Money Matters Episode 328- The Little Book of Data w/ Justin Evans

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Christopher Hensley: . [00:00:00]

Discussions in this show should not be construed as specific recommendations or investment advice. Always consult your investment professional before making important investment decisions. Securities offered through registered representatives of Cambridge Investment Research, a broker, dealer member FINRA Civic Advisory Services.

Through Cambridge Investment Research, Inc. A registered investment advisor, Cambridge and Houston First Financial Group are not affiliated.

Welcome to Money Matters with Chris Henley, where we spotlight financial literacy issues in the Houston community and beyond. And now your host, Chris Hensley.

Justin Evans: So I wanted to tell some stories and I, I get very emotional when I talk about this one, so apologies in advance. But I met this, uh, I, I, I interviewed this woman in the book I call Priya. She asked for her identity to be, uh, suppressed. Um, she was using analytics to [00:01:00] find and pinpoint sex traffickers. And to save the girls that had been, I don't know what word to use.

Christopher Hensley: Imagine waking up and realizing you've been leaving money and power on the table every single day just because you didn't understand the hidden language of data. In today's world, data isn't just for techies and analysts, it's the new currency of influence, decision making, and wealth building.

But what if I told you. That most people and even smart business owners are still stuck in the data denial phase. Our guest today wants to change that. Justin Evans is not your typical data scientist. He's a master storyteller who's taken his decades of experience working with brands like Nielsen, Comcast, and Samsung, and turned it into a powerful little book with a.

Big message, the Little Book of data. In this episode, we'll unpack why data is your overlooked superpower, [00:02:00] how ignoring it could cost you your career, and what simple mindset shifts can help you move from overwhelmed to data savvy. No coding degree required. Justin's new book drops in June and you'll wanna get your hands on it, but today you'll get a backstage pass into his brain right here on Money Matters.

Let's dive in. Justin, thank you so much for being on the show today. So great to be here. Thank you for having me. Absolutely. Absolutely. So I was lucky enough to get an advance copy of the, uh, of the book, and I loved it. , Really, really good information. , For listeners, they'll, we'll have this, so if you're driving, don't, you don't have to pull over the car, right?

So if you're driving, we're gonna have, uh, podcast notes and we'll have a link to it where you can get access to it. But I know it is dropping here, uh, next month coming up. So let's start right away, Justin, with what was your motivation in writing the little book of data?

Justin Evans: Well, I, I got to the point of my career 20 years in where suddenly you kind of look around and [00:03:00] you say, oh, I'm the data expert in the room.

And that's a nice feeling. With that was seeing some people that were, my colleagues and often beloved colleagues, kind of drop out of advertising and marketing the industry I work in. Because they weren't able to adapt to a data-driven world. And marketing and advertising is very data driven now.

And so I put those two things together and I just, did I get frustrated, angry, something? I just felt like there's gotta be a way to explain data in a way that. People can understand in their guts. It won't be intimidating. It won't be about the math, it won't be about the technology because data's really about ideas.

And I, I, I came to believe that as, uh, uh, as now the data expert, it was when I would talk to people and I would explain the, the ideas to them, I realized that we had to speak in language. [00:04:00] In English and, and we had to speak in business, and I passionately have come to believe that any data idea, no matter how complicated, can be reduced to business in English language and can therefore be accessible to somebody who's an expert in their own field, but not an expert in data.

Christopher Hensley: I love it. I love it. Kind of translating it to the masses here, putting it into business. Speak for us, people who speak business speak. Right. , But really big idea. And I, I love it. And I, and the stories that you used throughout the book really drew me in as a reader. It kept it interesting. It kept me wanting to get to the, you know, keep going to the end of the book there.

Uh, let ask you another question here. In the book, you talk about data is everywhere, but we're not always aware of it. Can you tell us a little bit more about that?

Justin Evans: Uh, the first chapter of the [00:05:00] book, I, I do tell a story in, in that one, I'm the main character. Um, I thought it'd be kind of funny to talk about the morning that I went for my first colonoscopy, and it was sort of like, you know, the, the, the metaphor of intrusiveness was intentional.

Um, but I just wanted to see, okay, let's, let's see how many data collection points you experience as a normal person over the course of half of a day. And so I, um, I, I got on the website and got my Apple data, my Google data, all this data. And I found that, you know, for the moment, I woke up and I turned on my phone.

My phone was calling a data center in Northern Virginia to access my Google administrative id. Um, you know, 40 times per second. And when I played my iPhone to listen to music on the way to the colonoscopy, there were all sorts of IDs and, and music assets that were being logged. Associated with my id.

There was the Uber that I [00:06:00] called, which accessed GPS data out of my phone. You know, talking to three different satellites that were swirling around at, you know, miles above the surface of the earth. Then that. On the other end, the colonoscopy clinic that I was going to, I think it was uh, east Side Endoscopy, uh, was in the Uber system because of places of interest data where Uber logs all, all the data.

When I got to the clinic, there was a, a coding system that has the, the diagnostic treatment, diagnostic treatment that I was, um. Receiving, which is maintained by the World Health Organization in Switzerland and, and, and so on and so on. And, and, and this was just like three hours. And, and that's happening to me.

Uh, and by the way, I'm not even really on social media, so I mean, you know, who, who knows what, what my kids would, would, would add to that as layers with TikTok and Instagram and so forth. [00:07:00] Um, so if that's happening and can be recorded just over the course of a few hours. Clearly we're in a position where we as human beings are kind of like shedding data.

We're just sort of creating it. We're, we're, we're, we're leaving it behind that sort of, in elegant metaphor I like to use as kinda like dandruff. You know? It's just like you just mm-hmm. Don't even know. Um, and so that's the, that's the world we live in. And, and I also compare it a little bit to electricity, which is, you know, it's not cool.

Um, it's happening in the background. It's literally behind the walls. Um, but you know, like electricity in the, in the 20th century, you know, where it played a, of course, massively transformational, um, role in society. I think really data is playing that role in the 21st century as this kind of invisible.

Slightly, not sexy, [00:08:00] but incredibly ubiquitous and influential force in our lives.

Christopher Hensley: I love that example because, uh, money matters. We focus on retirees. Retirement age. I, when I have to get gauge and guess what age you're at? I just had my colonostomy last year. So that comes on our, our to to do list magically when we hit that 50.

Right. So, so for our listeners that, that's a great way to, to start it. I love your examples here. You talked about, you know, just getting in the car and getting, you know. Touch points of data, uh, the, the, the diagnostic coding, the, uh, Uber's data centers knowing, you know, whether people had gone that route before or not.

You gave the idea of dandruff or, or electricity of things that aren't, aren't super exciting, but they're there, right? They're just kind of shedding all around us. So I love that kind of a way to. To set up the idea of data. Um, another, you know, you build a, a [00:09:00] case in this book that data tells a story as part of my research, I had listened to another podcast that you were on where you talked about, uh, let me see if I'm gonna frame it this way.

What does the movie 300 have to do with data?

Justin Evans: I tried to, to tell, uh, these stories always with a hero. Um, so what kind, kind of how the, the, the book started to take shape in addition to the, in sort of in original info I mentioned a few beats ago. I would, I would, once I decided I was gonna write the book, I tried to kind of write down what are the core ideas of data that I think are just sort of.

I'll just say it again, core to using data as a professional. And so I wrote down, well, there were four superpowers that I, ident that I identified, and then another eight, um, kind of key use cases, key ideas. And once I had those 12 core concepts and a few others, some about like [00:10:00] ethical data, I ethical use of data, I, I was, I tried to tap into specialists or experts who were.

Practitioners of data whose work on new data concepts illustrated those eight, those 12 ideas. And so in each case, I, I, I really wanted to show, um, these data concepts as a way of making them accessible through these people's stories. And for, for one of the superpowers, the superpower is, um, data directs resource data directs resources to where they are most needed.

And the hero of that story is an epidemiologist named Sharon Green, who works at the Bureau of Communicable Diseases in New York City, the BCD, which is in Queens. And what they do is they track leprosy and gonorrhea and wow. West Nile and Zika and Anthrax and [00:11:00] everything that, that, that could be communicable and could kill New Yorkers.

They're there to protect us against. And one day in February, 2019. They were briefed on this disease in China that had, um, that was fatal and or could be fatal and had no test and no cure, which is of course the beginning of the covid epidemic epidemic. And Sharon Green's team set out to use everything they had done before with say, salmonella to identify hotspots and apply that to the Covid crisis.

Because the theory is, you know, any communicable diseases, if, if you can stamp out the hotspot, you can stamp out an outbreak or a broader outbreak. And what was happening with Covid at that time in New York was, it was really the, the, our death rate was the highest in the world. It was higher than Delhi, higher than Hong Kong, higher than London.

And I don't know, nobody really knows why. Um, and they thought that if they could [00:12:00] bring down the. Uh, if they could stamp out the hotspots, they could bring, bring down the death rate, which they did, and I did the calculations and I think they probably saved 300,000 people, um, by getting it right, um, which is extraordinary.

Anyway, all of this also has a corollary in the world of military theory and military history. And if you go to, um, west Point and you take the classes on military strategy, you will learn the theory of mass. And the theory of mass is you need to deploy your forces to the most critical point in the battle because you have scarce resources and you need to apply them to this critical point and the most.

Potent example of that probably in history was the battle of Thermoy. Um, when the Persians marched over to attack Greece with hundreds of [00:13:00] thousands of soldiers and 300 Spartans and others, uh, d defended Greece at this little, tiny 70 foot pass at Thermoy. And it's, uh, it's this great example of mass and that was exactly what Sharon Green and the Bureau of Communicable Diseases was trying to do with Covid.

And so to step back a little bit and kind of talk as an author, what's, what's, what's fun and what was fun for me in writing the book is like, can I juxtapose two crazily different things to entertain you as a reader that like just, just when you get to know Sharon Green, I'm gonna take a hard left turn and introduce you to Xerxes and King Lee, Anita of the battle with mpo.

Christopher Hensley: I love it. I love it. Um, you know, a lot of stuff there. We talked about just the idea of the hotspots and kind of the, it, you know, the idea of things being concentrated and having a bigger compound effect when you, when you hit in those areas, that theory that you talked about that came through West Point.

But [00:14:00] it being, uh, you know, started by the conversation of Covid and how. The hundreds of thousands of lives were saved by putting more effort into one area. And this all came through data, through data science. So it kept me interested when I was here. It's like, oh, this is good. Tell me more. This is, this is fantastic stuff.

I wanna pivot a little bit and talk about, you know, what do we got about. 15, 13 minutes or so. So maybe we'll touch on AI here, which is the big, everybody wants to talk about AI right now, but let's go a little bit past that and talk about ethics, right? Because you, there's a good bit of your, your book where you dive into that kind of, you know, that thing that nobody really wants to talk about is the ethics behind it, and you do a really good job of, of that discussion.

Can you tell us a little bit about, you know, what you touch upon when we talk about the ethical side of data?

Justin Evans: I, I took the same narrative approach to ethics that I did with the other points of the [00:15:00] book. Um, I'm not a lawyer, I'm not a professor of ethics to, when you read an exhaustive book about data ethics, it's highly educational and guaranteed to put you to sleep.

Uh, so I wanted to tell some stories and I, I get very emotional when I talk about this one, so apologies in advance, but I've met this. Uh, I, I, I, I interviewed this woman in the book I call Priya. She asked for her identity to be, uh, suppressed. Um, she was using analytics to find and pinpoint sex traffickers and to save the girls that had been.

I don't know what word to use, put to use, kidnapped, whatever. Um, by, by sex traffickers. And they had this analytical technique, which she explained to me when she also asked me not to put in the book. Um, so it wouldn't tip people off. [00:16:00] Um, but she just had this extraordinary experience where she obsessively, once she realized that there was one woman that she could save by identifying a single sex trafficker.

Um, she, she realized she was working on a ticking against a ticking clock and 24 7 she was thinking about this girl and how she could save her and doing the proper analysis to catch the guy and, uh, because the, the clock was ticking because when these girls turn 18, they are deemed adults and therefore whatever circumstance they're in is deemed to be their choice and it's harder to prosecute.

The offenders and hard to, to get them free. Um, so Priya succeeded in her mission, uh, in saving the girl. But what was sort of most arresting in this was she kind of just like, she kept using this language in the interview [00:17:00] about, if I don't do my job, this girl is not gonna see the light. And it just, it gets me every time.

And she, even, like one time I was trying to kind of pivot off that point and she stopped me and she said, you don't know what, your world is so different than these, these girls' world. You have a family and you have a home and you have a job. And you go there and you smile and you see daylight and these people are in the darkness and it's my job to save them.

And I, I thought that was so. Um, dramatic as that is. I, I see that as very typical of the attitude of a data person. What I think is a good data person, because a good data person is someone who has this expert knowledge. They know how to query and analyze data. They know how to find the sources, and there's this, there's just a mentality after like hiring like dozens and dozens and dozens of data, people over the years, there's [00:18:00] just this mentality where people are in their guts, are just so committed to.

The mission that their client, whether another department in their company or an actual client has given them. And it's like a fiduciary duty. It's a duty of faith. And I think Priya was just such a beautiful example of that. And I can, I can contrast this to some people I met at Facebook and other places, um, in the same era who seemed to have, you know, let's just say.

The opposite point of view about data and how to use it, and, and so I, I let the narrative tell the tale versus sort of laying out any kind of scheme of precisely how to use data in an ethical fashion.

Christopher Hensley: Oh, I love it. Great answer there. By the way, I had it on mute here, but that, that was moving the idea of, I, I couldn't think of anything better.

When you talk about good data, this, this person who had a mission, a mission really to save those girls. Um, [00:19:00] and that's, we talk about things like fiduciary responsibility. These are those, those things that. Sometimes this stuff rolls out and then we go back and have to put the, go back and think like, how are we gonna come around these, these ideas of ethics.

So I love this, I love this, this idea, but let's juxtapose the good data with the bad data because you talk about something called bad data as well. Uh, you caution about bad data. What is bad dad?

Justin Evans: Well, I don't go too deep into it. Um. My, my goal in the book is to inspire people to feel comfortable with data, feel empowered about data, and feel confident to use it for their own mission.

And that's really my goal. Um, that said, there are a number of different ways for people to be bad data people. Um, one I feel is in kind of the most common is for people to be data bullies, which is. I'm going to intimidate you. I'm gonna make you feel stupid. I'm going to therefore [00:20:00] try and prevent you from asking the common sense questions.

And as we started out this conversation, one thing I want people to walk away with is any data idea can be expressed in English and business language. So in a way, no one should ever feel like they cannot talk data to an expert. So data bullies are, are, are, are kind of the core way that, that we are all put off and prevented from achieving our, our data superpowers.

Christopher Hensley: I, I love that term data. I got, I got a picture of that in my head here. A couple of people fall in line with it when I think about that. So, uh, let me ask you, so instead of talking about ai, we've got about seven minutes here and there's a lot in the book, but I'm gonna ask you something that, um, I was at a tech conference recently and one of the words that's really popular right now is, uh, data Lake.

Mm-hmm. Can you tell us what that is and, and maybe explain it for somebody who hasn't heard that [00:21:00] term before?

Justin Evans: Yeah. A, a a data lake is, , a data lake is a, uh, an asset that any organization can build for themselves, and it's where you have lots of different types of data that you wanna put in one place so you can analyze it.

It can be very complicated because these different data sources might become from different data sources and have different taxonomies, different languages. They can be organized or clean at different levels. Um, they might not speak to each other. So it's actually a pretty big challenge in the industry, uh, at large, and it's an even more pressing challenge in the era of ai.

When you, you really want to inject data into multiple aspects of your business or your workflow, and if the data isn't clean and accessible, then uh, you really [00:22:00] can't work with it. So there are, uh, a number of companies, including a company I'm an associate I'm associated with, called Above Data, which is creating these layers so that different aspects of it, of, of it.

A data lake can speak to each other or speak to AI applications in a much faster way.

Christopher Hensley: Love it. Love it. Uh, this book is, is also very visual and, and bite sized. Was that design intentional to match how we consume information today? And how do you think visual communication affects data comprehension

Justin Evans: , The book was the, the, I tried to bring each chapter in at 10 pages. Uh, I, I wanted to provide lots of space, uh, in the book visually to express through graphic design and through the length of the book and the accessibility of the book. The main idea of the book, which is that it's, it's this, this should be something you can read and enjoy and feel comfortable with.

Um, and, and I, I always find, [00:23:00] you know, separately. I don't have a great anecdote about this, but just for me, it's, data's really about visualization and it's the best way to articulate these, these core ideas. And I'm sort of, they're kind of, I'm, I'm teased at work for always going to the whiteboard.

Christopher Hensley: I love it.

I love it. The book, the Little Book of Data, uh, coming out in June, uh, Justin Evans, the author. Justin, before we go today, what have I forgot to ask you that you wanna leave listeners with today?

Justin Evans: Well, the, the, the one of the main things I want folks in business who have leadership roles, even middle manager roles, especially middle manager roles to take away is that.

Data has in, in your business, in your organization, there is a kinda latent power for growth or if not growth, than to do something extraordinary. [00:24:00] Uh, and it's, it's sort of sitting there embedded in the data you already have. Every business exists for a reason. There's a customer or a geography or a market space that you occupy, that you've been able to sustainably occupy, and that's your thing.

And so you know that. You being the expert in your space, you can apply data to articulate that in multiple ways. One example I use in the book, uh, was the am example of Amazon using its e-commerce data to launch an advertising business because knowing that you buy Pampers or knowing that you buy. Uh, uh, standing lamps allows the manufacturer of those items to advertise to you.

And in the Amazon ecosystem, this created, um, positive effects all round that you as a manufacturer could advertise and sell more. The person who was buying it was buying something that they ostensibly needed, and Amazon gets money all round. Um. I [00:25:00] also though have seen this in the nonprofit space where, uh, uh, a nonprofit I advised for a time called Denver Urban Gardens, was using data to try and articulate what's the impact, the social impact, and the e ecological impact of putting a garden in the space.

That used to be a, a, a bunch of gravel, and once they could articulate that, they could. Go to that story to funders and to other stakeholders and express like, this is how we're having an impact on the world. So all the way from Amazon to, uh, a, a local nonprofit, there are ways to use data to further your mission, and I want everyone to feel like that's something that they can reach out for and grab for themselves.

Christopher Hensley: I love it. I love it. Justin, thank you so much for being on the show today. Uh, as far as it being a page turner, you would think data, it'd be hard, but this book kept me wanting, it kept me wanting to keep going there and get to it. You're such a great storyteller. Thank [00:26:00] you for being on this show. Have a good rest of the day there.

Justin Evans: Appreciate it. Thank you.