct line
into the value that the work was having.
Whereas, if you imagine the same workbeing done, but being communicated
in a really complicated way, or,you know, really going deep into the
(07:30):
statistical methods instead of pullingback to say, What does this mean?
What does this mean for you,the audience, or the person, the
people to whom I'm communicating?
What does it mean for our people?
Business, how do we put that complicatedstuff into words that makes sense to
somebody who wasn't intimately involvedin the process that when you don't
(07:50):
take the time to do that, it can reallyeasily become a barrier to the good
work that's being done actually havingthe impact that it otherwise could.
And that's what I think when we spendtime thinking about how do we make
this make sense to someone else?
How do I look at something and say,all right, this might be what made
(08:11):
sense to me, or it's the view thathelped me reach that aha Eureka moment,
but it doesn't mean that that's thesame view or the same path that's
going to serve my audience best.
And so it really is this paradigmshift because I think often and
I think Especially people intechnical roles, we, we get so used
(08:35):
to seeing things a certain way.
And I think for me, at least asI look back, there was joy in
figuring out the puzzle, right?
Figuring out how the pieces fittogether when it wasn't obvious.
And so I think there's part of somethingin us that wants us to then be able
to kind of show that puzzle to someoneelse, but have it not be clear so that
(08:55):
we can have them experience some ofwhat we did, but that does a total
disservice because what that does isbasically take the value that we could
have added and obfuscate it insteadof saying, all right, I did this work.
I've, I've found, you know, the,the interesting thing now, rather
than me take my audience throughall the details and the work I went
to to get to the interesting thing,it's actually just lead with that.
(09:19):
And we may, in some cases, not evenhave to get into any of the detail.
I think sometimes that.
Feels bad when itshouldn't, that is success.
That means your audience trusts you.
It means they trust your findingbecause I can remember times I can
remember times at Google, I can remembertimes at banking back prior to that
in private equity, where I worked,where my team and I would spend a ton
(09:41):
of time on an analysis or on a study.
And then putting together a reallydense recount of what we did and what
we found in all of the methodology and.
When it didn't get presented afterat the end of all of that work,
that would feel bad when reallythat was a success scenario.
It didn't not get presentedbecause we didn't talk about it.
(10:01):
We talked about it and actually didn'teven need to go into all of that detail
because of the trust over time thatwas established to our stakeholders
were able to go in with the storyand then have the conversation focus
on really understanding that and it.
Understanding how we apply thatto the business going forward.
And it doesn't mean we didn'tneed to spend all that time
putting together the document.
(10:23):
We needed to have that.
We needed to do that work in order toget to the, the answer or the finding
or the interesting thing to communicate.
And there will be times whereyou do need to take your audience
through a lot of that detail.
And so you need to have it there, but.
The dense communication isnot the, the, the goal, right?
Going through that is not the goal.
It's having the impact through the work.
Avery (10:44):
I love that.
And I think in today's society, as muchas all of us might enjoy working on
something we're passionate on, uh, Ithink people rather be doing their hobbies
or spending time with their families.
And so if you can just make your resultsas clear as possible, as quickly as
possible, uh, that bodes well for you.
Because some, sometimes I think.
(11:04):
As technical workers, we wantour work to speak for itself.
Uh, and we want them to recognize,yes, I did all this work to actually
accomplish this, but the sad truthis most businesses don't care.
Just give us the results,tell us why it matters.
And a lot of the time I even saw thispost, um, from Kelly Adams on LinkedIn.
She's like a LinkedIn creator.
She was like the most of the time my bossdoesn't ask me how I, how I even got to.
(11:28):
Like doesn't ask to see my code ever.
It doesn't ask to like actually figureout how I came to my conclusion.
They just trust me to, to do theanalysis and come to the right point.
Cole (11:36):
Well, and I think that's part of the, part of the magic magic.
It's not quite the right word there,but is really assessing a situation and.
Anticipating what is going to be neededand what level of depth you're going to
need to be able to walk someone throughor show someone, uh, because when you
can make that match the situation,that's when when things go really well,
(11:59):
because you could easily take that andsay, okay, well, so my manager trusts me.
And that means.
You know, I still need to bebuttoned up on my work, but maybe I
don't need to show all of my work.
But then as soon as you get the questionback, or you, you, if you misanticipated
that or misread that, and now you have,or you're using that and going in front
of another audience who actually isgoing to want to be convinced of the
(12:21):
robustness of the analysis that wasdone, you need to be able to anticipate
that so that you can meet that.
Need.
I think that is where things mostoften fail, where we create a report
or a presentation for, for ourselvesor for our data for the project and
not specifically for the person or thepeople to whom we're communicating.
(12:44):
That's that paradigm shift I wasreferring to before that when we can
get out of our own heads and reallythink about, all right, here's what I
did, but now how do I make this work forthe people who need to understand it?
And take measures to make it work forthem, both through the visual design
and through how we talk about our work,how we communicate directly, that that's
(13:05):
where all of that can work really well.
Avery (13:07):
So I think if, if I understand what you're saying correctly is your
presentation, your communication,maybe even your, your graphs should
almost dynamically change basedoff of who you're showing it to.
Cole (13:18):
Yeah, I mean, ideally, so if it's a critical scenario and you
have audiences who are, whose needsare sufficiently different, then you
may want to think about, there willbe times where it would make sense
to have different communicationsfor those different audiences.
Now, in practice, that rarely happens.
In practice, we try to createthis one size fits all, but it's
(13:40):
easy through doing that to thennot exactly meet anyone's needs.
So, I think A lot of the time we can getto the good enough scenario where, you
know, if we, if we craft the communicationand it's 80 percent meets this audience
and 80 percent this audience, right,there's some overlap and that's
probably okay, but where audiences arecaring about really different things.
So bring up an example from Google, sincewe talked about this a little bit earlier,
(14:04):
internally, our main audiences were.
Engineers on the one hand, highlytechnical, needed to be convinced
that the methodology was sound,wanted very detailed information.
We needed to get them on boardbefore we even did the research a
lot of the time so that they wouldeventually buy into the results.
And then on the otherhand, we had the staff.
Sales organization whose generalsentiment was leave us alone.
(14:27):
We're the ones out heremaking the company money.
And so for them, we needed to bedirect and short and concise, focused
on what mattered to them and notuntil they needed to act upon it.
And it was like, it was, it was.
After trying to communicate to bothof those audiences simultaneously at
first and just failing for a varietyof reasons that are obvious in
(14:48):
retrospect, that we decided, you knowwhat, that's not the right approach.
We actually do need to communicateto these audiences separately, not
only in what we share and how wetalk through it or show it, but also
even when we communicate to them.
Avery (15:02):
I think there's, there's people listening who, who might be
thinking, well, the analysis is theanalysis, but it's so funny because.
You wouldn't necessarily think this,but the packaging that you put are
around your analysis really matters.
And oftentimes, like if, if let'sjust say we're, we're almost in
the holidays, let's just say I'mgiving you a Christmas present of
some, some new headphones, right?
Like if, if the headphonesjust in a cardboard box.
(15:25):
They're not going to be as valued as ifI put these headphones in like a really
nice, like box that has really good,like opening mechanisms and really good
wrapping paper and a bow and a nice card.
Even
Cole (15:37):
though even the wrapping paper, right.
It's going to be differentaround the holidays than around
birthday or something else.
So yeah, it's the same contents,but the way you present it
will and should be different.
Avery (15:48):
Let's, let's talk about some of the ways that, that we can present well.
So we talked about like.
Addressing your audience.
So if you're, if you're talking toyour boss's boss, you're going to
present it differently than to likeyour colleague or a engineer or a
programmer or something like that.
What are some other things that peopleshould know when they're, when they're
making data visualization and presenting?
Cole (16:06):
I think one thing to be clear on is that you likely know the situation, you
know, the data better than anyone else.
And what happens through that Is whenyou look at the graph you made or the
slide you made, it's super obviousto you where to look and what to see.
But to make those things as obviousto someone else, it means you have
to do things to make that happen.
(16:28):
And so when it comes to the designof the graphs and the slides, you
can think about how you might employvisual contrast, for example, sparing
use of color to show your audience.
where you want them to look and thenusing words either through your spoken
narrative or written directly with thegraph or on the slide or a combination
(16:50):
of those two things that tell youraudience why you want them to look there.
And a lot of the time, justthose two simple things.
So making it clear where to look andwhat to see, even if it's maybe not
the perfect graph type for what you'reusing, or there are some, you know,
there's some clutter or, or somethingelse, uh, You can still get your
message across and it gets the job done.
Avery (17:12):
That's something that I think you, you cover really
well in storytelling with data.
Um, just like the idea of how dowe, how do we declutter our graphs?
Because you know, it's funny, you're,you're, you're big enough that, um, maybe,
maybe, you know, the answer to this.
Um, but, but in this book, like you doall of this, I'll call it pretty ization
of, of data visualization in Excel.
All of the graphs that you do inthe book are, are done using Excel.
(17:35):
And what I mean by, by you're bigenough, like your brand and your, uh,
recognition has gotten to the pointwhere it's like, can't Excel start?
Like, It's actually a lot of work tomake a graph look pretty in Excel.
Can we talk to someone at Microsoftand have it like he defaulted better?
Cause one of the things that Microsoftdefaults does is if you have like
eight different lines on your chart,they're the all different colors.
(17:57):
And one of the things that, you know,you talk about is like, okay, let's only
use color on one or two of these lines.
Like why, why does Excel make it so hard?
Cole (18:05):
Well, I don't think so.
No tools trying to makeyour life miserable, right?
Um, that, uh, any tool is trying tomeet the needs of so many different
situations, all at once that it's nevergoing to exactly meet any of those, right?
Take the example.
You say like, why, whyis everything colorful?
Well, because if, The legend is,you know, off to the side or at the
bottom, which is how that charts goingto be at the beginning, then you have
(18:27):
to have color as a differentiator.
So you have some way to tie those back.
The way that you can get aroundthat when you are intentionally
designing is you figure out, well,where could I label those lines where
proximity is the thing that ties theminstead of the similarity of color?
But yeah.
You have to make that decision inlight of the data because it depends
on how it lays out on the graph to say,well, can I label it within the graph?
(18:50):
Or is that going to make it hard to read?
Or there simply isn't space to do so.
And so there are all thesedecisions that we make every
time we're working with data.
And you're even, you're implicitlymaking decisions when you're not
changing these default things,because then you're letting the
tool make the decisions for you.
And.
It's funny because I, I had thoughtfor a long time, like, Oh, I should
(19:11):
make myself my own template in Exceland make, make it just really easy.
So I can have the startingpoint that I want.
And I made several of these yearsago and found that I never used
them because for me, part of theprocess was looking at the thing that
was never going to be quite right.
And then figuring out how to intentionallymake it work for what I need.
And I think there's value inthat and in the time and thought
(19:36):
that it takes to do that.
But we have to be intentional aboutdoing it because otherwise we can
just plug data into any tool andit will spit out something and it's
never going to be what we need.
You know, we pick on Excel, butthis is not unique to Excel.
Uh, it's, it's anythingyou're working with.
And so I think there's an important partof the process that comes into play when
(19:56):
we are taking the time to make thosedecisions and change the default settings
to make them work for our given situation.
I guess it takes
Avery (20:05):
time.
It takes human brain and it's justthe laziness inside of me that
wants it done automatically, butit's also, it's also probably.
Something to look forward to forme and our listeners, because it
also keeps us employed, right?
Because if it was done out ofthe box automatically, perfectly,
then maybe we wouldn't have jobs,but it requires a human brain.
So that's good.
I want to, I want to transition intotalking about, uh, you know, a lot of
(20:27):
people who listen to this podcast aretrying to land their first day at a job.
They're transitioning into data careers.
Um, maybe they're teachers or physicaltherapists, or they're in sales.
Do you think there's room for them?
To, to stand out using data visualizationand ultimately pivot into analytics.
Cole (20:43):
Yeah, I, so I would say for the person who is trying to make
that pivot and is in a role that isnot working with data on a regular
basis, currently, first thing is tolook for opportunities where you.
Where is their data and what you'redoing today that you could work with?
Because that almost always exists.
If it really doesn't, then you can lookelsewhere in the community for ways
(21:06):
of practicing and honing those skills.
For example, we have our onlinestorytelling with data community
where we host a monthly challenge.
That's always something very, um,specific in theme, but open ended
in term of how you address it,where typically you're finding data.
Data that's of interest to youand doing something with it.
I think the one we have goingon currently, uh, so November,
(21:27):
2024 is just finding a graphin the wild that isn't perfect.
And then taking steps to improve it.
Uh, we also have an exercise bank thathas hundreds, probably at this point
of exercises that are more focused ondeveloping a specific skill where the
data, the instructions, it's all about.
All provided.
And so all you need is, you know,five minutes, 30 minutes and something
(21:51):
you want to work on, uh, in termsof practicing, whether it's, you
know, like we talked about, maybeit's taking a graph and figuring out
how to change the color of just oneline and make everything else green.
Gray or, uh, designing a slide.
Um, and there's a varietyof other things as well.
So looking for ways to practice to honeyour skills, which I would say again,
first look within your role to see ifthere's anything you could be doing there
(22:15):
or more broadly at your organization.
Some will allow there to be moonlightingor, you know, shy of an internal transfer,
but still getting some exposure toskills that you would want to be using.
So look for those, if not in yourcurrent role, then look to the community
to see where you might do that.
And then I think for anyone who is notcurrently in a data role, but wanting
(22:37):
to get to where they're working withdata, visualizing data, communicating
data, the thing to not overlook is howyou communicate, how you communicate
verbally, and how you talk about yourselfin terms of, you know, how do you
introduce yourself, or how do you portrayYour work history and your skills when
(22:59):
you are interviewing or doing thingslike that and spending time working on
that, uh, and also how you engage youraudience through the way that you speak.
Um, because this is one of the thingsthat over the years, and I think again,
as I look back, it's not surprisingand seems obvious, but it wasn't until.
Fairly long into things that it reallybecame clear to me that the graph or the
(23:23):
data visualization is really just onepart of the puzzle because you can have
the most beautiful graph in the world.
And if you can't subsequently talkabout that in a way that makes
other people want to listen andpay attention and do something
with it, the beautiful graph fails.
And so I think both for those who arewanting to transition into data roles.
(23:47):
Also, I would say for those who arecurrently in a role working with data
and communicating data work on yourselfbecause you can be just as strategic
when it comes to how you speak about yourwork, how you portray yourself, how you
communicate as you can with, you know,what graph you're choosing and how you're
choosing to portray things visually.
(24:09):
And when those two go together,you've made a good graph.
And you can get other people'sattention through how you speak
and through the passion you showfor the work that you've done.
That becomes a reallypowerful combination.
Avery (24:23):
It's, it's a great point.
Um, and whether we like it ornot, we live in a world, uh, where
your appearance really matters.
You know, it's not, if you're trying toland the data job right now, it's not the.
The smartest person or the person who'sbest at at sequel that lands the job.
It's the person who's able to bestportray their skills that they'd be,
you know, able to help the company.
(24:44):
And the same is true.
Once you land a job, it's not necessarilythe best employee that gets the promotion.
It's the employee that appears thebest or gets portrayed as the best.
And they, you know, itreally doesn't stop until.
You become like the CEO.
And then even then likeappearances still really matter.
So it's, it's maybe unfortunate and you'dwant maybe just pure talents and skill
(25:05):
to win, but the way that I think this is
Cole (25:07):
part of the talent as well, being able to being able to communicate
adeptly and one resource that I'll pointpeople to in case like, okay, I get
this, but how do I actually do that inthe yellow book storytelling with you?
This is the one that goes back to.
There's data visualization in it,but it goes beyond the data into how
can you develop yourself to be ableto plan, create and deliver content?
(25:33):
Uh, the penultimate chapter is craftingthe story of you, and it's basically
taking people step by step throughhow you can be really thoughtful and
robust in how you plan and how you talkabout the story of yourself, which can
be useful in a variety of scenarios.
And it's actually, it really, it becomesan interesting case study and way to
(25:56):
practice a lot of the other thingsthat are introduced that are grounded
more in how you would communicate data.
But things like, you know,brainstorming on sticky notes and
really considering your audience andmaking all of that work together with.
Using a subject that peopleknow really well themselves.
Um, but then after going through thatchapter, you can come out of it with a
(26:19):
really clear plan and ways to practicewhen it comes to talking about yourself
that you can then translate intotalking about other things as well.
Avery (26:29):
That sounds like a superpower to master that.
I don't have the yellow book, somaybe I'll, I'll have to look it up.
Look into that one.
Let's talk about your,your brand new book.
Daphne draws data.
Uh, tell us a little bit about whatit is and why you decided to do this.
Yeah.
Look at it.
Cole (26:43):
Yeah.
So Daphne is a delightful pinkdragon who has a unique talent.
She enjoys drawing.
That's not so unique.
Well, maybe for a dragon it is, but thething that she likes to draw the most is.
Data.
She likes to draw graphs.
And so the story is a really fun, I mean,it's a picture book, really fun, brightly
(27:06):
illustrated, uh, about Daphne's adventure.
She decides, well, if she's not beingappreciated at home, she's going to go
off and find a place where she can fit in.
And so she goes to the jungleand outer space and underwater
and all sorts of places.
And in each location, sheencounters some creatures.
Uh, and a problem they'refacing, and then helps them solve
(27:29):
their problem by drawing data.
So she collects it, she draws itin very pictorial forms of graphs.
Uh, the word graph I don't thinkis used once in the book though.
It's really introducing the conceptsthrough story and through pictures.
And then, uh, I won't giveaway the ending, uh, other
than to say it's a happy one.
And the story ends, but then the bookcontinues into a graph glossary that goes
(27:53):
more into what the graphs were that Daphneused over the course of her adventure.
So there's a page each devotedto bar charts, line graphs.
pie charts and scatter plots, uh,showing examples from her adventures,
helping kids understand how to read themwhen they work, and then introducing
activities that kids can undertake usingdata that's of interest to them because
(28:17):
one great Parallel that we can makeacross adults communicating with data
and kids and the use of data and graphsis to make it about something that's
meaningful and something that can beacted upon because when I see my kids
come home with graphs from school, sofar, I've been pretty disappointed because
they're graphing things like the weather.
(28:38):
The weather in September,okay, it was sunny.
You experienced that.
It's not so interesting now to draw itin a graph or they'll do things like
roll a die, uh, you know, a bunch oftimes to see that, you know, and then
graph it to see, okay, I rolled all thenumbers about the same amount of times.
This isn't anything that they canthen Use to understand things better.
(28:59):
Uh, and so I really would like to makethe data that we're having kids work with
be something that they're interested in,because I think this is such a, it could
be such an amazing way into mathematicsin a way that isn't portrayed as boring
or complicated or completely abstractwhen it comes to kids day to day.
(29:21):
Uh, so, you know, let's have them track.
How many hours they're spending on ascreen every day and how they feel plot
that, or, uh, you know, where's theirfavorite place to read and, you know,
how might we then emulate some of thosethings in the classroom to promote
more reading, like things that we canactually, uh, help kids learn about
themselves and about the world aroundthem in ways that is fun and engaging
(29:45):
because what I've seen through my kids.
And their friends is thatkids are fantastic and love
doing a couple of things.
One, asking questions,particularly like, I don't know,
kindergarten, first, second grade.
There's no filter yet and kidsare so curious and they ask
questions about everything.
And if we could teach kids how tohone and get really good at asking
(30:09):
questions that can subsequently beanswered with data, that is going to
be an amazing foundation for everyone.
Any sort of problem solving,critical thinking, analytical
career, and they also love drawing.
And so if we can let them take some ofthat creativity and do it with a graph
and with numbers and let kids approachthat creatively, I think it's a very
(30:33):
refreshing change from math beingsomething that's either right or wrong
because graphs, there's more leeway.
Uh, there can be creativity.
People can approach things.
differently, and we can celebratethat and learn from that rather
than say, no, don't do it that way.
Do it this way.
And so for me, I think it was acombination of just going back to
the impetus for writing the book, acombination of, you know, seeing the
(30:55):
adults who we teach and so many saying,I wish I had learned this sooner or
earlier, and then seeing my kids andhow, just how they learn about the
world around them, how they develop.
Language and logic and realizing we couldtake the visual language of numbers and
(31:15):
introduce that a lot earlier than we do,sort of those two things coming together.
I think there's an opportunity to reallyhelp our kids recognize this superpower
of comfort with numbers and askingquestions and answering those questions
and drawing and plotting things that,um, It'll be a great foundation for
(31:37):
them for so many things going forward.
Avery (31:39):
You're building the next generation of data analysts and a
data viz specialist, a ripe young age.
So, uh, that is very cool.
Where can people find this book?
Cole (31:50):
Oh, anywhere books are sold.
So yeah, favorite independent bookseller.
You can order it.
It's on Amazon.
Uh, and, uh, yeah, is around the world.
Avery (31:58):
Okay.
Awesome.
Well, I haven't checked it out yet.
I'll have to check it out.
I'll have to check out the yellow book.
Um, but I'm also a huge fan of, of thestorytelling with data original book.
So if you guys haven't checked thoseout, be sure to check them out.
We'll have links to all of themin the show notes down below.
Uh, Cole, thank you so muchfor coming on our show.
We appreciate it.
Cole (32:16):
Thanks for having me, Avery.
You can have the most beautiful graph in the world, and if you can't
(00:04):
subsequently talk about that in a waythat makes other people want to listen
and pay attention and do somethingwith it, the beautiful graph fails.
Avery (00:14):
Okay.
Cole, welcome to the Data Career Podcast.
So glad to have you.
Hi, Avery.
Great
Cole (00:18):
to be here.
Thank you.
Avery (00:19):
Yes.
So if you guys haven'theard of Cole before.
Uh, she is the author of thebook Storytelling with Data.
It is one of the, uh, best books onstorytelling with data, but specifically
like data visualization and how topresent and convince people at your
workplace, uh, of your findings.
She's also the, the author of the newbook, Daphne Draws Data, which we'll
(00:40):
talk about in this episode as well, whichis, which is more for kids, right, Cole?
Cole (00:43):
It is, yeah.
Younger audience, but interestingly, it'sa lot of the same lessons that apply.
Avery (00:48):
Okay.
And let's, let's get intosome of those, those lessons.
Um, I want to start off withactually a little bit about, about
your career because you studiedmathematics in college, right?
Cole (00:58):
Yeah.
Math.
I have an undergrad in math, uh,or applied math and, uh, an MBA.
Avery (01:04):
Okay.
And when you graduated, did you ever seeyourself becoming like the author of a
storytelling with data book and, and kindof this whole career that you have now?
Yeah.
Cole (01:12):
No, it didn't exist as as a career.
I don't think at that point I, as Imentioned, I majored in math and I, I
remember getting into my senior year incollege and still trying to figure out
what do I want to be when I grow up?
And I remember going to a series ofsessions that were, you know, like,
What profession to have as a math major.
(01:34):
And so I listened to the actuariesand the, the finance people, and
I had this moment of, or longerthan a moment, you know, the, the
crisis of like, Ooh, none of thesecareers sound like what I want to do.
Uh, and I remember then getting some ofthe best advice that I have received,
I think, as I look back from mymother, which was finish the degree.
(01:56):
And so, so.
Finished my math degree andthen got a job in banking.
Uh, not in finance though, incredit risk management, where I was
building statistical models, uh,forecasting loss, try to understand
how we should reserve for the bank.
And I loved, I loved the technicalside of it, but also being able to
Bringing creativity in and where Ibrought creativity and was in how I
(02:20):
was visualizing the data, simple thingslike colors and some inadvisable things.
As I look back like shadowsor cram as many graphs on a
slide as you can get on there.
But interestingly, what I foundover time was when I spent.
Time and thought on the design of thevisuals, people ended up spending more
(02:41):
time with my work, and so that becamea self reinforcing thing where other
people would come to me, and I becamethe sort of internal expert when it comes
to how do you show data fast forwardthrough a few career changes, and I.
Was it Google still using a lot ofthe same statistical methods, but
(03:01):
now in an analytics role in HR.
So people analytics forecastingthings like who's likely to leave the
organization and when, and what sort ofthings can we test out to change that?
And I still spent a lot of timeon the visuals and the team I was
on, we were doing a lot of reallycomplicated things that we needed to
(03:22):
communicate to the engineers at theorganization and the sales people at the
organization and everybody in between.
So audiences with widely varying.
Needs, technical skills,familiarity with data.
And so that was really interestingto see how do you change how you
show things depending on who you'reshowing it to and where, where is that?
(03:46):
How can that be more successful whenyou think about it from that standpoint?
So also, while I was at Google, I part ofa training program or part of developing
a training program where I was creatingcoursework on data visualization,
which was fantastic because it gaveme a chance to pause and research and
read everything I could get my handson at that point, which was not a lot.
It was like, Tufti, Stephen Few, I thinkhis first book was out at that point,
(04:11):
but really start to get an understandingof why some of the things I'd arrived at
through trial and error over time, youknow, why they work and why some things
work better or worse, and really turnthat around to be able to teach others.
And so I did that at Google, uh, taughtcourses across the organization for a
number of years and around the world.
(04:32):
And then realized that it's not just.
People in technical roles or at atechnology company who need to learn how
to communicate effectively with data.
These aren't skills that we naturallyhave, even though a lot of the things
and we can get into this, a lot ofthe lessons are really Practical and
(04:52):
maybe even obvious once you say them,but until somebody points them out
and shares them, we are sometimesour own worst enemy when it comes to
trying to communicate effectively.
Uh, and so it was, let's see,back in 2012 when I left Google
and started storytelling withdata, uh, which is what I've.
Poured the last decade plus intoreally with the goal of helping people
(05:18):
create graphs that make sense, butalso going beyond the graph to, you
know, you don't want to just show data.
We want to take the data that wework with and learn something new
from it and help communicate thatnew thing to other people so that
we can help drive smarter decisions.
Uh, reinforce that we're doing thingsthe right way or that we should change
(05:39):
how we're doing things and really havesmarter conversations, not about the
data, but using the data to have smarterconversations about the business.
And so we do that mainlythrough workshops.
Uh, there's the book that you mentioned,um, a couple more after that as well.
One focused on practicing anotheron you as the person who is
(06:00):
creating and communicating the data.
And then the latest onefor kids, as you mentioned,
Avery (06:06):
that's such a wild and cool story.
Congratulations on all the success.
I actually attended a, uh,storytelling with data workshop at
my company at ExxonMobil in 2020.
And it was, it was awesome.
And, and obviously I've, I've read thebook and, uh, I actually have multiple
copies, one of all the success in this,this really cool career that you've had.
If you go back to that first job, youknow, one of the things that you said
(06:28):
was that if you designed your charts.
Well, and you use best practices fordata visualization, your boss and your
boss's boss would care about them moreand pay more attention to your work.
And actually I was, I was rereadingyour book and I pulled this quote
and you said, I quickly learned thatspending time on the aesthetic piece,
something my colleagues didn't typicallydo met my work garnered more attention
(06:50):
from my boss and my boss's boss.
And I just want to kind of talkabout that for a second, because.
It's not necessarily that you weredoing better work or that your analysis
was better or it was more meaningful.
It was just easier for them to understand.
And because it was easier forthem to understand, they valued
it more and they valued you more.
Is that true in your career?
Cole (07:10):
I think, yeah, I think it's exactly that, that it became When the
graphs made sense and the messages madesense, it was more of a direct line
into the value that the work was having.
Whereas, if you imagine the same workbeing done, but being communicated
in a really complicated way, or,you know, really going deep into the
(07:30):
statistical methods instead of pullingback to say, What does this mean?
What does this mean for you,the audience, or the person, the
people to whom I'm communicating?
What does it mean for our people?
Business, how do we put that complicatedstuff into words that makes sense to
somebody who wasn't intimately involvedin the process that when you don't
(07:50):
take the time to do that, it can reallyeasily become a barrier to the good
work that's being done actually havingthe impact that it otherwise could.
And that's what I think when we spendtime thinking about how do we make
this make sense to someone else?
How do I look at something and say,all right, this might be what made
(08:11):
sense to me, or it's the view thathelped me reach that aha Eureka moment,
but it doesn't mean that that's thesame view or the same path that's
going to serve my audience best.
And so it really is this paradigmshift because I think often and
I think Especially people intechnical roles, we, we get so used
(08:35):
to seeing things a certain way.
And I think for me, at least asI look back, there was joy in
figuring out the puzzle, right?
Figuring out how the pieces fittogether when it wasn't obvious.
And so I think there's part of somethingin us that wants us to then be able
to kind of show that puzzle to someoneelse, but have it not be clear so that
(08:55):
we can have them experience some ofwhat we did, but that does a total
disservice because what that does isbasically take the value that we could
have added and obfuscate it insteadof saying, all right, I did this work.
I've, I've found, you know, the,the interesting thing now, rather
than me take my audience throughall the details and the work I went
to to get to the interesting thing,it's actually just lead with that.
(09:19):
And we may, in some cases, not evenhave to get into any of the detail.
I think sometimes that.
Feels bad when itshouldn't, that is success.
That means your audience trusts you.
It means they trust your findingbecause I can remember times I can
remember times at Google, I can remembertimes at banking back prior to that
in private equity, where I worked,where my team and I would spend a ton
(09:41):
of time on an analysis or on a study.
And then putting together a reallydense recount of what we did and what
we found in all of the methodology and.
When it didn't get presented afterat the end of all of that work,
that would feel bad when reallythat was a success scenario.
It didn't not get presentedbecause we didn't talk about it.
(10:01):
We talked about it and actually didn'teven need to go into all of that detail
because of the trust over time thatwas established to our stakeholders
were able to go in with the storyand then have the conversation focus
on really understanding that and it.
Understanding how we apply thatto the business going forward.
And it doesn't mean we didn'tneed to spend all that time
putting together the document.
(10:23):
We needed to have that.
We needed to do that work in order toget to the, the answer or the finding
or the interesting thing to communicate.
And there will be times whereyou do need to take your audience
through a lot of that detail.
And so you need to have it there, but.
The dense communication isnot the, the, the goal, right?
Going through that is not the goal.
It's having the impact through the work.
Avery (10:44):
I love that.
And I think in today's society, as muchas all of us might enjoy working on
something we're passionate on, uh, Ithink people rather be doing their hobbies
or spending time with their families.
And so if you can just make your resultsas clear as possible, as quickly as
possible, uh, that bodes well for you.
Because some, sometimes I think.
(11:04):
As technical workers, we wantour work to speak for itself.
Uh, and we want them to recognize,yes, I did all this work to actually
accomplish this, but the sad truthis most businesses don't care.
Just give us the results,tell us why it matters.
And a lot of the time I even saw thispost, um, from Kelly Adams on LinkedIn.
She's like a LinkedIn creator.
She was like the most of the time my bossdoesn't ask me how I, how I even got to.
(11:28):
Like doesn't ask to see my code ever.
It doesn't ask to like actually figureout how I came to my conclusion.
They just trust me to, to do theanalysis and come to the right point.
Cole (11:36):
Well, and I think that's part of the, part of the magic magic.
It's not quite the right word there,but is really assessing a situation and.
Anticipating what is going to be neededand what level of depth you're going to
need to be able to walk someone throughor show someone, uh, because when you
can make that match the situation,that's when when things go really well,
(11:59):
because you could easily take that andsay, okay, well, so my manager trusts me.
And that means.
You know, I still need to bebuttoned up on my work, but maybe I
don't need to show all of my work.
But then as soon as you get the questionback, or you, you, if you misanticipated
that or misread that, and now you have,or you're using that and going in front
of another audience who actually isgoing to want to be convinced of the
(12:21):
robustness of the analysis that wasdone, you need to be able to anticipate
that so that you can meet that.
Need.
I think that is where things mostoften fail, where we create a report
or a presentation for, for ourselvesor for our data for the project and
not specifically for the person or thepeople to whom we're communicating.
(12:44):
That's that paradigm shift I wasreferring to before that when we can
get out of our own heads and reallythink about, all right, here's what I
did, but now how do I make this work forthe people who need to understand it?
And take measures to make it work forthem, both through the visual design
and through how we talk about our work,how we communicate directly, that that's
(13:05):
where all of that can work really well.
Avery (13:07):
So I think if, if I understand what you're saying correctly is your
presentation, your communication,maybe even your, your graphs should
almost dynamically change basedoff of who you're showing it to.
Cole (13:18):
Yeah, I mean, ideally, so if it's a critical scenario and you
have audiences who are, whose needsare sufficiently different, then you
may want to think about, there willbe times where it would make sense
to have different communicationsfor those different audiences.
Now, in practice, that rarely happens.
In practice, we try to createthis one size fits all, but it's
(13:40):
easy through doing that to thennot exactly meet anyone's needs.
So, I think A lot of the time we can getto the good enough scenario where, you
know, if we, if we craft the communicationand it's 80 percent meets this audience
and 80 percent this audience, right,there's some overlap and that's
probably okay, but where audiences arecaring about really different things.
So bring up an example from Google, sincewe talked about this a little bit earlier,
(14:04):
internally, our main audiences were.
Engineers on the one hand, highlytechnical, needed to be convinced
that the methodology was sound,wanted very detailed information.
We needed to get them on boardbefore we even did the research a
lot of the time so that they wouldeventually buy into the results.
And then on the otherhand, we had the staff.
Sales organization whose generalsentiment was leave us alone.
(14:27):
We're the ones out heremaking the company money.
And so for them, we needed to bedirect and short and concise, focused
on what mattered to them and notuntil they needed to act upon it.
And it was like, it was, it was.
After trying to communicate to bothof those audiences simultaneously at
first and just failing for a varietyof reasons that are obvious in
(14:48):
retrospect, that we decided, you knowwhat, that's not the right approach.
We actually do need to communicateto these audiences separately, not
only in what we share and how wetalk through it or show it, but also
even when we communicate to them.
Avery (15:02):
I think there's, there's people listening who, who might be
thinking, well, the analysis is theanalysis, but it's so funny because.
You wouldn't necessarily think this,but the packaging that you put are
around your analysis really matters.
And oftentimes, like if, if let'sjust say we're, we're almost in
the holidays, let's just say I'mgiving you a Christmas present of
some, some new headphones, right?
Like if, if the headphonesjust in a cardboard box.
(15:25):
They're not going to be as valued as ifI put these headphones in like a really
nice, like box that has really good,like opening mechanisms and really good
wrapping paper and a bow and a nice card.
Even
Cole (15:37):
though even the wrapping paper, right.
It's going to be differentaround the holidays than around
birthday or something else.
So yeah, it's the same contents,but the way you present it
will and should be different.
Avery (15:48):
Let's, let's talk about some of the ways that, that we can present well.
So we talked about like.
Addressing your audience.
So if you're, if you're talking toyour boss's boss, you're going to
present it differently than to likeyour colleague or a engineer or a
programmer or something like that.
What are some other things that peopleshould know when they're, when they're
making data visualization and presenting?
Cole (16:06):
I think one thing to be clear on is that you likely know the situation, you
know, the data better than anyone else.
And what happens through that Is whenyou look at the graph you made or the
slide you made, it's super obviousto you where to look and what to see.
But to make those things as obviousto someone else, it means you have
to do things to make that happen.
(16:28):
And so when it comes to the designof the graphs and the slides, you
can think about how you might employvisual contrast, for example, sparing
use of color to show your audience.
where you want them to look and thenusing words either through your spoken
narrative or written directly with thegraph or on the slide or a combination
(16:50):
of those two things that tell youraudience why you want them to look there.
And a lot of the time, justthose two simple things.
So making it clear where to look andwhat to see, even if it's maybe not
the perfect graph type for what you'reusing, or there are some, you know,
there's some clutter or, or somethingelse, uh, You can still get your
message across and it gets the job done.
Avery (17:12):
That's something that I think you, you cover really
well in storytelling with data.
Um, just like the idea of how dowe, how do we declutter our graphs?
Because you know, it's funny, you're,you're, you're big enough that, um, maybe,
maybe, you know, the answer to this.
Um, but, but in this book, like you doall of this, I'll call it pretty ization
of, of data visualization in Excel.
All of the graphs that you do inthe book are, are done using Excel.
(17:35):
And what I mean by, by you're bigenough, like your brand and your, uh,
recognition has gotten to the pointwhere it's like, can't Excel start?
Like, It's actually a lot of work tomake a graph look pretty in Excel.
Can we talk to someone at Microsoftand have it like he defaulted better?
Cause one of the things that Microsoftdefaults does is if you have like
eight different lines on your chart,they're the all different colors.
(17:57):
And one of the things that, you know,you talk about is like, okay, let's only
use color on one or two of these lines.
Like why, why does Excel make it so hard?
Cole (18:05):
Well, I don't think so.
No tools trying to makeyour life miserable, right?
Um, that, uh, any tool is trying tomeet the needs of so many different
situations, all at once that it's nevergoing to exactly meet any of those, right?
Take the example.
You say like, why, whyis everything colorful?
Well, because if, The legend is,you know, off to the side or at the
bottom, which is how that charts goingto be at the beginning, then you have
(18:27):
to have color as a differentiator.
So you have some way to tie those back.
The way that you can get aroundthat when you are intentionally
designing is you figure out, well,where could I label those lines where
proximity is the thing that ties theminstead of the similarity of color?
But yeah.
You have to make that decision inlight of the data because it depends
on how it lays out on the graph to say,well, can I label it within the graph?
(18:50):
Or is that going to make it hard to read?
Or there simply isn't space to do so.
And so there are all thesedecisions that we make every
time we're working with data.
And you're even, you're implicitlymaking decisions when you're not
changing these default things,because then you're letting the
tool make the decisions for you.
And.
It's funny because I, I had thoughtfor a long time, like, Oh, I should
(19:11):
make myself my own template in Exceland make, make it just really easy.
So I can have the startingpoint that I want.
And I made several of these yearsago and found that I never used
them because for me, part of theprocess was looking at the thing that
was never going to be quite right.
And then figuring out how to intentionallymake it work for what I need.
And I think there's value inthat and in the time and thought
(19:36):
that it takes to do that.
But we have to be intentional aboutdoing it because otherwise we can
just plug data into any tool andit will spit out something and it's
never going to be what we need.
You know, we pick on Excel, butthis is not unique to Excel.
Uh, it's, it's anythingyou're working with.
And so I think there's an important partof the process that comes into play when
(19:56):
we are taking the time to make thosedecisions and change the default settings
to make them work for our given situation.
I guess it takes
Avery (20:05):
time.
It takes human brain and it's justthe laziness inside of me that
wants it done automatically, butit's also, it's also probably.
Something to look forward to forme and our listeners, because it
also keeps us employed, right?
Because if it was done out ofthe box automatically, perfectly,
then maybe we wouldn't have jobs,but it requires a human brain.
So that's good.
I want to, I want to transition intotalking about, uh, you know, a lot of
(20:27):
people who listen to this podcast aretrying to land their first day at a job.
They're transitioning into data careers.
Um, maybe they're teachers or physicaltherapists, or they're in sales.
Do you think there's room for them?
To, to stand out using data visualizationand ultimately pivot into analytics.
Cole (20:43):
Yeah, I, so I would say for the person who is trying to make
that pivot and is in a role that isnot working with data on a regular
basis, currently, first thing is tolook for opportunities where you.
Where is their data and what you'redoing today that you could work with?
Because that almost always exists.
If it really doesn't, then you can lookelsewhere in the community for ways
(21:06):
of practicing and honing those skills.
For example, we have our onlinestorytelling with data community
where we host a monthly challenge.
That's always something very, um,specific in theme, but open ended
in term of how you address it,where typically you're finding data.
Data that's of interest to youand doing something with it.
I think the one we have goingon currently, uh, so November,
(21:27):
2024 is just finding a graphin the wild that isn't perfect.
And then taking steps to improve it.
Uh, we also have an exercise bank thathas hundreds, probably at this point
of exercises that are more focused ondeveloping a specific skill where the
data, the instructions, it's all about.
All provided.
And so all you need is, you know,five minutes, 30 minutes and something
(21:51):
you want to work on, uh, in termsof practicing, whether it's, you
know, like we talked about, maybeit's taking a graph and figuring out
how to change the color of just oneline and make everything else green.
Gray or, uh, designing a slide.
Um, and there's a varietyof other things as well.
So looking for ways to practice to honeyour skills, which I would say again,
first look within your role to see ifthere's anything you could be doing there
(22:15):
or more broadly at your organization.
Some will allow there to be moonlightingor, you know, shy of an internal transfer,
but still getting some exposure toskills that you would want to be using.
So look for those, if not in yourcurrent role, then look to the community
to see where you might do that.
And then I think for anyone who is notcurrently in a data role, but wanting
(22:37):
to get to where they're working withdata, visualizing data, communicating
data, the thing to not overlook is howyou communicate, how you communicate
verbally, and how you talk about yourselfin terms of, you know, how do you
introduce yourself, or how do you portrayYour work history and your skills when
(22:59):
you are interviewing or doing thingslike that and spending time working on
that, uh, and also how you engage youraudience through the way that you speak.
Um, because this is one of the thingsthat over the years, and I think again,
as I look back, it's not surprisingand seems obvious, but it wasn't until.
Fairly long into things that it reallybecame clear to me that the graph or the
(23:23):
data visualization is really just onepart of the puzzle because you can have
the most beautiful graph in the world.
And if you can't subsequently talkabout that in a way that makes
other people want to listen andpay attention and do something
with it, the beautiful graph fails.
And so I think both for those who arewanting to transition into data roles.
(23:47):
Also, I would say for those who arecurrently in a role working with data
and communicating data work on yourselfbecause you can be just as strategic
when it comes to how you speak about yourwork, how you portray yourself, how you
communicate as you can with, you know,what graph you're choosing and how you're
choosing to portray things visually.
(24:09):
And when those two go together,you've made a good graph.
And you can get other people'sattention through how you speak
and through the passion you showfor the work that you've done.
That becomes a reallypowerful combination.
Avery (24:23):
It's, it's a great point.
Um, and whether we like it ornot, we live in a world, uh, where
your appearance really matters.
You know, it's not, if you're trying toland the data job right now, it's not the.
The smartest person or the person who'sbest at at sequel that lands the job.
It's the person who's able to bestportray their skills that they'd be,
you know, able to help the company.
(24:44):
And the same is true.
Once you land a job, it's not necessarilythe best employee that gets the promotion.
It's the employee that appears thebest or gets portrayed as the best.
And they, you know, itreally doesn't stop until.
You become like the CEO.
And then even then likeappearances still really matter.
So it's, it's maybe unfortunate and you'dwant maybe just pure talents and skill
(25:05):
to win, but the way that I think this is
Cole (25:07):
part of the talent as well, being able to being able to communicate
adeptly and one resource that I'll pointpeople to in case like, okay, I get
this, but how do I actually do that inthe yellow book storytelling with you?
This is the one that goes back to.
There's data visualization in it,but it goes beyond the data into how
can you develop yourself to be ableto plan, create and deliver content?
(25:33):
Uh, the penultimate chapter is craftingthe story of you, and it's basically
taking people step by step throughhow you can be really thoughtful and
robust in how you plan and how you talkabout the story of yourself, which can
be useful in a variety of scenarios.
And it's actually, it really, it becomesan interesting case study and way to
(25:56):
practice a lot of the other thingsthat are introduced that are grounded
more in how you would communicate data.
But things like, you know,brainstorming on sticky notes and
really considering your audience andmaking all of that work together with.
Using a subject that peopleknow really well themselves.
Um, but then after going through thatchapter, you can come out of it with a
(26:19):
really clear plan and ways to practicewhen it comes to talking about yourself
that you can then translate intotalking about other things as well.
Avery (26:29):
That sounds like a superpower to master that.
I don't have the yellow book, somaybe I'll, I'll have to look it up.
Look into that one.
Let's talk about your,your brand new book.
Daphne draws data.
Uh, tell us a little bit about whatit is and why you decided to do this.
Yeah.
Look at it.
Cole (26:43):
Yeah.
So Daphne is a delightful pinkdragon who has a unique talent.
She enjoys drawing.
That's not so unique.
Well, maybe for a dragon it is, but thething that she likes to draw the most is.
Data.
She likes to draw graphs.
And so the story is a really fun, I mean,it's a picture book, really fun, brightly
(27:06):
illustrated, uh, about Daphne's adventure.
She decides, well, if she's not beingappreciated at home, she's going to go
off and find a place where she can fit in.
And so she goes to the jungleand outer space and underwater
and all sorts of places.
And in each location, sheencounters some creatures.
Uh, and a problem they'refacing, and then helps them solve
(27:29):
their problem by drawing data.
So she collects it, she draws itin very pictorial forms of graphs.
Uh, the word graph I don't thinkis used once in the book though.
It's really introducing the conceptsthrough story and through pictures.
And then, uh, I won't giveaway the ending, uh, other
than to say it's a happy one.
And the story ends, but then the bookcontinues into a graph glossary that goes
(27:53):
more into what the graphs were that Daphneused over the course of her adventure.
So there's a page each devotedto bar charts, line graphs.
pie charts and scatter plots, uh,showing examples from her adventures,
helping kids understand how to read themwhen they work, and then introducing
activities that kids can undertake usingdata that's of interest to them because
(28:17):
one great Parallel that we can makeacross adults communicating with data
and kids and the use of data and graphsis to make it about something that's
meaningful and something that can beacted upon because when I see my kids
come home with graphs from school, sofar, I've been pretty disappointed because
they're graphing things like the weather.
(28:38):
The weather in September,okay, it was sunny.
You experienced that.
It's not so interesting now to draw itin a graph or they'll do things like
roll a die, uh, you know, a bunch oftimes to see that, you know, and then
graph it to see, okay, I rolled all thenumbers about the same amount of times.
This isn't anything that they canthen Use to understand things better.
(28:59):
Uh, and so I really would like to makethe data that we're having kids work with
be something that they're interested in,because I think this is such a, it could
be such an amazing way into mathematicsin a way that isn't portrayed as boring
or complicated or completely abstractwhen it comes to kids day to day.
(29:21):
Uh, so, you know, let's have them track.
How many hours they're spending on ascreen every day and how they feel plot
that, or, uh, you know, where's theirfavorite place to read and, you know,
how might we then emulate some of thosethings in the classroom to promote
more reading, like things that we canactually, uh, help kids learn about
themselves and about the world aroundthem in ways that is fun and engaging
(29:45):
because what I've seen through my kids.
And their friends is thatkids are fantastic and love
doing a couple of things.
One, asking questions,particularly like, I don't know,
kindergarten, first, second grade.
There's no filter yet and kidsare so curious and they ask
questions about everything.
And if we could teach kids how tohone and get really good at asking
(30:09):
questions that can subsequently beanswered with data, that is going to
be an amazing foundation for everyone.
Any sort of problem solving,critical thinking, analytical
career, and they also love drawing.
And so if we can let them take some ofthat creativity and do it with a graph
and with numbers and let kids approachthat creatively, I think it's a very
(30:33):
refreshing change from math beingsomething that's either right or wrong
because graphs, there's more leeway.
Uh, there can be creativity.
People can approach things.
differently, and we can celebratethat and learn from that rather
than say, no, don't do it that way.
Do it this way.
And so for me, I think it was acombination of just going back to
the impetus for writing the book, acombination of, you know, seeing the
(30:55):
adults who we teach and so many saying,I wish I had learned this sooner or
earlier, and then seeing my kids andhow, just how they learn about the
world around them, how they develop.
Language and logic and realizing we couldtake the visual language of numbers and
(31:15):
introduce that a lot earlier than we do,sort of those two things coming together.
I think there's an opportunity to reallyhelp our kids recognize this superpower
of comfort with numbers and askingquestions and answering those questions
and drawing and plotting things that,um, It'll be a great foundation for
(31:37):
them for so many things going forward.
Avery (31:39):
You're building the next generation of data analysts and a
data viz specialist, a ripe young age.
So, uh, that is very cool.
Where can people find this book?
Cole (31:50):
Oh, anywhere books are sold.
So yeah, favorite independent bookseller.
You can order it.
It's on Amazon.
Uh, and, uh, yeah, is around the world.
Avery (31:58):
Okay.
Awesome.
Well, I haven't checked it out yet.
I'll have to check it out.
I'll have to check out the yellow book.
Um, but I'm also a huge fan of, of thestorytelling with data original book.
So if you guys haven't checked thoseout, be sure to check them out.
We'll have links to all of themin the show notes down below.
Uh, Cole, thank you so muchfor coming on our show.
We appreciate it.
Cole (32:16):
Thanks for having me, Avery.
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