This week on The Data Stack Show, Eric and Kostas chat with Benn Stancil, the Chief Analytics Officer at Mode. During the episode, Benn discusses what data engineering was like a decade ago, technology inside and outside Silicon Valley, and the importance of gaining data context before creating.
Highlights from this week’s conversation include:
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.com.
Eric Dodds 0:05
Welcome to The Data Stack Show. Each week we explore the world of data by talking to the people shaping its future. You’ll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by RudderStack, the CDP for developers. You can learn more at RudderStack.com.
Kostas, today we’re going to talk with what I think is actually a really interesting company in terms of the context of both the problem they’re solving, and then how long they’ve been doing it. So we’re going to talk with Ben Stancil, from mode. And they’ve been around for almost a decade, doing data visualization, and sort of analytics workflow stuff, which is super cool. Ben writes a very popular blog, lots of people, including myself, I love to read his thoughts on everything. And this isn’t going to surprise you. But I want to know why he started mode. There were some huge enterprise incumbents. When he started mode, it was just right at the genesis of the cloud data warehouse, which is a really interesting time, you actually have similar experience with us starting a, you know, sort of data company right around the same time. So I just want to hear about that. What was he thinking? What was he doing? And what led to him starting it?
Kostas Pardalis 1:27
Yeah. I mean, for me, it’s a special episode, to be honest, because Ben is one of the first people that I’ve met in person while I was still like building Blendo.
Eric Dodds 1:40
Really? Like on your first trip to San Francisco?
Kostas Pardalis 1:42
Yeah, yeah. So he’s a personal like, I mean, he’s kind of special to me, and also is a person that I really appreciate both his opinion and the way that he expresses his opinion, because that’s also on. So yeah, I’m really looking forward like to chat with him today and talk about like, all the things that have changed like these past 10 years in the industry, and I’m pretty sure we are going to be surprised by his thoughts and opinions.
Eric Dodds 2:11
I agree. Let’s dive in.
Ben, welcome to The Data Stack Show. Super excited to chat with you today.
Benn Stancil 2:17
Thanks for having me. Excited to be here.
Eric Dodds 2:19
Okay, let’s start where we always do. Give us your background. And kind of what led you to what you’re doing today at that moment.
Benn Stancil 2:28
So I am one of the founders of Mode, it’s basically a BI product built for analysts can get into more specifically what it is, and the conversation knows around for a while. So we started node in 2013 or so. So one of the kind of early cloud data tools or modern data tools that we now have plenty of. Prior to that, and to where it originally came from, I worked on a data team at a company called Yammer, along with a number of other folks, three of us left to go start mode. So basically, Powell, a lot of like exposure to how people are thinking about data, that company saw some of the things that that we thought might make an interesting product and interesting business started mode. And then since being at mode is kind of bounced around from the numbers of things. Most of my time has been spent in either thinking about our own internal analytics and kind of data infrastructure. And spending time on kind of the landscape. And thinking a lot about like, what’s going on in the Data World Data community, we’ve been talking about what kinds of things are kind of the trends that are, you know, more than anybody else’s kind of writing, what might be popular, what won’t stick that kind of stuff. And then in addition to that, as, as you kind of tend to do at startups bounced around for another jobs and like being involved in marketing or customer success, or solutions or products, or, you know, running the exec team, whatever kind of stint you have, and kind of your various rotational programs over the course of eight or nine years at a startup.
Eric Dodds 3:54
Yeah, absolutely. I mean, that’s a long tenure for a startup. So congratulations. I’d love to actually dig into what you saw as the problems you wanted to solve. Even back in 2013, you know, like, the Cloud Data Warehouse is still like, fairly new, you know, kind of emerging. But there were still like, massive incumbents in the analytics space. So you had, you know, whatever Tableau and, like visualization, had sort of some major incumbents that like the enterprise level. So what did you see that made you say, like, Okay, I want to go build something new because there’s some sort of need or like something that’s not some itch that’s not being scratched in the market?
Benn Stancil 4:45
So the basic answer to that is the problem that we had at Yammer. So we were a data team. At the time Yammer was so Yammer got acquired by Microsoft in 2012. We were the company has about 400 people or so got acquired. We had a data team of 20, roughly, including data engineering. And we, we represented this kind of like new version of what data teams will be coming in Silicon Valley, where our job was to work with other people or other business to help them make decisions. And we were not a BI and reporting team, like we weren’t there to just build dashboards and sort of binders that execs are supposed to look through for their weekly meetings. But we also want a team full of statisticians who are trying to work in sort of capital D data science type of places. And so what we’re trying to do is like sit next to businesses, like people who business and say, which marketing campaign should we run? Which products should we bill? How do these AV tests perform that kind of stuff? Yep. The way that we needed to do it was we wanted tools for ourselves who were relatively technical data analysts. So technical sense that we could write sequel are a little bit Python kind of stuff. But we also need to share that stuff with other people. Like our job was very much business oriented. We weren’t, we weren’t deploying code to production, anything like that. Yep. And so that the tooling to us was either the traditional BI tool and you’re talking about so Tableau was kind of the cutting edge of that, but also all of the, you know, the micro strategies and business objects and yet to things. Or it was stuff until the very technical so that basically Statistical Tools, Data and saps and those kinds of things, or, you know, our studio Jupyter notebooks were around that point as Python, I think at that point.
Eric Dodds 6:33
Yeah, like visualization, or like really gutsy, heavy-duty stuff.
Benn Stancil 6:38
Yeah. And while we are like desktop, sequel editors, you know, like, like, data grip, and you get that sort of stuff, sequel workbench, those tools would have worked for us, the more technical ones, because that kind of set what we were trying to do. But we couldn’t actually share any of that stuff with anybody else. Like, it didn’t work to the rest of the business, because we can’t send to the CEO here as IPython notebook like, you know, here’s an instruction status and let them run it. We just couldn’t send them a SQL query and have them run it, we couldn’t send them like we weren’t gonna pay for SES, because we were startup. So all those things didn’t really work for us. And the BI tools were very aimed at like these non-technical folks were just like, our job is to build dashboards that you put up onto the screen. Like that’s not the job we wanted to get. So we there’s like, we need to do this ourselves. We need to we ended up the team at Yammer built an internal tool that looked what it is essentially, like a very stripped-down version mode. That was basically a sequel editor and a browser with charts on top. And so what we would do is we like do analysis and sequel write queries, save those like charts, send them to people and be like, you know, here’s why campaign a did better than campaign be or here’s some analysis that tells her story. And so what we ended up seeing kind of was two kind of big trends that were emerging around this time. This is more or less why we left to start mode. One was called Data Warehouse. And so at Yammer, we were spending a whole bunch of money on data infrastructure, like we had a Vertica cluster, we’re probably spending half a million to a million bucks a year on it like that was kind of par for the course if you’re using Vertica or Oracle, Teradata, whatever. The other thing is, we have this kind of team that we thought was specialized was like this is not many companies have teams like this. Both of those things changed where Cloud Data Warehouses made the cost of the infrastructure were cheaper. So you could run Redshift for a few 1000 bucks a month. And then the team that we represented this kind of like analytics team that thinks about data, not as a reporting function or not as like an actuarial function became much more of the way that a lot of companies thought about data that, that people started pulling off, what was the Facebook model, the Airbnb model, that kind of stuff and saying, our data genes are not it genes, they are not also a bunch of statisticians in the basement. They are like, people to help the business to kind of deploy around the business and to be, you know, data experts to help people make decisions. And those were really the two trends that we were writing.
Eric Dodds 8:56
Yeah, super interesting. Okay. Question for you. So like, the term data engineering is actually, like, relatively young. How did you refer to the team back then, because that was sort of, you know, maybe say, like, before data engineering became as formal a term as it is now, in terms of like, you have a data engineering team, or like, a head of data engineering role, or whatever. I’m just interested to know, like, I mean, so that’s like, a decade ago, right? How did you refer to it? Because it also sounds what’s interesting is like, it also sounds that at Yammer, that data team was operating in a very forward-thinking way. You know, it’s like we’ve heard it called like structured embedding or something right where you have like someone from a data team like sitting with a someone you know, in a business function and like, working on that stuff. So just interested to know like, the ways that you thought about it, and maybe even the terminologies like a decade ago.
Benn Stancil 9:57
So actually, the terminology was easier than And then it is now we that Yammer was a yam was like a relatively forward thinking company, I will give it credit for that. And in a couple of particular ways, the two kinds of additional ways that we are writing are sort of like the data moving to the cloud. And like the rise of these sort of new data teams and he new waste, a couple of things that Yammer was writing was what was referred to as the consumerization of IT. So basically, like business products are getting built like consumer products, where instead of it being a thing that you build, and you go sell it and like nobody likes, but it does, because it checks various boxes, you should go build something that the end users themselves will like and pressure it to get. The other thing that that jammer did sort of pioneering kind of a little bit wrong word, but was one of the early companies in was thinking about data for a business, the way the consumer companies did to where we had a lot of AP tests, we did a lot of the stuff that was meant to say, how do we build a better product, a stickier product and more viable product, in the same way that really likes gaming companies actually pioneer like Zynga in the world where a lot of the people who really thought about this stuff and had sort of very robust ways. Yeah. And we were basically picking off some of the easy stuff from what they had done.
Eric Dodds 11:17
One quick question there. Do you think that what Yammer and actually maybe this sounds funny, because like, I remember Yammer, I remember using Yammer but do you mind just giving like a quick explanation of what Yammer is for listeners who might not know for what it was? Who might not know especially like, as it existed pre Microsoft.
Benn Stancil 11:42
Yammer was Facebook for work before Facebook for working system. It was like, quite literally, it was, it was intended to be something that felt exactly like Facebook, where you have a feed, you’ve got groups, you’ve got, like, the experience was meant to say, hey, Facebook works really well for getting like a bunch of viral adoption around your friends. What if we do the same thing for schoolwork? In effect? No, it’s I think it’s interesting. The product actually worked really, really well. If you used it right. Because I’m in right if you use it, like it basically went all in on it, because it was I am a personally I hate slack. I think slack is a disaster for society. It was but it does some things well, which is like on silos communication or whatever it gets everybody in one place. It has some nice stuff, even budget email threads are bad about, but it doesn’t the way it is just this like 40 fire hoses that are constantly hitting you in the face to the point where you can read everything and like pray. Yammer actually worked a little bit like our Facebook actually works where you can check Facebook. And if you log like Eric least Facebook used to 10 years ago, if you log into Facebook, and like kind of read it 20 minutes a day, you’ll end up picking up pretty well. And like what people are doing, you suddenly had an ambient awareness of what’s going on with all of your friends. Sure, because you don’t see everything they say. But you’ll see this picture, whatever. And that was basically what Yammer was trying to capture was create the same kind of feed that you’re not supposed to read everything. But if you check it periodically, you’ll see a bunch of conversations from other people around the business. If I go, I’m gonna think about marketing. If it looks interesting to me, I will kind of read the thread up. So it’s actually like a kind of digestible thing similar like reading the news. But it was very much like the product itself was very much like Facebook, some work like it was a feed threaded messages.
Eric Dodds 13:28
Yeah, it’s just super interesting, because when you think about that happening a decade ago, it’s a really interesting context of having a B to be like SaaS product in terms of the way it’s going to market. But delivering the product is very much a consumer experience. It just happens to be like inside of a business. Do you think that that dynamic sort of helped catalyze some of the ways that you were approaching, like working with data, where like, it was a b2b company, but you had a very consumer mindset?
Benn Stancil 14:04
I think it did it. Yes. And yes, I certainly did like that. That was certainly you know, me and the other two folks who started note as well, some of the early employees who came from Yammer. All have the mindset that we were kind of instilled with mammer which was around this idea of build a consumerized products, think about marketing and more consumer way think about growth in a more consumer way. I think that served us well in some respects and battling others to be entirely honest and I think like interesting that still applies to data companies today. And we’ve moved a little bit past this idea of like, you know, growth hack your way into success, which is where that goes. That was like at the height of this remote growth hacking.
Eric Dodds 14:44
Oh, yeah. I remember that. That’s like what the negative part was like it served me well in some ways, but like not another’s. What were the not another’s?
Benn Stancil 14:52
I think it serves you well, and as you think about the Unity you think about that. And probably the people like the product is to focus on the downside. I don’t think is businesses buy things differently. And like they’re you, you develop kind of a disdain for traditional marketing. And like, the value of the pay to have a coffee with the CIO and talk to that person, you know, like, like, there is just some, like, write the white papers, do the stuff that people have done in sort of b2b marketing forever. Talk to analysts do all that kind of legwork, that you’re sort of like, we don’t need that if we have a good enough product didn’t sell itself. Or better than that. Exact I mean, yeah, and like there are every once a while companies that seem to do this, that you’re like, Oh, of course, we can do that, like Slack was on extended this. But it’s hard because these products look like there’s sort of a natural limitation to how viral they could be. Businesses are still like, the purchasers are still often it. There are a lot of concerns that businesses that are not, especially in the area where like security and privacy and all sorts of things matter where you’re not selling a consumer product, like you can kind of think of it that way, but you’re not. And so I think I think that, that there are some places where like, early we had this idea of like, yeah, we’ll build a viral thing. We’ll focus on all that. And I think there is there is some legwork to be done on building an actual enterprise-grade products that you’d have to spend on pretty early to do it. And thinking about like, growth hacking or whatever you want to call it. The actual path of success, I think, I think is a little bit of distraction.
Eric Dodds 16:25
Yeah. Super interesting. Okay, Kostas, I’m gonna hand the mic over to you because I’m having too much fun. Please chat.
Kostas Pardalis 16:34
You can join the conversation whenever you want. But yeah, it was very interesting, like to listen to both of you. And, like, I have a question based on the stuff that you were discussing towards the end. So you’ve talked about like b2c b2b, like growth hacking? Like, why are our growth and like, all these things? And what like, comes to my mind is like, as equation like, how things are different about like, the stuff that we talk and care about in the valley in the Silicon Valley, and outside Silicon Valley, because, okay, we build, let’s say, products, in a kind of, like, bubble, right? Like, it doesn’t mean that the rest of the world out there operates or work in exactly the same way. And I think this is like even more evident when we are talking about b2b because, okay, b2c is like a different kind of beast, and how you can approach people and do marketing and selling all that stuff. But with b2b like things are different in terms of like how fast things can change and behaviors can change, right? Like, Ben, you said something about, like, the CIO, drinking coffee with them, like talking to analysts, like all these things are still there, right? And even like with the success of Snowflake, so like Snowflake, didn’t do that stuff anyway, even if like they had the more product lead, let’s say, growth, strategy or whatever. So based on your experience, like how different are things inside outside Silicon Valley when it comes to like to building technology and selling technology?
Benn Stancil 18:10
I’m in Silicon Valley, so I have no idea. My general take on that is there’s there like a couple of ways, which it’s very different. And a couple of ways actually not? Like I think there is there is this sort of impression, and that you see, if you read like political journalism, there’s a lot of political journalism about like, the DC elite, going out to the diner in Kansas and talking about this is what real people think. And like, they mostly think the same things that people in DC think they don’t pay as much attention. But they’re not like sitting around, you know, the only thing I care about is like gas prices. And that’s it like they are entertained by sort of the political theater the same as everybody else is. I think the same is sort of true for tech, where they don’t spend as much time thinking about this stuff. They don’t have podcasts about data infrastructure tools. Most of these people are not like, driven that much in their jobs by like, I care about the craftsmanship of the technology that I am using. But I think a lot of us have problems aren’t that tick. Like these businesses are trying to solve the same sorts of things. They see the stuff that people in Silicon Valley have done just as startups look at Airbnb and want to emulate some of the stuff that they did or success or Facebook or Uber, you know, sodas, such and such company in Dallas, that is trying to build a similar business. They care about what Facebook is doing. And they talk to the Gartner analysts, the Gartner analysts are looking at what Facebook is doing. So, I don’t think it’s that those things aren’t that different. Like, you know, they want products that work, they want products, they enjoy using products that make them miserable when they have to use it. But it was just it was a lot less time talking about it. And to me, the biggest place where that like actually manifest itself is there are certain things that that we probably like in Silicon Valley. My contention is we overvalue certain things because We ourselves value them like craftsmanship and software as part of that, where it’s or sort of philosophical stances about software. And one of the ones that I like, more recently than thinking about is, like modularity in the data stack, this has become a thing that is like a little bit of a, a best practice about building data tools is you want stuff that’s modular, you want things where you can like plug and play. And you can have kind of a Mr. Potato Head type of deal, where you choose the mouth, and the ears and all fits together really great. And like philosophically great, I think that makes sense. I think everybody wants that. But if you don’t care that much about it, you actually, there’s a thing that’s even more valuable, which is you could buy all the pieces to Mr. Potato Head one pack. And like, I don’t need to get her 10 stores to buy a leg and an arm. It’s like, I do want the nice leg. And I do want the nice arm. But it also saves me a lot of time to just bite on one place. And if this place has the discounted leg that it works, I’ll take it. And so I think there’s stuff like that with more around how people buy software, the things they value that is different. But I don’t think it’s like some crazy different world where they’re, you know, where aliens and they’re not or whatever, like it’s, yeah, everybody’s still trying to kind of solve the same problems and fundamentally driven by the same principles.
Kostas Pardalis 21:08
Yeah, makes total sense. So, okay, you mentioned the modularity and of like, the modern data stack, or whatever we want to call it. Do you think that the reason that we ended up like, with so many different, let’s say, tools that you have to use in order to build like, let’s say, the stack is mainly, like what you mentioned about like this kind of like software engineering, kind of mentality in Silicon Valley? Are there also other reasons that contributed to these unbundling of the data stack or whatever, like, it’s called? What? What are your thoughts on that?
Benn Stancil 21:47
And then we have all these tools, because it was easy to raise VC money. Like, I mean, I think it’s, I think it was basically that, that Silicon Valley, poured a whole bunch of money into the space, you could start a company and raise 20 million bucks with pitch Jack, Ma, especially if you were someone who came from a reputable like data team and Silicon Valley, if you were, you know, head of data infrastructure at x notable startup VCs would throw money at. And there’s this kind of interesting dynamic to where, if you are a data person, your career is sort of tapped, like, it’s hard to know where to go, it’s hard to know, where senior director of data infrastructure at, you know, what’s that kind of suddenly accommodate it? Who cares? Some $10 billion startup goes, you know, say you work at Coinbase, the Senior Director of data infrastructure Coinbase, where do you go next? Like, what’s your next step? I don’t know, actually, like, you move into just sort of like general business leadership, maybe but like, as a data person, that the career path is sort of like not there yet. I think it will be. And I think these folks can become just generally leaders to business or whatever. But if you’re an engineer, like you can move up to being you know, DPS, and CTOs and things like that, if you’re in sales, you can vote up the top or you can just make a whole bunch of money as like an IC sales reps. as a, as an analyst, or a data engineer, it’s like I didn’t kind of hit the ceiling. And then your boss is now a CTO or your boss is the VP of Finance and like, the data engineer does not get promoted to being a VP of Finance. And so I think the combination of that sense, plus the fact there’s a whole bunch of money out there, and it’s clearly like the market was basically willing to accept anything, drew a lot of people and doesn’t take that much to really saturate the market with tools, but drew a lot of people who were at that point in their careers to be like, Yeah, I’ll start a company, why not like, These things seem like they basically hit the ground as successes, it gives me a chance to do a thing I’ve never done before, that was part of motivation behind why I did it was like, I’m a data person, when I’m ever going to be on, you know, the ground floor of a startup, like we don’t hire data people until like employee lift. And so I think there’s kind of a why not. And so as a result of that, we got a lot of people who were looking for small problems to solve, or trying to solve, like the small wedge of an issue that they solved within their other companies, which isn’t that sort of putting any moral judgment on that, like, that’s fine. That’s what exactly what I did. But when you do that, 1000 times over, you end up with a whole lot of tiny wedges, that can’t be viable businesses, unless they go into big wedges. And then you have yourself a problem because all of them are going to be done.
Kostas Pardalis 24:27
Okay, these are some excellent point. So, okay, the career path is not there yet. So people in data out there, I mean, they will be like, let’s say stuck in this ladder for a while more but at the same time. I mean, it seems at least that like, there’s not that much money anymore of the right like so. What do you think that it’s going to happen next? I mean, there’s like already quite a few companies out there like pretty much every category has a number of vendors already. What’s next? Like okay for the VCs? I mean, okay, they have their portfolio anyway, like, I am not that much worried about them and their returns, like they will figure it out. But like for the founders who have started, like a data company out there like, and especially first-time founders, what your intuition says that it’s going to follow? Are we going to have like merges? Are we going to see like companies starting down? Like, what do you think?
Benn Stancil 25:29
I mean, inevitably hit to some extent, yeah, Lincoln, and I don’t know how bad that’ll get like if I knew that I would have a different job and be making a lot of money doing that to find— it’s interesting because so if you go back to like, the prior tech bubbles, and things like that, obviously, there was a lot of pressure on startups then. And a lot of them shut down. The two things now that feel very different about it is, especially in the data world, is a lot of these companies a lot of money. Like they went out and raised a whole bunch of money, and probably haven’t spent that much. But now there are some that probably raised money a year ago, that like ramped everything like crazy. And those are the ones that are gonna be a little bit of trouble were like, Oh, my God, I thought I was a company that had $100 million in the bank and was worth a billion dollars with 5 million bucks in revenue. And now, I have a 200-person team supporting that. And like, that’s way too big in America, actually, today would value me $200 million. And like, what do I do? Okay, that’s, that’s like getting out pretty far, like, in dangerous territory. But if you raise money six months ago, pay raise 10 $100 million, and your net worth billion dollar companies outwardly dollars, and then what we’ve been raised today, it’s still be worth 200 million, 200 million, whatever. But you have a whole bunch of money in the bank, and you probably actually change your business that much. And so really, what you’re doing is like, Well, now let’s just make our runway six years. And you could do that. And so I think like, that’s a very different world where a bunch of companies have runway for six years, to be able to get through something like this and casework, like you have runway for two. And you’re desperately trying to figure out how to like, basically catch up to your valuation so that you’re not completely underwater. That’s one dynamic third element is I think the data industry is still really big. Like, regardless of what happens with these, you know, sort of a, a recession slash correction, slash them or call it, these companies are still selling into something that’s really big, I don’t think it’s big enough to support all of them, certainly not to support them at the valuations that they had six months ago. Certainly not like all of these companies that had their wedge was, you know, this is a company that’s worth a billion dollars because that’s a wedge that we think can turn into something that generates $100 million in revenue, there’s a whole lot of wedges that are gonna overlap with each other. But the industry I think, is big enough to support a lot of this. And so my guess is the ones that that are in relatively good financial flooding, we’ll figure out ways to come out of it, that won’t necessarily be like $10 billion companies or anything like that. But you know, being a billion-dollar company is actually very hard, despite the last couple of years, making it seem quite easy. So I think it’ll basically just be like, that stuff is hard again, but I don’t think these companies are gonna necessarily die, they’re just gonna be a lot of VCs that make like pretty middling returns on some aggressive investments, I suspect.
Kostas Pardalis 28:07
That’s pretty cool. So, okay, you’ve been, I mean, Mode started like in 2013 like, two days, like where the 2022 main thing things have changed. And you have also gone through like us, I mean, back then, like we were talking about, like the big markets, there was a lot of competition there. We had like luchar we had Periscope data and we had like a whole group of like companies that they ended up either like getting acquired many things happened in this market right like it’s it was like very interesting for me to observe as an outsider to be honest, because I wasn’t working like in a BI tool. But Mode is still there. And obviously like has done like has evolved? So can you share like a little bit with us like these evolution and like also like the journey through this evolution together with like, how this is affected by the market out there right like because you don’t do the combined in the product like in a vacuum that’s why I did those who like all these introduction about how the market has changed. So yeah, I’d love to hear from that because that’s a pretty unique experience that you have and I think it would be awesome like to hear from you like how you experience it.
Benn Stancil 29:29
Yeah, I mean, so I think Mode has been the there’s a question Harley’s have changed you question sort of like why is most still standard What Why are some these other companies not around or been acquired and why is Mode still standing in that regard? I you know, I think a lot of that is luck. Like, to be honest, I think that that we had the initial thought for what mode was was something that was born out of our own experience. I think that saved us a lot. Were in the sort of like frosty versions of the market, you hear a lot of different things for customers, everything is moving really quickly. And I think we always had, like some grounding of the product that we wanted to use. And I think that, that, that those guesses that we make about what makes that product important about what it is like analysts will actually want to use, what is it they won’t want to use? What are the things they value versus the things they don’t value? We got lucky in that, I think they were mostly right. That doesn’t mean we like did everything perfect by any means. There are plenty of places where like, you know, we didn’t execute on things we should have executed on or made bad decisions or built the wrong thing or whatever. But generally, I think like in broad strokes, if we obviously didn’t know exactly where the world was headed, but if we had looked at the world in 10 years ago, and said, This is what it looked like in 2022, we would have been like, that’s not a bad one for us. Obviously, we know that we would have done some things differently. But it’s a world that generally set the direction we were kind of I don’t want even say betting like we were that that would give us more sort of like agency. And this I think was really true. Like it was the world where we were intentionally or not building tools. And so I think we got lucky in that where things like sequel state popular, one of our big bets was like, Yeah, SQL based, big on basically shirked lean on SQL, that could have not happened. And at the time, that was a little bit unconventional at the time, that was like, no, look, ML is the right way to do stuff. No, people are all gonna move to like Hadoop-based stuff. And we are now pretty sure SQL, and that was one we’re where we could actually still make a bet. And it landed pretty well. But things like notice of BI tool without much of a governance layer. Like we didn’t focus a lot on semantic modeling and things like that we focused on like rapid iterative analysis. Things like DVT introduced a semantic layer that we could use. And so that made it such that the version of BI that we had built, which didn’t depend heavily on this, like sort of built-in semantic layer actually kind of fit. Had that not happened had there been nothing like that in the market had there been just like, oh, actually the way to do this, as you build stuff like look ml or you build really deep versions of back, like Yeah, node would not have have not fared well in that world. Now, again, one of the reasons we did do that is because within Yammer, we built these tools, we’d actually built an internal thing that sort of look like DVT. And so we were building kind of to the world that we created, we got lucky in the sense that that that partly, like, you know, we built a thing that just had some it works, but we got lucky in that bet that that was actually the direction to things panned out. So I think that like part of it was, you know, that’s the big reason to me why Motors has been around as like, in a macro sense, a lot of these trends were things that played out. Now, you know, I would also be remiss to not say another big reason why it’s panning out, obviously, is because there’s been a ton of hard work by a lot of people, there are a lot of very smart people on the team that made good decisions that just put in the hours to make work. You know, startups are both a combination of luck and sort of the grind. And I’m very fortunate that to have been part of a team that was that was able and willing to put in that grind. So I’d be remiss not to mention that. But you know, I think it turns out, it’s changed like, mode is actually kind of interesting. And it has ever been through a pivot. Like a lot of data companies go through sort of pivots in their life like motors ever really pivot. There’s, it’s sort of like winded a little bit, there have been some big swinging turns in some ways we’re talking about let’s talk a little bit more on this or focuses on this. But if you just showed me again, mode, the moment we found that it compared to like if the day of founding me had seen mode as it exists today, I would be like, okay, yeah, fits, like it would have still stick the general direction. So, you know, for us, like not that different, I guess. But again, a lot of that was just sort of like Lucky macro guesses largely in the direction the market was going.
Kostas Pardalis 33:50
Yeah, you actually, like pretty much also answered like the next question that I had, which is, like, based on your experience, like, do you think that there was, let’s say, more change that was required for the product or for the business of mold? Because of the changes out there? Like what, where was like more pressure? And from what I understand like, yeah, okay, like, market changes, maybe we have also like to adapt on how we do business and how we go to market. But you didn’t have like, really to go through like some kind of big change on like, what the product is?
Benn Stancil 34:27
No, we haven’t gotten to that. And I think that there have been changes on the market. And it’s more of like market position. It’s more of like, if you just like were to map out the landscape mode as a product is still similar to what it would have been, you know, day one really like again, obviously has a lot more capabilities and things like that, like it’s not, if you squint enough, it looks the same mom, however, that the particular place that it fits has become very different. So 2013 was a time when If people were skeptical of data in the cloud, there was no sort of like they ELT was barely a thing. Most people did not have cloud data warehouses that are starting to, there was nothing like DVT or transformation. There was certainly nothing like orchestration, certainly nothing like data catalogs and observability knows of things. There was no real concept of there was like a very strong concept of exactly what VI was, there was no real concept yet of like analytics teams and what they were supposed to do, like, our first customers were data science teams, what are they trying to do is little bit different. We had to explain to a lot of people that there’s this thing, and like, I remember the very first pitches we ever gave, it was like, oh, there’s this thing that’s outside of the BI T, that are the people like writing SQL queries, answering questions. And like one of the things that we got a lot of questions over the pitches was, how many people write SQL really, like a sequel really mattered. And so that obviously, like that positioning has changed. There’s like data change analytics teams, where analytics engineering represents was not a thing at all. So like, all of those worlds have changed in a way that that, again, actually grows around us in a decent way. We’ve gotten sort of lucky in that. But I think that’s largely the thing that has changed is like, how do we position exactly what mode is in the market? Positioning is both about what we had and what the market has in itself? And so I think like, that has certainly changed. But again, the specific of what it is, is is an extract.
Kostas Pardalis 36:30
Okay, and how much does the markets change since 2013?
Benn Stancil 36:41
I’ve been kind of interested in your view on that, too, is someone who was in it, then as well, on my view was a tollen. I mean, I think that there’s a handful of things, I think they’re very different. One is the cloud, like probably the biggest one is just like that has become much more ubiquitous, much more widely accepted. Certainly there are some companies that are still hesitant about it, you know, you can’t sell AWS to Walmart type of stuff, it’s still there. But like, hey, we have a cloud tool used to be like a deal breaker for a lot of people. Now, it’s kind of expected, and up and down the stack. The other really big change is broadly speaking, the idea of analytics engineering, that’s a little bit of a nebulous term. I don’t quite mean it like, exactly is the people who do what you would find on like, DVTs. blog is a web analytics engineering is, to me analytics engineering is more representative of like, the new structure of what data teams look like. And this gets to Eric earlier question about like data teams and how they’re different. When I was a Yammer, we had data engineers, which was responsible for like building embraces building ETL, keeping up a warehouse, basically being DBAs, all that sort of stuff. And then analysts were just strictly writing queries on top of it and built nothing. It was an easy split, because then we’re like, the builders and the analysts. And that is very clear. In this world, it’s a lot fuzzier. But as a result of that, you get a lot more of like, people creating stuff with fast tools. And the broader acceptance of the data team isn’t just a bunch of like business analysts, it just broader team was trying to, like solve big hard problems, changes a lot in the landscape. And in terms of how people like to build tools, what they’re trying to solve with them. So millage is a tool that sort of fits those, those types of people fits a little better in that landscape than one where it’s like, well, we have data engineers, and you have a bunch of big angles.
Eric Dodds 38:34
One question for you on that actually. So when you had the builders and the analysts like, and you had sort of a, you know, a really clear delineation there of jurisdiction, say? Where their context problems, right, because the people building, you know, are like building to spec, right? Say, like, Okay, we need to get data from here over to here. But the analysts, like have a lot of context from the business users. And I agree that today, like, I think there’s a really healthy crossover between, like, the building and the analysis. And like, a lot of the tooling allows you to, like almost combine, like the context from the data to like the business problem, I mean, modes a great example of that. But back then, do you feel like there was a challenge with the builder getting enough context to create sort of a data set that like served the analysts needs, and then ultimately, the business users needs or the consumer.
Benn Stancil 39:37
So I’m sure what they like we didn’t have that much problem with that at Yammer, because of the way the team was structured and because of tooling that we had. So the way our team was structured was data engineering and the analytics team all recorded up to the same person like it was all part of one org and so we were they were, we were their costs are they are out We were their customers, I don’t know, whichever one we were sort of like the people they sat next to. And the data engineers were basically building to serve us as analysts. And so it was like a pretty tight relationship there. The other thing is in like the analytics engineering world, we had built a tool that was a SQL-based transformation tool, we had built a tool that again, like looked similar to what DBT does today. So that the pipelines were in like, they’re just a data transformation. The pipelines were built by hands like we ourselves, were building that I think that solved a lot of that problem where the data engineering folks were much more focused on, how do we give you the tools to do that? How do we give you query tools, dashboarding, tools, those kind of things. Whereas whatever went in those things were our responsibility. But I mean, yeah, this was a problem back then there was there’s I don’t know when this blog post is from Vegas, like 2015 or 2016, a blog post on stitch fixes blog, which I think is kind of one of the big trajectory, changing things has happened over the last decade or so. There was, I think the title of it something like analysts shouldn’t, or data science or data engineers should not write ETL. Where it was essentially arguing for data engineers being out of the building pipelines and transformations, because to exactly what you’re saying, like, you can’t, it’s a hard thing to spec out. And this is very bad back and forth. A very capable data engineer writing a bunch of like, SQL to transform data in ways they don’t really understand. And the analysts like writing is better. It just doesn’t make sense. And so I that the reason that blog posts probably had to get written was because that was exactly the problem you’re describing was exactly what, what was happening. We were a little bit ahead of that at Yammer. And, you know, the folks who came before me who built all this stuff, anticipated that and built some solutions for it. But I’m sure industry-wide that was not the case.
Kostas Pardalis 41:47
Yeah, 100%. That’s my experience, like by starting a company back then. And, I mean, I also had, let’s say, the luxury, let’s say, the experience of being one of I would say the first categories of this unbundling, right? Because, okay, it’s one thing like to go out there and be like, I’m building a BI solution, okay, it’s going to be like, a very different way of doing BI, but it’s a BI solution. And another thing like to do what, like companies like Fivetran, were doing back then, or like, six data on and including, like, Blendo, where we were going out there, and we’re saying, Well, you know, like, you can move your data around by just clicking like a few buttons. And you can do it on the cloud. And you don’t really have like to write your own transformations like to move the data there. And like all these things, and like one of like, the biggest, like changes that I have seen these, like in the behavior of like, the markets towards these kind of tools, like if, like, if you’re not like playing like, we started the conversation we were talking about, like, how many different tools we have today, right? Like back then you were saying that, outside of like BI in the warehouse, I’m also giving you like a platform that’s going to do like this one single thing, which is like moving the data and like doing the extraction and the loading of the data. And even that was like, Why do I need that? Why like lookers not gonna do that? Why is not five, AWS with Redshift is not gonna do that, or why I’m not just asking my data engineer to go and maintain and build like the python script like to do that. Today, we don’t talk that much about that. Like, it’s pretty much like a nonexistent conversation. And, like, a big change that has happened, like, definitely, in terms of like, the perception that people have on like, how, like this infrastructure should be built and maintained. And I think it has a lot to do with, like, the empowerment and like the different, let’s say, position that like data teams have in the companies right now. Right? Like, it’s not like an IT function, it’s like something else. And that I think, like has missed, like, a huge difference, both like from the user perspective, because of like all the different tools out there, like I’ve I love, love, love to use, but also like from being someone who wants to build it, right. Like it’s a completely different experience to go to market right now. And I would say that, for me, it’s probably like a little bit of more of a technical, like catalyst that I think that has happened is the clouds warehouse changed things a lot. And it’s not that much because people moved into the cloud is because of like the elasticity that it provided for computing. That made the eel t like reality, because it’s a completely different game to start about to discuss about data infrastructure like 20 years ago, when you needed the appliances. You need. It’s, let’s say, writing ETL meant that any logic is part of the pipeline and like, do we want like to remodel the table like yeah, we have like to go through like cutes deploy meant to do that, right? None of that stuff would have happened if we didn’t have, let’s say a way for people to be free from like how many resources they’re going to need and like, budget, like the computation and like the infrastructure upfront. So these are like the things that they have some, like change the loads and like, really like Arctic, like as catalysts to get today to where we are. And together with the money from the VCs, like we have ended up like having all these like, worlds of innovation happening. So that’s how I see it.
Benn Stancil 45:36
The one thing I would add is it also compounds in ways that I think are hard to anticipate? You know, that there’s like the, when Eva was early, there was a lot of stuff of like, oh, well, it’s obviously the cap was a taxi industry, right? Like, we will use Uber for Uber and taxi industry that are a zero-sum thing. And basically, what Uber was, like, I guess, proved to some extent, was, if you make this easy enough, people do a lot of things they wouldn’t typically do with taxis, whether you were they’ll commute to work or to Uber, because it’s just straightforward. And you can schedule it now or whatever, like I would never get in a taxi, but with Uber wanting them. And so I think that are like, you know, I live in a neighborhood that doesn’t always have taxis, and it’ll take it to the grocery store. I think a lot of stuff happens, where I’m like data infrastructure like that, to where once things like the cloud storage became so cheap, and it became fast enough, we started building a lot of things on top of it that we wouldn’t really want it otherwise. Or once Fivetran The stitches and Windows, the world became a thing. It was like, Oh, actually, yeah, I’d like to get data out of my, like, applicant tracking system. I don’t think I need to do analysis on that. But if it’s straightforward, yeah, I’ll do that. We’re like, we sort of catalyzed a bunch of demand because we’ve made stuff that was so much harder, easier, in a way that that the ecosystem really kind of ballooned from.
Kostas Pardalis 46:51
Yeah. 100%. Eric, my turn to say that I monopolized the conversation, so it’s yours.
Eric Dodds 47:00
No, that’s great. Benn, I’ve read a lot of your writing over the years and really appreciate it. So thank you for the time and thought. By the way, I’m sure that a lot of our listeners read your stuff but if they don’t, where can they find your musings online?
Benn Stancil 47:18
So most of them, aren’t I substack? It’s been.substack.com need to work on the branding.
Eric Dodds 47:26
I like it.
Benn Stancil 47:28
It doesn’t have a punchy name or whatever. Never got past that point. Yeah, that’s like I if you want to follow me on Twitter, great. That’s much less interesting and infrequently. And I don’t know, I’m, you know, keep myself sane by not saying too much on Twitter. Yeah, the, the longer form thing, if you have trouble sleeping, or looking something to help knock you out at night? You can check it out.
Eric Dodds 47:54
It is really good stuff. But, you know, right, sort of writing like, is a form of, in some ways, at least in my opinion, like, sort of really testing your thinking. You know, because you’re actually putting something down into words. And one thing you mentioned to know, like, you think a lot about the data space in general. Trends, like you’ve seen it over a decade, like, you know, I’m tempted to ask you the standard question of like, what’s interesting or whatever. But what I what I really want to know is, what is a trend that you’ve seen that you look at, and you’re like this is happening, but it’s probably not a good thing in the long term?
Benn Stancil 48:44
Probably a couple of things. So first of all, so I’ll say that Yes. People say that writing is meant to help clarify your thoughts and test things out. I do most of my testing and production on that. So read the blog.
Eric Dodds 48:59
Yeah, yeah. Apologies. The Jet Force pushed a Master. Yeah, exactly. The hope for the best because, you know, sometimes it works on it doesn’t.
Benn Stancil 49:08
It’s okay. So I think I think there are a couple of things. These aren’t really trends is like, in terms of like big trends, I think there’s an oversaturation in the marketplace. There are a lot of small problems that we’re trying to turn into big problems that just don’t need to be solved in that way. Yeah, or they need to be solved. Like they’re better office features and not products. That doesn’t seem super interesting. I think most people can agree with that. There is usefulness in having companies trying to solve those things that eventually like, we’ll figure out good ways to solve them and incorporate that solution into something bigger. Yeah. But just like as an economic reality, I think it’s hard for that stuff to come via one thing. One of the things though, that I think is interesting about that there’s another trend in the data world that I think it is kind of a bad dynamic. That mode is like everybody’s every data company is guilty of this and I think it makes for ultimately worse products. Which is everybody tries to be Switzerland. There is this general sense. I think DBT did this really well. And I think a lot of people basically are trying to do the thing that they did, where you are competitive with nobody. And you’re like, Oh, yes, we are agnostic to every other part of the stack. We work great with every database work great. Whatever ETL tool we work great with everything else. There is this kind of like reluctance to compete. Unless you are, say, squarely in someone’s face that you can’t let do it like Stalis and Hightouch. fight each other. It’s kind of fun. I like it. I like that they like yell at each other about performance nonsense. 100 year for it. As as an aside, I’m also a small personal investor in census but like, don’t care. I’m just here to watch these people fight it out. I think that’s good. I think it’s actually good that Snowflake and Databricks yell at each other about performance and stuff like that and are not shy about the fact that they’re trying to do that.
Eric Dodds 50:53
Buy expensive billboards.
Benn Stancil 50:56
Which snowflakes billboards are terrible. But, you know, clearly some 60-year-old dad wrote all their billboards, and it’s just terrible.
Eric Dodds 51:04
I love it. That’s one of the best spicy takes I’ve had.
Benn Stancil 51:08
Which I guess you know, here we are talking about him. So job well done. Yeah. Well, I think there is this general thing of like, we all have to play super nice with each other. And I don’t know that that actually leads to the best experience for customers, where you sacrifice a lot of, well, if we just worked really well as others people, if we focused on making really great experience with these two or three other folks, we could actually solve really good blood problems in really, really robust ways. Because like, there is this kind of fragmentation problem where the experience is kind of all disjointed. I think that’s problematic. The way you get around that is not by trying to make everything work with everything. That I do think there will be some that has maybe a positive change around what is happening in the market now is there will be winners that start to emerge. There’ll be clear like, Alright, I’m going to align myself with the winners and say, Actually, I don’t want to work with the people who are struggling. Again, that’s that’s in some ways, like, it’s a cutthroat dynamic. It’s a dynamic that will lead to some people losing. Yeah, maybe node. I don’t know what the point is, I think like in terms of as the customer of those things. I would rather that dynamic and think greened up long term, just a better product than if we end up with something where everybody’s kind of like, trying to play a little bit nice. And it’s not the thing is it’s not even nice. It’s like trying to play like passive aggressively Nice. Where you’re positioning is like, Oh, yes, we’re all friends. Like, I think there’s some just like, Nah, let’s actually just bite it out. So you can make the best products, actually is going to be useful.
Eric Dodds 52:32
Yeah. Love it. Yeah, it’s interesting, because that’s like, a very, like, a strange flavor of hypocrisy in some ways. Because, you know, your go-to-market is like, we’re all friends. And your investors are like, we’re not all friends. Yeah, like, if you get the outcome that I want, like, we’re not all friends. But Ben, this has been such a fun conversation. It felt like we were talking for like five minutes. But it was an hour. So thank you so much for your time. And we’d love to have you back sometime to come back and give us some spicy takes.
Benn Stancil 53:09
Yeah, thanks for having me. It’s been fun.
Eric Dodds 53:14
And I think my biggest takeaway from that conversation Kostas was just how transparent and I think real Ben is, I mean, you kind of get this from reading his blog. But like, it’s just so refreshing to hear someone say like, man, VCs are pouring a bunch of money into companies that, you know, are basically should be features of the X. And just to hear him talk about that, and even sort of be dispassionate about Mode, right, like, he was very clear, like, maybe Modi gets it wrong, you know, and sort of like goes to the grinder. Yeah, they’ve survived 10 years. So I don’t think that’s likely but like, I don’t know, I just appreciated that. Like he was just us probably one of the most transparent guests who like will just say, say it how it is and like, is okay with that. Also incriminating, like himself and his company?
Kostas Pardalis 54:15
Yeah, yeah. Yeah, I think that Ben is like probably one of the most, let’s say, clear and consistent voices out there in the industry. And that’s something that like I think everyone appreciates not just me and you and that’s one of the reasons although I’m so happy that we had him today like on the show. What a tip from our conversation to be honest with like the last part of it’s about competition and how competition is important to build at the end like a better product for the customers out there and how this particular industry right now as it is like it’s at least at the surface level, like tries to avoid competing I have a feeling that like reality is going to accelerate things. So yeah, we’ll see more competition happening out there. And I’m looking forward to it.
Eric Dodds 55:08
Indeed. All right. Well, thank you for joining us to tell a friend about the show if you haven’t, and we will catch you on the next one.
We hope you enjoyed this episode of The Data Stack Show. Be sure to subscribe on your favorite podcast app to get notified about new episodes every week. We’d also love your feedback. You can email me, Eric Dodds, at firstname.lastname@example.org. That’s E-R-I-C at datastackshow.com. The show is brought to you by RudderStack, the CDP for developers. Learn how to build a CDP on your data warehouse at RudderStack.com.