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

2

: 00:00:04

subsequently talk about that in a way

that makes other people want to listen

3

: 00:00:08

and pay attention and do something

with it, the beautiful graph fails.

4

: 00:00:14

Avery: Okay.

5

: 00:00:14

Cole, welcome to the Data Career Podcast.

6

: 00:00:16

So glad to have you.

7

: 00:00:17

Hi, Avery.

8

: 00:00:17

Great

9

: 00:00:18

Cole: to be here.

10

: 00:00:18

Thank you.

11

: 00:00:19

Avery: Yes.

12

: 00:00:19

So if you guys haven't

heard of Cole before.

13

: 00:00:21

Uh, she is the author of the

book Storytelling with Data.

14

: 00:00:24

It is one of the, uh, best books on

storytelling with data, but specifically

15

: 00:00:29

like data visualization and how to

present and convince people at your

16

: 00:00:34

workplace, uh, of your findings.

17

: 00:00:36

She's also the, the author of the new

book, Daphne Draws Data, which we'll

18

: 00:00:40

talk about in this episode as well, which

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

19

: 00:00:43

Cole: It is, yeah.

20

: 00:00:44

Younger audience, but interestingly, it's

a lot of the same lessons that apply.

21

: 00:00:48

Avery: Okay.

22

: 00:00:49

And let's, let's get into

some of those, those lessons.

23

: 00:00:51

Um, I want to start off with

actually a little bit about, about

24

: 00:00:54

your career because you studied

mathematics in college, right?

25

: 00:00:58

Cole: Yeah.

26

: 00:00:59

Math.

27

: 00:00:59

I have an undergrad in math, uh,

or applied math and, uh, an MBA.

28

: 00:01:04

Avery: Okay.

29

: 00:01:04

And when you graduated, did you ever see

yourself becoming like the author of a

30

: 00:01:09

storytelling with data book and, and kind

of this whole career that you have now?

31

: 00:01:12

Yeah.

32

: 00:01:12

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

33

: 00:01:16

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

mentioned, I majored in math and I, I

34

: 00:01:21

remember getting into my senior year in

college and still trying to figure out

35

: 00:01:26

what do I want to be when I grow up?

36

: 00:01:27

And I remember going to a series of

sessions that were, you know, like,

37

: 00:01:32

What profession to have as a math major.

38

: 00:01:34

And so I listened to the actuaries

and the, the finance people, and

39

: 00:01:39

I had this moment of, or longer

than a moment, you know, the, the

40

: 00:01:43

crisis of like, Ooh, none of these

careers sound like what I want to do.

41

: 00:01:48

Uh, and I remember then getting some of

the best advice that I have received,

42

: 00:01:52

I think, as I look back from my

mother, which was finish the degree.

43

: 00:01:56

And so, so.

44

: 00:01:57

Finished my math degree and

then got a job in banking.

45

: 00:02:00

Uh, not in finance though, in

credit risk management, where I was

46

: 00:02:03

building statistical models, uh,

forecasting loss, try to understand

47

: 00:02:08

how we should reserve for the bank.

48

: 00:02:09

And I loved, I loved the technical

side of it, but also being able to

49

: 00:02:15

Bringing creativity in and where I

brought creativity and was in how I

50

: 00:02:20

was visualizing the data, simple things

like colors and some inadvisable things.

51

: 00:02:25

As I look back like shadows

or cram as many graphs on a

52

: 00:02:28

slide as you can get on there.

53

: 00:02:30

But interestingly, what I found

over time was when I spent.

54

: 00:02:34

Time and thought on the design of the

visuals, people ended up spending more

55

: 00:02:41

time with my work, and so that became

a self reinforcing thing where other

56

: 00:02:47

people would come to me, and I became

the sort of internal expert when it comes

57

: 00:02:51

to how do you show data fast forward

through a few career changes, and I.

58

: 00:02:58

Was it Google still using a lot of

the same statistical methods, but

59

: 00:03:01

now in an analytics role in HR.

60

: 00:03:05

So people analytics forecasting

things like who's likely to leave the

61

: 00:03:09

organization and when, and what sort of

things can we test out to change that?

62

: 00:03:13

And I still spent a lot of time

on the visuals and the team I was

63

: 00:03:19

on, we were doing a lot of really

complicated things that we needed to

64

: 00:03:22

communicate to the engineers at the

organization and the sales people at the

65

: 00:03:28

organization and everybody in between.

66

: 00:03:30

So audiences with widely varying.

67

: 00:03:33

Needs, technical skills,

familiarity with data.

68

: 00:03:36

And so that was really interesting

to see how do you change how you

69

: 00:03:42

show things depending on who you're

showing it to and where, where is that?

70

: 00:03:46

How can that be more successful when

you think about it from that standpoint?

71

: 00:03:50

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

a training program or part of developing

72

: 00:03:55

a training program where I was creating

coursework on data visualization,

73

: 00:03:58

which was fantastic because it gave

me a chance to pause and research and

74

: 00:04:03

read everything I could get my hands

on at that point, which was not a lot.

75

: 00:04:06

It was like, Tufti, Stephen Few, I think

his first book was out at that point,

76

: 00:04:11

but really start to get an understanding

of why some of the things I'd arrived at

77

: 00:04:17

through trial and error over time, you

know, why they work and why some things

78

: 00:04:20

work better or worse, and really turn

that around to be able to teach others.

79

: 00:04:25

And so I did that at Google, uh, taught

courses across the organization for a

80

: 00:04:30

number of years and around the world.

81

: 00:04:32

And then realized that it's not just.

82

: 00:04:35

People in technical roles or at a

technology company who need to learn how

83

: 00:04:41

to communicate effectively with data.

84

: 00:04:43

These aren't skills that we naturally

have, even though a lot of the things

85

: 00:04:47

and we can get into this, a lot of

the lessons are really Practical and

86

: 00:04:52

maybe even obvious once you say them,

but until somebody points them out

87

: 00:04:57

and shares them, we are sometimes

our own worst enemy when it comes to

88

: 00:05:01

trying to communicate effectively.

89

: 00:05:03

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

back in: 2012

90

: 00:05:08

and started storytelling with

data, uh, which is what I've.

91

: 00:05:12

Poured the last decade plus into

really with the goal of helping people

92

: 00:05:18

create graphs that make sense, but

also going beyond the graph to, you

93

: 00:05:23

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

94

: 00:05:25

We want to take the data that we

work with and learn something new

95

: 00:05:29

from it and help communicate that

new thing to other people so that

96

: 00:05:33

we can help drive smarter decisions.

97

: 00:05:36

Uh, reinforce that we're doing things

the right way or that we should change

98

: 00:05:39

how we're doing things and really have

smarter conversations, not about the

99

: 00:05:43

data, but using the data to have smarter

conversations about the business.

100

: 00:05:48

And so we do that mainly

through workshops.

101

: 00:05:51

Uh, there's the book that you mentioned,

um, a couple more after that as well.

102

: 00:05:54

One focused on practicing another

on you as the person who is

103

: 00:06:00

creating and communicating the data.

104

: 00:06:02

And then the latest one

for kids, as you mentioned,

105

: 00:06:06

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

106

: 00:06:08

Congratulations on all the success.

107

: 00:06:10

I actually attended a, uh,

storytelling with data workshop at

108

: 00:06:14

my company at ExxonMobil in 2020.

109

: 00:06:16

And it was, it was awesome.

110

: 00:06:18

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

book and, uh, I actually have multiple

111

: 00:06:21

copies, one of all the success in this,

this really cool career that you've had.

112

: 00:06:25

If you go back to that first job, you

know, one of the things that you said

113

: 00:06:28

was that if you designed your charts.

114

: 00:06:32

Well, and you use best practices for

data visualization, your boss and your

115

: 00:06:36

boss's boss would care about them more

and pay more attention to your work.

116

: 00:06:39

And actually I was, I was rereading

your book and I pulled this quote

117

: 00:06:42

and you said, I quickly learned that

spending time on the aesthetic piece,

118

: 00:06:46

something my colleagues didn't typically

do met my work garnered more attention

119

: 00:06:50

from my boss and my boss's boss.

120

: 00:06:52

And I just want to kind of talk

about that for a second, because.

121

: 00:06:55

It's not necessarily that you were

doing better work or that your analysis

122

: 00:07:00

was better or it was more meaningful.

123

: 00:07:02

It was just easier for them to understand.

124

: 00:07:04

And because it was easier for

them to understand, they valued

125

: 00:07:07

it more and they valued you more.

126

: 00:07:08

Is that true in your career?

127

: 00:07:10

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

exactly that, that it became When the

128

: 00:07:13

graphs made sense and the messages made

sense, it was more of a direct line

129

: 00:07:18

into the value that the work was having.

130

: 00:07:21

Whereas, if you imagine the same work

being done, but being communicated

131

: 00:07:25

in a really complicated way, or,

you know, really going deep into the

132

: 00:07:30

statistical methods instead of pulling

back to say, What does this mean?

133

: 00:07:35

What does this mean for you,

the audience, or the person, the

134

: 00:07:38

people to whom I'm communicating?

135

: 00:07:39

What does it mean for our people?

136

: 00:07:40

Business, how do we put that complicated

stuff into words that makes sense to

137

: 00:07:45

somebody who wasn't intimately involved

in the process that when you don't

138

: 00:07:50

take the time to do that, it can really

easily become a barrier to the good

139

: 00:07:56

work that's being done actually having

the impact that it otherwise could.

140

: 00:08:01

And that's what I think when we spend

time thinking about how do we make

141

: 00:08:06

this make sense to someone else?

142

: 00:08:07

How do I look at something and say,

all right, this might be what made

143

: 00:08:11

sense to me, or it's the view that

helped me reach that aha Eureka moment,

144

: 00:08:17

but it doesn't mean that that's the

same view or the same path that's

145

: 00:08:22

going to serve my audience best.

146

: 00:08:24

And so it really is this paradigm

shift because I think often and

147

: 00:08:29

I think Especially people in

technical roles, we, we get so used

148

: 00:08:35

to seeing things a certain way.

149

: 00:08:37

And I think for me, at least as

I look back, there was joy in

150

: 00:08:41

figuring out the puzzle, right?

151

: 00:08:43

Figuring out how the pieces fit

together when it wasn't obvious.

152

: 00:08:47

And so I think there's part of something

in us that wants us to then be able

153

: 00:08:51

to kind of show that puzzle to someone

else, but have it not be clear so that

154

: 00:08:55

we can have them experience some of

what we did, but that does a total

155

: 00:08:59

disservice because what that does is

basically take the value that we could

156

: 00:09:03

have added and obfuscate it instead

of saying, all right, I did this work.

157

: 00:09:08

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

the interesting thing now, rather

158

: 00:09:12

than me take my audience through

all the details and the work I went

159

: 00:09:15

to to get to the interesting thing,

it's actually just lead with that.

160

: 00:09:19

And we may, in some cases, not even

have to get into any of the detail.

161

: 00:09:23

I think sometimes that.

162

: 00:09:24

Feels bad when it

shouldn't, that is success.

163

: 00:09:28

That means your audience trusts you.

164

: 00:09:30

It means they trust your finding

because I can remember times I can

165

: 00:09:33

remember times at Google, I can remember

times at banking back prior to that

166

: 00:09:37

in private equity, where I worked,

where my team and I would spend a ton

167

: 00:09:41

of time on an analysis or on a study.

168

: 00:09:43

And then putting together a really

dense recount of what we did and what

169

: 00:09:49

we found in all of the methodology and.

170

: 00:09:52

When it didn't get presented after

at the end of all of that work,

171

: 00:09:56

that would feel bad when really

that was a success scenario.

172

: 00:09:59

It didn't not get presented

because we didn't talk about it.

173

: 00:10:01

We talked about it and actually didn't

even need to go into all of that detail

174

: 00:10:06

because of the trust over time that

was established to our stakeholders

175

: 00:10:10

were able to go in with the story

and then have the conversation focus

176

: 00:10:14

on really understanding that and it.

177

: 00:10:16

Understanding how we apply that

to the business going forward.

178

: 00:10:19

And it doesn't mean we didn't

need to spend all that time

179

: 00:10:21

putting together the document.

180

: 00:10:23

We needed to have that.

181

: 00:10:24

We needed to do that work in order to

get to the, the answer or the finding

182

: 00:10:28

or the interesting thing to communicate.

183

: 00:10:30

And there will be times where

you do need to take your audience

184

: 00:10:32

through a lot of that detail.

185

: 00:10:34

And so you need to have it there, but.

186

: 00:10:37

The dense communication is

not the, the, the goal, right?

187

: 00:10:41

Going through that is not the goal.

188

: 00:10:42

It's having the impact through the work.

189

: 00:10:44

Avery: I love that.

190

: 00:10:45

And I think in today's society, as much

as all of us might enjoy working on

191

: 00:10:50

something we're passionate on, uh, I

think people rather be doing their hobbies

192

: 00:10:54

or spending time with their families.

193

: 00:10:55

And so if you can just make your results

as clear as possible, as quickly as

194

: 00:11:00

possible, uh, that bodes well for you.

195

: 00:11:02

Because some, sometimes I think.

196

: 00:11:04

As technical workers, we want

our work to speak for itself.

197

: 00:11:09

Uh, and we want them to recognize,

yes, I did all this work to actually

198

: 00:11:13

accomplish this, but the sad truth

is most businesses don't care.

199

: 00:11:17

Just give us the results,

tell us why it matters.

200

: 00:11:19

And a lot of the time I even saw this

post, um, from Kelly Adams on LinkedIn.

201

: 00:11:22

She's like a LinkedIn creator.

202

: 00:11:23

She was like the most of the time my boss

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

203

: 00:11:28

Like doesn't ask to see my code ever.

204

: 00:11:30

It doesn't ask to like actually figure

out how I came to my conclusion.

205

: 00:11:33

They just trust me to, to do the

analysis and come to the right point.

206

: 00:11:36

Cole: Well, and I think that's part

of the, part of the magic magic.

207

: 00:11:40

It's not quite the right word there,

but is really assessing a situation and.

208

: 00:11:45

Anticipating what is going to be needed

and what level of depth you're going to

209

: 00:11:49

need to be able to walk someone through

or show someone, uh, because when you

210

: 00:11:54

can make that match the situation,

that's when when things go really well,

211

: 00:11:59

because you could easily take that and

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

212

: 00:12:03

And that means.

213

: 00:12:04

You know, I still need to be

buttoned up on my work, but maybe I

214

: 00:12:07

don't need to show all of my work.

215

: 00:12:09

But then as soon as you get the question

back, or you, you, if you misanticipated

216

: 00:12:13

that or misread that, and now you have,

or you're using that and going in front

217

: 00:12:17

of another audience who actually is

going to want to be convinced of the

218

: 00:12:21

robustness of the analysis that was

done, you need to be able to anticipate

219

: 00:12:26

that so that you can meet that.

220

: 00:12:28

Need.

221

: 00:12:28

I think that is where things most

often fail, where we create a report

222

: 00:12:34

or a presentation for, for ourselves

or for our data for the project and

223

: 00:12:39

not specifically for the person or the

people to whom we're communicating.

224

: 00:12:44

That's that paradigm shift I was

referring to before that when we can

225

: 00:12:47

get out of our own heads and really

think about, all right, here's what I

226

: 00:12:50

did, but now how do I make this work for

the people who need to understand it?

227

: 00:12:54

And take measures to make it work for

them, both through the visual design

228

: 00:13:00

and through how we talk about our work,

how we communicate directly, that that's

229

: 00:13:05

where all of that can work really well.

230

: 00:13:07

Avery: So I think if, if I understand

what you're saying correctly is your

231

: 00:13:10

presentation, your communication,

maybe even your, your graphs should

232

: 00:13:15

almost dynamically change based

off of who you're showing it to.

233

: 00:13:18

Cole: Yeah, I mean, ideally, so if

it's a critical scenario and you

234

: 00:13:22

have audiences who are, whose needs

are sufficiently different, then you

235

: 00:13:28

may want to think about, there will

be times where it would make sense

236

: 00:13:31

to have different communications

for those different audiences.

237

: 00:13:34

Now, in practice, that rarely happens.

238

: 00:13:36

In practice, we try to create

this one size fits all, but it's

239

: 00:13:40

easy through doing that to then

not exactly meet anyone's needs.

240

: 00:13:44

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

to the good enough scenario where, you

241

: 00:13:49

know, if we, if we craft the communication

and it's 80 percent meets this audience

242

: 00:13:53

and 80 percent this audience, right,

there's some overlap and that's

243

: 00:13:56

probably okay, but where audiences are

caring about really different things.

244

: 00:14:00

So bring up an example from Google, since

we talked about this a little bit earlier,

245

: 00:14:04

internally, our main audiences were.

246

: 00:14:07

Engineers on the one hand, highly

technical, needed to be convinced

247

: 00:14:11

that the methodology was sound,

wanted very detailed information.

248

: 00:14:16

We needed to get them on board

before we even did the research a

249

: 00:14:19

lot of the time so that they would

eventually buy into the results.

250

: 00:14:22

And then on the other

hand, we had the staff.

251

: 00:14:23

Sales organization whose general

sentiment was leave us alone.

252

: 00:14:27

We're the ones out here

making the company money.

253

: 00:14:30

And so for them, we needed to be

direct and short and concise, focused

254

: 00:14:35

on what mattered to them and not

until they needed to act upon it.

255

: 00:14:39

And it was like, it was, it was.

256

: 00:14:41

After trying to communicate to both

of those audiences simultaneously at

257

: 00:14:45

first and just failing for a variety

of reasons that are obvious in

258

: 00:14:48

retrospect, that we decided, you know

what, that's not the right approach.

259

: 00:14:51

We actually do need to communicate

to these audiences separately, not

260

: 00:14:54

only in what we share and how we

talk through it or show it, but also

261

: 00:15:00

even when we communicate to them.

262

: 00:15:02

Avery: I think there's, there's

people listening who, who might be

263

: 00:15:05

thinking, well, the analysis is the

analysis, but it's so funny because.

264

: 00:15:09

You wouldn't necessarily think this,

but the packaging that you put are

265

: 00:15:12

around your analysis really matters.

266

: 00:15:14

And oftentimes, like if, if let's

just say we're, we're almost in

267

: 00:15:16

the holidays, let's just say I'm

giving you a Christmas present of

268

: 00:15:20

some, some new headphones, right?

269

: 00:15:22

Like if, if the headphones

just in a cardboard box.

270

: 00:15:25

They're not going to be as valued as if

I put these headphones in like a really

271

: 00:15:29

nice, like box that has really good,

like opening mechanisms and really good

272

: 00:15:34

wrapping paper and a bow and a nice card.

273

: 00:15:36

Even

274

: 00:15:37

Cole: though even the

wrapping paper, right.

275

: 00:15:39

It's going to be different

around the holidays than around

276

: 00:15:41

birthday or something else.

277

: 00:15:42

So yeah, it's the same contents,

but the way you present it

278

: 00:15:46

will and should be different.

279

: 00:15:48

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

the ways that, that we can present well.

280

: 00:15:52

So we talked about like.

281

: 00:15:53

Addressing your audience.

282

: 00:15:54

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

your boss's boss, you're going to

283

: 00:15:57

present it differently than to like

your colleague or a engineer or a

284

: 00:16:00

programmer or something like that.

285

: 00:16:02

What are some other things that people

should know when they're, when they're

286

: 00:16:04

making data visualization and presenting?

287

: 00:16:06

Cole: I think one thing to be clear on is

that you likely know the situation, you

288

: 00:16:11

know, the data better than anyone else.

289

: 00:16:13

And what happens through that Is when

you look at the graph you made or the

290

: 00:16:18

slide you made, it's super obvious

to you where to look and what to see.

291

: 00:16:21

But to make those things as obvious

to someone else, it means you have

292

: 00:16:26

to do things to make that happen.

293

: 00:16:28

And so when it comes to the design

of the graphs and the slides, you

294

: 00:16:32

can think about how you might employ

visual contrast, for example, sparing

295

: 00:16:37

use of color to show your audience.

296

: 00:16:39

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

Share Episode

Shownotes

Transcripts

Follow

Links

Chapters

Video

More from YouTube