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From Data Literacy to Storytelling: Insights from The Little Book of Data
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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.
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Transcript
Introduction and Book Overview
00:00:13
Speaker
Welcome back to the Policy Viz Podcast. I'm your host, John Schwabisch. Hope you're well. Hope you are enjoying season 12 of the podcast thus far. On this week's episode of the show, I welcome Justin Evans, author of The Little Book of Data.
00:00:29
Speaker
Actually, not that little, you know, it's about 250 pages or so. ah really enjoyed this book. This is a nice read. I have to tell you, subtitle to the book is understanding the powerful analytics that fuel AI make or break careers and could just end up saving the world.
00:00:47
Speaker
I'm not sure about saving the world, but it is an enjoyable
The Role of a Data Person
00:00:51
Speaker
book. Justin does talk about his experience working in a variety of different organizations and sectors. He does talk about what it means to be a data person. And we talk in this episode about...
00:01:03
Speaker
whether anyone can be a data person or whether we need special skills or special insight or special training. ah We also talk about some of the most effective ways people and organizations can build data and data visualization literacy within with their staffs, which is something that Justin has spent a lot of time working on.
Engagement with Data Dashboards
00:01:24
Speaker
We also talk about dashboards. It's something that's sort of been rumbling around in my brain over the last few months is whether or to what extent dashboards are useful. Public dashboards are useful. When we post these data dashboards out to the world on websites, do people actually use them? Do people actually download the data and use them? Do they make the graphs and and use them? I'm not really sure.
00:01:46
Speaker
I've been thinking about this for a little bit. There is an interesting paper out of Tableau Research ah showing that at least in there, I think like two dozen or maybe 30 people that they interviewed that most people didn't actually use the dashboard, that they explored it for a second and then they downloaded the data and did their own analysis.
00:02:03
Speaker
So Justin and I talk a little bit about dashboards in this conversation because it's something I've been thinking about a lot.
Federal Data Challenges and Career Shift
00:02:09
Speaker
um We also talk about some of the challenges that are happening at the federal level when it comes to data.
00:02:14
Speaker
And we talk about being able to trust the producers, collectors, and communicators when it comes to federal or government data sort of more generally. um Again, I really enjoyed this book. It was a nice little read. i got to read it over the summer, which of course is always nice when you are reading during the nice weather.
00:02:31
Speaker
um So let's ah turn it over. Here's my interview with Justin Evans, author of the new book, The Little Book of Data. Hey, Justin, good to meet you. Thank you, John. Great to be here with you.
00:02:44
Speaker
ah Thanks so much for for coming on Little book of data. I've got the fun galley copy, so there's no index. I had to put notes in the book, which I hate to do. I had put notes in the book to make sure I knew where to go back to.
00:02:57
Speaker
um You have to have that classic undergraduate ballpoint pen, perception versus reality note. Yeah. Yeah. Yeah. And try not to highlight every sentence because but um so I appreciate you coming on the show.
00:03:14
Speaker
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
Data Denial and Career Survival
00:03:20
Speaker
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?
00:03:29
Speaker
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.
00:03:44
Speaker
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.
00:03:57
Speaker
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.
00:04:09
Speaker
And what I was noticing is that the people who embraced data and that next generation of marketing and advertising that was based on data and machine learning were persisting in their careers
Writing Process and Storytelling in Data
00:04:23
Speaker
and succeeding. And the people who couldn't embrace it for whatever reason were parachuting out.
00:04:29
Speaker
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. And it made me think, what's the what's the matter with data that people can't get themselves geared up to learn it?
00:04:47
Speaker
And i started to sort of investigate that and think about the 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.
00:05:03
Speaker
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.
00:05:22
Speaker
So, so I ended up, kind of writing the 20-ish core ideas that I felt were really true of data that would want to tell that person or tell my younger self.
00:05:34
Speaker
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.
00:05:52
Speaker
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. And that went much better and ended up being a ah complete joy meet these data professionals who I regarded as sort of heroes in a way.
00:06:19
Speaker
and hear their stories and turn those stories into the heart of the book. So you're learning the lessons of data, right? 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.
00:06:37
Speaker
Right, because it's the thinking about data problems that's the first step. That's right. Like you get into code later, but but the the the human part of it, i mean, we're not gonna talk about AI. i can't I can't talk about AI anymore, but like the AI part of it is like a whole other thing, but like the human part is the critical thinking about data.
00:06:55
Speaker
That's right. And I would add the the problem solving part of data is really the essential part of it. I think even for people who do it every day,
00:07:06
Speaker
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 math, but I've been in the data business a long time.
Vision and Ethics in Data Work
00:07:19
Speaker
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 a data opportunity.
00:07:31
Speaker
And it's fun. And if you have to, once you get some of the kind of core principles in your head, 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.
00:07:56
Speaker
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. Right.
00:08:07
Speaker
So I wanted to ask you about the data people, 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...
00:08:30
Speaker
It does get in the weeds, but, but you're seeing how people have had to address these challenges with data. So I'm all in on the storytelling to sort of get that message across. But I wanted to ask about, about data people.
00:08:43
Speaker
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?
00:08:54
Speaker
Like, like what makes a data person, a data person? 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 try to think about what made, what was similar between those people.
00:09:13
Speaker
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.
00:09:25
Speaker
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 coining the phrase, a fiduciary duty. I'm probably applying it to the data world maybe for the first time.
00:09:42
Speaker
That people had a duty of faith to the client and a sense of mission. And they just, they really cared about getting the right answer for the client.
00:09:56
Speaker
And there was 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.
00:10:09
Speaker
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,
00:10:24
Speaker
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 capture verse on Nielsen methodology, on the Nielsen TV ratings, but they couldn't tell you how to solve a business problem.
00:10:46
Speaker
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 honorable title of data person as well.
00:11:16
Speaker
yeah 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.
Data Professionals in Leadership Roles
00:11:27
Speaker
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. You'll see financial people rise to be CEO.
00:11:38
Speaker
You'll see salespeople rise to be CEO, occasionally you're marketing 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. You can't copy money and then try another whack at it. You know, you've already you've already gained or lost it.
00:11:57
Speaker
So data people definitely have, again, in my personal estimation, a certain dream equality that is, again, my eyes, laudable and even essential.
00:12:10
Speaker
They have to be able to close their eyes and and think about what's possible. But that sort of <unk> connection to reality can sometimes be a liability in other parts of their business life.
00:12:24
Speaker
Yeah. It's interesting the way you describe that. ah You didn't use a phrase like a programmer, a mathematician, ah statistician.
00:12:38
Speaker
It's almost like you take more of a humanities view of what it means to be someone working with data. I do. i think I think data people have to fall in love with the problem.
00:12:49
Speaker
and And once you do that, you'll do anything to solve it. You'll dream any dream and you'll work any hours. And working hard is part of the commitment. But indeed, the the hardcore quants and the hardcore coders, I i don't see that as essential to the to the the data personality, so to speak.
00:13:11
Speaker
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.
00:13:22
Speaker
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?
00:13:42
Speaker
Yeah. so So you're asking what
Data Literacy and Visualization
00:13:44
Speaker
what makes, how how does one become data literate? Is that a good paraphrase of your question? Yeah. yeah The core ideas I really put in the book and tried to illustrate with some examples. And sometimes these things have to be learned by instinct and exposure. you know, one concept is the concept of identity, which is I can i can put a name on and an object, even an an abstract data object, and I can always come back and find that.
00:14:15
Speaker
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. They're just, they just they're not cool.
00:14:35
Speaker
And he, there was, the the little label in the corner of Progressive Grocer had an identification number that was unique to the addressee.
00:14:47
Speaker
And that unique number actually meant that this person was the general manager of the Piggly Wiggly in Charleston, South Carolina.
00:14:58
Speaker
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.
00:15:17
Speaker
And it became this decoder ring for the manufacturers like Procter and Gamble and Unilever to know exactly where they were sending their cookies and their detergent.
00:15:30
Speaker
And they knew not just that it was a Piggly Wiggly, but it was store number 243 out of a thousand 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.
00:15:51
Speaker
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 a way to uncork millions of dollars of value for these manufacturers.
00:16:08
Speaker
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 yeah of data, and then you can play the game.
00:16:38
Speaker
Right. Yeah. So 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.
00:17:00
Speaker
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.
00:17:18
Speaker
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.
00:17:33
Speaker
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 probably primarily internally um and then sort of building out people's data viz literacy in organizations.
00:17:54
Speaker
Well, the story, Freddie H, that story is actually a Samsung story. And this and the and the story was um the Samsung, we make money from selling advertising on streaming TV.
00:18:08
Speaker
And in 2020, when that story is set, the pandemic had just started. Everyone was now walked at home and everyone's, a billion people globally started streaming television overnight.
00:18:23
Speaker
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.
00:18:34
Speaker
What the hell is going on? how what Where are my customers watching TV? but Where can I reach them at ads now that the world is upside down? i really know how to reach anyone. Yeah. And the.
00:18:45
Speaker
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. 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.
00:19:10
Speaker
And so we we ginned up a dashboard really quickly And it became 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.
00:19:30
Speaker
yeah And if it was ah a moment where we created a lot of clarity for clients who were desperate and we created a lot of transparency or light in a dark room is a phrase I point 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.
00:19:59
Speaker
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 yeah at the time. But but in that case, it was it was a distribution mechanism that was what was powerful about it.
00:20:21
Speaker
But it also sounds like the reason it was successful is that people had fairly specific questions that they wanted to answer. That's right. And actually, but that's a really great hook into the essence of your question, which is why a why a dashboard and why a visualization?
00:20:39
Speaker
And the contrast you could make is to a dashboard, which is designed for i call, it a general user. In our case, it was salespeople who were generally accessing the dashboard, people who are ad salespeople who are not data people.
00:20:58
Speaker
And you can contrast that with a power user who's where where the interface is designed for someone to go in 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 in a dashboard.
00:21:14
Speaker
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 the, at that time, the narrative that we created was if you,
00:21:40
Speaker
advertiser know who your audience is, I will tell you how they are watching streaming television. That was the question we were answering. And we kind of, it it was literally a ah flip book. We would say, okay, how many of your audience are there?
00:22:00
Speaker
It's 40 million. And flip the page. How many of them watch linear televisions? 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. 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.
00:22:32
Speaker
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, yeah we have these sort of rhetorical questions that are then answered by the data.
00:22:43
Speaker
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. And it's efficient for the data team because if the data team only has to answer those questions.
00:22:58
Speaker
They're not creating some power user information. deep dive tool where you can ask it any question. 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
Pandemic and Data's Role in Crisis
00:23:10
Speaker
everybody. And the beautiful thing is if you have ah team of people who are working 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.
00:23:29
Speaker
Right. if you do that upfront investment. 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.
00:24:02
Speaker
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.
00:24:15
Speaker
And i don't want to get too deep into Samsung stuff, but the, what in the in the world of television, where we we collectively are still on a path to go from small data to big data, where have the Nielsen sample of tens of thousands of households being measured to big data sets, which are tens of millions.
00:24:36
Speaker
Right. 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, yeah,
00:24:53
Speaker
It's at that time we were much, we collectively, the advertising marketing industry were earlier in the cycle of still relying on small data. I gotcha. Okay.
00:25:04
Speaker
So this was moving in the direction of getting more, more households, more real time across multiple channels and by channels, meaning, you know, different delivery services, but also the different actual channels.
00:25:19
Speaker
That's right. 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.
00:25:40
Speaker
And I'm curious if you have thoughts about where we are in the U.S., on, you know, I would say pretty dramatic changes to the federal statistical agency structure.
00:25:56
Speaker
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,
00:26:11
Speaker
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?
00:26:28
Speaker
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.
00:26:45
Speaker
the The first demographer slash data scientist is credited to be this fellow named John Grunt, G-R-A-U-N-T, who was a haberdasher in London in the 1640s.
00:27:02
Speaker
who, I don't know, he maybe he just spent a lot of time with measuring tape, but he but became he really fell in love with numbers. 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 they're all these kind of villages that were strung together. And at the time, it was the same thing, only more so.
00:27:25
Speaker
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.
00:27:40
Speaker
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,
00:27:55
Speaker
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.
00:28:07
Speaker
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 Grant 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.
00:28:25
Speaker
And then he showed trends over time and you would see that someone died of of, ah you know, drowning in the bath at six people per year. And then people died of the plague is 60,000 people per year. And you would see it by neighborhood.
00:28:42
Speaker
And that's really when data science came alive, being using data to answer questions. Right. 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 ah statistician who
00:29:17
Speaker
helped make battlefield weapons for the United States government and helped use, create Cold War spy planes for the US government before settling into being a Princeton stats professor.
00:29:30
Speaker
And he too saw the the the relevance of using data from diverse sources and sometimes just good enough data, not great data to answer these life and death questions.
00:29:41
Speaker
All of 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.
00:29:53
Speaker
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 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.
00:30:31
Speaker
Yeah. And do you...
Private Sector's Role in Data Gaps
00:30:35
Speaker
foresee a spot. Now yeah you've worked in the private sector, you know, different places. Do you foresee the private sector stepping in different ways with different data to fill in those gaps.
00:30:49
Speaker
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.
00:31:09
Speaker
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.
00:31:20
Speaker
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.
00:31:35
Speaker
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 up front. to gather data, clean it, figure out the use cases, make it available.
00:31:50
Speaker
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.
00:32:02
Speaker
yeah So the the notion that 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.
00:32:19
Speaker
Yeah. Well, ah yeah, I don't, I don't disagree with you and it will be interesting to see how, how things evolve over the next three, four years.
00:32:31
Speaker
um Okay. So the book, just to wrap the book is Little Book of Data. Yeah, by the way, yes, we completely went into a depressive hole there. And now we're going to pull up. butre We're going to pull up.
00:32:42
Speaker
We're going to pull Yeah, we're going pull Yeah, we ended up in a dark place there. so um So people should certainly check out the book. um They can get the book with the index and the author's notes, which would be ah helpful. 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?
Conclusion and Future Engagement
00:33:05
Speaker
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. ah But the little book of data is available in stores, including in airports. I'm delighted it's in Hudson News in the airport. Yeah, that's fun.
00:33:23
Speaker
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. Okay, terrific. I didn't know there was an audiobook for him. That's super fun.
00:33:35
Speaker
I, of course, have the paperback. I'm just going to keep it on the shelf here. This is great. 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. Appreciate it, John. Thank you so much.
00:33:48
Speaker
Thanks everyone for tuning into this week's episode. Hope you enjoyed that. Hope you will check out Justin's book, Little Book of Data. I will link to it in the show notes. And I hope you're enjoying this season of the show so far. I've got some really exciting interviews coming your way.
00:34:03
Speaker
And let me know if there are folks that you would like to hear from. If there are people, if there are organizations that you are paying attention to in your life ah that you would like me to try to reach out to and talk to, ah to see how they approach data, data visualization, presentations, tools, and so on and so forth, I will see if I can get them on the show.
00:34:22
Speaker
So until next time, this has been the Policy of His Podcast. Thanks so much for listening.