Data Overload to Data Literacy- Navigating the Zetabyte Era

 Recorded live from the NSTA conference in Anaheim, this episode features Zarek Drozda and Hannah Kurzwell from Data Science for Everyone — a national initiative working to bring data literacy into K–12 classrooms across every subject area. We dig into what "data science" actually means for students (hint: it's not about becoming a data scientist), why the ability to think critically about data matters more than ever in an AI-saturated world, and how science educators can start building these skills without a data science degree. From the Zettabyte Era to garbage-in-garbage-out, this conversation is equal parts eye-opening and empowering — and a must-listen for any educator wondering how to make their classroom more relevant to the world students are actually entering.

Transcript

Andrew 0:00

Patrice, we are here and we are live in Aheim at the NSDA conference. It's crazy. There's a lot of people here. We're at a very impressive booth. We've got people outside who are looking at us, which is interesting when you podcast and you never see anyone. Yeah. My mom always told me that I had a face for radio. Wow. That's out there. That's nice. Your mom's nice. Yeah, she's a lovely lady. We have with us two individuals from Data Science for Everyone. Yeah. Zarek and Hannah. Welcome to the show.

SPEAKER_00 0:30

Thank you so much. So excited to be here. I'm told these are new microphones. They are new microphones. We've got the new microphone smell, new car smell.

Andrew 0:38

That's right. They're new to us. I think they're new to everybody. They are new.

SPEAKER_02 0:42

I'm just kidding.

Andrew 0:42

I was trying to get a reaction. It didn't work.

SPEAKER_02 0:44

Oh, yeah.

Andrew 0:45

All right. So if you wouldn't mind sharing with the NSDA voices just a little bit more about yourselves and maybe what it is that you do with data science.

SPEAKER_00 0:53

So my name is Eric Drosen, the executive director of data science for everyone. We're a national initiative associated with the University of Chicago. It's been, you know, working to advance data science and data literacy skills for students for about the past five, six years. If you all know the free economics books and podcasts, we got started from economist Steve Levitt, who's based at the university. And our whole movement was sparked by a free economics podcast episode. So this is very fitting. You know, like five, six years later, we're keeping the tradition alive.

SPEAKER_04 1:20

That's really coming full circle.

SPEAKER_00 1:22

Yeah, yeah, exactly.

Andrew 1:23

The podcastees have become the podcasters. Ah, there we go.

SPEAKER_04 1:27

Yeah, it's it's really great to be here. I have attended NSTA as a science educator, now a little bit different in the framing of the data science lens, but excited. My name is Hannah Kurzweil. I'm the community manager for Data Science for Everyone. And my passion is working with teachers. So we're ready to dive into it.

Andrew 1:47

Well, here's the question that's on everyone's mind who's listening to this podcast is what does data science for everyone do?

SPEAKER_04 1:54

You're gonna get a very different answer to that question, depending on which team member you ask, because we are pushing data science on all fronts. That's good because it's needed. It is needed. Yeah. We have, I think, a like a lot of different ways that we're like tackling essentially our core mission, which is how do we get data science education into K-12 classrooms? We want every student to be equipped with data literacy skills when they graduate. And you can't tackle that problem with only one foot and one door. So we bust down doors at policy level, state level, at the content and curriculum and teacher education and preparation level.

SPEAKER_02 2:29

So if I'm completely new to this world, can you break it down for me to really understand when you say data science, what do you mean?

SPEAKER_04 2:36

That's a great question. Having a common definition is really important when you're speaking about language, especially in an education sense. If I'm gonna make it a little bit more broad, data literacy in general, because we don't want people to walk away thinking that we're expecting students to graduate as seniors in high school and become data scientists. Yeah. Obviously, some will. We're stoked on those people as well. But what we really want is for our educational system to equip students for the world that they're facing. And so when we talk about like data science and data literacy, again, in a more broad sense, what we're really saying is how can we equip students with the necessary tools in their tool belt in terms of critical thinking and working with the massive amounts of data that they're inundated with? Like you cannot escape it.

SPEAKER_02 3:19

Yeah.

SPEAKER_04 3:20

And recognizing that data is coming at them from all angles. It's not just the traditional data of like you're seeing a spreadsheet with a bunch of rows and columns. This is talking about imagery and text and news articles, and how can we teach them to sift through that, recognize what's misleading, recognize what questions to ask that are going to help them expand their information and make decisions moving forward. Amazing critical thinking skills. Yeah.

SPEAKER_00 3:42

And if you had to put it in a sentence, like the shortest possible definition is deriving insight from data. That's really what the whole practice is about. But if you want to go into academia land, it's a combination of a little bit from mathematics, a little bit from computer science, a little bit from the domain of which you're working, right? So if you're a data scientist working on and in healthcare, you better know something about the healthcare industry. Right. And it's the same would be true if you're in agriculture, the same, you know, true if you're in you're in biostatistics, right? You have to have domain knowledge about that area. Yeah. And our goal is to get students to a place where they have confidence with any technology tool in which they are having to solve a problem that has data about the real world.

SPEAKER_03 4:24

I love that.

Andrew 4:25

It sounds like you're intentionally taking on some of the biggest enemies of science, which would be opinion and emotion. And you're you're taking on and say, let's eliminate that part. Let's actually talk about the facts and the data in the name, right?

SPEAKER_04 4:40

Yes, it's interesting that you say that. My initial reaction led with emotion, ironically enough, is both yes and no to that. Like we do actually highlight a really strong branch of like data narratives. Yeah. And like recognizing like emotion and your feeling aren't the enemy of data. And actually, how can you exhibit and back the things that you might be feeling or things that you want to convey that you could feel very strongly about, but in an evidence-based way and tell a story to the correct audience. And how does the story need to change and shift in terms of the visuals that you're creating to back that up, depending on who you're giving it to, right? And so just being as most effective as you can with the things you want to say. And it's okay if emotion is in there that certainly cannot be the only thing that's in there. There should be the data component.

SPEAKER_02 5:22

And I think that's what's really missing in a lot of well, I think that's what makes it beautiful is the marrying between the two, because some people are moved by the emotion, but it's backed by the data, which makes it plausible and more reactive.

SPEAKER_00 5:35

In an ideal world, it provides that zone of exchange that is common for everyone.

SPEAKER_02 5:40

Yeah.

SPEAKER_00 5:40

Right? Common fact base, a common language to talk about difficult issues. It puts you in a really grounded space, ideally.

SPEAKER_04 5:47

Yeah. And it helps you think really critically, right? Like I have seen back-to-back headlines. Drinking a glass of wine a day is better than going to the gym. And then an opposing headline that says, you know, drinking a glass of wine a day is shaving five years off your life. And so it's interesting.

SPEAKER_02 6:02

Better articles because I've been reading the shave the five years off you. I want the other articles.

SPEAKER_04 6:06

Yes. But you can find, you can find, you know, the echo. You can find data to support you in any way. Problem. It is a real problem. So how do we think critically about like, okay, both of those are quote unquote true and somehow backed by evidence. So what are the deeper questions that I need to be talking, like thinking about as I read these two very conflicting headlines? And if students are afraid to dive into the data, they're never gonna figure out what's true for them in terms of, oh, well, what variables were studied here, what questions were asked, and like how framing a question is one of the most critical pieces of finding out the outcome. Um and that's how a lot of misleading can occur.

SPEAKER_00 6:44

The amount of data that is hitting people on a daily basis is insane. It's insane. It's itself is emotionally overwhelming. Yeah. Right. When you're getting inundated with like fax, figures, claims, the the speed of the people you consume social media, TikTok videos, yeah, you know, regular news media, whatever it is, it's really easy to shut down. And it's very frustrating experience for someone who doesn't feel confident in it going in. And and so to like nerd out a little bit here, in the year, I think 2016, it might have been 2014, we hit something called the Zetabyte era.

SPEAKER_03 7:16

Oh, what's that?

SPEAKER_00 7:16

Officially, which is around that time, humanity hit the milestone of having generated one Zeta byte's uh worth of data.

SPEAKER_03 7:23

Oh my gosh.

SPEAKER_00 7:24

You might be thinking, like, what's a Zeta byte? Right, yeah, yeah.

Andrew 7:29

And I'm like, oh, that sounds impressive.

SPEAKER_00 7:30

So I said, wow, but I have no idea what you're doing. It sounds like something from Star Wars, right? Like it's like a billion pentillion gigabytes or something like that. And I those units are wrong.

SPEAKER_02 7:39

Okay, but yeah, no, the words sound great and it's huge.

SPEAKER_00 7:42

It's a large amount of data if you measure it in terms of phone books, right? If you think about all the data that's hosted in a phone book, right? Uh just as a way to illustrate this, you know, all think of all the names of businesses, all the phone numbers, etc. You know, like a hefty phone book from like I don't know, like a metro area. If you try to equate that to the amount of data we've generated, it would stack beyond the solar system.

SPEAKER_02 8:01

Oh physically. Oh, my gosh.

SPEAKER_04 8:03

Which I think is incredible visual when you think about the Artemis mission. And like this is the furthest that humans have ever reached into space. We are already trying to think of the miles of the solar system, like over 16 times past that in the amount of data that's being generated. And that's expected to double, right?

SPEAKER_02 8:18

Like and it doubles relatively quickly because we're at this like impetus. It's not slowing down happening.

SPEAKER_00 8:24

Yeah, AI is accelerating it, right? It's just yeah, it's all getting faster.

SPEAKER_02 8:28

So I could nerd out about that for a while. What I'm hearing is Andrew says no.

SPEAKER_04 8:35

I like to go in with like the the deeper, to me, like the deeper philosophical question of like, oh, that's the spatial awareness visual for amount of data. And I like to then say, so what's the weight of data that's been generated? And to me, I always think of it because right, you can think about it in the ones and zeros, in which case there's no weight to data. And then you can think about it in the storage facilities and water use.

SPEAKER_02 8:57

I was gonna say, like the consumption, the economic yeah, yeah, environmental consumption is crazy.

Andrew 9:02

There also has to be different value to that data. Just because we have the data doesn't mean that it's actually accurate or progressing or helpful, not that everything has to progress something forward, but is it really helpful data or is it just information that's out there? And imagine that part of what we're trying to achieve is to help people be able to make that differentiation between is this something that will help me with my perspective and help me understand further, or is this just nonsensical, is there any point data that is, you know, helps us reach beyond the solar system?

SPEAKER_00 9:31

We live in like a an increasingly ever-expanding wild jungle of data. Right. And we we want to help our young people sort through it with confidence rather than getting overwhelmed by it. Because when you get overwhelmed, you shut down. So how do you can't engage with others?

SPEAKER_02 9:44

Yeah. So how do you help your young people?

SPEAKER_00 9:46

Well, this is where teachers can come into play, right? To be building the muscles that students need to navigate the new world we now live in. And we think it's feasible because there's ways to teach science or math or social studies, but with using data sets as a way to bring some of the material alive and to have students learn something about ecosystems on a data set about a real species chain in the Amazon, rather than reading it in a textbook and just looking at pictures of it. Right. So if we can build the muscle there, yeah, and then you get practice with it over time as you go through high school, by the time you graduate, you'll be way more confident with both the sort of intimidating technology tools, but also just like the pattern and sort of like the automatic intuition you have around sorting through data tables, deciding what's the question, knowing which questions to ask. Yeah, exactly.

SPEAKER_02 10:31

Knowing which questions. That's a tough one.

Andrew 10:33

Now I'm hearing what sounds like almost the rumblings of kind of a workforce readiness. Like you're trying to prepare these students and transition them. Is it kind of an accurate statement or am I misreading?

SPEAKER_00 10:44

I think that's accurate. Like there's I mean, there's two angles to this, right? There's the workforce readiness. We know that emerging technology, data, AI changing the economy.

SPEAKER_03 10:51

Yeah.

SPEAKER_00 10:51

And we're gonna have to get our kids ready, not just for like let's predict what job will exist five years from now, but have them prepare to transfer between jobs over time as technology changes, right? We really want to get the transfer muscle and comfort built up on the workforce side. There is also a civic dimension in an everyday life dimension to these learning goals. Right. I think we would be limited if we're just thinking about workforce outcomes. That's like a huge part of this. Yeah. But you know, we know that data, AI, social media, it's having big impacts around our democracy. And you know, honestly, like if you're trying to make personal decisions about finance or you're trying to navigate healthcare policies and like which one to choose. Yeah. Or if you're looking, you're trying to decide how much wine to have at night, right? Like and you're looking at all those competing health claims on the internet because Google says everything.

SPEAKER_03 11:36

Right.

SPEAKER_00 11:36

You know, uh, there's a couple multiple dimensions here, but I think workforce is a an important, you know, we got to make sure the kids are ready.

SPEAKER_04 11:43

Speaking of data, we have the data that shows. I think it's like one in four like job sectors are saying that the jobs that they have have to require some amount of like data literacy skill to be able to be effective and to stay and have growth projections within that career path. At the core of it, we always want what's best for our kids. And part of that is a secure job at the end of like some pathway that they can see going forward. And I think that what we're recognizing is that more and more of those pathways into this jungle are filled with data. And so it's like pretty irresponsible for us to, you know, put blinders on and think that the way that the current education system that let's just remind ourselves is currently set up based on an industrial revolution and like cogs in the machine. And we've talked over and over in the educational space. So, like that in and of itself isn't working for kids and isn't working for jobs now. Yeah. And so, what an opportunity we have to say, like, let's make what we're talking about in the classroom relevant. There is, there's bias here, but there's obviously not much more relevant that like data that you can bring in your classrooms, and those things can still really be impactful and niche to communities, right? You don't have to work with data that you just scrape off the internet. You can work with the data from your local businesses, you can work with environmental data from your backyard. And there's so many rich opportunities here to help students see themselves and empower them to like then go off and be flexible in a job market that we cannot predict.

SPEAKER_03 13:08

Yeah.

SPEAKER_04 13:08

If we are just thinking of workforce readiness, we cannot just say, Oh, we need you to learn Python. We need you, I mean, yeah, I'm dating myself, but like if that was the case, right, I'd be stuck at any job that's simply going in Java and C. Like, that's not gonna work.

SPEAKER_02 13:21

No, yeah.

SPEAKER_04 13:22

But if we are thinking of it from a the holistic perspective, we really are trying to do in terms of the muscle that Zarek was mentioning. It's about how can we teach them to be adaptable and flexible with how things change because they are changing, I don't know, very, very do you all want to guess the top sector for data skills these days?

SPEAKER_00 13:39

Which sector do you think is like asking for data skills the most? It'll surprise you.

SPEAKER_02 13:43

This is gonna sound crazy, but I definitely think it the more like plumbers, the electricians, those yes, you're adjacent. Yeah, yeah. Like you're on the right path. Those are in need of those kinds of skills.

Andrew 13:54

Yeah, no, no, I was not on the right path. If that's the right path, I was not on the right path. No, what are you gonna say, Andrew? Well, maybe it was the right path. This is out of nowhere. I was thinking like agriculture.

SPEAKER_00 14:04

Agriculture. Well, all paths are good here. Agriculture's number three. Oh, it's pretty far up there. You know, if I beat her, is it higher than hers? So manufacturing is number two.

SPEAKER_02 14:13

Yeah, no, see, I said, yeah, no, I'm claiming manufacturing is kind of adjacent to what I was saying.

SPEAKER_00 14:19

Like in the ballpark. Yeah, like loosely. You're stretching your data clearly. Yes, go ahead. But number one is energy management. Really?

SPEAKER_02 14:27

Well, we are consuming so much. Wow. Right.

SPEAKER_00 14:31

Like both of you are like the person that's literally responsible for balancing the grid, but also if you're you know a trader, if you're trying to manage supply for a company, like it's just it's very data intensive these days. But this is true across any sector. Yeah, whenever I play this game, the punchline is like all answers are right. Yeah, because it's really all sectors, right? Like education, especially.

SPEAKER_02 14:50

Think how of all the formative assessment data in the last like 15 years, the amount of data that teachers are having to sift through. Yeah.

SPEAKER_00 14:58

That's happening in every sector.

SPEAKER_02 15:00

Yeah.

Andrew 15:00

And really that's pointing to convergence learning. That well, in an educational setting that was established during the industrial revolution, that side load studying of different topics made sense. Now we're like, we need all hands on deck, and we're not just gonna science at this time and we're not gonna math at this time. Like we need everybody, regardless. Like, we need to work on these problems together.

SPEAKER_02 15:22

I just think what they're saying is like data is the way to do that. Yes, that is exactly.

SPEAKER_04 15:27

Yeah, like you're like that's what breaks down like walking into a web classroom, yeah, and then turning that part of your brain off to then go into social studies. We're saying, nope, you don't have to do that. Like, in fact, this is all related, and we have a lot of big projects that we're doing. One of the ones that I feel really proud of is bringing together a bunch of industrial people, educational researchers, teachers, student voice, higher education, kind of all across all the sectors that we think that this touches, which is all of them. But to come together and create a framework for how we think data science looks from kindergarten through 12th grade and graduating. And so we put out that framework in our DSE K-12 conference in 2025. And then our work since then has been to refine it and to help the different core subject areas see, help them see where they land in that. And so we are coming out with a science-specific version of that, coming out with the social studies, coming out with math. So that way they can see this holistic puzzle a little bit better and also recognize that like it's not as scary. You know, if you have never heard the word data science, or you certainly are like, well, I don't have a degree in data science, so how could I teach that? The answer is that you you are probably already tangentially doing a lot of these skills and you know, just a really quick like focus into intentionality behind those will go a long way in helping build these muscles for the students, and then also see how their learning connects to the real world and their own lives, but also how it connects. So we quit getting students to say, I'm a math person, I'm an English person. It's like, no, actually, like we're connecting that. And so all of you have this role to play. I think kind of empowering as an educator. I see how I can like really grab onto that to make that actionable change that that is what keeps a lot of educators in the classroom.

SPEAKER_02 17:17

Where can they find that? Like you said, you put it out. Was it just for the conference, or is it like out for public consumption?

SPEAKER_04 17:22

Yes, yeah, we have we have a website, data sciencelearning.org, and we have a lot of like other projects in the works to help support that in terms of what type of audience and is going to get the most out of looking at those. And so we keep on iterating as everyone should and everything they're doing. Iteration, a big piece. But yeah, those are all out there and we're circulating them. We were at this conference doing a lot of work with the science educators to help us reframe that language in a way that makes sense for science classrooms, and we're really excited to keep that work going.

SPEAKER_00 17:52

As Hannah's mentioning, for science education specifically, Data Science for Everyone and STA are partnering on developing a science-specific progression of like what does data science and data literacy look like for you know our science education community. We kicked that work off at this conference. We just hosted a focus group for the past two days where we were both mapping like what are all the resources and professional development opportunities and sort of like just strategic milestones we need to hit over the next couple of years. And then we also started going through sort of the early consensus on what K-12 appropriate data literacy, data science education looks like and say, what does that concept look like in sixth grade for the science unit? Yeah. Or what does it look like for ninth grade physics? Yeah. And so we're starting to map that out, and we'll hope to have a much more refined progression soon.

Andrew 18:37

That's fantastic. What I'm hearing is through data science, you're creating pathways for learners to enter into the space, but also it's bridging the gap that can exist between other content areas so that we can be more focused on the overall solution, not just doing things in isolation, but being able to work collaboratively to find that solution.

SPEAKER_00 18:57

I think our vision is that the adding data skills, you know, data science topics to the curriculum in multiple school subjects will be the zone of exchange and zone of collaboration for the math and the social studies teacher to collaborate. Or for the science and social studies teachers to collaborate on a lesson plan for the 11th graders.

SPEAKER_03 19:13

Right. Yeah.

Andrew 19:14

So learn more about how to move out of the industrial revolution and into the Xerek Revolution. Or what was that? What did you call it the Zetabyte?

SPEAKER_00 19:22

The Zetabyte Revolution.

Andrew 19:23

The Zetabite Revolution. We better not complete those. I don't know. You want to move it named after you? Yeah.

unknown 19:29

Yeah.

Andrew 19:30

The Z revolution.

SPEAKER_02 19:32

Is there a way if I'm a teacher and I want to learn more or if I want to get involved, is there a way on your website that I can do that? Is there a way to contact you, or how can you teach?

SPEAKER_04 19:41

I would love to send teachers to me. I am on, you look at the About Our team, Hannah Kurzweil. I am our community manager. I'm the one who's the Lorax for the teachers. I speak for the teachers. Please come find me. Send us an email. Okay. You can also send an email at just to our general page, info, like a contact us. And if it's about teachers or teachers needing help or resources or any questions, I host office hours on a monthly basis for teachers. We do a lot of webinars with ED and outreach. So yeah, fantastic.

Andrew 20:08

So with everything we've talked about, I think we'd be remiss if we didn't bring up the two-letter acronym of AI. That seems to be a big part of everything lately. Of everything that everyone does. Actually, when we have people ask us questions, they're like, Well, I asked so-and-so, meaning AI, one of the their favorite ones first, before I asked you. I asked Claude. Yeah, me too.

SPEAKER_02 20:30

I love it.

SPEAKER_00 20:31

Chat said this. Yeah.

SPEAKER_02 20:32

We need a Claude that. It's really, it's really sad how much I enjoy him. I've like, and I totally made it a man. I know it's bad. It is Claude. But that's why I'm advocating for Claudette for Claudette. Claudette. Yeah. Not sponsored, by the way.

Andrew 20:46

Yeah. Within this whole conversation, where do you see AI fitting into that? Or more specifically, where do you see data science working into the conversation of AI?

SPEAKER_00 20:59

AI is such a lightning rod in education. Yeah. Right. It's changing so much about school management, how we think about our classrooms. And then we also just don't know where it's all going to land. I want to focus and kind of guardrail our conversation on just student learning goals.

SPEAKER_03 21:14

Yeah.

SPEAKER_00 21:14

You know, what skills, dispositions, habits are they going to need to succeed in a world that's getting changed by AI? You know, one of the things that like I see really clearly going into the next five, 10 years is as companies mature around how they think about AI, how they use it for a variety of their internal functions, how they train their employees on it. I think we'll move towards a world in which people are customizing AI tools for really specific problems that they have. Because your general Chat GPT is like not that useful if you're a lawyer and you need really fine tuned, detailed legal precedent that cannot just be recombined randomly. It has to preserve case law very precisely. So you're gonna have folks building AI tools for that use case. And the same is gonna be true in agriculture, you know, the same is gonna be true in the sectors we mentioned earlier. Customize an AI tool is with the data that you train it on. And so data literate students are going to be durably prepared for a world in which this is a tool that's getting integrated into a variety of sectors and a variety of places.

SPEAKER_03 22:12

Yeah.

SPEAKER_00 22:12

And then you, I mean, there's the classic like garbage in, garbage out.

Andrew 22:15

Yeah. It's literally what I was thinking about. I was like, how am I going to say garbage in, garbage out?

SPEAKER_00 22:19

That's like the old data science adage, right? Like if you put garbage data in, you're going to get a bad output. The last thing I'll just say on this is like we want students to understand the mechanics of how these tools work.

SPEAKER_03 22:29

Yeah.

SPEAKER_00 22:29

If they understand that an AI tool is really just a set of mathematical functions trained by data done automatically by code, maybe they won't rely on the tools for social purposes as much. But they'll see it as just one imperfect tool for research, for inquiry, for building things.

SPEAKER_02 22:45

The imperfection piece is a hard one. I have a 14 and a 12-year-old, and they just think it's it's magic. Right. Yeah. Everything's right. It was online. I can believe it. Like, oh no.

SPEAKER_00 22:55

And it's not, it's just a statistical prediction device. Yeah. And it's just churning on a lot of data and it's gotten really good because it's scaled so much, but it's not magic. So anyway, we're gonna dive way deeper into this at a panel conversation on Friday.

SPEAKER_02 23:08

Awesome.

SPEAKER_00 23:08

10 40 a.m. This is my soft pitch, shameless plug. Yeah, definitely. Myself, Trish Shelton from NSTA will be hosting. We'll have uh someone from the National Academies who was on a recent consensus study around the foundations of data and computing and how you know that will help set up the K-12 sector well to think about what are the true fundamental ideas that are gonna last over the next decade or two that kind of cut through the hype. And it's like, no, these are some of the fundamental ideas that we think are really gonna prep students to succeed in this age. We're gonna get into all of this in even more depth at the conference here.

Andrew 23:37

That sounds like a lot of fun. You know what I felt when you were saying that is that we're actually possibly more prepared for the AI revolution than we think we are. We just need to, instead of purely leading on human emotion with it, let's use these resources, specifically what you're talking about. Let's look at the data science. What things do we consider here so we can pave through intellectually, not just emotionally? But the other thing to type back to what we talked about in the beginning is that human aspect is very important. So it sounds like data science for everyone is looking to make all those connections to say humanistic nature, opinion, emotions, that's what makes us human. How do we connect the data to that information to almost fact check ourselves or a kind of an accountability piece? Well, thank you for coming by and spending your time with us and joining us for NSDA Voices.

SPEAKER_04 24:26

Yep, thank you so much for having us. And stop by after the panel if you want to continue the discussion, we'll be there.

SPEAKER_00 24:31

This was awesome, y'all. Thanks so much.

SPEAKER_04 24:33

Yeah.