In this week’s episode of the show, I sit down with Justin Evans, author of The Little Book of Data, to talk about what it means to truly think like a data person. Justin shares insights from his 20-year career in data and advertising, reflecting on why so many professionals struggle to embrace data and how his book helps break down those barriers. We discuss the “four layers of data denial,” the qualities that make someone a data person, and the importance of storytelling in making data engaging and useful. Justin also offers stories from Nielsen, Samsung, and beyond to illustrate how data literacy and visualization can create clarity, solve problems, and unlock value. This conversation is both inspiring and practical for anyone working with—or intimidated by—data.
Resources
Check out Justin’s book, The Little Book of Data.
Guest Bio
Justin Evans is a twenty-year veteran of the data and technology industry, whose innovations have generated hundreds of millions in revenue for Fortune 500 companies and startups. His mission as a communicator is to demystify data and AI, and empower every leader to use their “data superpowers.” He is a frequent conference speaker, the author of the DataStory substack, and a novelist whose fiction has been named a Top 100 book by the Washington Post. Evans is a graduate of Columbia University and NYU Stern where he received an MBA.
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Transcript
00:02.06
Jon
Hey, Justin, good to meet you.
00:04.88
Justin Evans
Thank you, John. Great to be here with you.
00:06.78
Jon
ah Thanks so much for for coming on Little Book of Data. I’ve got the fun galley copy, so there’s no index. So I had to use that. I had to i had to like put notes in the book, which I hate to do. to put notes in the book to make sure I knew where to where to go back to.
00:20.84
Jon
Yeah.
00:22.75
Justin Evans
You have to have that classic undergraduate ballpoint pen perception versus reality note.
00:28.24
Jon
yeah
00:29.28
Justin Evans
Yeah.
00:29.93
Jon
Yeah. And try not to highlight every sentence because then they’re just highlighting everything.
00:33.89
Justin Evans
but
00:35.51
Jon
um So I appreciate you coming on the show. um ah So I thought we’d start sort of like the basics, like, you know, who are you? What’s your background? and then And then maybe just get right into the book. Like, what is the, you know, what is the overarching sentiment of the book and who do you think will benefit most from reading it?
00:55.25
Justin Evans
Well, again, thanks for having me. I’m Justin Evans. I wrote the little book of data. I wrote it about 20 years into my data career, which started at the Nielsen company, the big TV research company.
01:10.16
Justin Evans
And at the to leap forward a lot, I was about 20 years into my career and working at Comcast, a big cable company in one of their advertising divisions.
01:24.30
Justin Evans
And what I was observing is that a lot of mid-career people who are really rock stars kind of in the old traditional TV world were bailing out of their careers or being bailed out.
01:37.01
Justin Evans
And what I was noticing is that the people who embraced data and that next generation of marketing and advertising that was based on marketing and machine learning, ah sorry, based on data and machine learning, were persisting in their careers and succeeding.
01:49.07
Jon
Yeah. Yeah.
01:53.16
Justin Evans
And the people who couldn’t embrace it for whatever reason were parachuting out. And actually found this kind of distressing because the people that I was seeing leading the business were great people and great professionals and great people leaders.
02:04.53
Jon
yeah
02:07.43
Justin Evans
And it made me think, what’s the what’s the matter with data that people can can’t get themselves geared up to learn it? And i just, I started to sort of investigate that and
02:18.18
Jon
yeah
02:23.15
Justin Evans
think about what I call the four layers of data denial, which is that you you think it’s too hard or you don’t have to learn it or it’s too complicated or you’re just intimidated by it.
02:34.96
Justin Evans
And i thought to myself, well, what what if I could write a book that was almost written for that person, the person who is a leader or a general business person, but who doesn’t understand data, but has an inkling that, you know, this this data thing could be powerful and could mean something in my career.
02:53.15
Jon
Yeah. Right. right
02:54.81
Justin Evans
so So I ended up kind of writing the 20-ish core ideas that I felt were really true of data that I would want to tell that person or tell my younger self.
03:06.06
Justin Evans
And then try to collect stories about those ideas. and and full full transparency for your listeners, I wrote a very bad first hack at this that was, I’m just going to tell this data idea in the simplest possible language.
03:24.09
Jon
Right.
03:24.20
Justin Evans
And my friend read it and said, this is just, you know, not that it was unreadable, but it’s not fun. Make it fun, tell stories around it. So what I did was I then started the process of trying to find people whose careers, whose work illustrated these 20 ideas.
03:31.24
Jon
Yeah.
03:39.55
Justin Evans
And that went much better.
03:40.36
Jon
Mm-hmm.
03:40.75
Justin Evans
And I ended up in ended up being a ah complete joy meet these data professionals who I regarded as sort of heroes in a way and hear their stories and turn those stories into the heart of the book. So you’re learning the lessons of data.
03:57.85
Jon
right
03:58.08
Justin Evans
You’re learning how to think like a data person, but you’re not being burdened by the technology and code. You’re actually just learning it by seeing how other professional data people think about data problems.
04:09.77
Jon
Right. Because it’s the thinking about data problems that’s the first step. Like you get into code later, but but the the the human part of it, I mean, we’re not gonna talk about on AI.
04:14.59
Justin Evans
That’s right.
04:19.68
Jon
i can’t I can’t talk about AI anymore, but like the the AI part of it is like a whole other thing, but like the human part is the critical thinking about data.
04:29.20
Justin Evans
That’s right. And I would add the so problem solving part of data is really the essential part of it. I think even for people who do it every day and do it for a living, i mean, I can’t code my way out of a paper bag and i’m really not even that strong with maths but i’ve been in the data business a long time i am a data person i think like a data person and there’s almost no business problem or a life problem that you can’t put on me that i can’t turn into ah data opportunity and it’s fun and if you have to once you get some of the kind of core principles in your head
05:05.17
Jon
Mm-hmm. Mm-hmm.
05:12.20
Justin Evans
then it starts to flow. And in my experience, and I’m not diminishing the importance of people who are quantitative and are coders and are math people, but generally it’s the vision about what you can do and why you need to do it that drives a process. And if you have a vision about what and why you need to do, then you can find the technical people to make it so.
05:40.50
Jon
Right. so So I wanted to ask you about the the the data people, um because you have a whole chapter in the book entitled Data People and Why I Love Them, um which was one of my ah favorite chapters. I’ll also say, just going back to your part about stories, like that’s why that’s why I was able to read the book like by the pool this summer, because it was just kind of an enjoyable read. It wasn’t like…
06:05.34
Jon
it does get in the weeds, but, but you’re seeing how people have had to address these challenges with data. So, so I, I, I’m all in on the storytelling to sort of get that message across, but I wanted to ask about, about data people.
06:18.65
Jon
And you’ve already alluded to this a little bit but like, do you think people are intrinsically data people? Do you think it’s a skill that needs a four year degree?
06:30.59
Jon
Like, like what makes a data person, a data person?
06:36.31
Justin Evans
What I tried to do in writing that chapter was think about all the people I had hired over the years, which is probably in the hundreds and trying to think about what made, what was similar between those people.
06:50.90
Jon
Mm-hmm.
06:50.95
Justin Evans
And especially when I was leading teams that were going through a period of transition where we had to let go a certain kind of person and hire a different kind of person.
06:51.06
Jon
Mm-hmm.
07:02.97
Justin Evans
And upon that reflection, I just thought that the the data people I had hired had a sense of what I call a, well, I’m not quite to phrase a fiduciary duty.
07:16.63
Justin Evans
I’m probably applying it to the data world maybe for the first time that people had a duty of faith to the client and a sense of mission.
07:23.29
Jon
Mm-hmm.
07:25.43
Justin Evans
And they just, they really cared about getting the right answer for the client. And there is a sort of thrill in that responsibility and a thrill in the adventure of going into data and trying to come back with an answer.
07:46.69
Justin Evans
And the duty of faith part also meant that there was an ethical component as well. And when I think about the people that I exclude from the honorific of data people,
08:02.05
Justin Evans
I exclude people who just love the fact that they know more than somebody else. Especially in TV research, there’s this class of person who could quote you, chapter and verse on Nielsen methodology, on the Nielsen TV ratings, but they couldn’t tell you how to solve a business problem.
08:18.67
Jon
yeah
08:24.28
Justin Evans
And in a different end of the spectrum, heading west towards Silicon Valley, There are people who can tell you how to steal intellectual property and use data unethically and addict people against their will while using data in a clever way, but their lack of ethics excludes them from my for my honorable title of data person as well.
08:51.61
Jon
who yeah
08:54.95
Justin Evans
And actually had someone on my team Now, the other day, read that chapter and say, oh, that that chapter really describes me, especially the part you write about money. And I make the observation, i think I think I’m right about this, that I’ve never seen a data person rise to be CEO.
09:13.72
Justin Evans
You’ll see financial people rise to be CEO.
09:15.58
Jon
Yeah.
09:16.15
Justin Evans
You’ll see salespeople rise to be CEO. Occasionally, a marketing or product person, but you never see a data person rise to be CEO. And I think that’s because money is so final.
09:22.65
Jon
Yeah.
09:26.54
Justin Evans
You can’t copy
09:27.10
Jon
Yeah.
09:28.22
Justin Evans
money and then try another whack at it you know you’ve already you’ve already gained or lost it so data people definitely have again in my personal estimation a certain dreamy quality that is again my eyes laudable and even essential they have to be able to close their eyes and and think about what’s possible but that sort of
09:32.27
Jon
Mm-hmm.
09:41.75
Jon
Mm-hmm.
09:55.94
Justin Evans
tenu connection to reality can sometimes be a liability in other parts of their business life.
10:01.95
Jon
Yeah. It’s interesting the way you describe that. ah You didn’t use a phrase like a programmer, a mathematician, ah statistician.
10:15.90
Jon
It’s almost like you take more of a humanities view of what it means to be someone working with data.
10:24.20
Justin Evans
I do. i think I think data people have to fall in love with the problem. and And once you do that, you’ll do anything to solve it.
10:28.08
Jon
Mm-hmm.
10:31.78
Justin Evans
You’ll dream any dream and you’ll work any hours.
10:31.97
Jon
Right.
10:34.81
Justin Evans
And working hard is part of the commitment. But indeed, the the hardcore quants and the hardcore coders, I don’t see that as essential to the to the the data personality, so to speak.
10:49.38
Jon
right you You also spent a bunch of time kind of throughout the book talking about data literacy. I would throw sort of data visualization literacy and in there as well.
11:00.43
Jon
um In your experience, what are some of the like more effective ways both people, but but sort of more broadly, I think organizations build that sort of data literacy skill ah throughout their their kind of their data workflow or data ecosystem?
11:20.43
Justin Evans
Yeah. so So you’re asking what what makes, how how does one become data literate? Is that is that a good paraphrase of your question?
11:26.84
Jon
Yeah, yeah, yeah, yeah.
11:31.63
Justin Evans
the The core ideas I really put in the book and tried to illustrate but some examples. And sometimes these things have to be learned by instinct and exposure.
11:45.40
Justin Evans
you know, one concept is the concept of identity.
11:46.36
Jon
Mm-hmm.
11:48.94
Justin Evans
which is i can I can put a name on and an object, an abstract data object, and I can always come back and find that. And in the book, I tell the story of Scott Taylor, who is an employee at the Nielsen company, who just kind of wandered into ah world where this trade magazine called Progressive Grocer, and B2B trade magazines, you got to love them.
12:14.34
Justin Evans
They’re just, they just they’re not cool.
12:14.84
Jon
Yeah.
12:17.18
Justin Evans
And he there was the The little label in the corner of Progressive Grocer had an identification number that was unique to the addressee.
12:28.63
Justin Evans
And that unique number actually meant that this person was the general manager of the Piggly Wiggly in Charleston, South Carolina.
12:39.90
Justin Evans
And it was associated with the store code. And what he found was that Progressive Grocer, this B2B trade magazine, had the most comprehensive database of all stores, ah grocery stores and store locations in the United States.
12:57.53
Jon
Mm-hmm. Mm-hmm.
12:59.60
Justin Evans
And it became this decoder ring for the manufacturers like Procter & Gamble and Unilever to know exactly where they were sending their cookies and their detergent.
13:12.66
Justin Evans
And they knew not just that it was a Piggly Wiggly, but it was store number 243 out of 1000 in the Southeast. And they knew that this guy who is the manager of the store was one person they had to reach, but they had 200 and some other other general managers to reach.
13:33.44
Justin Evans
And therefore they knew how, what their sales penetration was. And they knew where they stood with that particular chain of retailers. And so this weird little label in the corner of Progressive Grocer Magazine became ah way to uncork millions of dollars of value for these manufacturers.
13:46.52
Jon
Right.
13:49.81
Jon
right
13:50.05
Justin Evans
And so if someone can wrap their head around a concept of identifiers like that, then they can really wrap their head around a core concept of data. And i think once you build up the idea of identification, the idea of matching from one database to another, the idea the idea of scoring different items in a database, you’re you’re kind of building up sort of the forehand, backhand, and serve of of data, and then you can play the game.
14:16.79
Jon
Yeah.
14:20.18
Jon
Right. Yeah. What about, I’m curious. Well, okay. So let’s, let me get to the, to the other story that I love in the book again, also about Nielsen. i have in my notes, I think it was your friend, Freddie H. I don’t know Freddie H, but, but that was the, that was the name of the, of the person building dashboards to sort of, to, to provide these insights on these data. And I’m curious,
14:45.50
Jon
I guess on a couple of things, like on the data data visualization literacy, like what has your experience been sort of in how people improve their data visualization literacy? And also i’m I’m curious about your thoughts on dashboards, both internally and externally.
15:01.26
Justin Evans
Mm-hmm.
15:03.20
Jon
um i’m I’m personally sort of going through this like thought experiment of like, or or experience really that internal dashboards have a lot of value because you and I work in the same company. We look at it we look at data in real time together and that’s useful.
15:18.91
Jon
But if I put it on a website, it’s just another tool that you know just blows by most people and they’re not actually going to use it. And so I’m curious in your experience about people using dashboards like probably primarily internally um and then sort of building out people’s data viz literacy in organizations.
15:39.44
Justin Evans
Well, the story, Freddie H is, that story is actually a Samsung story. And this and the and the story was um the
15:45.92
Jon
OK. Yeah.
15:51.03
Justin Evans
Samsung, we make money from selling advertising on streaming TV. And in 2020, when that story is set, the pandemic had just started, everyone was now walked at home.
16:05.94
Justin Evans
And everyone’s a billion people globally started streaming television overnight and all of the marketers and advertisers and the ad agencies were calling us and saying, Samsung, you have a lot of data on people’s TV viewing.
16:12.02
Jon
yeah
16:21.85
Justin Evans
What the hell is going on? what Where are my customers watching TV?
16:24.71
Jon
Yeah.
16:25.42
Justin Evans
but Where can I reach them at ads now that the world is upside down? I really know how to reach anyone.
16:31.24
Jon
yeah
16:31.51
Justin Evans
And the. It was one of those sort of emergency moments. I mean, it was much more fun than working in an yeah ER at the time, but it was an emergency moment in the ads business where we had to tell these clients immediately where to find their customers.
16:47.70
Justin Evans
and And because streaming TV libraries were so deep, people were spending lots more time with streaming and there’s only so much linear TV you can take in that you want.
16:56.06
Jon
Right.
16:57.81
Justin Evans
And so we we ginned up a dashboard really quickly And it became
17:07.02
Justin Evans
a really great way to distribute a lot of data to a lot of people who needed it right away in in this aggregated form that told people answers to this question of where their audience is.
17:19.86
Jon
Yeah.
17:20.61
Justin Evans
And it it was ah a moment where we created a lot of clarity for clients who were desperate and created a lot of,
17:32.22
Justin Evans
transparency or light in a dark room, the phrase I coined in the book, for clients who were afraid of or just unfamiliar with streaming TV behaviors and it made them feel safe as a place to advertise.
17:35.15
Jon
Yeah.
17:48.97
Justin Evans
So in that way, you know, at the time we were using the phrase democratizing data or simply sharing it at scale. I wouldn’t say there was anything particularly strong about the visuals we created at the time, but but in that case, it was it was a distribution mechanism that was what was powerful about it.
18:02.78
Jon
yeah
18:08.69
Jon
Yeah.
18:11.73
Jon
And, but it also sounds like the reason it was successful is that people had fairly specific questions that they wanted to answer.
18:22.04
Justin Evans
That’s right. And actually, but that’s a really great hook into the essence of your question, which is,
18:29.85
Justin Evans
why a why a dashboard and why a visualization? And the contrast you could make is to a dashboard, which is designed for, call it a general user.
18:33.37
Jon
Yeah.
18:43.53
Justin Evans
In our case, it was salespeople who were generally accessing the dashboard, people who were ad salespeople who were not data people.
18:43.66
Jon
Mm-hmm. Mm-hmm.
18:51.15
Justin Evans
And you can contrast that with a power user, who’s where where the interface is designed for someone to go and really crank on data and it’s ugly and it’s, hard to manipulate, but you can really go deep and ask it very refined questions.
19:05.61
Justin Evans
In a dashboard, what’s elegant and super fun actually about creating a good dashboard is that you are telling a story and you’re guiding someone through a narrative that actually they may not have even known that they wanted.
19:23.93
Jon
hmm.
19:25.39
Justin Evans
The At that time, the narrative that we created was, if you, advertiser, know who your audience is I will tell you how they are watching streaming television.
19:40.27
Justin Evans
That was the question we were answering. And we kind of, it it was literally a oh flip book. We would say, okay, how many of your audience are there?
19:53.88
Justin Evans
It’s 40 million.
19:55.73
Jon
Right.
19:56.03
Justin Evans
and flip the page. How many of them watch linear television? 20 million. How many watch streaming television? 10 million. How much time do they spend on linear television? I mean, I’m making it sound very boring, but the you you have to create a narrative.
20:06.85
Jon
right
20:09.28
Justin Evans
And I actually, actually, I intend to do a ah workshop on this with my team. So thank you for reminding me, which is what what I force them to, when when we did that one and when we’ve done it since, what we do is we, I force them to use the English language and just ask the questions.
20:25.82
Jon
Yeah.
20:26.05
Justin Evans
How many of my audience are there? Where can I reach them? How much time are they spending? Can I even put ads in that environment? And we just we have these sort of rhetorical questions that are then answered by the data.
20:32.03
Jon
Yeah.
20:36.41
Justin Evans
And if those questions have a logical flow, then what’s useful about that to the user is you’ve done the work to to to to tell the narrative.
20:46.79
Jon
yeah
20:46.75
Justin Evans
And it’s efficient for the data team because if the data team only has to answer those questions. They’re not creating some power user deep dive tool where you can ask it any question.
20:51.90
Jon
Right.
20:56.92
Justin Evans
They’re only doing the analysis to answer those questions or setting up the data to be queried to answer those questions. So becomes efficient for everybody.
21:02.35
Jon
here
21:03.65
Justin Evans
And the beautiful thing is if you have a team of people who are working up front to answer those questions and doing that work and just shutting up, answering those questions and those questions only to create clarity, you have all these downstream clear clarifying effects.
21:22.27
Jon
Right.
21:23.29
Justin Evans
if you do that upfront investment.
21:25.27
Jon
so So was the… were Were these advertisers, I mean, you know, Netflix was hugely popular before the pandemic. Obviously, you know, all these streaming services exploded during the pandemic, but were they not deep into data prior to the pandemic? was like what ah like what were How were they making decisions without that sort of in-depth analysis that you just explained that you and Freddie and others on the team sort of built out at the time?
21:59.69
Justin Evans
Well, in that and that moment, and actually we’re still in it in the TV and advertising industry, there were only there there was only Nielsen and similar data, which is based on smaller samples.
22:12.67
Jon
Yeah.
22:12.76
Justin Evans
And i don’t want to get too deep into Samsung stuff, but the when in the in the world of television, where we collectively are still on a path to go from small data to big data.
22:26.96
Jon
Great.
22:27.03
Justin Evans
where have the Nielsen sample of tens of thousands of households being measured to big data sets, which are tens of millions.
22:33.81
Jon
great
22:34.09
Justin Evans
And there are pros and cons of using both kinds. just The small data set, you can demographically weight and balance. Larger data sets are much more accurate because there’s more data, but you have to make certain adjustments. So it’s at at that time, we were much we collectively,
22:54.72
Justin Evans
advertising marketing industry were earlier in the cycle of still relying on small data.
22:59.61
Jon
I gotcha. Okay. So this was moving in the direction of getting more more households, more real time across multiple channels. And by channels, meaning you know the different delivery services, but also the different actual channels.
23:16.05
Justin Evans
That’s right.
23:16.97
Jon
Yeah. um Okay. So on this topic then of different kinds of data from small to big, from occasional to real time, um towards the end of the book, you talk about the consumer price index and how it’s collected and all the work that goes into that.
23:37.12
Jon
And I’m curious if you have thoughts about where we are in the U and on, you know, I would say pretty dramatic changes to the federal statistical agency structure.
23:53.38
Jon
um And we know that, for example, there the BLS is is cutting staff, that’s, you know, whose job it is to create the CPI. And I wonder, I guess, from like, you know, whether you have any just thoughts on that generally, but also like,
24:01.98
Justin Evans
yeah
24:08.08
Jon
What do you think data, back to your data person, like what should your data person be thinking about in this kind of maybe new, effort for for for folks who are relying on federal data for lots of different things, like what should they be thinking about in their own day-to-day work?
24:25.08
Justin Evans
If you go back to the origins of demographics and you go back to the origins of data science, data science really came alive in moments of life or death.
24:42.62
Jon
Mm-hmm.
24:42.97
Justin Evans
the The first demographer slash data scientist is credited to be this fellow named John Graunt, G-R-A-U-N-T. who was a haberdasher in London in the 1640s, who, I don’t know, even maybe he just spent a lot of time with measuring tape, but he became he really fell in love with numbers.
24:56.16
Jon
Mm-hmm.
25:06.73
Justin Evans
And he got really frustrated with the way the London authorities were dealing with the plague around London, because there were all these different neighborhoods in London. you know London, when you go there now, when you go to the tube stops, you can see how there are all these kind of villages that were strung together. And at the time, it was the same thing, only more so.
25:22.46
Justin Evans
And John Grant was concerned that all the data that was being gathered about causes of death was not being used by the authorities to help manage the plague and keep more people alive.
25:36.98
Justin Evans
And the technique at the time for gathering cause of death data was someone would die. They would ring the bell in the church and what he calls ancient matrons, so old ladies, would sort of trundle over and they would make a note somehow of the cause of death that they observed or that they learned from the person who brought the body.
25:54.14
Jon
Yeah.
26:05.94
Justin Evans
And these were actually tallied up on a weekly basis and called the Bills of Mortality, and they were published within the city. And John Gron took all the bills of mortality and tabled them up and made them consistent and did all the things you do with data to make it usable.
26:24.29
Justin Evans
And then he showed trends over time and you would see that someone died of, you you know, drowning in the bath at six people per year.
26:33.26
Jon
yeah
26:34.38
Justin Evans
And then people died of the plague is 60,000 people per year.
26:35.06
Jon
Yeah. Right. Right.
26:38.02
Justin Evans
And you would see it by neighborhood.
26:38.68
Jon
right
26:40.61
Justin Evans
And that’s really when data science came alive. being using data to answer questions.
26:48.29
Jon
right
26:49.42
Justin Evans
And that was its birth. and And the other moment of this that i point out in the book is this Princeton stat statistician named John Tukey, who really foresaw a lot of what’s happening now with the bridging of data and and computer science to answer big questions. And John Tukey was a Cold War and and Second World War.
27:14.03
Justin Evans
a statistician who helped make battlefield weapons for the United States government and helped use, create Cold War spy planes for the U.S. government before settling into being a Princeton stats professor.
27:28.75
Justin Evans
And he too saw the the the relevance of using data from diverse um diverse sources and sometimes just good enough data, not great data, to answer these life and death questions.
27:42.01
Justin Evans
All of it is just a long way around to say that there are, there’s so much tradition of data being, public data being used to answer important questions.
27:53.56
Justin Evans
And I feel as ah as a data person to read about databases and data access being deprecated and being deprecated for what one can only guess are political reasons or ideological reasons is…
28:12.43
Justin Evans
both heartbreaking for the endeavor of humanity to advance itself with knowledge and science, but also entirely against the grain of the tradition of data science, which to me goes back to John Grant and using data to save people’s lives and public data to use data to save people’s lives.
28:31.27
Jon
Yeah. And do you… foresee a spot. Now yeah you’ve worked in the private sector, lot ah ah you know, different places. Do you foresee the private sector stepping in in different ways with different data to fill in those gaps.
28:51.12
Jon
I mean, not necessarily as a public service, but that those data will be the data that we will have to then rely on to, you know, maybe it won’t be as ah comprehensive as the CPI, but we’ll be able to track, you know, prices of ads on streaming television in a way that maybe the government is no longer able to do.
29:10.44
Justin Evans
You know, data businesses are not easy, even today when data appears to be cheap and plentiful. Most data businesses, you have to have a paying customer.
29:21.95
Jon
Mm-hmm.
29:21.97
Justin Evans
And so having 60,000 scientists who are going to log into your system and download a couple of tables in order to answer an obscure question in their lab, to me, does not sound like a great customer base, unfortunately.
29:36.83
Justin Evans
and The other thing about the data business, again, even in this world of cheap and plentiful, is it’s a fixed cost business. you you exite You exert a lot of effort upfront to gather data, clean it, figure out the use cases, make it available.
29:51.90
Justin Evans
And that upfront investment means you’re putting a lot of money and effort in long before you’re breaking even, much less making a profit.
29:59.59
Jon
Yeah. Yeah.
30:03.44
Jon
yeah
30:03.77
Justin Evans
So the the notion that
30:08.38
Justin Evans
entrepreneurs would be jumping into these gaps left by government cuts to resupply all the impoverished scientists with data, unfortunately seems unlikely for those reasons.
30:21.02
Jon
yeah Well, ah yeah, i don’t I don’t disagree with you. And it will um be interesting to see how how things evolve over the next three, four years.
30:32.65
Jon
um OK, so the book, just to wrap the book is Little Book of Data.
30:36.98
Justin Evans
Yeah, by the way, yes, we completely went into a depressive hole there.
30:37.49
Jon
Yeah. yeah yeah Yeah, we totally did.
30:41.03
Justin Evans
And now we’re going to pull up.
30:41.73
Jon
We should come back.
30:42.35
Justin Evans
but’re We’re going to pull up.
30:42.92
Jon
yeah
30:43.79
Justin Evans
going to pull up.
30:44.19
Jon
Yeah, we’re going to pull Yeah, we we ended up in ah in ah in ah in a dark place there. um
30:48.49
Justin Evans
Yeah.
30:49.36
Jon
So, um, uh, where, um, okay. So, so people should certainly check out, check out the book. Um, they can get the book with the index and the author’s notes, which would be, uh, helpful.
31:00.88
Jon
Um, where can people find you to, to get, you know, get in touch with, you know, more requests, more information, you know, workshops, whatever, whatever it is, like, where, where can they find you?
31:01.49
Justin Evans
yeah
31:11.54
Justin Evans
My home base is LinkedIn. ah You can look look look me up, Justin Evans. I’m the one that says dad and author on the on ah my slug. It’s got a big picture of the the little book of data as my background photo.
31:23.08
Justin Evans
ah But the little book of data is available in stores, including in airports. I’m delighted it’s in Hudson News in the airports.
31:28.71
Jon
Yeah, that’s fun.
31:29.78
Justin Evans
And ah if you like the sound of my voice at all, you can hear me for four hours reading the book in an audio book form.
31:37.44
Jon
Okay, terrific. I didn’t know there was an audio book form. That’s super fun. um I, of course, have the paperback. I’m just going to keep it on the shelf here. This is great. ah Justin, thanks a lot for coming on the show. it was really fun to chat. And best of luck with the book. I hope ah i hope people will check it out.
31:52.89
Justin Evans
Appreciate it, John. Thank you so much.
