Will a Duck DB-Like Excel Emerge by 2075? And Is Data Every Company’s Most Valuable Asset? Featuring Benn Stancil of Mode

Eric Dodds 00:06
Welcome to the data stack show.

John Wessel 00:07
The data stack show is a podcast where we talk about the technical, business and human challenges involved in data work.

Eric Dodds 00:13
Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Welcome back to the show, everyone. We are here with Ben Stancil. You probably all read his newsletter. I know we do. Ben, welcome back to the show. It’s been years, I guess, at this point, since you were first

Benn Stancil 00:41
on, yeah, awesome, yeah. Thanks for having me. I’m excited to be here. All right, so

Eric Dodds 00:45
much to talk about, but just give us a quick background.

00:47
Yeah,

Benn Stancil 00:48
so I started my career at a startup called, well, I started my career in DC, doing my policy research for a couple years. Ended up a startup in San Francisco, shortly on a data team there. Shortly after that, I was acquired by Microsoft. I worked at Microsoft briefly, most of my career was then after that, me and a couple other folks started an analytics bi we’ll figure that out later, I guess, a company called mode, which sold a way for folks in data teams to make DAC boards and charts and do analysis and things like that. Worked on that for about 10 years. About a year ago, we sold to thoughtspot, which is a larger startup BI tool. Was it thoughtspot For a little bit, and as of now, a few months ago, no longer with either of them. And so I am unencumbered, I suppose, yes.

John Wessel 01:32
Unencumbered, yes. All right. So then for the show, we started talking a little bit about Excel and whether we think that will exist in 50 years. So that’ll be a fun topic. You know, AI was part of the conversation there, and just how we work now. But what are some topics that you want to dive into? Like

Benn Stancil 01:49
I said, I am now unburdened by

Eric Dodds 01:53
say whatever you want. Yeah, to the credit

Benn Stancil 01:56
of both the mode and thoughtspot marketing teams, they were never emphasized. A whole lot of editorial oversight, but yeah, I mean all that stuff is, it’s like a lot of stuff going on, interesting to kind of to see where it’s headed and get y’all text Great.

Eric Dodds 02:10
Well, let’s dig in. Ben, so excited to chat with you, and what an interesting time to you. Know, recently you sort of ended a decade long journey building mode, and sort of began a new journey with mode as part of thoughtspot. You’ve written, you know a lot about that, but I thought it’d be really interesting for me personally, but also our listeners, just to reflect on that a little bit. And so I’m going to position you as an advisor to your younger self. So this is a completely unfair and unrealistic, like, you know, hindsight view. So, but also, I have a couple questions, but the first one is about the sort of strategy and vision for a company, which over a 10 year period, I think is a really interesting thing to think about, because that can change so drastically for a number of different reasons. And you’ve seen, you know, really a space sort of eMERGE and change drastically over that time period. The underlying technologies change drastically. But if you had to go back to younger Ben as an advisor and say, Okay, from a strategic perspective, like, here are the most important things to keep in mind. What do you think you’d

John Wessel 03:29
say? And I want to add one quick thing to it. I because this is very much related. I’m really curious if there’s a focus like, Hey, I wish I had spent more time doing like X, like I spent more time with this team, or developed this team faster, whatever. I think that would be really nice about it here.

03:50
Good one. So, okay, so

Benn Stancil 03:53
there’s a handful of ways I’d answer that, though they all kind of have the same theme. Probably there are a lot of things I would tell about my past itself, and most of them are actually kind of framed around one rough idea, which is, like, you can do whatever you want. That I remember early on, I was talking to an engineer. This was so I’m not an engineer. I’ve learned how to do a little bit of stuff between now and 10 years ago, but I’m not an engineer. And so I was talking to one of our engineers, very early in the morning, and I remember him telling me, I was like, asking, Can we do this, or is it possible to make this thing? And his answer was, essentially, it’s a computer. It can do anything. It just depends on how long, much time you’re willing to spend doing it. But like, yeah, we can make it do anything. And there’s like, an element of that in running a startup, where you kind of feel like, okay, there are things you are supposed to do, there are rules, there are ways that this has to be done. If you’re doing something, it seems like it’s working, but you’re not doing the stuff that the textbooks say you’re supposed to do, the stuff that the textbooks say. There’s a bunch of stuff that you find yourself, especially when it’s the first time you’re doing it, which is the first time all of us know who it’s. Start a note. We’re doing it. You kind of run the playbook. That is the thing that the internet tells you to do. And I think most of the things I would have done differently are like, tell myself You can do this the way that you want to do it. And so there’s two examples of that. I think. One is early on, and this actually lasted for a long time. I remember it feeling like weird that people joined to work on that I was not very old, I wasn’t terribly experienced, and, like, we had some really good engineers join early and some really good folks in the marketing side and design and all that, that I was kind of like, why are you here? Like, what? Right? What are you doing? I could never quite shake the feeling that they were like, doing me a favor. So on one hand, I think that’s not terrible, like, okay, great. That means you’re not going to be like an Evil Pirate. I’m sure I was an evil tyrant in plenty of other ways. But like you, you try not to make them all mad. But on the other hand, you end up sort of realizing, like you don’t, if they’re things that you want to do, you kind of feel like you have to have permission to do it. And I think one of the things that we should have done, Me, personally and as a company more generally, is like, hey, we want to do these certain things. We believe this is the thing we want to build. We don’t really care what other people say. This is the way we’re going to do it. And so one example of that is and to your to John, to your question, like, what’s something I would have spent more time on when we first started mode, I had nothing to do, like, there were three of us, the Derek, who was our CEO, was personable and could talk to investors, and he was like a good face at the company, so he went off and did those things. Josh, who was our CTO, was chained to a desk building a product, and I wasn’t capable of either this. I was like, not the person we would have put in front of investors. I was certainly not capable of building a product. And as I was like, kind of had nothing to do, like, my background was as a data person. And so I started writing a blog. It was not like the it was essentially like analysis of public data that the very first blog post on modes website, which I think is still up, is this analysis of Miley Cyrus and the VNAs, like, I don’t know what it’s about. And so we did, I did that, like, partly because I was interested in it. It was kind of fun, because it was like, All right, this is a good way to just sort of talk about data things. And it sort of worked as, like, content marketing, because it wasn’t like, here are five things that you should do as a data team number five is buy mode, right? It was more like 538 style stuff. And it was actually pre 538 being what five it was, 538 was just like a Nate Silver blog on New York Times at that point, data driven journalism. And so, like, it had a bit of an oddity, some people started reading it and had a bit of an audience, and eventually it became like, Okay, now we have customers. Now we have other things to do. And we looked at that as like, All right, well, this blog is not the right thing to do. This, clearly, isn’t. It’s not in the playbook to write a weird blog like, therefore I should go off and do other stuff. And so I did, and I think that was a mistake. I think it was, it was like we found something that sort of works. Just keep doing the thing that works, even though it doesn’t feel like the right stuff. And he can find 100 examples of that, I think, of places where you are reluctant to do things the way that you think you should, or that it works, or whatever, because there’s kind of, like, a that’s not the right way to do it. And so I don’t know, I think I would have basically wanted to tell myself, like, Hey, do this the way that you feel like it works, and don’t worry that much about, like, whether or not it fits the sort of place. Sort of playbook or

Eric Dodds 08:24
not. One question on that. Well, I’m sure there. I will have more questions on that, on this, because it’s so fascinating. But how much of that pressure to follow the playbook do you think is self imposed? Because I’ve felt that as well, right? You know, where you I felt the same thing starting a company, you know, it’s like there are these really successful people, you know, even especially people who have, like, serial success, who like write about these, you know, patterns that they’ve seen or that they’ve experienced or applied, you know. And so there is that pressure. But then there are also other other pressures, right? Like, if you raise money, you know, I’ve had experiences before where it’s like, okay, well, you know, these are successful investors. They’re looking at these patterns and sort of this playbook. And so there can be pressure to, like, do things a certain way, or, you know, sort of model things in a certain way. So in your experience, how much of that was sort of internal and self imposed versus, like, sort of external,

Benn Stancil 09:30
I think it’s mostly self imposed, but all of the like you’re on to me a gentle slope, like the external slope is gentle, but pointing to, pointing you towards the playbook. And so you do have to resist a little to your point about investors. Investors are going to mostly give you kind of the standard, here’s the way to do stuff, kind of thing. And yeah, so it’s not going to be if you try to do something totally crazy, they might get on you and stuff. But, like, they’re not going to be that most of the time. Obviously. There can be evidence, I’m sure, like, authoritative about, like, you have to do it this way, yep. Honestly, they become more of that later stage as, like, when they Yeah, but early stages are typically not that hands on anyway, yeah, but there is, like, a gentle pressure there. I think, to me, the thing that, like, the way I would frame it, is startups. Businesses in general, are basically like this factory that produces money and fame and success and all that stuff. And as a person who runs one of those businesses, you’re essentially sitting there with like, 1000 levers in front of you that are somehow connected to this factory that produces money,

Eric Dodds 10:39
right? I love this mental image

Benn Stancil 10:42
for a startup, you have 1000

John Wessel 10:44
minions. That’s my mental image of like,

Benn Stancil 10:52
and there’s wires going from the levers back into the industry, and the wires are all on this giant knot. And some of them, you pull it, and it takes an enormous amount of effort to pull, but you can’t see what happens after you pull it. You can’t see if, like, maybe there’s electricity flowing through the wire really slowly. Maybe the electricity turned into a fuse and it’s about to blow up, like you have no idea what’s actually happening, like you basically pull it up. Sometimes something happens immediately. Sometimes nothing ever happens. And to me, that’s really hard like that to me is why startups are hard. It’s like, yes, they’re a lot of work, but it’s not what I want to be like, not to say that being an Olympian is not hard. I obviously am not an Olympian. I imagine it’s very hard. But there is a part of me that seems like to be an Olympian, you have to work incredibly hard, but there is a little bit of a path there. You have a trainer who tells you what to do, and obviously they may be wrong. They may be wrong. They have to choose a trainer and stuff like that, but like, there is just, I have to put in the effort. I have to put in the hours. It’s a tremendous amount of effort and hours, but I sort of know at the end of the day if I get there, if I don’t, it’s because either I didn’t put in the time or I just, like, I wasn’t able to do it. I just don’t run fast. Yeah, for a startup, you don’t know, you just don’t know what to do most of your time is like, the business isn’t going the way you want it to, or something’s hard, something’s working, something’s not, whatever. And you’re like, how do you fix it? And you’re like, I have 1000 levers, and I know any of them do, yeah.

John Wessel 12:11
So the context here is really funny, right? Because this is a data startup, right? So, like, a lot of the point of your tooling is to give somebody insight into their data so they know what to do, right? Is it? I mean, but of all companies like you would think, like, oh, there’s a data company. They’ve got a lot of smart people, they’ve got analysts, they’ve got, like, way more resources than me. But like, it’s still true of a data company like, you know, Silicon Valley, you have pretty good access to talent, like, all that’s true, and it’s still the same problem, right?

Benn Stancil 12:41
Yeah. And especially for an early company, it’s just like, yeah, like, one of those Levers is, do we build giant feature x or giant feature y? Like, I did. I don’t know. I don’t know how that’s not how that’s connected to the engine. And when you’re a big company, like, it’s still hard, and you don’t know what to do, but there’s a lot of you’ve untangled a lot of the wires, yeah? You’ve figured out a lot of those things, yeah. And for a startup, you have it. And I think back to the sort of original point of, like, just have some confidence in doing what you want to do. In a lot of ways, I think I’m a lousy founder. Like, I’m just constitutionally a lousy founder, because you can react to that confusing set of levers and engine by, like, carefully pulling stuff and being like, what happened? What happened? And the answer is, like, nothing ever is going to happen. Like, the thing is just going to do a bunch of random stuff and everything. I think the only way you really solve that is you’re just like, You know what? I really believe this is the lever that it’s going to be. I’m going to pull this lever. Like, the outcome be damned. For two years I’ve been pulling these levers. I know that’s what it’s going to work. Sometimes you miss, but I think you have to have, like, that kind of delusional commitment to almost know what these levers and wires do, and yeah, some people do and some people don’t. Well, we’re a bunch of analysts, like, we, we pull the lever slowly and try to

John Wessel 13:54
figure out what happened. Well, it’s such a complex relationship between did this work? Because I believed it would work so much and like, you know what I mean? Because, like, it’s not like a linear thing where it’s like, Oh, there’s one right thing, or three combinations of right things. Part of the impact of it, I think, from a founder standpoint, is, like, part of this working is that people can tell that you believe it’s going to work, so your team’s behind you, and your customers are like, Man, I believe. You know, so it’s not, it’s not even like something completely determinable via analytics,

Benn Stancil 14:21
yeah, yeah. And I think in a lot of these cases, like, the decision isn’t the lever you pull doesn’t matter that much, so pulling it right, like, you could build a lot of products that work. You could build a lot of things that yeah, eventually some things may be terrible ideas, but like a lot of things, if you sit around and think about it for a week, idea one and idea two probably are both fine. Like, the thing that you need to do is just commit to one. And I think right, the challenge is if, if you are analytical, really, yeah, and you spend a lot of time, like, trying this, and that’s like, yeah, that, yeah, whatever happens with, like, which lever you pull, the one where you’re just constantly fiddling with everything, that one doesn’t work. Yeah?

Eric Dodds 14:59
Go. That reminds me, John. So John, actually, we had a chance to, we were part of a group, actually, at rudderstack. We were doing this kickoff, and the former CFO of Atlassian, Alex Estevez, who’s a, you know, he’s, you know, sort of famous for a number of reasons, like really well known and super successful. And he came and gave a talk, and we did a Q and AQ & A, which was awesome. And one of the things he said that you kind of know in your head, but really echoes what you were saying, Ben, was he was like, Okay, in this startup, you make 1000s of decisions every year. And he said, but really in terms of the trajectory of the company, there are probably, like, one or two key decisions that actually, like, have a material impact on the trajectory of the company you know

John Wessel 15:48
that you may not know at the time, and if you may not know, could never have known. Yeah, right,

Eric Dodds 15:53
right. Yeah. That is Yeah. That’s really fascinating. Yeah. And I think that’s

Benn Stancil 15:59
Basically, like, it’s tough, because some things are a little bit contradictory to say this based on, like, just pull a lever and commit to it, but also part of it’s like, you just have to try. You have to just, like, try to pull the thing and see what happens. And it doesn’t mean just, like, willy nilly Yank everything all the time, but you can’t stare at the knot and figure out where things go, yeah? Like, you got to just start pulling stuff. Yeah, not all the time, but like, You got to start pulling stuff. Yep.

Eric Dodds 16:25
I think another thing, the Olympian analogy, I think, is really helpful, because the other thing is, you understand the inputs, but you also understand a lot of the benchmarks, right? Like your coach has studied, like all the major athletes have studied, you know, all of the mechanics and the physics of how to compete, and like body composition and all of that sort of stuff. But when you’re trying to build a startup, a lot of times, you’re trying to build a novel approach to solving some problem. And so there are also, like very few benchmarks, that you have this tangle of stuff, but then also you even if something you’re doing something right, it’s hard to know, it’s hard to feel that in a visceral way, because it’s like, well, we’re trying to do this differently than a lot of other people have done it before, and so like the physical sensation of a benchmark, or like comparing yourself, is almost non existent as well, especially in the early stages. I think, I think that’s

Benn Stancil 17:24
mostly true. The one semi caveat to add to that is, I do think there’s a lot of startups, particularly when things are struggling, a lot of startups that actually, like you do have some feel like you have a part of it, just like anxiety. But you kind of can tell when something doesn’t work like you often will feel it before it shows up in anything else. You’ll kind of realize, like, how you talk to these customers, and nobody really seems that excited about it, or, like, just pulling teeth to get this thing to work like I think there’s a lot of a lot of like, the job as a founder, as this sort of delusion, but, but you also, I think, have to pay attention to, like, where are you in the dark moment? Like, not dark in terms of bad, but like, you’re lying in bed at night. How does it actually feel? And if it’s like, I feel something that’s not good, or I’d be like, Hey, this is there’s really something here. I like, feel a spark, or something like, there’s usually something in that, that, I think, is, like, it’s not saying, hey, just like, go with your gut on everything, but it’s that your gut tells you something, and if your guts telling you something. I think there’s a lot of startups that know it doesn’t work for a long time before they, like, actually really make the drastic changes they try to do, to, like, change something. There’s a lot I kind of can tell this idea is not going to work, but I’m like, fiddling with it for too long because, I mean, you get attached to it, it’s hard to change that. But, like, most of the time when people shut something down or realize I have to pivot, they knew a long time before that happened that, like,

Eric Dodds 18:57
man, yeah, something wasn’t right here. Yep. Man, that’s yes, yeah, that internal benchmark of like, yeah, that’s, I think those are such wise words. Well, shifting gears just a little bit. You’ve obviously spent a lot of time thinking about the data landscape generally, right? And so in terms of something working, or sort of a data product working really well in the current landscape, are there, like, a set of ingredients that you think map to the landscape that are sort of required in order for a data company to be successful? And the current landscape, like,

Benn Stancil 19:40
If you were starting a data company or whatever, like, what’s the inputs that make it good?

Eric Dodds 19:46
Yeah, or even, like, the inputs that make success like a viable possibility, right? Because there’s so many things you can screw up, or, you know, or things that don’t work out. But, like, Are there any core ingredients? You think it is just mandatory.

Benn Stancil 20:01
I would say, like, I mean, nothing’s like, really mandatory. I would say there’s a handful of things that I would like to feel better about. One is related to this, like, kind of have an idea, commit to it, know what you’re doing. The way I frame that with, like, a data startup. It’s more like, know your boundaries, that the data landscape and, like the tooling landscape is, really overlapping, like there’s a ton of stuff that’s all mashed up together where it’s hard to know what bleeds into what and so, like bi and what mode the space mode operated was very much like this, where we were a SQL, ie, there were notebooks attached. It was a kind of data science. It was mostly analytics. It was also kind of like capital D data science of people building, of people building models that obviously bleeds into dashboarding and bi, which bleeds into things like alerting, and then starts to touch on things like data discoverability. And how do you like to monitor things? And there’s you end up being able to basically be adjacent to everything, right? And so I think it’s really hard to build something not knowing where, like exactly you are saying, this is the line that we won’t cross, because your customers will pull you into all of those adjacencies. Yep, right? And so part of it is just like a clear sense of vision of where you want this thing to be, and being disciplined enough to stick to it, because, again, you’re going to get pulled in those adjacencies. You’re in those adjacencies. There’s always going to be, like, a big customer if we only build this one thing. And I think that the real thing that happens there is you end up, especially early, you end up building a core set of features that everybody buys. And then one customer is like, we really want this one thing. And you’re like, Okay, we’ll make that. And then other customers like, really want this other thing. And you kind of make that. You will tell yourself you built the same product for both of them, but in reality, you’ve built two different products where, like, they believe your roadmap is moving in their direction. And so, like, you’ve actually sold nine different roadmaps that way. And so you have to have a lot of discipline of being like, no, look, that’s we’re not going to build that, not because it’s not adjacent to what we’re doing, but because we know where it’s going to go and it’s going to be this huge surface area and stuff like that. Like, there’s, again, so much, like space in the ecosystem of just like, so much sort of ground to cover that I think we have to be like, we’re going to be really good at this particular piece of it. That’s what we’re going to do. And if we’re either going to live or die on that piece being the thing that we think it can be.

John Wessel 22:21
Is there a partnership play there too, where you like, where, like, if you have a, like, more specific boundaries. Does that mean? Does that happen? Think it make easier to have like, partnerships, like, I’m thinking about like, BI tools with a data warehouse, or even inside a bi like, well, we don’t really do notebooks, but like these guys do notebooks well, like the is that because, you know, large companies, like a lot of them, have a, you know, a bunch of different BI tools, not like they just have one, is that a part of it, or is it more just, like, about the focus and like, that doesn’t really matter. I

Benn Stancil 22:52
like, informally, sure, I think that’s fine. I don’t know that. I think partnerships are often a distraction for folks. So, there’s a couple reasons for that. One is that a lot of times people will be like, Look, we want to be this thing that works really well with the ecosystem, right? The ecosystem doesn’t want to work with you. Like, there’s actually work with, you know, if you get big enough or popular enough, that changes. If you’re stuff, like, if you’re DBT, if you’re fivetran, if you’re Databricks, a handful of others. Like, yeah, you can kind of, you will have the gravity to have people come work with you, but you’re not going to get there from day one, there’s no like, oh, we want to have partners observed even day one, sure. Plus, you can’t build good experiences with those things. Like, I don’t think you can say like, Hey, we’re like, you can’t be like, we’re going to be great with all these database partners. Like, you’re not going to make a good product there either. I think the only way that partnerships really work is you choose, like, one person that you’re saying we are absolutely riding these people to coattails, yeah? That we are going to be basically an extension of snowflake, or Databricks or some Amazon thing, or GCP or data DBT or whatever, yeah? Where you want to be so good with that thing that their sales team sells you, yeah? But the idea of like, oh, we’ll be on some partnership websites, that’s a waste of time. Waste

John Wessel 24:02
of time. Sure, yeah, yeah,

Eric Dodds 24:05
yeah, that makes total sense. So kind of related back to the conversation, or back to the point you made about knowing you know if something’s working or something’s not going to work. And sort of feeling that deep in your bones speaks a little bit to product market fit. Like, how would you even think back on your mode experience or just your current thoughts on that? There’s so many thoughts on product market fit, but just interested again, thinking about, like, building a company. Mentioned focus. Like, how do you think about product market

Benn Stancil 24:38
fit? It’s a feel thing. I think it’s a lot of it’s just like, how hard is it to sell and who can you sell it to that there’s not, you know, there’s not, like, a you don’t have it. A lot of people have said this before. It’s not like you don’t have product market fit. You do you sort of, it’s like you have a product market. It is what it says. You have a particular product that fits a particular market. And basically, I would like to find, do you have a product that fits a market? Can you sell it pretty easily? Do people. Come to you to buy it. Like, the feel part of that, I think, is actually like, if you’re on sales calls, it’s kind of like, how excited are you to join the sales call? You know you’re about to give a demo to somebody? Are you like, dreading it because you know you’re about to just get hammered by a bunch of questions that you can’t answer, and they’re like, you’re going to have to disappoint this person over and over again, because you know you can’t solve this problem. Be like, hell, yeah. I’m fired up for the sales call because I know I have a thing that they’re gonna be excited about. I’m excited to see the look on their faces when they see this thing. Like, that’s almost a better like, yeah, okay, fine. Look at retention, look at growth, whatever. But I think, like, that feeling is almost as good of an anything measure as anything. Because you know how you feel going into these calls. You know how you feel when you’re, like, getting on a call with a customer? If you’re like, God, like, every time these things happen, they’re upset about something, or if, like, you get on and they’re like, I know they’re gonna love it. I know they’re gonna talk about how great it is. Yeah, they’re gonna have some things they want differently or whatever. But like, they’re excited customers and they’re excited to be on this call because they want to make this thing better together, versus like they want to yell at me about why they hate it. And so I think the product market is, to some degree, just that feeling. It’s but I think the thing that you, you have to be careful about is like, that doesn’t just mean like, hey, do we have product market fit or don’t. It’s like, oh, we actually work really well with this type of person, but not this type of person or whatever. And so it’s, can you, is that type of person big enough? I think one of the things, for instance, like mode, the place where mode like, one of the reasons we actually ended up doing the acquisition on a much longer time horizon, basically, was the market for a data tool, like an analytics focused data tool for technical folks. Isn’t that big. It’s just not that big of a market to sell a productivity tool for analysts. It’s why all the tools that do that end up kind of becoming BI is because, like, the only way you can either get there is either sell it for something that’s really expensive, which people now are kind of allergic to. Like, nobody wants to pay $500 a month for software like that just doesn’t feel right anymore. They want to pay $25 and say, like, Oh, we’re gonna sell $25 seats. Well, I guess we gotta sell to everybody. And so you can have product market fit with, like, the analysts, where you sit on calls with them and, like, it’s great, but then you get on a call with the head of marketing, and they’re like, how do I do XYZ that I’m used to on my BI tool? Can’t do it. So that’s where, like, I think you have to be honest. We’re like, yes, we have product market fit. Doesn’t mean we’re set. It means, like, we can sell to this particular group. And how easy is it to find them? How many of them are there? That kind of stuff of like, where actually is the draw? Yep.

John Wessel 27:29
So I think I have an interesting perspective on this, because my previous company, we bought, we did a whole, like, selection process for BI tools, and went with mode around 2018 I think, and there are some things that since then, I’ve realized were not typical of the company. And one of them was that the analysts were very much involved in the process, which is, ironically, I do not think that is often true in the selection process. And we compared a lot of tools, and absolutely, like, the analyst, like, at the time that was involved in the selection was like, This is great. Like, like, this. These people get me, like, this is how I want to work. Like, that was a big point, you know, part of the decision. And then we did a lot of, we had a lot of what we call them, civilian analysts, like, a lot of, like, dual role type people that picked up a little bit of SQL, so it just worked really well in that environment. And then I think, and since then, I’ve learned that, like, oh, like, most companies, this is not how, like, it works. You know, I like, we had a sales manager that was pretty good at SQL and would, like, use mode. Like, that’s not super normal. But on the product market fit side, I’m curious. You’ve got two components, so the product part and the market fit part, which do you think there’s over indexing on one of those where, like, people are typically keep tweaking the mark the product part and like, we want to cram into this market. It doesn’t make sense. Where the other side, where, like, we keep in search of a market for a product that maybe doesn’t exist. Like, What angle do you think is like, do people over index on real quick,

Benn Stancil 28:54
whether on the like selling to y’all, Dad, you’re right. Like, that’s not that common. Thought that was uncommon. But this is one of the reasons why selling a product like mode is to get it really big is hard, it’s very hard to identify. It’s really hard to figure out which companies are like that and which and so you end up having to cast away like you can’t have a very targeted net because you’re basically talking to everybody and then figure out based on conversations like, how much say the data team has. There’s not a good signal that it’s not like a Facebook ad target. You can’t get an audience. Yeah, crap. Data Team influence, whatever. Well, that was very much like a thing for us. Was like, if the data team had a big voice in the buying decision, we were great. If the data team did not, then there’s a struggle. So you’re like, do you over index on the product or the market? I think that most people mess up like, they either don’t interpret the market as like, they basically over index on their own experiences. I think it’s fine, like, build a purpose that’s all great, but they over index on like, how common their experience is, or like, they really think this thing is great, the market will love it, and there’s just, like, not that many buyers, right? The other thing I think that a lot of companies do is. Yeah, they’re selling cultural change too. The product isn’t just a product, it’s a new way of doing things. And like, you can pull that off, but you basically pull that off by building something that just has such a good product market fit that, like, people adapt around your thing, like DBT, was DBT pulled off cultural change, and to some extent that was the intent from the beginning. But it wasn’t successful because it was intentional from the beginning. It was successful because people just like, they built a product that people adopted like crazy, and so a bunch of people changed it to sort of fit their way of doing stuff around it. But there’s a lot of companies that are like, we’re going to come make you data driven, or whatever. We’re going to change your communication into some now you’re going to be a transparent company. You’re going to be a company that runs in new ways. Like, yeah, you’re going to make those things. If you make the product just so appealing that people start using it, and your product encourages that behavior, you’re not going to make it that way because people are like, I’m adopting your product for a new cultural thing. Like, there might be some buyers who say that, but they’re not. That’s not going to be, you know, like, like, Zoom can’t go to people and be like, we’re going to make you vote for the first company. Like, adopt a remote first culture with Zoom. And like, yeah, you’re right. This is a good tool for making us remote first. It’s like, No, you’re gonna make a good product. And then people will be like, actually, we can kind of be remote because this product is so good, then you can encourage it. But like, I think there’s a lot of data companies. That you send your points about, like, what are data teams doing stuff like that? Like, look at it like, we need to also encourage cultural change. And it’s just like, you’re selling them two really hard things, and not just one. Yeah,

John Wessel 31:34
sure, yeah, yeah. Speaking of data teams on the like, Data Function side, I’m curious, like, how you’ve maybe seen over the last like 10 or so years that evolve, because we talked before the show data teams haven’t always existed, right? And then, and then in the last 10 years, I think there’s been some evolution. But curious how you have seen that evolve, I guess, basically with mode customers, or just in general, in the ecosystem, it’s so

Benn Stancil 31:58
weird. So, when we first started selling mode, we liked the first pitches to it to VCs. We were like, look, we’re building this thing for data folks. They write SQL. This is what they’re going to do. And we got a ton of responses that were just like, What are you talking about? Who are these people? These aren’t people that are like, we have to prove the market for SQL. We have to, like, explain what these analysts are like, are they data scientists? Like, we know data scientists are a sexy job. Are you building for that? Like, no, not really. They’re like, Well, why aren’t you building for that? And like that has completely gone away. The idea of semi technical analysts who write SQL and are, like, trying to answer questions, and it’s not just building dashboards, but it’s not like, proper data science is a pretty standard thing now, in a way that very much was not when we were first getting started, obviously, like a lot of stuff with, like, you know, transition to cloud and stuff like that, was also early back when we started, where a lot of people like about Cloud Data Tools, and now that’s snowflake hacks notwithstanding, something that most people are pretty comfortable. The thing that I think is, like, in some way, I can’t decide, actually, if that’s a good or bad thing that’s happened. Like, in some ways it’s like, great. This is amazing. We have those people who are going to think about these problems and be analytical and business are going to be way better. But like, we haven’t done that really. Like, are we off making better decisions? Not really. Like, we still are in the same still talk about things in the same way. Of, like, how do we get out of making dashboards and get onto the more impactful work. Like, this has been the line forever, and I don’t know, I struggle sometimes with, like, is there actually more impactful work there? Like, we’ve had a lot of opportunities to do it, we’ve built a lot of tools to try to make it more possible, and yet we’re not that good at it. And so I don’t know. I think it’s like, there’s been a lot of evolution around the belief that you give smart people big data sets that they will go and find smart decisions, that has become, like a faith that a lot of people have adopted. But I don’t know if it’s the thing that, like, the faith has delivered the goods yet.

Eric Dodds 33:56
Why do you think people believe that? Like, where does that core faith come from, because it is a non-trivial like it is a very widespread belief. I mean, literally, entire companies that make a lot of money are built on that.

Benn Stancil 34:12
I think I have two answers. I think one is Nate Silver, like basically that. There’s a handful of very popular things that have happened, where people are like, look at the data solved this problem, be it Nate Silver, be it Moneyball, be it Sure, a few things that are like, high profile, you know, the warriors start, or Steph Curry started. A lot of trees. People were like warriors. Really analytical, all this stuff like, you can point to a handful of examples where, yeah, analysis in some form or another seem to be very successful,

Eric Dodds 34:46
investing Renaissance, and the whole, you know, yeah, computers doing this and all that, yeah, yeah,

Benn Stancil 34:50
The Renaissance is like, I mean, well, that’s a decent example in some way. So, the reason I think it’s a decent example is, like, the second thing I think that happened was, it was tech companies. It was. A lot of likes were Facebook, Google, Netflix, whatever. And I think the thing with that, which is also true for something like Renaissance, is they’re solving problems that are, like, just fundamentally well suited for that, yeah, yeah, this enormous optimization around like, yeah, Google can make enormous amounts of money by optimizing ads on search results, because there’s a trillion of them a day. Or how I use it. I don’t know, yes, there’s leverage there. Renaissance can make a ton of money because there is leverage in making a billion trades. And financial markets that are automated based on, like, small little market signals, yep, basketball and Moneyball and, like, predicting an election, it’s like, it was yard signs, and now it’s like, let’s be a little bit more scientific, and there’s something very useful there. But that doesn’t mean that, like, the same method works for sure. We’re a 100 person business trying to figure out who we sell. Like, it’s just the data is fundamentally not even not that valuable. And so I think a lot of people who saw the early successes in data were people who had data that had high leverage, like, kind of obviously, like, that’s why they were successful. They had high leverage stuff. And so then there was somewhat, like, of a kind of cargo culting around, ah, the data must have been the thing that, like, the analysis must have been a thing. Have data. It’s our most valuable asset too. And it’s like, it’s probably

Eric Dodds 36:16
not, yeah, yeah. That is super interesting. Yeah. I

John Wessel 36:20
think another interesting trend here that I’ve seen is you had, like IT teams, for example, right? That had to developers say you had a product, or say, say you didn’t even have a product. You’re a company, and you have to integrate with other companies. You don’t even have like a core, like SaaS product. You’re just doing integration. So you have a small IT team, and then from there, like, I think before data was a separate team, it would be a business analyst or business user that goes to the IT team, like, hey, I need some data. And like, what do you need? And then, like, you know, they pull some data down and give it to you in a spreadsheet. And like, okay, so, and at least companies I’ve worked for, like, the data team started out of like it was done messing with it. Like, they’re like, this is a waste of our time. Like, let’s stand up. Let’s, like, hire a person, or, like, stand up this data team. So we don’t have to mess with this because it’s a waste of our time. We’re focused on our critical client integrations, or our, you know, our SaaS product or whatever. So I don’t know that’s true everywhere, but I think for a lot of like industry, business, like, in my like, supply chain, like, history, that was true. And, I mean, that’s not a, that’s not a great start to a team, right? Like, as far as, like, yeah, like, I don’t know, like, we don’t really want to do that. Like, you guys do that. And then you can end up with some awkward, like, they don’t have quite enough business context to be super useful. They’re not as technical as, like this, like, formal IT team. And you can end up with some data teams on a really kind of weird spot, yeah. So I don’t know if that’s an industry trend or just kind of my experience, but

Benn Stancil 37:49
I mean, there’s a guy who reached on product at mode is a guy named non he now runs product at Lenny here the like Asana competitor, yeah. And he gave a talk at the figma conference a few months ago. He gave a talk about it. And the kind of core point of the talk was, one of the ways that product teams really mess up is they have this kind of notion, and it’s like, largely drawn from Spotify, like squads and tribes, whatever. But they have this kind of notion that, like, Okay, we have a new area of the product, we’re going to assign in a PM, two designers and six engineers, and this, like squad of people is going to go off and build the admin functionality, and this other team is going to go off and build our mobile app, and this other team is going to go off and build whatever other thing. And his point is, like, you end up creating a lot of symmetry because it looks nice and because it sort of feels organizationally tidy, right? But in reality, like, that’s really bad, because probably the admin page is not as important as a lot of those other things. But once you have a team that’s assigned to it, that’s their job. That’s the thing they’re excited about, they’re going to go figure out ways to, like, push it as far as they can. Because what else would they do? Yeah, and I think, like, you could kind of apply the same thing for some data teams, where people like your point about the IT thing. I was like, this is a pain tire data team to go get rid of. Make it not a pain for us. Right now, you have these people that are like, we’re going to push this as far as we can. We want

Eric Dodds 39:11
to be great. Sure, it’s how they interpret their own success, right? Like, it’s the lens through which they can actually, it’s their barometer. If

Benn Stancil 39:19
you were on that team, that is the sensible thing to do. Like, sure, push it as far as you can, but it doesn’t mean that, like, you’re pushing something terribly important. And so that doesn’t mean, like, data stuff is important, but I think it is very important for some like, what’s the business? How’s it performing? Kind of operating is, like, some degree of senses, but I think there’s a notion of like, yes, there’s hidden gold everywhere. There are needles in these haystacks. And it’s like, yeah, it is

Eric Dodds 39:48
interesting. I mean, I kind of think about data tooling in general, and we have to get to the Excel question. But for sure, you know, I work on cars sometimes, and I. You know, there’s this sort of, like, if you have the right tool, it can make certain things so much easier, you know, and so, but then you also see this huge over indexing on, like, the tools, right? And it’s like, well, actually, like, if you’re good at it, you can just use a tool that’s pretty good, and, like, you just do the work and it’s fine. And so to some extent, like data tooling in the data industry, like, sometimes it seems like it can over index on the tool, and it’s like, this is actually just a means to an end, right? It needs to be good enough to, like, to your point, Ben, like, help the business, you know, achieve, like, X, Y or Z,

Benn Stancil 40:37
yeah, yeah. The tooling stuff I get it’s having built one, you know? And there’s, like, people who live in them, you want to make them good, sure, right? And I yeah, there’s, I think it’s fine, but it doesn’t mean that it’s producing something terribly useful, right? Yeah, again, maybe that’s okay. Having a comfortable chair at the office does not necessarily make me way more productive, but it is nice. Yeah. So,

Eric Dodds 41:05
yeah, yeah. All right, the Excel question, John, yeah, I’ve been waiting this whole time. I’ve

John Wessel 41:11
been waiting this whole time. Yeah, so we’re talking before the show about a little bit about Excel spin around. I don’t know what the Excel launch date is. Like, roughly, like, how long has it been around? That’s

Eric Dodds 41:23
a good question.

John Wessel 41:24
Get somebody to look that up. Anyways. So it’s been around a long time. We’re talking about, will it be around in 50 years? And we also talked about data teams, like, there’s at some point where almost no one, or no one had a data team, right? And then at some point that happened. And then we were talking about AI, like, does that impact data teams going forward? Do they look different? Do they, you know, are they more decentralized, kind of embedded into the business? Are they more centralized? Yeah, so that’s kind of a starting place. But yeah, Ben, just curious, your high level thoughts on Excel first, and then we can dive into some of the other stuff. Yeah. I

Benn Stancil 41:57
I mean, I think that the Excel stuff looks like it was released in 1985 okay, yeah. Okay, wow. I like it. Question about the Excel take is, yeah, it’s been around, I guess, for 40 years. What if we imagine a world in 50 years where it’s not the thing that we all use? What does that mean? What does that look like? How do we actually get away from it? I think that the parties I ask are, like, there’s a lot of companies that attempt to kind of replace it. Like, there’s a lot of companies that they’re like, their market is Excel users. Like, look, Excel people, they’re doing all this stuff wrong. Don’t want to do this. And I think it kind of misses, like, there’s a lot of anti patterns in Excel that are actually the reasons that people use it. And I think there’s a lot of effort in like sales companies to solve the things that are wrong with Excel, that sort of miss that. That’s the reason Excel is good.

Eric Dodds 42:44
What’s one example of that? Like one anti pattern versioning,

Benn Stancil 42:49
that there are tools that are like, no, we want it to be connected to live data. You don’t want to have to send around all these versions of things. And it’s like, you ever sat in like, a sales forecasting call you ever talk to, like, you want, like, the person who’s sitting there and, like, looking at their spreadsheet of all the current deals in the pipeline for that thing that you like, randomly update all the time. Do you have any idea how upset they would get? Yeah, like, like, the thing that I think is useful about Excel is, one of the things, anyway, is when I send you an Excel file. I have sent you everything. I have sent you a standalone product. It is the data and the view of the data on top. It isn’t just a prism that is looking at something that could change. It is like the database plus the BI tool all in one

Eric Dodds 43:36
and the logic, yeah, the logic is completely transparent. There’s something

Benn Stancil 43:40
really that is, it’s not going to change. I can futz with it, and I’m not going to break anything that it’s like, this nice little package that all exists in one in one thing. And again, if I have, if I’m a salesperson, I’m doing a pipeline thing, I can be like, Yeah, I want this thing so I have a file that I know is not going to change. And like, you could say, Okay, well, what about Google Sheets? That’s, I think, half true, because, like, people don’t really use the Google like, the Google Sheets connected to a database, is not the thing that people want to use it for. For sure, they use it as like, oh, okay, it’s nice that if we’re collaborating on this thing together, yeah, I don’t have to worry about, like, merging updates. There’s not, like, a track change merge that has to happen. But it’s not so much like, oh, the input data is just randomly changing edges that you have to, like, very manually say, Okay, I’m going to put something new into this thing. And so I know there’s just, like, a really nice package. It’s a nice thing to have that the versioning is kind of the point. And so a lot of tools are like, No, we got to get rid of that. We got to be a SaaS product. We got to be all this stuff that’s more modern. It’s like, I don’t know, a desktop app with files is sometimes pretty good. I

John Wessel 44:48
I think another anti pattern that’s even more fundamental is that Excel will primarily use it to work with data sets. Each cell is addressable, right? I can free form 10. Type into each individual cell. And I see a ton of Excel documents where it’s like, it’s not a data set, like, it’s like, I type this thing here, I type this thing here. I wrote a note under that. I like NoSQL. You know, it’s unstructured, sometimes structured as well, but it has, it’s addressable down to the cell level. It’s not just a data set. Yeah, there’s a lot of business users that use excel in a non data set way, whether it’s just maybe there’s one tab where they’ve kind of, like, typed in some things and the rest of its data sets, but I think that’s a really common pattern that you don’t really see. And like FBI tools, yeah, and you

Benn Stancil 45:35
can, like, touch it. It’s a thing where it’s like, you can get your hands around. You can understand what you’re doing. There is a, I think people like, this is one of the reasons I think DBT is pretty good, is it’s data pipelines that is, like, table by table, and for like, okay, that has problems, and it can create a lot of mess and be expensive or whatever, if you do it badly. But there is a nice thing where you’re basically, like, able to see each step of the pipeline. And just like, I can run it, I can see it. Did it work? Okay? I can go to the next one, I can see it. And Excel is a version of that where, like, I can go in and I can futz with things, and I can play with it. I can understand it. And, like, BI tools and anything that just sort of sits on top of the database, puts this kind of unknown abstraction. Sure, like, I don’t really know what I’m manipulating. Like, if you work with an OLAP cube, for instance, like, an OLAP cube is just, it’s hard to get your head around what you’re doing. And I, like, Excel sort of solves that. Like, yeah, you can create it, and you can create a pivot table, and you, like, you can sort of hand write it the way that it did a thing. Whereas, if you’re looking at something, you’re like, I don’t really know, am I looking at this, right? I don’t really know. Like there’s just this, this, like, lack of transparency around it that I think Excel makes really nice. And to your point, like, it seems broken because it’s not structured. It’s not like, governed in the way that things should be governed. It’s not Persian control. Like, yeah, that’s what makes it nice. Yeah.

Eric Dodds 46:54
If Excel, you know, was sort of dethroned, what would you how would you even think about what would replace it in 50 years, for what would

John Wessel 47:05
have happened? Right? Like, yeah, like, this cosmic data event happened.

Benn Stancil 47:12
Yeah? Like, my, I haven’t ever quite put this idea together, but I would my version of it’s basically gets replaced with a different version of Excel that, like, uses the internet more smartly, that it’s again, a lot of tools that are spreadsheet based, BI tools that sort of claim to be the next Excel don’t let you. Don’t let them sort of do this easy versioning stuff. They don’t let you, like, share files around really easily. They don’t let me, like, double click on a CSV and open it. There’s this, like, Oh, it’s this heavy thing. In some ways, I think, like a tool that would sort of be interesting to me is one that basically, like, has this kind of hybrid model, where it actually, you just download a thing on your computer, is a piece of software. You double click on a CSV, you open it up. But the So, the way that Mother Duck works, which is like Duckdb. They basically build, like a hosted sort of version of duckdb. Yeah, they do this kind of hybrid thing where you, like, you can run it locally. Because of duckdb work, you can run it locally, but, you know, those are just, like, push things up to either resources on the cloud, or there’s a way to sort of sync. So I have my version of the Mother Duck database. Basically you have your version. We can kind of say, like, actually, let’s read off of this centralized one. But we can also pull down our local versions and futz with things there and stuff like that. It seems like there’s a version of that for Excel, yeah. Basically the aim is like, make it really fast. Make it so that it solves the scale problem. Make it so that you can manipulate across, like, really big things without giving up the like, close to the metal, I can play with this thing and just open files on my desktop, right? Yeah,

Eric Dodds 48:50
yeah. I thought, yeah, when you were describing that, when you were describing that experience, I actually thought of duck TV, because it has a lot of the same characteristics. One last question, because we’re at the buzzer, and Brooks is telling us in the Slack channel that it’s time to land the plane. So you know, you’re currently unencumbered, but if you were going to go solve, you know, another problem in the data space, what, or even, just like, build another tool, what would you like? What problem would you

Benn Stancil 49:23
Try to solve it? I don’t exactly know what sort of a tool like this is. The tool around this would look like my general belief right now about AI and sort of hate this as an AI answer, but whatever, it wouldn’t be. It

Eric Dodds 49:38
wouldn’t be a data set. Yeah, it’d

Benn Stancil 49:39
be 2024, if it was, yeah, my general belief around what AI does is, there’s a lot of people who basically like, Okay, let’s take AI and like, let’s make a better BI tool with it, where you ask it questions and automatically answer whatever. Okay, yeah. I think, like, the more interesting thing is, like, if we think about what, like, LLM specifically are good at, they can read really fast, and they can tell you what they read, and they’re good at. Summarizing that, there’s a lot of interesting information and signals in unstructured stuff, that there’s a lot of information and customer interviews or just like videos of the world and things like that. And that information is probably much richer and more useful, honestly, than like, the data that we collect that if you want to say, like, how do we make an intersection have better traffic patterns? I don’t know anything about traffic. There’s probably a lot of urban planning people that would disagree with all of this, but whatever, in my head, it makes sense if you want to make an intersection with, like, better traffic patterns. One way you could look at that is like, you could look at the way you could look at that is, like, you could look at the data. You could, like, instrument the intersection and measure where cars are, and you have, like, some structured, giant file of things and do a bunch of analysis on it. Okay, it’s kind of hard. Or in theory, you could have someone sit there and watch the intersection for a year and just watch everything. And if they remembered everything really well, they’d probably be like, You know what? I noticed some of these things. Maybe we should change that. And, like, actually, that second one is probably more useful in terms of figuring out something that might work, yeah, and handing this data to somebody and saying, like, go spelunk through a giant data set and try to find the insight. Like, right? If you just want stuff. If your job is to improve an intersection, I bet if you just watch it for 24 hours and you had this amazing memory, you’d probably come up with a lot of ideas like, hey, maybe we should try this. I noticed this thing that always happens. I notice whatever that I think is and like, that is what an LLM does. An LLM reads stuff. It remembers it like, is able to have all of it in memory and then sort of find these little patterns. I think it’s actually like enabling that isn’t taking the video and structuring it into a data set. It’s just like bypassing that entirely. It’s doing what, yeah, a user researcher with perfect recall would do, which is, they’re not like, trying to map everything to numbers. They’re just saying, like, look, I watched, I sat and watched people use this product, yep, for 1000 hours, and like, I saw some crazy things. You should know about those crazy things that, to me, is actually where it’s what data is trying to get at, but in a very indirect and hard way. And it’s like the reason we don’t do the other one is because it’s too hard to do anything with it’s too hard to take 1000 hours of video of customer interviews and, like, actually make any sense of it, because you have to watch it, and you have to have one person think about it and aggregate it, and they can’t do it. But if you didn’t have that problem, give me the interviews and so I think, like, there’s something in that to me, or like, what do you do if you’re trying to say, actually, we think most of the valuable information is in this unstructured stuff. And the tooling we should have is, like, make it so that, yeah, I can just watch things or interview people, or look at text and, like, find what’s interesting, do the analysis on the unstructured stuff, and not by structuring it, but by like, the way, someone they just observed. Yep.

Eric Dodds 52:59
Fascinating. Ben, this has been so fun how the time flew by, but thanks for giving us some of your time. Thanks for letting us encumber you momentarily, for sure. Thanks

53:10
for having me up.

John Wessel 53:11
Yeah, it’s just fun. The

Eric Dodds 53:13
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