142: Meet The Woman Who Changed Data Storytelling Forever (Cole Knafflic)

Cole: 00:00:00

You can have the most beautiful graph in the world, and if you can't

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subsequently talk about that in a way

that makes other people want to listen

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and pay attention and do something

with it, the beautiful graph fails.

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Avery: Okay.

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Cole, welcome to the Data Career Podcast.

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So glad to have you.

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Hi, Avery.

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Great

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Cole: to be here.

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Thank you.

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Avery: Yes.

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So if you guys haven't

heard of Cole before.

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Uh, she is the author of the

book Storytelling with Data.

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It is one of the, uh, best books on

storytelling with data, but specifically

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like data visualization and how to

present and convince people at your

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workplace, uh, of your findings.

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She's also the, the author of the new

book, Daphne Draws Data, which we'll

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talk about in this episode as well, which

is, which is more for kids, right, Cole?

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Cole: It is, yeah.

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Younger audience, but interestingly, it's

a lot of the same lessons that apply.

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Avery: Okay.

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And let's, let's get into

some of those, those lessons.

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Um, I want to start off with

actually a little bit about, about

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your career because you studied

mathematics in college, right?

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Cole: Yeah.

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Math.

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I have an undergrad in math, uh,

or applied math and, uh, an MBA.

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Avery: Okay.

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And when you graduated, did you ever see

yourself becoming like the author of a

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storytelling with data book and, and kind

of this whole career that you have now?

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Yeah.

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Cole: No, it didn't exist as as a career.

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I don't think at that point I, as I

mentioned, I majored in math and I, I

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remember getting into my senior year in

college and still trying to figure out

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what do I want to be when I grow up?

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And I remember going to a series of

sessions that were, you know, like,

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What profession to have as a math major.

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And so I listened to the actuaries

and the, the finance people, and

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I had this moment of, or longer

than a moment, you know, the, the

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crisis of like, Ooh, none of these

careers sound like what I want to do.

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Uh, and I remember then getting some of

the best advice that I have received,

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I think, as I look back from my

mother, which was finish the degree.

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And so, so.

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Finished my math degree and

then got a job in banking.

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Uh, not in finance though, in

credit risk management, where I was

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building statistical models, uh,

forecasting loss, try to understand

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how we should reserve for the bank.

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And I loved, I loved the technical

side of it, but also being able to

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Bringing creativity in and where I

brought creativity and was in how I

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was visualizing the data, simple things

like colors and some inadvisable things.

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As I look back like shadows

or cram as many graphs on a

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slide as you can get on there.

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But interestingly, what I found

over time was when I spent.

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Time and thought on the design of the

visuals, people ended up spending more

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time with my work, and so that became

a self reinforcing thing where other

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people would come to me, and I became

the sort of internal expert when it comes

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to how do you show data fast forward

through a few career changes, and I.

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Was it Google still using a lot of

the same statistical methods, but

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now in an analytics role in HR.

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So people analytics forecasting

things like who's likely to leave the

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organization and when, and what sort of

things can we test out to change that?

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And I still spent a lot of time

on the visuals and the team I was

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on, we were doing a lot of really

complicated things that we needed to

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communicate to the engineers at the

organization and the sales people at the

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organization and everybody in between.

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So audiences with widely varying.

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Needs, technical skills,

familiarity with data.

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And so that was really interesting

to see how do you change how you

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show things depending on who you're

showing it to and where, where is that?

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How can that be more successful when

you think about it from that standpoint?

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So also, while I was at Google, I part of

a training program or part of developing

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a training program where I was creating

coursework on data visualization,

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which was fantastic because it gave

me a chance to pause and research and

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read everything I could get my hands

on at that point, which was not a lot.

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It was like, Tufti, Stephen Few, I think

his first book was out at that point,

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but really start to get an understanding

of why some of the things I'd arrived at

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through trial and error over time, you

know, why they work and why some things

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work better or worse, and really turn

that around to be able to teach others.

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And so I did that at Google, uh, taught

courses across the organization for a

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number of years and around the world.

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And then realized that it's not just.

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People in technical roles or at a

technology company who need to learn how

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to communicate effectively with data.

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These aren't skills that we naturally

have, even though a lot of the things

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and we can get into this, a lot of

the lessons are really Practical and

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maybe even obvious once you say them,

but until somebody points them out

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and shares them, we are sometimes

our own worst enemy when it comes to

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trying to communicate effectively.

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Uh, and so it was, let's see,

back in: 2012

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and started storytelling with

data, uh, which is what I've.

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Poured the last decade plus into

really with the goal of helping people

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create graphs that make sense, but

also going beyond the graph to, you

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know, you don't want to just show data.

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We want to take the data that we

work with and learn something new

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from it and help communicate that

new thing to other people so that

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we can help drive smarter decisions.

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Uh, reinforce that we're doing things

the right way or that we should change

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how we're doing things and really have

smarter conversations, not about the

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data, but using the data to have smarter

conversations about the business.

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And so we do that mainly

through workshops.

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Uh, there's the book that you mentioned,

um, a couple more after that as well.

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One focused on practicing another

on you as the person who is

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creating and communicating the data.

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And then the latest one

for kids, as you mentioned,

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Avery: that's such a wild and cool story.

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Congratulations on all the success.

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I actually attended a, uh,

storytelling with data workshop at

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my company at ExxonMobil in 2020.

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And it was, it was awesome.

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And, and obviously I've, I've read the

book and, uh, I actually have multiple

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copies, one of all the success in this,

this really cool career that you've had.

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If you go back to that first job, you

know, one of the things that you said

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was that if you designed your charts.

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Well, and you use best practices for

data visualization, your boss and your

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boss's boss would care about them more

and pay more attention to your work.

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And actually I was, I was rereading

your book and I pulled this quote

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and you said, I quickly learned that

spending time on the aesthetic piece,

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something my colleagues didn't typically

do met my work garnered more attention

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from my boss and my boss's boss.

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And I just want to kind of talk

about that for a second, because.

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It's not necessarily that you were

doing better work or that your analysis

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was better or it was more meaningful.

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It was just easier for them to understand.

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And because it was easier for

them to understand, they valued

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it more and they valued you more.

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Is that true in your career?

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Cole: I think, yeah, I think it's

exactly that, that it became When the

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graphs made sense and the messages made

sense, it was more of a direct line

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into the value that the work was having.

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Whereas, if you imagine the same work

being done, but being communicated

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in a really complicated way, or,

you know, really going deep into the

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statistical methods instead of pulling

back to say, What does this mean?

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What does this mean for you,

the audience, or the person, the

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people to whom I'm communicating?

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What does it mean for our people?

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Business, how do we put that complicated

stuff into words that makes sense to

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somebody who wasn't intimately involved

in the process that when you don't

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take the time to do that, it can really

easily become a barrier to the good

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work that's being done actually having

the impact that it otherwise could.

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And that's what I think when we spend

time thinking about how do we make

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this make sense to someone else?

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How do I look at something and say,

all right, this might be what made

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sense to me, or it's the view that

helped me reach that aha Eureka moment,

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but it doesn't mean that that's the

same view or the same path that's

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going to serve my audience best.

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And so it really is this paradigm

shift because I think often and

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I think Especially people in

technical roles, we, we get so used

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to seeing things a certain way.

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And I think for me, at least as

I look back, there was joy in

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figuring out the puzzle, right?

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Figuring out how the pieces fit

together when it wasn't obvious.

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And so I think there's part of something

in us that wants us to then be able

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to kind of show that puzzle to someone

else, but have it not be clear so that

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we can have them experience some of

what we did, but that does a total

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disservice because what that does is

basically take the value that we could

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have added and obfuscate it instead

of saying, all right, I did this work.

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I've, I've found, you know, the,

the interesting thing now, rather

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than me take my audience through

all the details and the work I went

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to to get to the interesting thing,

it's actually just lead with that.

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And we may, in some cases, not even

have to get into any of the detail.

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I think sometimes that.

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Feels bad when it

shouldn't, that is success.

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That means your audience trusts you.

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It means they trust your finding

because I can remember times I can

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remember times at Google, I can remember

times at banking back prior to that

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in private equity, where I worked,

where my team and I would spend a ton

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of time on an analysis or on a study.

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And then putting together a really

dense recount of what we did and what

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we found in all of the methodology and.

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When it didn't get presented after

at the end of all of that work,

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that would feel bad when really

that was a success scenario.

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It didn't not get presented

because we didn't talk about it.

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We talked about it and actually didn't

even need to go into all of that detail

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because of the trust over time that

was established to our stakeholders

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were able to go in with the story

and then have the conversation focus

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on really understanding that and it.

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Understanding how we apply that

to the business going forward.

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And it doesn't mean we didn't

need to spend all that time

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putting together the document.

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We needed to have that.

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We needed to do that work in order to

get to the, the answer or the finding

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or the interesting thing to communicate.

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And there will be times where

you do need to take your audience

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through a lot of that detail.

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And so you need to have it there, but.

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The dense communication is

not the, the, the goal, right?

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Going through that is not the goal.

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It's having the impact through the work.

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Avery: I love that.

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And I think in today's society, as much

as all of us might enjoy working on

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something we're passionate on, uh, I

think people rather be doing their hobbies

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or spending time with their families.

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And so if you can just make your results

as clear as possible, as quickly as

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possible, uh, that bodes well for you.

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Because some, sometimes I think.

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As technical workers, we want

our work to speak for itself.

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Uh, and we want them to recognize,

yes, I did all this work to actually

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accomplish this, but the sad truth

is most businesses don't care.

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Just give us the results,

tell us why it matters.

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And a lot of the time I even saw this

post, um, from Kelly Adams on LinkedIn.

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She's like a LinkedIn creator.

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She was like the most of the time my boss

doesn't ask me how I, how I even got to.

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Like doesn't ask to see my code ever.

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It doesn't ask to like actually figure

out how I came to my conclusion.

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They just trust me to, to do the

analysis and come to the right point.

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Cole: Well, and I think that's part

of the, part of the magic magic.

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It's not quite the right word there,

but is really assessing a situation and.

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Anticipating what is going to be needed

and what level of depth you're going to

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need to be able to walk someone through

or show someone, uh, because when you

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can make that match the situation,

that's when when things go really well,

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because you could easily take that and

say, okay, well, so my manager trusts me.

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And that means.

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You know, I still need to be

buttoned up on my work, but maybe I

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don't need to show all of my work.

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But then as soon as you get the question

back, or you, you, if you misanticipated

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that or misread that, and now you have,

or you're using that and going in front

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of another audience who actually is

going to want to be convinced of the

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robustness of the analysis that was

done, you need to be able to anticipate

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that so that you can meet that.

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Need.

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I think that is where things most

often fail, where we create a report

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or a presentation for, for ourselves

or for our data for the project and

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not specifically for the person or the

people to whom we're communicating.

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That's that paradigm shift I was

referring to before that when we can

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get out of our own heads and really

think about, all right, here's what I

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did, but now how do I make this work for

the people who need to understand it?

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And take measures to make it work for

them, both through the visual design

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and through how we talk about our work,

how we communicate directly, that that's

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where all of that can work really well.

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Avery: So I think if, if I understand

what you're saying correctly is your

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presentation, your communication,

maybe even your, your graphs should

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almost dynamically change based

off of who you're showing it to.

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Cole: Yeah, I mean, ideally, so if

it's a critical scenario and you

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have audiences who are, whose needs

are sufficiently different, then you

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may want to think about, there will

be times where it would make sense

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to have different communications

for those different audiences.

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Now, in practice, that rarely happens.

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In practice, we try to create

this one size fits all, but it's

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easy through doing that to then

not exactly meet anyone's needs.

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So, I think A lot of the time we can get

to the good enough scenario where, you

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know, if we, if we craft the communication

and it's 80 percent meets this audience

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and 80 percent this audience, right,

there's some overlap and that's

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probably okay, but where audiences are

caring about really different things.

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So bring up an example from Google, since

we talked about this a little bit earlier,

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internally, our main audiences were.

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Engineers on the one hand, highly

technical, needed to be convinced

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that the methodology was sound,

wanted very detailed information.

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We needed to get them on board

before we even did the research a

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lot of the time so that they would

eventually buy into the results.

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And then on the other

hand, we had the staff.

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Sales organization whose general

sentiment was leave us alone.

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We're the ones out here

making the company money.

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And so for them, we needed to be

direct and short and concise, focused

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on what mattered to them and not

until they needed to act upon it.

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And it was like, it was, it was.

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After trying to communicate to both

of those audiences simultaneously at

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first and just failing for a variety

of reasons that are obvious in

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retrospect, that we decided, you know

what, that's not the right approach.

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We actually do need to communicate

to these audiences separately, not

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only in what we share and how we

talk through it or show it, but also

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even when we communicate to them.

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Avery: I think there's, there's

people listening who, who might be

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thinking, well, the analysis is the

analysis, but it's so funny because.

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You wouldn't necessarily think this,

but the packaging that you put are

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around your analysis really matters.

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And oftentimes, like if, if let's

just say we're, we're almost in

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the holidays, let's just say I'm

giving you a Christmas present of

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some, some new headphones, right?

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Like if, if the headphones

just in a cardboard box.

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They're not going to be as valued as if

I put these headphones in like a really

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nice, like box that has really good,

like opening mechanisms and really good

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wrapping paper and a bow and a nice card.

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Even

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Cole: though even the

wrapping paper, right.

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It's going to be different

around the holidays than around

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birthday or something else.

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So yeah, it's the same contents,

but the way you present it

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will and should be different.

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Avery: Let's, let's talk about some of

the ways that, that we can present well.

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So we talked about like.

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Addressing your audience.

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So if you're, if you're talking to

your boss's boss, you're going to

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present it differently than to like

your colleague or a engineer or a

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programmer or something like that.

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What are some other things that people

should know when they're, when they're

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making data visualization and presenting?

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Cole: I think one thing to be clear on is

that you likely know the situation, you

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know, the data better than anyone else.

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And what happens through that Is when

you look at the graph you made or the

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slide you made, it's super obvious

to you where to look and what to see.

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But to make those things as obvious

to someone else, it means you have

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to do things to make that happen.

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And so when it comes to the design

of the graphs and the slides, you

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can think about how you might employ

visual contrast, for example, sparing

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use of color to show your audience.

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where you want them to look and then

using words either through your spoken

297

: 00:16:45

narrative or written directly with the

graph or on the slide or a combination

298

: 00:16:50

of those two things that tell your

audience why you want them to look there.

299

: 00:16:55

And a lot of the time, just

those two simple things.

300

: 00:16:58

So making it clear where to look and

what to see, even if it's maybe not

301

: 00:17:02

the perfect graph type for what you're

using, or there are some, you know,

302

: 00:17:05

there's some clutter or, or something

else, uh, You can still get your

303

: 00:17:09

message across and it gets the job done.

304

: 00:17:12

Avery: That's something that

I think you, you cover really

305

: 00:17:14

well in storytelling with data.

306

: 00:17:16

Um, just like the idea of how do

we, how do we declutter our graphs?

307

: 00:17:20

Because you know, it's funny, you're,

you're, you're big enough that, um, maybe,

308

: 00:17:24

maybe, you know, the answer to this.

309

: 00:17:25

Um, but, but in this book, like you do

all of this, I'll call it pretty ization

310

: 00:17:30

of, of data visualization in Excel.

311

: 00:17:32

All of the graphs that you do in

the book are, are done using Excel.

312

: 00:17:35

And what I mean by, by you're big

enough, like your brand and your, uh,

313

: 00:17:39

recognition has gotten to the point

where it's like, can't Excel start?

314

: 00:17:43

Like, It's actually a lot of work to

make a graph look pretty in Excel.

315

: 00:17:46

Can we talk to someone at Microsoft

and have it like he defaulted better?

316

: 00:17:50

Cause one of the things that Microsoft

defaults does is if you have like

317

: 00:17:54

eight different lines on your chart,

they're the all different colors.

318

: 00:17:57

And one of the things that, you know,

you talk about is like, okay, let's only

319

: 00:18:00

use color on one or two of these lines.

320

: 00:18:02

Like why, why does Excel make it so hard?

321

: 00:18:05

Cole: Well, I don't think so.

322

: 00:18:06

No tools trying to make

your life miserable, right?

323

: 00:18:09

Um, that, uh, any tool is trying to

meet the needs of so many different

324

: 00:18:12

situations, all at once that it's never

going to exactly meet any of those, right?

325

: 00:18:16

Take the example.

326

: 00:18:17

You say like, why, why

is everything colorful?

327

: 00:18:19

Well, because if, The legend is,

you know, off to the side or at the

328

: 00:18:24

bottom, which is how that charts going

to be at the beginning, then you have

329

: 00:18:27

to have color as a differentiator.

330

: 00:18:29

So you have some way to tie those back.

331

: 00:18:31

The way that you can get around

that when you are intentionally

332

: 00:18:34

designing is you figure out, well,

where could I label those lines where

333

: 00:18:37

proximity is the thing that ties them

instead of the similarity of color?

334

: 00:18:41

But yeah.

335

: 00:18:42

You have to make that decision in

light of the data because it depends

336

: 00:18:45

on how it lays out on the graph to say,

well, can I label it within the graph?

337

: 00:18:50

Or is that going to make it hard to read?

338

: 00:18:52

Or there simply isn't space to do so.

339

: 00:18:54

And so there are all these

decisions that we make every

340

: 00:18:57

time we're working with data.

341

: 00:18:58

And you're even, you're implicitly

making decisions when you're not

342

: 00:19:02

changing these default things,

because then you're letting the

343

: 00:19:05

tool make the decisions for you.

344

: 00:19:07

And.

345

: 00:19:08

It's funny because I, I had thought

for a long time, like, Oh, I should

346

: 00:19:11

make myself my own template in Excel

and make, make it just really easy.

347

: 00:19:15

So I can have the starting

point that I want.

348

: 00:19:17

And I made several of these years

ago and found that I never used

349

: 00:19:21

them because for me, part of the

process was looking at the thing that

350

: 00:19:25

was never going to be quite right.

351

: 00:19:27

And then figuring out how to intentionally

make it work for what I need.

352

: 00:19:31

And I think there's value in

that and in the time and thought

353

: 00:19:36

that it takes to do that.

354

: 00:19:37

But we have to be intentional about

doing it because otherwise we can

355

: 00:19:41

just plug data into any tool and

it will spit out something and it's

356

: 00:19:45

never going to be what we need.

357

: 00:19:46

You know, we pick on Excel, but

this is not unique to Excel.

358

: 00:19:49

Uh, it's, it's anything

you're working with.

359

: 00:19:52

And so I think there's an important part

of the process that comes into play when

360

: 00:19:56

we are taking the time to make those

decisions and change the default settings

361

: 00:20:01

to make them work for our given situation.

362

: 00:20:04

I guess it takes

363

: 00:20:05

Avery: time.

364

: 00:20:05

It takes human brain and it's just

the laziness inside of me that

365

: 00:20:09

wants it done automatically, but

it's also, it's also probably.

366

: 00:20:12

Something to look forward to for

me and our listeners, because it

367

: 00:20:15

also keeps us employed, right?

368

: 00:20:16

Because if it was done out of

the box automatically, perfectly,

369

: 00:20:20

then maybe we wouldn't have jobs,

but it requires a human brain.

370

: 00:20:23

So that's good.

371

: 00:20:23

I want to, I want to transition into

talking about, uh, you know, a lot of

372

: 00:20:27

people who listen to this podcast are

trying to land their first day at a job.

373

: 00:20:30

They're transitioning into data careers.

374

: 00:20:32

Um, maybe they're teachers or physical

therapists, or they're in sales.

375

: 00:20:36

Do you think there's room for them?

376

: 00:20:38

To, to stand out using data visualization

and ultimately pivot into analytics.

377

: 00:20:43

Cole: Yeah, I, so I would say for

the person who is trying to make

378

: 00:20:46

that pivot and is in a role that is

not working with data on a regular

379

: 00:20:51

basis, currently, first thing is to

look for opportunities where you.

380

: 00:20:55

Where is their data and what you're

doing today that you could work with?

381

: 00:20:59

Because that almost always exists.

382

: 00:21:01

If it really doesn't, then you can look

elsewhere in the community for ways

383

: 00:21:06

of practicing and honing those skills.

384

: 00:21:08

For example, we have our online

storytelling with data community

385

: 00:21:11

where we host a monthly challenge.

386

: 00:21:13

That's always something very, um,

specific in theme, but open ended

387

: 00:21:17

in term of how you address it,

where typically you're finding data.

388

: 00:21:20

Data that's of interest to you

and doing something with it.

389

: 00:21:24

I think the one we have going

on currently, uh, so November,

390

: 00:21:27

2024 is just finding a graph

in the wild that isn't perfect.

391

: 00:21:32

And then taking steps to improve it.

392

: 00:21:34

Uh, we also have an exercise bank that

has hundreds, probably at this point

393

: 00:21:40

of exercises that are more focused on

developing a specific skill where the

394

: 00:21:44

data, the instructions, it's all about.

395

: 00:21:46

All provided.

396

: 00:21:47

And so all you need is, you know,

five minutes, 30 minutes and something

397

: 00:21:51

you want to work on, uh, in terms

of practicing, whether it's, you

398

: 00:21:55

know, like we talked about, maybe

it's taking a graph and figuring out

399

: 00:21:58

how to change the color of just one

line and make everything else green.

400

: 00:22:01

Gray or, uh, designing a slide.

401

: 00:22:05

Um, and there's a variety

of other things as well.

402

: 00:22:07

So looking for ways to practice to hone

your skills, which I would say again,

403

: 00:22:11

first look within your role to see if

there's anything you could be doing there

404

: 00:22:15

or more broadly at your organization.

405

: 00:22:17

Some will allow there to be moonlighting

or, you know, shy of an internal transfer,

406

: 00:22:22

but still getting some exposure to

skills that you would want to be using.

407

: 00:22:26

So look for those, if not in your

current role, then look to the community

408

: 00:22:30

to see where you might do that.

409

: 00:22:32

And then I think for anyone who is not

currently in a data role, but wanting

410

: 00:22:37

to get to where they're working with

data, visualizing data, communicating

411

: 00:22:41

data, the thing to not overlook is how

you communicate, how you communicate

412

: 00:22:48

verbally, and how you talk about yourself

in terms of, you know, how do you

413

: 00:22:53

introduce yourself, or how do you portray

Your work history and your skills when

414

: 00:22:59

you are interviewing or doing things

like that and spending time working on

415

: 00:23:03

that, uh, and also how you engage your

audience through the way that you speak.

416

: 00:23:09

Um, because this is one of the things

that over the years, and I think again,

417

: 00:23:13

as I look back, it's not surprising

and seems obvious, but it wasn't until.

418

: 00:23:17

Fairly long into things that it really

became clear to me that the graph or the

419

: 00:23:23

data visualization is really just one

part of the puzzle because you can have

420

: 00:23:28

the most beautiful graph in the world.

421

: 00:23:30

And if you can't subsequently talk

about that in a way that makes

422

: 00:23:34

other people want to listen and

pay attention and do something

423

: 00:23:38

with it, the beautiful graph fails.

424

: 00:23:41

And so I think both for those who are

wanting to transition into data roles.

425

: 00:23:47

Also, I would say for those who are

currently in a role working with data

426

: 00:23:51

and communicating data work on yourself

because you can be just as strategic

427

: 00:23:57

when it comes to how you speak about your

work, how you portray yourself, how you

428

: 00:24:01

communicate as you can with, you know,

what graph you're choosing and how you're

429

: 00:24:05

choosing to portray things visually.

430

: 00:24:09

And when those two go together,

you've made a good graph.

431

: 00:24:12

And you can get other people's

attention through how you speak

432

: 00:24:15

and through the passion you show

for the work that you've done.

433

: 00:24:19

That becomes a really

powerful combination.

434

: 00:24:23

Avery: It's, it's a great point.

435

: 00:24:25

Um, and whether we like it or

not, we live in a world, uh, where

436

: 00:24:29

your appearance really matters.

437

: 00:24:31

You know, it's not, if you're trying to

land the data job right now, it's not the.

438

: 00:24:35

The smartest person or the person who's

best at at sequel that lands the job.

439

: 00:24:39

It's the person who's able to best

portray their skills that they'd be,

440

: 00:24:43

you know, able to help the company.

441

: 00:24:44

And the same is true.

442

: 00:24:45

Once you land a job, it's not necessarily

the best employee that gets the promotion.

443

: 00:24:49

It's the employee that appears the

best or gets portrayed as the best.

444

: 00:24:54

And they, you know, it

really doesn't stop until.

445

: 00:24:56

You become like the CEO.

446

: 00:24:58

And then even then like

appearances still really matter.

447

: 00:25:00

So it's, it's maybe unfortunate and you'd

want maybe just pure talents and skill

448

: 00:25:05

to win, but the way that I think this is

449

: 00:25:07

Cole: part of the talent as well,

being able to being able to communicate

450

: 00:25:12

adeptly and one resource that I'll point

people to in case like, okay, I get

451

: 00:25:16

this, but how do I actually do that in

the yellow book storytelling with you?

452

: 00:25:22

This is the one that goes back to.

453

: 00:25:23

There's data visualization in it,

but it goes beyond the data into how

454

: 00:25:27

can you develop yourself to be able

to plan, create and deliver content?

455

: 00:25:33

Uh, the penultimate chapter is crafting

the story of you, and it's basically

456

: 00:25:38

taking people step by step through

how you can be really thoughtful and

457

: 00:25:42

robust in how you plan and how you talk

about the story of yourself, which can

458

: 00:25:49

be useful in a variety of scenarios.

459

: 00:25:51

And it's actually, it really, it becomes

an interesting case study and way to

460

: 00:25:56

practice a lot of the other things

that are introduced that are grounded

461

: 00:26:00

more in how you would communicate data.

462

: 00:26:02

But things like, you know,

brainstorming on sticky notes and

463

: 00:26:05

really considering your audience and

making all of that work together with.

464

: 00:26:10

Using a subject that people

know really well themselves.

465

: 00:26:14

Um, but then after going through that

chapter, you can come out of it with a

466

: 00:26:19

really clear plan and ways to practice

when it comes to talking about yourself

467

: 00:26:25

that you can then translate into

talking about other things as well.

468

: 00:26:29

Avery: That sounds like a

superpower to master that.

469

: 00:26:32

I don't have the yellow book, so

maybe I'll, I'll have to look it up.

470

: 00:26:34

Look into that one.

471

: 00:26:36

Let's talk about your,

your brand new book.

472

: 00:26:37

Daphne draws data.

473

: 00:26:39

Uh, tell us a little bit about what

it is and why you decided to do this.

474

: 00:26:42

Yeah.

475

: 00:26:42

Look at it.

476

: 00:26:43

Cole: Yeah.

477

: 00:26:43

So Daphne is a delightful pink

dragon who has a unique talent.

478

: 00:26:49

She enjoys drawing.

479

: 00:26:51

That's not so unique.

480

: 00:26:52

Well, maybe for a dragon it is, but the

thing that she likes to draw the most is.

481

: 00:26:56

Data.

482

: 00:26:57

She likes to draw graphs.

483

: 00:26:58

And so the story is a really fun, I mean,

it's a picture book, really fun, brightly

484

: 00:27:06

illustrated, uh, about Daphne's adventure.

485

: 00:27:10

She decides, well, if she's not being

appreciated at home, she's going to go

486

: 00:27:13

off and find a place where she can fit in.

487

: 00:27:15

And so she goes to the jungle

and outer space and underwater

488

: 00:27:19

and all sorts of places.

489

: 00:27:20

And in each location, she

encounters some creatures.

490

: 00:27:25

Uh, and a problem they're

facing, and then helps them solve

491

: 00:27:29

their problem by drawing data.

492

: 00:27:31

So she collects it, she draws it

in very pictorial forms of graphs.

493

: 00:27:36

Uh, the word graph I don't think

is used once in the book though.

494

: 00:27:39

It's really introducing the concepts

through story and through pictures.

495

: 00:27:43

And then, uh, I won't give

away the ending, uh, other

496

: 00:27:46

than to say it's a happy one.

497

: 00:27:48

And the story ends, but then the book

continues into a graph glossary that goes

498

: 00:27:53

more into what the graphs were that Daphne

used over the course of her adventure.

499

: 00:27:58

So there's a page each devoted

to bar charts, line graphs.

500

: 00:28:02

pie charts and scatter plots, uh,

showing examples from her adventures,

501

: 00:28:07

helping kids understand how to read them

when they work, and then introducing

502

: 00:28:11

activities that kids can undertake using

data that's of interest to them because

503

: 00:28:17

one great Parallel that we can make

across adults communicating with data

504

: 00:28:22

and kids and the use of data and graphs

is to make it about something that's

505

: 00:28:27

meaningful and something that can be

acted upon because when I see my kids

506

: 00:28:32

come home with graphs from school, so

far, I've been pretty disappointed because

507

: 00:28:36

they're graphing things like the weather.

508

: 00:28:38

The weather in September,

okay, it was sunny.

509

: 00:28:41

You experienced that.

510

: 00:28:42

It's not so interesting now to draw it

in a graph or they'll do things like

511

: 00:28:46

roll a die, uh, you know, a bunch of

times to see that, you know, and then

512

: 00:28:51

graph it to see, okay, I rolled all the

numbers about the same amount of times.

513

: 00:28:55

This isn't anything that they can

then Use to understand things better.

514

: 00:28:59

Uh, and so I really would like to make

the data that we're having kids work with

515

: 00:29:06

be something that they're interested in,

because I think this is such a, it could

516

: 00:29:10

be such an amazing way into mathematics

in a way that isn't portrayed as boring

517

: 00:29:16

or complicated or completely abstract

when it comes to kids day to day.

518

: 00:29:21

Uh, so, you know, let's have them track.

519

: 00:29:23

How many hours they're spending on a

screen every day and how they feel plot

520

: 00:29:26

that, or, uh, you know, where's their

favorite place to read and, you know,

521

: 00:29:32

how might we then emulate some of those

things in the classroom to promote

522

: 00:29:35

more reading, like things that we can

actually, uh, help kids learn about

523

: 00:29:39

themselves and about the world around

them in ways that is fun and engaging

524

: 00:29:45

because what I've seen through my kids.

525

: 00:29:47

And their friends is that

kids are fantastic and love

526

: 00:29:51

doing a couple of things.

527

: 00:29:53

One, asking questions,

particularly like, I don't know,

528

: 00:29:56

kindergarten, first, second grade.

529

: 00:29:58

There's no filter yet and kids

are so curious and they ask

530

: 00:30:01

questions about everything.

531

: 00:30:02

And if we could teach kids how to

hone and get really good at asking

532

: 00:30:09

questions that can subsequently be

answered with data, that is going to

533

: 00:30:13

be an amazing foundation for everyone.

534

: 00:30:15

Any sort of problem solving,

critical thinking, analytical

535

: 00:30:19

career, and they also love drawing.

536

: 00:30:21

And so if we can let them take some of

that creativity and do it with a graph

537

: 00:30:27

and with numbers and let kids approach

that creatively, I think it's a very

538

: 00:30:33

refreshing change from math being

something that's either right or wrong

539

: 00:30:37

because graphs, there's more leeway.

540

: 00:30:41

Uh, there can be creativity.

541

: 00:30:42

People can approach things.

542

: 00:30:43

differently, and we can celebrate

that and learn from that rather

543

: 00:30:46

than say, no, don't do it that way.

544

: 00:30:48

Do it this way.

545

: 00:30:49

And so for me, I think it was a

combination of just going back to

546

: 00:30:52

the impetus for writing the book, a

combination of, you know, seeing the

547

: 00:30:55

adults who we teach and so many saying,

I wish I had learned this sooner or

548

: 00:31:01

earlier, and then seeing my kids and

how, just how they learn about the

549

: 00:31:06

world around them, how they develop.

550

: 00:31:08

Language and logic and realizing we could

take the visual language of numbers and

551

: 00:31:15

introduce that a lot earlier than we do,

sort of those two things coming together.

552

: 00:31:20

I think there's an opportunity to really

help our kids recognize this superpower

553

: 00:31:26

of comfort with numbers and asking

questions and answering those questions

554

: 00:31:32

and drawing and plotting things that,

um, It'll be a great foundation for

555

: 00:31:37

them for so many things going forward.

556

: 00:31:39

Avery: You're building the next

generation of data analysts and a

557

: 00:31:44

data viz specialist, a ripe young age.

558

: 00:31:46

So, uh, that is very cool.

559

: 00:31:49

Where can people find this book?

560

: 00:31:50

Cole: Oh, anywhere books are sold.

561

: 00:31:51

So yeah, favorite independent bookseller.

562

: 00:31:53

You can order it.

563

: 00:31:54

It's on Amazon.

564

: 00:31:55

Uh, and, uh, yeah, is around the world.

565

: 00:31:58

Avery: Okay.

566

: 00:31:58

Awesome.

567

: 00:31:58

Well, I haven't checked it out yet.

568

: 00:32:00

I'll have to check it out.

569

: 00:32:01

I'll have to check out the yellow book.

570

: 00:32:02

Um, but I'm also a huge fan of, of the

storytelling with data original book.

571

: 00:32:07

So if you guys haven't checked those

out, be sure to check them out.

572

: 00:32:09

We'll have links to all of them

in the show notes down below.

573

: 00:32:13

Uh, Cole, thank you so much

for coming on our show.

574

: 00:32:15

We appreciate it.

575

: 00:32:16

Cole: Thanks for having me, Avery.