Collaborative Data Analytics on the Data Warehouse, featuring Rob Woollen & Stipo Josipovic of Sigma

Eric Dodds 00:03
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, I, you know, we’ve talked with a lot of database companies on the show. But I love talking with analytics companies, just because it seems like bulk databases in analytics tools are so pervasive, and seem to actually be proliferating at a pretty high rate. And so today, we’re going to talk with Sigma. And they have a fascinating approach to analytics, sort of a spreadsheet and SQL focus. So we’re gonna talk with Stipo and Rob, Stipo from the product side. Rob is CTO and co-founder. And I have so many questions. I mean, number one, spreadsheet and SQL, you know, the two fundamental tools for analytics, like why build a tool that is a spreadsheet and SQL, you know, I mean, that’s gonna be fascinating to hear. But they also have an interesting story, they sort of came of age, right on the heels of Snowflake, sort of out of the same group. I think that’ll be really interesting to ask about. So yeah, that’s, I think those are the things that I’m going to ask about. How about you?

Kostas Pardalis 01:33
Yeah, I definitely have many questions about how to breed the spreadsheets and the relational proteins together. And I think that’s like part of like, the successful sigma, that the models like, somehow like to do that. So one thing is to understand how they did it, what is left out there, because I’m sure that there is still work to be done. These are two, you know, like, two of the foundations of working with data that usually don’t overlap. Each one has its own pros and cons. There’s a reason that we have both and we don’t have only one, right. And I’d love to hear like, both the product and the technology story behind it. Because there’s like, very different challenges there. Right. And I think we have the right people to do that. It’s one of those rare occasions where we have both product and engineering presentations at the same time. So we can ask the same question from a different angle and see, like, the differences in perspective. So we’re definitely going to spend a lot of time in labs.

Eric Dodds 02:51
All right, well, let’s dig in. Let’s loot SIBO. Rob, welcome to The Data Stack Show. We have a million questions. So I’m very excited to get to it. But thanks for giving us some of your time. Thank you.

Stipo Josipovic 03:03
Happy to be here.

Eric Dodds 03:04
All right. Steve, I’ll start with you. Give us your background, tell us how you got into data and then ended up at sigma.

Stipo Josipovic 03:12
There. So I guess my career has been a little bit of in, you know, at the time felt a little bit like jumping through Goldilocks stepping stones of random jobs first, starting with working in public policy, doing data analysis, to which I had a strong reaction jumping, where, you know, if you’ve ever worked in government, you will know how much technology is involved. And for me, it was not enough. So I made a pretty hard pivot to the next Goldilock bowl. And I jumped into full stack engineering where I, where I worked on, somehow ended up inadvertently working in building a, a internal analytics app for the company I was working for. So inadvertently to a NBI without knowing what a and b, a and bi were after that, you know, I think my next journey was my next step. And my realization was that where I really wanted to be is not just in the processing data. And it wasn’t just really sort of building the tools and the infrastructure to work with data. But it was in decision making itself. And so that’s when I made a cut over to actual bi Management here in San Francisco with a company called pager duty. So as a practitioner, then a little bit into my journey, a tiny little startup decided to or decided to reach out to me to ask a little bit to ask what I thought about their product. And this company was sigma. And I was the second person outside of the company to see the product. And from that first time that I saw it, they might have oversold. And so clearly, I joined. I joined as a sales engineer and transitioned over to the first product manager and I’ve been doing this for about five years now.

Eric Dodds 04:59
Awesome. Okay, Rob, I have to know, Were you part of the group that got early feedback from CBOE and, and pager duty?

Rob Woollen 05:08
No comment.

Eric Dodds 05:12
We use the word oversold. So I had to ask him about that. But Rob, give us your background.

Rob Woollen 05:19
Absolutely. So let’s see, I’ve spent, gosh, more than 25 years in enterprise software. And I’ve always built things that in some ways, they’re not finished products or things that other people take, build on top of, or do something, build products with. So I actually started my career working in the Unix kernel. I thought that was like the coolest job I was, you know, the technology nerd and loved Unix, but in the end it really wasn’t actually that interesting, because it was always a lot of just like, People just expect kernels to work. It was not like a lot of the features they wanted. And so I remember in like the mid 90s, I was like looking at a bus signal. And trying to debug this, you know, crash. And all my friends were working on like these cool new.com startups, and I was like, I want to go do something that’s, that sounds a lot more fun like that. So I moved up to San Francisco and joined this company, B EA, that was building a product WebLogic. And the whole idea was really trying to build kind of an operating system for the web, and really sort of give developers a platform and Java at the time to build out. And I’m spending nine years there building that out taking them out to a pretty successful business, we were, you know, multibillion dollar business. And in about 2007, I got really excited about this idea of cloud computing, I remember talking to some friends at Salesforce. And even just the idea that, you know, I left a company where I think we had 40 versions of my own, you know, stack that was maintained, and they were like, we have only one version. And that was bad enough, I was like, Well, I’m moving a few blocks, I’m going to work there. So I spent seven years at Salesforce building out a cloud platform for them. I spent a lot of time trying to think about, again, what would you need to build out a product on top of the cloud stack. In Salesforce, this case, it was both, you know, their internal products like their CRM, as well as things that third parties would try to build. And I left Salesforce then in about the beginning of 2014, and joined a venture capital firm, Sutter Hill, and a join there because of a few things. One is, I was crazy enough to want to go start a company. But the second is, I really liked that. What makes Sutter Hill different was that instead of just like a normal VC firm trying to invest in the kind of, you know, interesting pitches they see, they actually tried to start companies from scratch. So I watched them do that very successfully. And I went and made that leap. And as part of that, they helped us find a sigma. And so we founded the company in 2014. And I’ve been our CTO, and obviously co founder ever since.

Eric Dodds 08:04
Very cool. And could you just give us the quick, what is sigma? Tell us what sigma is?

Rob Woollen 08:11
Absolutely. So the way I think about it is we started from this idea that people were going to move to cloud data warehouses. So we were early on seeing this transition to things like Snowflake, or Google’s BigQuery, or Amazon Redshift. And we thought about those environments and said, What is the interface that you’d want to build on top of one of these things? And in particular, like if you wanted to say, hey, I want everyone in my company to leverage this massively scalable repository of data, what is the interface you’d want? And so what we built is something that lets multiple people work together, collaborate, build out, very complex analysis. And the interface we chose to sort of base our product on is the idea of a spreadsheet. And so what you do in sigma is you can build these collaborative workbooks, where, say steeple, and I could build a bunch of analysis together, we can interact, live and share things back and forth. And we can write formulas, we can, you know, just like you would in any of these sort of traditional spreadsheets. But the magic of it is that everything you do in the product actually gets translated to SQL executed against that scalable data warehouse. So you’re combining all sorts of benefits of a traditional kind of centralized-like technical data warehouse with something that’s actually broadly accessible, and very powerful. So that was, that was the original kind of, you know, IDM pitch and what we built out, love it. Well,

Eric Dodds 09:37
I want to return to the founding. But before we do that, I want to go back to part of your story. So you built sort of homegrown analytics products inside of a company, as you know, sort of one of your first forays into full stack engineering. And you know, I’ve never done that personally, but knowing some people who’ve worked on that stuff you No, and knowing that there are companies like sigma who, you know, literally build, like big companies around this. Did you have any takeaways from that? You know, is there ever a point at which you said, like, maybe we should have bought something instead of trying to build this homegrown, maybe I have the use case wrong, that would just that seems to be sort of a formative experience. And so I’d love to hear about the lessons that you have taken away from that and sort of influenced the way that you think about building analytics products.

Stipo Josipovic 10:27
I love this question so much. The, I’ve never, I never regretted that decision. And even still, you know, knowing where the market is, I still I, you know, if I could go back in time with the same market, I barring sigma, you know, I would make the same decision again. The reality is that the way that people work with data right now, and with traditional BI, is so limited, and the behaviors and what we needed at the time, when at the company that I was at was just, there was no tool that could easily support it. And in reality, it wasn’t just choosing a tool, it was choosing an entire sort of infrastructure for how I needed to prepare my data to be able to work with it. So there’s these like layers and layers. It wasn’t just the top level of how users interact with this. And I, at the time, as an engineer, I was just trying to understand how many of these layers interact? What are these models that I have to think through? And if I’m having a hard time understanding how to work with this data, and I’m supposed to be the technical person doing this work? How the heck is anyone else supposed to work with this? And so, you know, I think it’s influenced a lot in how I think about the end state of where we need to go. But I also feel that all of my time working with traditional BI tools is just as much influenced not to love it.

Eric Dodds 11:52
Rob, let’s go back to you and give us a little bit of the founding story, but Snowflake was, you know, part of, you know, sort of one of the founding stories out of Sutter Hill, and was in pretty close timeline wise to sigma, right. I mean, you’re talking maybe a couple of years there. What I would love for you to dig into just a little bit relative to the founding story is an interface on top of the data store is a very, that’s not a new concept, right? And even when you started sigma that was, in the enterprise, at least sort of a prevailing paradigm of how you did you know, business intelligence, right? It’s like, okay, well, you have a data store, and we have some sort of layer on top of that, you know, whatever. What about the existing paradigm that, you know, from a basic architectural standpoint, Datastore interface? What about that wasn’t working? And like, what made you I mean, really, in many ways, it kind of sounds like you’re saying, like, the data store is completely different. And so we need to think about the interface completely differently. Is that a good summary of the way that you were thinking about it,

Rob Woollen 13:02
you ended there, and then stole the answer. But I will give you, I’ll give you a bit more color, as you’d imagine. So you’re right, we were, in fact, the Snowflake team had just left Sutter Hill, maybe six months to a year before I joined. My co-founder, Jason, was actually advising Snowflake during that first year, helping them build out their optimizer. And I remember, I was at Sutter Hill, when Snowflake came in, I think for their series, B, they did their pitch to sort of, you know, follow up with their investors and show off the product. And I vividly remember the presentation, because it was, they were showing off at the time how you could elastically scale, you could drag a slider and have it, you know, have more CPUs or less CPUs, and obviously, the cordage runs faster or slower. And from, you know, honestly, from the podcast, right now, people are gonna be like, yeah, that just sounds like what you do, right? But if you rewind your mind back to, like, 2014, like, what people used to have to do, and sometimes I, you know, feel like the old person, or I have to tell people like, back in my day, back in my day, you had to go like rack servers, right? So you know, if you wanted eight CPUs, you were like, dragging stuff around and putting cables and or most likely requisitioning someone from it to go do it. So as a, like, from a technology perspective, right, I was sitting there like this was mind blowing. But if you watch them, when I watched them demo, they literally had like, you know, pages and pages of these, like 700 lines, and the SQL varies. And so that was where I think a lot of the status quo has been in this environment, like the interface that people have traditionally built on top of warehouses is designed for a very small number of people. And it’s designed for a very small number of people on purpose. If you had a traditional that traditional warehouse where it was very painful to fix to expand it, then you’d be very protective of it, you would say, like, I do not want a lot of people running queries on this, the story I’ll tell you is even, even when I was at Salesforce, back in the 2000s, we had a, you’d imagine we had an on prem data warehouse, and at the end of the quarter, they would have to send it out things saying, please stop refreshing your reports, you know, the grass is getting overwhelmed. That was just the reality of how these systems work. When you step back and say, I have something where I can actually scale it out, I can partition resources, I can make all these decisions about how we want to manage the workloads. That’s when I think he really started thinking about how I want to redesign the interface. And that was the fundamental sort of technology that inspired us to start, you know, that whole journey from over again. We were, of course, fortunate that, you know, we were there to see these were at the same VC firm, we saw the early work, we knew the founding team there. So you know, we had sort of an early bird’s eye view into that change?

Eric Dodds 16:00
Sure. Well, I mean, that’s the wonderful thing about silicon, silicon valley, right, is that, you know, technologies can sort of eMERGE together. So Rob, that’s super interesting, you know, at the time, you know, 2014, so almost a decade ago, some people might have said, like, well, sort of the BI problem is solved, right? You can get the insights that you need. You can run queries on top of a data store, and you can visualize it great. You know, fast forward 10 years, maybe it’s even more of a salt problem now. But the market would actually suggest differently, right? I mean, in many ways, there are new analytics paradigms and technologies coming out that would, in some ways, suggest like, well, maybe we’re sort of in early innings in terms of what’s actually possible with analytics. So I guess this is a question for both of us. SIBO. Maybe I’ll start with you. Are both of those things true? Like, is it over? Are we in the early innings of analytics? Like, give us your perspective?

Stipo Josipovic 17:05
I guess I’d start with by saying that, the BI of the oldest dead? And, you know, I think there’s a new era, we’re in the early innings of a new era. And what I mean by this is, certainly we are, you know, I think these the BI tools of old did provide more access to the centralized data stores than all the power that’s there, and then the size of the data that’s there, right. I mean, crucially, that’s sort of the most important piece of what they offer, which is at the high trade offs that they require, which is users can’t work with data the way they’re taught to work with data, the way they think about data. Instead, they have to be limited in the ways that these tools provide. So I think, you know, that model, I think, is what’s really being challenged. And certainly, it’s what we’re that we are challenging. The core of this, and four is really about going back to the basics and the fundamentals, rather than giving tools to people and telling them to learn the tools to answer the questions that they have the new era of the eyes to give people the tools that meet them where they are, right to remove the friction, because being able to answer the question is almost the least interesting thing that they can do. But the most interesting thing is asking the question, and being able to get to the answer. And so and when it comes to and being able to do that, with massive amounts of data, spreadsheets are able to do that, and are able to do that on small amounts of data. So, you know, the, there are many things that we’re doing as a product right now, that’s trying to do this, for example, what we’ve just released, which is input tables, as and it’s almost unremarkable in in how I’m going to describe it, because it’s also seems just so absolutely obvious. And yet, it’s something that BI has not been in a position to be able to provide and technology hasn’t able to provide an input tables are very simply UI, manage tables, where users can add their own data, integrate it into their analysis, join it, and then modify that data, iterate on it, and it opens up a massive world of use cases that historically bi wouldn’t be dissolve. And now, these use cases that were once either in spreadsheets, offline, decentralized all the bad things about, you know, about working with data in modern times, or they would happen in centralized specialized tools that, again, even fewer people are able to work with and use. And so now there’s this middle ground, right, and that, that is the platform that we are building, which is it gives you the flexibility to work with that data and reestablishes that relationship that we as humans natively have with data, which is we want to be able to manipulate it. No one wants to have to think about it. I want to ask what if and so let me figure out a feature and take 20 steps to be able to do that. Instead, what they want to do is engage that data and modify it. And just by nature of doing that You can forecast, right? You can analyze data that doesn’t exist, because you know, the centralized data stores are not all the data that exists, right? There’s so much human context with everything that happens. And really, so when and then on top of that, once you capture that context, there’s the question of what now, right? At a certain point, when you’re able to capture data through your platform, you can capture decisions. And which is incredible, because up until now, the right DPI of old was at the end of the pipe. You perform your analysis, you get your insights, and then you bugger off and try and you try and take action on that. And it all happens in this ephemeral kind of organizational gray area that’s never captured, it’s very difficult to follow through on it’s, there isn’t a common understanding. And now you have this data, now you’re capturing this exactly where you’re performing your analysis. And you can capture these decisions, you can learn from them, right, you know, what you did last quarter, you know, what was effective and what wasn’t. And so you have the sort of compounding effect of what analytic analytics and bi can provide in this new world. And then on top of that, if you’re ready to capture your decisions, why can’t you take action? Why can’t you, you know, capture your decision that you’re going to go to X, Y, and Z place to update X, Y and Z things? Why can’t you just do it on a single platform? Right. So when I think about going back to your question of, you know, is, you know, where are we in the state of BI, and certainly, the old BI is dead. But when we’re the future is very much about maximizing your data and being able to work with it the way that you sort of think about working with it?

Eric Dodds 21:42
Yeah. So this is just a quick, nerdy personal question on infant tables. But if I understand correctly, you essentially have this spreadsheet interface, but it’s, you know, it’s actually a table that lives in the warehouse. And you can sort of augment that, and you’re interacting with that data. And so will it actually, you know, sort of, if you, whatever, if I’m running a calculation or a, you know, something of that nature, will it actually push that back down into the warehouse?

Stipo Josipovic 22:18
It’s your data. And I think this is, you know, going back to, you know, the very beginning of my, you know, to your question about the decision to build a custom app, you know, one of the things I mentioned is just all the layers that you had to really buy into on top of the BI tool, or analytics tool itself. So, we sort of sit directly on top of your warehouse, and your data is your own, and we will, we will, so input tables will write to your warehouse, we are just a simple, clean layer on top that allow you to maximize the value of what you have. And we also this is a pivotal moment where we’re allowing you to deal with the challenges of having a centralized data store, which is very simply that everything that you need isn’t always there. So yes, that’s exactly the point. And so what this means is, you can add your data, it gets written to your warehouse, and you then also have it available to use it anywhere else you’d need as well. We’re not locking you down, we don’t have, like, we’re not going to ingest your data and hold on to it. It’s your data. And it’s there in the warehouse for youtube, able to work and join it against all the other live data that you have.

Eric Dodds 23:29
Yeah. Yeah. I mean, that’s incredible. Because I think that one of the, one of the amazing ironies of, you know, sort of doing analytics, even in modern forward thinking companies, you know, it’s like, okay, we’re running Snowflake, we’re centralizing our data and all this sort of stuff. Like, I mean, they name a company where, you know, maybe it’s sigma customers who don’t do this. But you know, where there’s someone in marketing, someone in, you know, revenue operations or something, they ask the analysts to produce a view and Snowflake and export it so that they can work on it in a spreadsheet, right? I mean, it’s pervasively common, right? That is unbelievably ironic, right? It’s like an anti-pattern, you know, relative to the promise of, like, centralizing your data and querying it, right? It’s like, wow, we’re we did that literally just to like, get it back out into a spreadsheet.

Rob Woollen 24:23
Yeah. There’s an old joke that, you know, BI tools, re buttons that were by far the most popular. Okay, cancel and download to excel.

Eric Dodds 24:34
Absolutely. Absolutely. Well, Rob, that actually leads me to another question. So the reason that that is one of the most popular buttons is because this spreadsheet is, you know, an interface many people say, you know, no one’s ever going to kill the spreadsheet. Maybe it gets reinvented, right. But I think to your point, it’s Evo. It’s how people ask questions. I mean, visualization, you know, that problem was solved a long time ago. But to your point, the problem is that in order to understand what you need to visualize, you have to actually work with the data and ask a bunch of questions. And then like, Okay, once I get there, I can visualize it. The SQL is similar to the spreadsheet and that, you know, many people say, we’re, you know, no one’s ever gonna kill the SQL. Can you talk about the thinking behind sort of combining those two, the two most common analytical interfaces into sort of this new interface that you’ve built for modern data stores? I mean, to me, that’s fascinating. And it’s brilliant. Because you’re placebo, you’re meeting people where they are. But can you talk about the way that you were those in mind when you thought about creating a new interface? Or was that a conclusion that you came to after, you know, doing research and talking with potential users?

Rob Woollen 25:55
I’ll admit to you that it was not our first idea. Several rounds of failing, like many startups of figuring out what was the right way to solve the problem? So we were always, we’ve always been trying to solve the same problem. We’ve always looked at it. If I have, you know, these incredible advances in cloud infrastructure, how would I build the right interface for it? I think there was actually a big moment in sort of, like, like any of these things, it was a big moment, it took several months to realize it was a big moment. But a lot of our first efforts, I actually think, followed a similar paradigm to what many people do with dashboards, which is, they build something that they think other people want. And they basically, at some level, even though we don’t always want to admit this, as technologists, we think like, I’m a little smarter than the person is gonna use this. And so I’m gonna give them a link to very simple things for them to do. But I’m gonna take care of, you know, the hard part. And the irony in all this is like, we all step back and say, look, like, I don’t really know a lot about whatever department is consuming this. But in our minds, we’re still like, No, you know, I need to be the one to tell them what they need to analyze as Person X. So I think that was one of the real sort of big realizations, when we went and looked at, like spreadsheet users and what they actually built, most of the time, I was like, Oh, my God, that is like, this crazily complex thing that I had no idea of, you know, I don’t know anything about what these people are talking about. And I look at it and from, you know, math or statistics or, you know, an analytics perspective, I can follow it. But like the domain knowledge, I don’t have, I would not have known to do this analysis. And so that was a big pivot in our interface design. Because we stepped back and said, I want to build an interface that you can build the equivalent of any SQL query without writing code. And if you think about that challenge, that’s very different than starting from saying like, Okay, I just want to make it so that, you know, filter, or, you know, change the drop down, you instead say, x build interface, that’s really powerful. And that’s what makes it so hard is because, honestly, it’s very easy to say, I’m gonna build an interface that’s, you know, as powerful as SQL. But as simple as a spreadsheet, it’s very hard to actually make that possible. It’s taken us many iterations, we actually redid our Korean interface, even as recently as 2021. It’s, again, something that you’re going to iterate on with customers, you’re going to learn so much. And it’s fundamentally a hard problem to solve. On the plus side, it’s the kind of problem I love solving, because it’s just exciting technology to work on. And you can sort of incrementally discover things every year and make it better.

Stipo Josipovic 28:37
One thing that I would add, on top of all of this is, no, we’re not necessarily choosing those two things are not necessarily at odds, because there are people who love writing SQL, and there are people who love spreadsheets, and operate through spreadsheets. And what’s really neat about our platform is the ability for both of those people to be able to work together and also build on top of each other’s work. And so I think that’s one of the most critical aspects of being able to have a platform for all, and it is that we’re not compromising, we’re empowering everyone. And the fact that I can write SQL and hand something off to you and you can then build on top of this, whatever analysis you want, that transition is incredibly powerful, because it brings, you know, there’s wisdom that both parties bring, certainly the people who are tend to be more business oriented to have the knowledge in the context of the data, and the people who are technically oriented have more knowledge of the data structures, etc. And for those to be able to collaborate and solve a problem. Also in real time, I think it’s just absolutely game changing. And I think that’s it’s, you know, more and more is going to become table stakes for what this industry needs to deliver.

Rob Woollen 29:46
together and not over JIRA tickets is just like that is a fundamental change.

Eric Dodds 29:51
I hear great rejoicing among the audience.

Kostas Pardalis 29:56
See, well, I have a question you mentioned a couple of times about, let’s say the BI of the past, or like how bi was done until today or a couple of years ago before, like, let’s say sigma started. Can you give us a little bit more context on that? Like, what does it mean to do bi? In the old way? Right? Like, how does it look? What’s the process like? And what’s the experience that the user has? Absolutely.

Stipo Josipovic 30:25
So the big thing of the past is a lot of process. It is, it is a lot of time, and a lot of heartache. And I say that in the sense that at a certain point, when you have a reasonable question, and you’re, if you’re a curious person who’s trying to do their best to just to do their work, to answer their question, to accomplish something, you would be stuck in this, at this fork in the road, where you make this for you’re dependent on other people, and everyone is dependent on their people, you’re dependent on a bottleneck, to be able to get to what you need. And so you have to make a decision of, do I wait for my data? Or do I just make a decision now, and the cost here is going to be two, three weeks of my time. So from the user perspective, and from the business perspective, what you end up with is a lot of sub optimal issues, you in the end, are in suboptimal results, really, where you there’s no great path for you to success, despite trying to do everything you can now from the king of the entire process side of it, what is actually this flow? It is you who have people who are specialized in these analytics and BI tools, and it is there. As for those being those specialized people, right, I know this as having been one of myself, you get your job is to take whatever business questions come at you transform into one ensure the right the data is there, ensure that the question being asked is worthwhile? And is the right question to be answered, in order to make it worthwhile to invest in getting the right data in the right format. And then it has to build out what that person needs, then is to go to them and validate that with them and validate that question is actually answered. And if it’s not, you then have to go back to the drawing board, because you’ve messed up somewhere, or rather, there was some sort of communication gap between the two parties, and you have to go rectify that, and make work through that process with the data. And now it could be that, and this is in, you know, I see this agnostically without calling out a particular technology, because this is just the standard, like way to interact. And way to engage, you know, you could, it could be that all of your data is just in the warehouse, it could be that it’s a tool that requires ingestion, it could be like that. You have different levers and different amounts of mechanisms to be able to interact with the data in the end. But the story is always the same. And it’s just small little optimizations on that. And so when I say bi of old, that is really what I sort of talked about, what I think about and you know, I think it’s, it’s where this really lands, you are in a world of uninteresting questions being asked and answered. Because it’s far too much work to be able to ask the interesting ones because you need to be able to iterate. And that you know, that data, those questions, those answers come when you’re working with the data directly. It’s not going to happen in three, you know, three weeks cycle times to get one tiny little bit of obvious kind of feedback back.

Kostas Pardalis 33:32
Yeah, that makes sense. And I have a question, actually, for both of you. But from a different angle. One angle is going to be the more for the product and the user experience. And the other is a little bit more technical for you, Rob. So we are talking here about spreadsheets, which is like a very, I would say that like a well defined way of working with data. It’s almost like a part of the move, like computing or like writing code in a way, right? And then we have the relational model, right, the one that the data warehouses out there are using like Snowflake, BigQuery, etc, etc. Now, these two, although let’s say they have the same expressivity in a way, they are not exactly equivalent, like they have their pros and cons, they do some things better. Some other things are harder to do. Yeah, you can do everything if you want at the end. But it’s not going to be as easy. And you mentioned earlier that one of the benefits of working with something like sigma is that yeah, we have spreadsheets, they’re like you can use this environment. But at the same time if you prefer to write SQL, you can write SQL like we want to take people and collaborate regardless of what tools they want to use, right? So how is this achieved? Because personally as someone who has tried to do similar things like to take yellow bars or these different users and put them together work on the same platforms? It’s a really hard problem, right? So let’s start with step one, the product’s side of things is the user experience, right? How can you deliver, I would say, a consistent experience at the end, regardless of the person there. And let’s say the language that they are using. And then I’ll come to Europe and ask more of like, say, what happens behind the scenes, right, how we can translate one thing to the other. So

Stipo Josipovic 35:35
One of the most interesting things to me about sigma is the model through which you interact with data. Now, what I mean by this, and I’ll also take us back to noting, you know, when I, my first, when sigma oversold, what really oversold me, was, when I saw what the product was at the time, and at the time, it was a table, it was a table with pivots. Now it is the smallest of really small little Lego of what our product offers. But it was just a table on which you could perform pivots and add formulas. Now, the reason that was so that that hooked me so quickly, and so easily, and to such an extent, was because this model isn’t what I’ll go back to the traditional BI tools offer, I think what you typically get in a BI tool is the concept of measures and dimensions, right? The measures of dimensions, it’s like it’s aggregates, it’s group bys. Right? It’s very easy to translate dimensions and measures to the way that databases work. And I think that’s what Sigma offers. And the interface in a paradigm shift is that instead of working through dimensions and measures, while being on the warehouse, you’re working with data, and then building on top of that, right, so in the same way, you open up a spreadsheet, someone sends you some data, you want to perform some calculations, you’ll open up that data, and then you just you build your formulas, your aggregations, if you need to, you can work and transform the data. You don’t think about, okay, like what are the combinations of columns that I want? It is you have your data, and then you have the playground to be able to interact with it any way or any way, shape or form that you want. Now, that is I think that’s such a, it’s almost a subtle or an implicit shift. And when someone sees our product, they understand and I think this is i Sir, I certainly did. But it is, it’s a remarkable shift in the way that you think about data at scale. And so to be able to relate to the spreadsheet users, that’s what makes sigma incredible. On the SQL side, I think the interesting piece is because you know, you’ve got SQL users who are very familiar for writing SQL, and you want those users to be able to work with the spreadsheet users think the beauty of what the product offers is a single common language, which is we translate that the spreadsheet to SQL, behind the scenes, we have, you know, an incredible, incredible team here that’s built a compiler that will take whatever you do through our UI, translate it into performance SQL. And then by virtue of having that common denominator, you can join these data sources together, you can build on top of each other, and you can build on top of and reference each other’s work. So the way you can it’s kind of hard to say in my opinion that, you know, to talk about this as almost as an intended thing to have been built. It’s almost like these worlds sort of naturally come together. And they overlap. And what comes together is sort of this beautiful synergy.

Kostas Pardalis 38:46
Yeah, it makes all sense. And, Rob, same question from a different angle to you. I remember. And I think everyone who has used both SQL and something like a spreadsheet knows that doing, for example, pivots on the spreadsheet is very easy, let’s say, right? doing that. In the database system, it’s not the most straightforward thing to do. And in a similar way, but opposites joining data, something very natural to do using a relational model. But joining data on the spreadsheet is not exactly straightforward, right? So I’m giving the two very obvious, like examples of like, the differences between the two, I would say paladins. How, from a technical perspective, do you breed these differences? And how do you create a model that can deliver, let’s say, the best from both worlds at the end? Right.

Rob Woollen 39:43
Right. I think the best of both worlds is a great way to think about it. If you step back and look at a spreadsheet. No one ever talks about a spreadsheet in these terms, but if you think about it, it is essentially functional reactive programming, right and there’s no spreadsheet you’re set Stan says, I’m ready to do some functional reactive programming. That’s fundamentally what you’re doing. Right? If you’re trying to explain to someone who’d never ever seen a spreadsheet before, you would say, there’s this amazing thing, you change this value here, and everything that depends on it updates. And that core idea, I think, really sort of is pervasive throughout a lot of how we think about the interface. And also the technology, like one of the fundamental things we built is, that’s true for, you know, columns and formulas. But it’s also true even as you combine different things together, and in a document, what we call a workbook. So you might build, you know, a table element, where you’re gonna do a bunch of transformations, you might build visualizations on top of that, you may have other table elements that you’re linked to. And fundamentally, you have this directed graph of here, all of the sources that come in and the different calculations, their dependencies. And the way I like to think about our technology is we’ve built essentially, even though we don’t mark it this way, the technology we’ve built is essentially a data warehouse. What’s different about it is instead of it being backed, normally you back a data warehouse by disk, we are backed by another data warehouse. And so that means we do everything from, you know, we do local execution, we actually do execution in your browser, if we have a subset of the data available. And we can do execution there. We’ll do execution there, we do optimization. So we have to do things like, hey, you’ve got, you know, multiple different visualizations and dependent things, we’re actually going to, you know, optimize that it only issues one query to cover all of these things. So we have a lot of the elements of sort of a traditional database or data warehouse, the back end of it happens to be that we’re going to as the last step, translate our representation now down to a SQL representation, instead of reading blocks off the disk, we’re essentially reading from a backing data warehouse. So that’s kind of from a technical perspective. And then to your point, there are different aspects of this, you think about in the interface, and you’re thinking about design in the product and how you want to expose things. So I’ll give you one example, because he raised it as a very traditional part of the relational model. When you go to an Excel user, you say the word joint, they may or may not even know what you’re talking about. When you say VLOOKUP, they light up and they’re like, yes, the lookup is by that’s my thing. And so we actually intentionally have both concepts in our interface, you can write a formula, which is a lookup, and is very close to a VLOOKUP. In Excel, it is, you know, hey, I want to join, I want to grab some information from somewhere else, and it doesn’t change cardinality. So it’s a very safe thing to do, you’re not going to change, you know, your results, or, you know, twist things around. Whereas you can also go into the interface and say, you know, instead of writing a formula, I want to explicitly do a left join, for example, right, and that is where, or, you know, sophisticated or technical or just data where a user is going to perform that operation, but we treat them separately explicitly, because they tend to be separate audiences or separate use cases. And so there’s cases like that, where we try to embrace both, and there’s cases where we very explicitly, you know, take a concept like this, you know, reactive, and apply it everywhere throughout our product.

Kostas Pardalis 43:19
That’s super interesting. And one of the innovations, let’s say that they had that maybe, like, also quite popular, was something similar to what you say about having, let’s say, more like technical persons that can go and write like a SQL in their case, they had LookML, right. So it was like this language there. That allows you to do some kind of modeling, let’s say that then can be used and like consumed by the business user and work in like, they’re more abstracted environment. And it was super successful, right? Like these, creating these like clear boundaries between, let’s say, the engineering persona and the business persona. It seems that it is important. How is this done? In sigma? Right? We have, let’s say, a very business driven by rodding, which is the spreadsheet. And we can also write SQLs but how do you help the engineer, let’s say, to do the modeling to do all the things that they have to do for their job before the business user starts consuming? The data?

Rob Woollen 44:30
Right? I think about the world and really sort of two big camps of work I have to do. I have the things that are centralized and governed and known. And that known party is a really big important part of it. And then I have things that are ad hoc, we are discovering we are still trying to figure out we are iterating on and so either of these two extremes is bad. If you send an interface and you say, Hey, you can only model things and look ml and the business user can’t change anything. It is a terrible environment, right? Any change you want, you have to go back to some centralized person say, can you change this? And to be honest, you don’t really know what you want to change, right? So you end up just sort of iterating on the centralized model, and just getting things wrong constantly. And both sides are frustrated. The other extreme, we all know, is also wrong, right? If you just say, do whatever you want in Excel, I don’t care. I’m sure you’ll get it right. We all know the answer is you probably won’t get it. Right. Right. So bad, that’s also wrong. So what we tried to espouse and sigma is to say, look, there are a set of known things. And that set of non things is going to change over time, right, we’re going to learn more and more about what other metrics we want to govern, what other dimensions tables, all those things, once you sort of have a known set of things to govern things. So those are the things that you push down into version control, we’ve seen a rise of DBT. And so you know, we see a lot of people say, and we’re going to use DBT, because I want something that is, you know, open source and not our proprietary way to model things. But they’re gonna use that for sort of their centralized modeling layer, you’re gonna expose those models into sigma. And then the power of what we try to provide for people on top of that is to say, there’s gonna be a million things that people don’t even know yet they want to ask about, we want to give them the power to figure those things out. And we may then push things back down. And part of this steep was talking about earlier, there’s benefit of having everyone the same plot, is that the, you know, the data engineer can actually see what’s going on, what queries are they running, where the analyzing, and they can actually say, oh, you know, I’m gonna pull that down, and I’m gonna make something and govern it and pull it into a, you know, say, like a DBT model. That was never really possible before, right? Like, when someone downloads it to Excel users? Like, I don’t know what they’re doing and hopefully it doesn’t show up in the, you know, board decK? It probably will. That’s kind of the problem we’re trying to avoid. You’re trying to keep people on a central governance, governance system of data, and let them bring things into that governance overtime.

Kostas Pardalis 46:53
Yeah, yeah. 100%, that actually looks very interesting, what you mentioned about the data engineer, like also taking a look into things like SQL and all that stuff. And I have a question about that. Because I’ve seen what it was like with a looker. But when you have like systems that generate SQL, right, like, automatically generate SQL, you very easily end up with code that has been generated, that’s like, really hard, both like to debug, to optimize and also to understand in some cases, right, like, there are bugs also, like in these systems. And it is a hard problem, like to create code that, you know, it’s ideal for the machine, but also easy to depress, like from the human side. So how do you do that? I mean, and what you’ve learned by trying to do that, because I’m sure you’ve done a lot, and there’s probably still a lot to be done, like it’s a very interesting area of like, research and development in general.

Rob Woollen 47:59
Yeah, so I mean, to your point, it is a journey, it is a hard problem. Obviously, there are trade offs. And, you know, for instance, you may want to write SQL that is going to optimally perform, but maybe then harder for a human to understand. And so making those trade offs, a lot of what we’ve tried to do is provide tooling at sort of the higher level. So you can understand just even in our interface, a, what are we running what, you know, what is what, what generated this query, like, so you can map those things, we also try to push all that down into the warehouse itself. So it sounds like such a simple thing, but actually even just tagging every query with a comment in the query text of what actually generated the query. So one of the things that I feel passionate about is trying to, you know, different audiences are going to choose different tools for how they want to discover these things, right? So some part of the audience may say, like, Hey, I only want to use, you know, the warehouses and query plans to look at what’s actually going on. For, you know, someone who’s writing the spreadsheet formula, that is not going to be a good interface, right? Well, they’re going to want a much higher level of understanding. So trying to show things in the right language for the right user, but offer different choices. Because the reality is, it’s often going to be a team effort, or things that are, you know, complex or working at scale. Yeah,

Kostas Pardalis 49:22
It makes a little sense. Okay, one question, Rob about you more as a founder, like someone who starts something from zero and takes it to one before you keep scaling, right? You make the decision at some point to use something like a spreadsheet, right? Like as your interface with a user out there. And spreadsheet is okay obviously something that it’s like very well adopted and very well understood by many users out there, but at the same time, there are like very well established vendors, right you have Microsoft On one side with Excel, which is huge, right? And you also have Google on the other side with the free version of Google Sheets. And you are in your startup, right? And you have these two back and forth like that, that owns distribution at a scale that is like, I mean, can’t even imagine, right? How do you build us? And how do you go to markets? With that?

Rob Woollen 50:31
Absolutely. So I mean, the first part of the answer to your question is, if you pull out the logical thread, when you’re starting in any company, you’ll quickly realize it’s like a terrible idea, right? We always say the company will be like, you know, you’re gonna work super hard, you’re honest, Netflix, acid really low. And there’s all these established companies already, you know, in this space, it’s easy to basically convince yourself that like, this is not a good idea. And you know, also being fair, a super high percentage of startups fail. So there’s a logical progression that just says, this is never a good idea. And yet, obviously, we all know, some startups succeed and make it. So it’s hard to pull in the threads and get to the logical thing. I think, for us, we tried to do two things that were early on that I think were important, like how we went to market. And then there’s also sort of the product side, I mean, start with the product side, and then I’ll talk about the go to market thing. First thing I want to mention on the product side, when you look at a term like spreadsheet, one of the challenges you have early on is how do you actually describe your product to people. And when I was describing something significant in the show, I purposely didn’t say spreadsheet, probably till about a minute into it. Because I think if you start with a spreadsheet, people’s orientation is going to be exactly like this existing product. And then they’re gonna be disappointed when they try it, they’re like, but it doesn’t, you know, doesn’t do exactly the same thing. And even though in their mind, they know, that’s obviously you know, it’s something different, it’s very hard, it’s, you know, anchoring yourself and one of those things immediately, they just expect a clone. From a go to market perspective, I think for us, it’s interesting when you’re doing a product that is, in many ways, a kind of universal platform, right? The way people use big databases, spreadsheets, it’s not just like, hey, it’s only done in the finance department, or it’s only done in marketing, you know, we’re not, there’s not like a particular by looking at a breakdown even of our own users and customers, it tends to be a fairly even distribution between all of the sorts of departments you’d suspect finance, sales, ops, marketing, HR people ops, you know, product management, and even the use cases people have, you’ll see some, you know, concentration on things like, you know, financial ops, or supply chain management, or embedding and building out sort of custom data apps. So you’ll see these concentrations, but you’ll also see a long tail of people doing all sorts of things with these products. And that makes a good market hard for a platform because, you know, the worst pitches, you can do anything with it, like a customer doesn’t match my problems, right? Like, I need a real problem. I think for us, it was a few things. One was, for larger customers, you know, the enterprise customers we have, you cannot go in with a pitch of like, hey, you know, we’re gonna make your dashboards, like, they already have dashboards, like, this is not a problem you’re trying to solve. So we had to find new problems that we were uniquely able to solve, like, we were not competing against an existing product we were competing against, they couldn’t solve this problem. And then we had to find motions in the mid market and below, where we could just go head to head with existing products. And that was a strong flow for us. Obviously, partnerships were a huge part of it. So we’ve been, you know, these data products are interesting, because by themselves, they’re actually kind of useless, right? Like, what would you do with a warehouse that had no data in it? What would you do with an ETL tool that couldn’t talk to anything, or, you know, BI product that couldn’t talk to anything, and so you end up actually having to buy a stack. And that means you have a natural set of partners to go to market with. And so that’s also been a huge part

Kostas Pardalis 54:06
of our motion. That’s super interesting. Okay. And let’s talk a little bit about the future now. Right. Bi is not like a new concept, obviously, like it’s been there for I don’t know, since we started having mainframes and databases and businesses that wanted to use technology to drive growth. What’s next? I mean, I think if you ask someone about something like bi, the first thing that comes to mind is reports, visualizations, that kind of stuff. And at the same time, we believe like in an era where like everyone’s talking about AI right now there is a ml becoming much more accessible and more and more people like they can use ml as part of working with data. From spreadsheets, right like something that exists for a very long time, it’s very mature. It’s something that, like everyone, it’s like a lingua franca to have data for business users out there to AI and large language models or whatever. It’s like the next thing that will come up in the next couple of weeks or whatever. What do you see there? Like, what’s next for? For both the BI industry and for sigma? And more specifically, right. Sure,

Rob Woollen 55:30
I get started when I prefer people to chime in as well. I think, you know, as you mentioned, everyone’s talking about AI. And I think what’s interesting when you look at this, so you know, we tend to, as an industry, go through these different waves of innovation or concentration of what people are talking about. And obviously, right now, everyone is talking about generative AI chat GPT like this, you know, even my mom is very interested in this. And so I think, the question we think a lot about is like, for the industry, how was that? Like, what’s going to change? And are we gonna see a new set of players? Are we going to see, you know, some fundamental transformations, I think, this is a tough one. Because you know, on one hand, it’s changing so quickly, that like, on some level, I’m like, I don’t even want to touch predicting what’s going to happen on the air landscape. Because next week, there’ll be something so different. But I will say what I’ve seen so far, especially in the last year, is there’s been a wave of things. I’ve seen a lot of things I would describe as features that everyone or is going to already have. And that’s probably on the one hand, it’s been this huge, enabling technology, right? Almost any product right now, is building out or has a natural language interface. And so I think if you’re a startup and you say, you know, the only thing we built is a natural language interface, I think that’s a hard thing to be defensible on. Because I think every product is gonna have that right every day, some product analysis, hey, now we have, you know, product food GPT, with our natural language interface, right. And so, I think you’re gonna see every product that has that you’re gonna see every product enhanced by AI. And I think if we look at the BI and dataspace, there’s a set of common features that people are generally doing. And I think the existing players honestly will probably check the box on all of those. What I haven’t seen yet is what is like the wave of features that like we haven’t even honestly maybe even dreamed up yet. But this technology is going to enable it. I think that’s kind of an interesting thing to watch over the next six months to a year. And it’ll be an opportunity for people to react or not. I think the challenge for new entrants here is that everyone’s eyes on AI, which also makes it so that even all the big players are reacting to it. A lot of what you count on as a startup is that the big players are asleep, and that you can sneak in and get past yet. I think that the challenge for the new entrants here is that no one’s asleep on AI, because we’re all looking at it. Yeah. 100%. And they have the benefit of forming a distribution, which is huge, right? Like,

Kostas Pardalis 58:08
I don’t think I mean, if you haven’t tried to start a company, you don’t really understand how important distribution is, you can’t grab, like, you might say, Yeah, I understand, like all the millions of like users that already use Excel, for example. But unless you try to build something you can’t realize how important I am, what the luxurious thing is, it is like super, super important that, as you say that I’m also like, very interesting to see how things will change in the next like six months or a year. stipple. Bob, what do you think about that?

Stipo Josipovic 58:46
I, you know, I think the, you know, thinking about this, you know, this topic pragmatically, I think what the tool at our disposal is the ability is really human, great processing of data, without any human being involved. And what’s so incredible about that is, I think it just lowers the barrier, you know, barrier to entry for anyone to be able to perform their analysis. So now you can immediately get the context of the data that you need to work with something that just came out of the warehouse, you now have a way to be able to get going with that and understand what’s going on there. You know, despite the space of sort of cataloging already existing, and the convenience of being able to just ask someone, or ask an API, and be able to get that context. It’s just super, super critical. So there’s this, there’s so much I mean, there’s so much in the analysis that is done today that requires processing, like if you have data coming from two different sources, and they need to be matched together. There’s some amount of human processing that’s required there. And is it really required? Well, not anymore, right? You have these tools that you can now lean on. And so I think we can always speak Killian, where’s the end state going to be before where these tools are today? I think that’s the play. That’s the area that is going to be the most, the most impactful in our day to day.

Kostas Pardalis 1:00:11
Yeah. 100%. All right, one last question from me. And that’s for Rob. And then I’ll give the microphone back to Eric. So, Rob, you mentioned at some point about the excitement you had when you first showed the promise of cloud computing, right? Back in 2007 -2008. We are like, so many years after that, right? Do you think that cloud has delivered? Are we there? Are we done with clouds actually, or there’s still a lot of potential. And when it can go, when it comes to the clouds parting?

Rob Woollen 1:00:50
For me, the amazing thing was just how fast you can move, right. And so if you think about, if you were even just starting a company or starting a project at a company, 20 years ago, or even more, when I first started my career, like, we wrote all the software in our stack, we didn’t use it any there’s no like kind of equivalent of open source, like you just you had to build everything. And then you had to get all the hardware to deploy all these things that you sort of do. And so for me, there’s something amazing about like, how quickly you can get started on things, how quickly you can iterate how quickly you can, you know, spend your time on sort of building out new things for value for your users, versus spending time on sort of, honestly, the same old problems? And so, you know, I think we are well, on our way, I don’t think we’re anywhere near done yet. I think in some ways, we’re still learning a lot. I think it’s part of the honestly, the great part about this industry, right, is that we’re actually I think, in a lot of ways learning how to, together move faster, right? In some ways, open source is a way that we sort of learned, like, you know, if you talk to a lot of people and say, what if we just gave away our product for free? It sounds crazy. And yet developers have sort of learned that there’s a huge value in doing it for a lot of developer oriented things. Because it’s a way to promote community, it’s a way to build on top of things, it’s a way to have building blocks that kind of act like compound interest and build on top of things. And so I think spreading those ideas further. There’s a lot of opportunity there.

Kostas Pardalis 1:02:26
100% Very cool. Yours.

Eric Dodds 1:02:29
Okay, I’m gonna, I want to ask this a little bit, going back to the AI question, but maybe I can turn this into a good, concluding question. One really interesting thing about sigma is that in many ways, it gets people a lot closer to the data, you know, then you see, but when you were talking about traditional BI, you sort of have this vertical integration, right? And the consumers like at the very end, and so they’re actually not very close to the data. When you think about giving someone a spreadsheet interface, it gets them very close to the data. And anyone who’s tried to answer a question knows that. In terms of ergonomics, like looking at the data, and just sort of trying to wrap your mind around, like looking at a spreadsheet, gives you a sense of what questions to ask and orient you, in many ways to actually perform analytics, maybe even before you start, you know, doing analytics formerly. One thing that’s interesting about some of the AI, especially the chat interface that sort of has become the predominant paradigm, is that it obfuscates a lot of that, right? So when you think about closeness to the data, in terms of sigma, and in terms of analytics, in general, I just love to conclude on hearing about, like, how do you value that as a, as someone who like works with and uses and produces Analytics, you know, every day as part of my job? That’s really valuable to me. And I think it’s an important part of it, yet, at the same time, it can turn into a lot of, you know, unnecessary work. What do you think about closeness to data?

Rob Woollen 1:04:12
I guess a few things jumped out at me, you know, as you were talking about the spreadsheet interface, and I mean, they’re, they’re, they’re funny things, right? Like you, if I just showed someone who was not, you know, as a very much a novice in analysis, I just showed him some column. And it has, you know, some data value that’s very different from the others, like their eyes would naturally go to that their questions would naturally go to that. They don’t necessarily have to have a concept of, you know, DVT standard deviations and outliers or anything like that. But it’s just, it’s sort of human intuition to be curious about certain different things. And I think, you know, early on, sometimes we thought about, like, what’s the right interface for a curiosity? And so at some level, an interesting interface for curiosity is a chat interface, because it’s a very lightweight interface for asking like, you know, I’m generally curious about like, how are we doing in marketing? And so it’s a very good interface for that. It’s a very good, I think paradigm for sort of getting, hey, here’s like a general high level, you know, here’s how we’re doing in marketing. It’s not necessarily a great interface for like a very detailed set of things. And so I think you’re always going to see a spectrum of like, what is the right, for lack of a better thing, a tool for the job. But you know, I love getting people engaged. I think one of the things that I’ve always been passionate about when I watch spreadsheet users is the spreadsheet user gets in front of the data. And it’s like, they, they kind of almost want to, like, hug their Excel, and they’re like, you know, now that I have it here. I can do it, I can do anything. Yeah. And they like, just jam out for like, the next 30 minutes. And I remember watching like early sigma users that see the same thing, I’d watch them like, you know, do like 3000 things in a row, that type of thing and building out those types of interfaces, right? I don’t necessarily want to type 3000 questions into the GPT chat. But if I could type in like three really interesting ones that start me on a journey. That’s a great, that’s sort of a great win.

Eric Dodds 1:06:05
Yeah, no, that makes sense. I think. When you think about hugging the spreadsheet, I actually think it’s a very apt analogy, because in some sense, when you encounter a new data set, you need to understand the physical dimensions of it, if that makes sense, right? Like, it’s almost like, you know, walking into a new house, and you kind of need to understand, like, you know, how it’s arranged and the size, and other things like that. But at the same time, like having an interface that encourages curiosity. Yeah, that’s a wonderful answer. SIBO. Any, any additional thoughts? Before we close it out?

Stipo Josipovic 1:06:40
You know, the thing that I’ve done here is it’s really about how people think, right? There’s people who are very verbal, right? There’s, you know, they think in words, there’s people who think in sort of images and diagrams, and I think when it comes to data exploration, it’s very tough to do it all verbally. And so in this regard, would I sort of expect is, so first off as you’re verbally asking all of these questions, but the second off is, you know, there’s, you need to have a level of transparency of what you’re getting, because you need to see and understand, you know, what happened behind the scenes to vet it. And I think this is a state of the technology, and certainly where it is today, but to get a sort of a blind answer. And just in the same way that if you show up to a meeting, someone gives you aggregate numbers, you want to be able to drill down and actually understand how the heck did they come up with the numbers, especially if it’s something something that’s that seems something that seems amiss, or stands out as an outlier? And so, you know, to Rob’s point, I, you know, I would just sort of underscore and agree that there’s, it’s a great place to sort of start for a high level and to ask the first question, but that those deeper questions and the way that those spreadsheet huggers work, you know, you really need to be able to see the data and the way that you sort of iterate on the next question to even your point when you’re describing this question is that you sort of see the data and that drives, how you interact with and what you’re going to ask next. And what you’re going to do, and you don’t need words, you just need the actions, I notice that going from what you see and what you need to do, to translating that to words, to then to data and going back and forth. It’s almost a suboptimal way to go back and forth. But it’s really sort of curious to see how technology will kind of progress in this realm.

Eric Dodds 1:08:27
Absolutely. Well, we are very excited to see what you and the team at sigma build as we figure out how these new technologies are going to influence analytics. Thank you so much for giving us time. Wonderful episode. And we’d love to have you back. So let us know, you know when you can come back on the show.

Stipo Josipovic 1:08:51
All right. Thank you both. Thank you.

Eric Dodds 1:08:53
Well, Costas, what a fascinating conversation with SIBO. And Rob from Sigma. They have built a modern analytics tool that is a spreadsheet and SQL interface. So relying on the two most common paradigms for modern analytics. And it was fascinating to hear them talk about how and why they made the decision to pursue that kind of interface. I think two things stuck out to me. One, they didn’t start there. That seems like an obvious, you know, it seems like an obvious decision. Well, let’s just, you know, bridge this gap between the spreadsheets and SQL, which have the most usage in terms of analytical interfaces than, you know, any other tooling in the world, but they didn’t actually start there. Rob, as a founder, told us this, you know, told us that they went through many iterations and did a ton of research and watched, you know, tons and tons of analysts use a bunch of different tools. and ultimately came to that conclusion that to me was fascinating. The other piece that I really appreciated was, they really had kind of a humble nature to them in terms of the way that they described, coming to their conclusions about the product. And I think Rob made a great point, and that a lot of analytics tools are pretty opinionated, because they maybe think that they need to create a lot of guardrails for the end users, because they sort of know how analytics need to be done better than the end user. And Rob was pretty clear, that’s not actually true. And that he seems to have a fundamental belief that that’s a pretty bad way to build products in general. And, you know, from their success, it’s clear that I think they made the right choice and sort of, you know, giving different kinds of advanced tooling to different users. I loved it so much, it was absolutely a great episode.

Kostas Pardalis 1:11:03
Oh, yeah. 100%. I mean, there are a couple of things that I found, like, I’m very fascinating, and like, there was a wealth of like, let’s say, wisdom there from some people with a ton of experience in the industry. I really enjoyed the part, the parts where we talked about the differences between the relational model and the spreadsheets and how these can be breached. Like, what does this mean in terms of, well, like the product that we are building in terms of like, the experience that you try to build there, and there were like, some very interesting insights there. But also, from a technical perspective, like, what it takes to engineer a system like that, which is also like, super, super interesting. And gives like,

1:12:02
like, helps

Kostas Pardalis 1:12:03
us like, understands of like, the difficulty and the complexity of the problem that we are dealing with. And at the same time, it’s like, very interesting to hear that. It is a German, like, you never like solving a problem is always like in Germany, and like, that’s what I like, I really keep like from this conversation with both like steeple and like robe, like, yeah, we have done so far, like, and I mean, like, I would say, they’ve done an amazing job. They didn’t say the word amazing, because they are very humble. But sigma is like an amazing tool in terms of like how, like the experience that he delivers at the end. But Rob was like, very explicit on that. It’s still like, a lot of work to be done there. And many things that I like can be improved. And the other thing that I’d say, and I’m not going to disclose exactly what because I want to keep that as like a surprise, like for our audience is that there’s also like a hidden gem in this conversation about go to market and building products and companies, especially when you are going out there to compete against some very established and big companies.

Kostas Pardalis 1:13:21
I would recommend to anyone who likes to pay attention to some very interesting parts of the conversation not only from the technical and the product perspective, but also the business perspective.

Eric Dodds 1:13:34
I agree. All right. Well, thank you for listening. Another great episode in the books, subscribe if you haven’t on your favorite podcast platform, and tell a friend if you haven’t. We always love getting new listeners. And of course, give us feedback. You can go to the website, submit the form, tell us what you like and what you don’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 to 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 eric@datastackshow.com. 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.