In this bonus episode, Eric and Kostas talk shop surrounding data infrastructure.
The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.
RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.co
Eric Dodds 00:05
Welcome to The Data Stack Show shop talk Kostas, we have talked with people who built amazing data technology companies like Netflix, Uber, and LinkedIn. But you and I actually don’t record our talks about data very much. But we actually talk about data together a ton. And so Brooks had this amazing idea of just recording some of the conversations that you and I have before and after the show about data and our opinions on it. And really, this has been my favorite things that we do. So welcome to shop talk. It is where Costas and I share opinions and thoughts on a personal level about what we’re seeing in the data space. And it really is simple. We ask one another a question, and the other one tries to answer it. So without further ado, here is shop talk. Welcome to The Data Stack Show Shop Talk where Costas and I talk back and forth about the topics that interest us. And we’ve learned a ton. These are great. I’ve learned a ton in these talks. And I believe it’s your turn today, Costas. So what’s been on your mind?
Yeah. So I have a question for you. You.
Kostas Pardalis 01:21
You’ve been at RudderStack, like some very early stage in the company, right? Yep. But this is not your first startup. But I think it’s your first startup has to do with data infrastructure. Right?
Correct. So my question
Kostas Pardalis 01:41
and now that you have been like, for a while, building your business, in this industry, I’d like to hear from you like, what are the differences that you see between like building a business around the product, like in the data infrastructure compared to your previous experiences, and I must think that big is okay, but I’ve been working like, here quite a while, like with startups, but pretty much all of them have become like beta. So I’d like to take the opportunity to experience like how it is to be with like, a technology company and be like, something different. So tell me, how does it feel the difference?
Eric Dodds 02:32
war? That’s a great question. Maybe would it be helpful if I described previous companies, so there’s like a baseline, I won’t spend too much time, but maybe that’ll be helpful context. So the first company was an education startup, and specifically was actually for profit education. And what people today know is like coding boot camps. And, you know, there’s, I haven’t looked at the industry in a while there was kind of like this big bubble at a point with like, you know, people are going because demand for software developers or whatever. And that was, I think in like, 2011 2012. Actually, back then there really, there were really only a few like code boot camps, as people call them today, like one in New York, and like, I think they’re a couple in San Francisco. And that started out as a very traditional business, like it was for private education, teaching people how to code and then placing them in jobs. And we actually started out with all physical location. So it’s done. One point, we had, like, 30 campuses or something like that. So it became like a pretty large business. And then of course, we like, we’re working on digitizing it, you know, and like productizing it so we actually built, you know, essentially an LMS bought this like a standalone product as an acquisition fee for the in person product and then also to, like, facilitate the in person product. So it was really interesting, and then ended up selling that to a large publicly traded company, and then, you know, whatever the other did, whatever, there have been a couple of like, small like, like, whatever would you what the venture, you know, world would call like a lifestyle business, like a consultancy or whatever, just a basic cash flow business. And then in terms of, like, technology startups, there’s also I actually don’t know if I’ve ever told you about this. We built a technology that allowed you to like take your retargeting audiences from your website, and essentially resell them to other to like, advertisers.
Kostas Pardalis 04:44
That sounds very evil to me. Like,
Eric Dodds 04:47
Scott. I mean, yeah. It’s like, yeah, totally up to you. It sounds it would sound totally evil for a marketer. It’s a dream, right? Like, if I could, like serve like people visit your website. And then they go out and browse other websites. Normally, you would retarget them with your own ads, but it would allow me to retarget your audience with my ads. But you know, based on that stuff, which for certain businesses is very powerful, right. So like, if you have to non competitive businesses, it’s a way for them to create a ton of value without having to like share data. So in many ways, actually, like one of the big use cases was, was actually protecting privacy, no possibility for abuse, significant. But used correctly actually was like, really cool. And COVID basically decimated that business, right. Like when COVID hit. The everyone basically just completely stopped spending on anything exploratory from an advertising standpoint, it just essentially killed the business. So okay, so what’s different? So one thing I would say is that the,
Eric Dodds 06:02
the marketing is way harder. And I’m not, I don’t say that to diminish. And really, like, the main reference point is, well, in both of those cases, like we can compare about the examples, right? I’m not diminishing, like, they’re like, consumer marketing is very hard, right? Especially with like, you know, if you think about consumer mobile, or, you know, consumer mobile games, or whatever, like it is phenomenally difficult. So I’m not diminishing the difficulty. It’s just difficult, like, it’s a different type of difficulties. So for example, the main marketing message for the education startup was learn to code, get a job, right? I mean, that’s, it doesn’t get more simple than that. And that’s also like, an extremely powerful value proposition, right. And, you know, whatever, like that business happened to like, provide a very relevant product at a relevant time. And like, the message really resonated. And it was like, very simple. And even with the other one you like, you talk to a marketer, and it’s like, well, would you like to serve my retargeting audience ads? And they’re like, yes, I would, right? Yeah, I’m a marketer, I will follow everyone everywhere. When you talk about when you think about data infrastructure, the nature of the problem is, in reality, a lot more complex than just is right. Like, the actual, like, what does this infrastructure do? How does that fit into the larger picture of, like, technology within a company? How is it differentiated from like, other infrastructure? The details there? Are, it just takes a lot? It’s, I would, in some ways, trying to think of the right word here, because I don’t want to diminish, like, how hard consumers because that, like, especially the brand buildings on it, that was really hard. But
this is what I would say,
Eric Dodds 08:19
one of the major things that I’ve noticed is that the there’s a lot more requirements to take a very high level of complexity and distill it down. And that process of distillation is very difficult, not only from like, A, not only from the standpoint of like, how do we describe our own product, but also like, how do we differentiate it? Right? And like a great example is, okay, you have like, ETL, like, this is your space, right? Like you had ETL vendors, and it’s like, well, how is your ETL pipeline different than this one? Right. And it’s like, well, I mean,
Kostas Pardalis 09:01
meaningfully communicating about the differences without bogging people down, and like, technical details that aren’t necessarily helpful to explain, like, how it can help them is really hard. Like, that’s really hard. So I don’t know that’s the main that’s the first thing that came to mind. Yeah, yeah. Yeah, that’s, that’s super interesting. And I have build blocks too when I was trying to to build like, mine serves business because good like do you know like, when you’re lifting for marketing, your marketing advice and you have like zero marketing experience, for example, like all the advice that you will get from people usually comes like from consumer related marketing, right, so yeah, like, you will get advice about doing stuff that sound very reasonable, but probably they’re like, super hard. It’s like to do because you have to distill some very abstract concepts into something tangible. Like, yeah, learn to call and get a job. That’s great. Amazing. Yeah, sure. Like, but how do you phrase and communicate the same light, let’s say amount of information for building a pipeline and moving the data around, right? Oh, yeah. I, I fill you. And okay, so if you have to choose your next stopped up to workouts,
Eric Dodds 10:37
like an existing one, or just,
Kostas Pardalis 10:39
I mean, it’s not about like, the startup itself. It’s more about, like, let’s say the industry you want like from mythic to, like the the infrastructure?
Eric Dodds 10:47
What do you win the Tour de tuer and evil advertising technology? Yeah, like, we
Kostas Pardalis 10:54
will keep these like, yeah, you know, like, under the radar, because we love you, and it’s okay. We all make mistakes, you know, it’s like, it’s, it’s okay.
Eric Dodds 11:04
I definitely would never do adtech. Again, I can tell you that.
What you will do next?
Kostas Pardalis 11:11
Is that, like any industry out there, like everything, like, for example, like, okay, like the data industry is huge, right? Like someone who’s working like getting them at Lopes is like, probably, like, it’s not something that I would like, easily do like Frogger. I don’t I don’t know enough to go and do it, although I’m working like in the data industry. Right. But yeah, regardless of like, how much you know, from all the things that you have seen so far, and like all the stuff that you have been exposed to, because of the show so far, right? Yeah. Part of the beauty of this show is like getting in touch and bragging about like stuff that we wouldn’t otherwise works. Excited to you like what you would do next.
Eric Dodds 11:52
With this may not have been part of the question, but I think that I would definitely stay in the data industry. Part just because I love I love the technology side of things. And I mean, whatever you can argue that like, all technology is data related. But you know, I think, obviously, you and our listeners understand we’re talking about, you know, the types of stuff we have on the show. And it’s really I mean, it really is fascinating. And I think like, one of the main reasons I would stay I wouldn’t like stay involved is because I think we are in what we will look back on is like one of the very exciting times maybe there are tons of exciting times ahead. But there’s just a lot of new frontier being developed, right in front of our eyes. You know, so what an exciting time to work in this general space. Okay, I’m just going to, I haven’t thought a lot about this. So I’m just going to tell you it first came to mind. So there are two things that first came to mind. One is more of like a category, or like a problem, a type of problem that I think could apply to like multiple different data technologies. And I’ll explain what I mean by that. The problem type is the automation of things that are currently still like pretty manual. So a couple of examples of that, that come to mind are things like like data quality, you know, or whatever, there are a number of terms there, like data quality, observability, blah, blah, blah, right? But messy data is pervasive. And at least as far as I can see, even though there’s really cool technologies sort of emerging around it. It’s still a very widespread problem. And I don’t think that anyone has really figured out how to solve it in a great way. I’m not saying that I’m the person to figure that out. But I’m saying like as a type of problem. I’m very intrigued, this is probably a better way to say it. I’m very intrigued at data related technologies, that free up mindspace for people working with data to solve harder problems, provide more value, etc. Right? Like there’s sort of, it seems like, there are still a significant number of like fairly low level problems that require an unnecessary amount of, you know, sort of call it like manual labor. So that’s really interesting. The other one, like, this is, this may sound crazy because there’s, there’s no way that I’m qualified to do this at all that you actually mentioned ml ops. The reason that is interesting to me, is because every time it comes up on the show You can see like the searing pain of difficulty, that is ml ops right now. Right? And that’s not because, like, it’s an ignored problem, right? I mean, we’ve talked with some amazingly brilliant people trying to solve this problem. And so maybe this is a little bit masochistic. But I like really challenging problems for some reason. And so that, you know, that, to me is really intriguing. Because the pain is so palpable, like when you talk about it, you know, so it just seems like this huge problem space. But, you know, I don’t know anything about ml ops. So that would be a steep learning,
Kostas Pardalis 15:44
I don’t think that you will have like a big problem, to be honest, I’m pretty sure that you will be like, super successful in doing it, and any company will target you would be really, really blessed.
Eric Dodds 16:02
That is so flattering of you to say, Yeah,
Kostas Pardalis 16:04
but if you are a recruiter don’t reach out to him. Because like, very serious work to do.
Eric Dodds 16:14
No, that’s a great question. That is really interesting to think about that. Okay. I think we’re really close to the buzzer. But I have to ask you, what, what problem would you go?
Kostas Pardalis 16:23
I don’t know, I think I think I would stay like ingredient. Like store ads and database. Like, space. I think there are like Blendo opportunities and challenges right now. So I’m gonna probably do that. But yeah, anything Beleriand like is very fascinating, because I just like, haven’t done it. And I’d love to do something new and like more like it’s feels more exotic. You like it’s
Eric Dodds 16:57
a Yeah. Like the Miami. You know, the data world? I told them, This
is the crypto
Eric Dodds 17:08
No, crypto is the crypto web three. Yeah. What’s the Vegas of the data world? The world needs to be something that’s like, pretty extreme, pretty expensive and like doesn’t really result in anything. Except maybe you like wake up and you discovered you’re married? Yeah. Oracle. Oracle is you wake up in a marriage that you realize you signed a 10 year contract for and you can’t get out
Kostas Pardalis 17:50
I said the right word, because then it’s like this thing that you can carve until you realize the consequences of your choices, that you are legally bound to that for
Eric Dodds 18:05
Oracle, as the Vegas of you know, cost us we learned so much from the data leaders that we talked to, but I learned so much from picking your brain. And actually your questions really make me think really hard. So I appreciate shoptalk. I think it makes me a sharper think.
Kostas Pardalis 18:24
Well, it’s, it’s fun. What? I think it’s good to exhaust Seaton Shutterbugs, the stuff that we experience. And yeah, I think like, I felt like people enjoy it. That’s why I’ll keep asking for people to reach out, please do this guidebook. Like, you can’t do that, like, send an email. Yeah, let us know how you feel. And like, what are your opinions of like, your experience with the SOAP? So please do that to me. And there we go. Keep a copy.
Eric Dodds 19:00
Of course. And of course, we try to take the same types of questions to, you know, data leaders from all sorts of companies, large and small. So definitely subscribe to the main show, if you haven’t yet. tons of really good episodes there. And tons of really good thoughts from data leaders, you know, really around the world. So definitely subscribe if you haven’t, and I will catch you on the next shop talk.