 Welcome back to Vegas, guys. Lisa Martin and Dave Vellante here, wrapping up our coverage of day two of Snowflake Summit. We have given you a lot of content in the last couple of days. We've had a lot of great conversations with Snowflake, Spokes, with their customers, and with partners, and we have an alumni back with us. Please welcome back to theCUBE by Palmer, CEO of Sigma Computing. Mike, it's great to see you. Thanks for having me, and I guess again. Exactly. It's fantastic to hear. So, talk to the audience about Sigma before we get into the Snowflake partnership and what you guys are doing from a technical perspective. Give us that overview of the vision and some of the differentiators. Sure, over the last 12 years, companies have benefited from enormous investments and improvements in technology, in particular, starting with cloud technologies, obviously going through companies like Snowflake. But in terms of the normal user, the one that makes the business decision in the marketing department and the finance team, and the works in the back room of the supply chain doing inventory, very little has changed for those people. And the time had come where the data availability, the ability to organize it, the ability to secure it, was all there, but the ability to access it for those people was not. And so what Sigma's all about is taking great technology, finding the skills that they have, which happens to be spreadsheets. There are a billion licensed spreadsheet users in the world, and connecting that skillset with all of the power of the cloud. And how do you work with Snowflake? What are some of the, what's the joint value proposition? How are they as an investor? That's what I want to know. Quiet, which is the way we like them. No, I'm just kidding. Snowflake, well, first of all, investment is great, but partnership is even better, right? And I think Snowflake themselves are going through some evolution. But let's start with the basics of technology, where this all starts, because all of the rest doesn't matter if the product is not great. We work directly on Snowflake. And what that means is, as an end user, when I sit on that marketing team and I want to understand and connect, how did I get a customer where I had a paid ad and they showed up on my website and from my website they went to a trial and from there they touched a piece of syndicated content. All of that data sits in Snowflake. And I as a marketer understand what it means to me. So for the first time, I want to be able to see that data in one place and I want to understand conversion rates. I want to understand how I can impact those conversion rates. I can make predictions. What that user is doing is going to Sigma, accessing live data in Snowflake. They're able to ask ad hoc questions, questions that were never asked, questions that don't exist in a filter, that were never prepped by a data engineer. So they can truly do something creative and novel in a very independent sort of way. And the connection with Snowflake's live data, the performance, the security and governance that we inherit, these are all facilitators to really expand that access across the enterprise. So at a product level, we were built by a team of people, frankly, that also were the original investors in Snowflake by two amazing engineers and founders, Rob Willen and Jason Franz. They understood how Snowflake worked and that shows up in the product for our end customers. So if I may, just to follow up on that. I mean, you could do that without Snowflake, but what, it would be harder, more expensive. Describe what you'd have to go through to accomplish that outcome. And I think Snowflake does a good job of enabling the ecosystem at large, right? But you always appreciate seeing early access to understand what the architecture is going to look like. Some of the things that I will, leaning forward that we've heard here that we're very excited about is Snowflake going to attack the OLTP market, the transactional market, one of the transactional database market. One of the things that we see coming and one of the bigger things that we'll be talking about in Sigma is not just that you can do analytics out of Snowflake, I think that's something that we do exceptionally well on an ad hoc basis, but we're going to be the first that allow you to write into Snowflake and to do that with good performance and to do that reliably, we go away from OLAP, which is the terminology for data warehousing, and we go toward transactional databases. And in that world, understanding Snowflake and working collaboratively with them creates, again, a much better experience for the end customer. So they allow us into those programs. Even coming to these conferences, we talk to folks that run the industry teams trying to up level that message and not just talk database and analytics, but talk about inventory management. How do we cut down the gap that exists between POS systems and inventory ordering, right? So that we get fewer stock outs, but also that we don't overorder. So that's another benefit. Strong business use cases. That's correct. And you're enabling those business users to have access to that data, I presume, in near real time or near real time so that they can make decisions that drive marketing forward or finance forward or legal forward. Exactly. We had a customer panel yesterday, an example of that. Gopuff is hopefully most of the viewers are familiar with. As a delivery company, this is a complicated business to run. It's run on the fringes when we think about how to make money at it, which means that the decisions need to be accurate. They need to be real time. You can't have a batch upload for delivery. When people are on the street and then there's an issue, they need to understand the exact order at that time, not in 10 minutes, not from five minutes ago, right then. They need to understand, do I have inventory in the warehouse when the order comes in? If they don't, what's a replacement product? We had a, Mike came in from Gopuff and walked us through all of the complexity of that and how they're using Sigma to really just shorten those decision cycles and make them more accurate. You know, that's where the business actually benefits. And actually create a viable business model. Can you think back to the early, think back to the dot-com days and you had pets.com, right? They couldn't make any money. But now Chewy, okay, it appears to be a viable business model, right? Part of that is just the efficiencies and it's sort of a, I don't know if those are customers that they may or may not be, but they should be if they're not. Chewy is, but you know, and that's another example, but I'll even pivot to the various REI and other retailers. What do they care about? Cohorts, I'm trying to understand who's buying my product, what can I sell to them next? That idea of, again, I'm sitting in a department that's not data engineering, that's not BI, now working collaboratively where they can get a engineer, putting data sets together, they have a BI person that can help in the analytics process. But now it's in a spreadsheet where I understand it as a marketer. So I can think about new hierarchies. I want to know it by customer, by region, by product type. I want to see it by all of those things. I want to be able to do that on the fly because that creates new questions. That sort of flow, if you've ever worked in development, we use the word flow constantly, right? And as people, that flow is when we have a question, we get an answer that generates a question, we just keep doing that iteratively. That is where Sigma really shines for them. What does a company have to do to really take advantage of this? I mean, if they're kind of starting from a company that's somewhat immature, what are the sort of expectations, maybe even out of scope expectations so they can move faster, accelerate analytics, a lot of the themes that we've heard today? What is an immature company? It's actually a question in and of itself. I think a lot of companies consider themselves to be immature simply because for various constraint reasons, they haven't leveraged the data in the way that they thought possible. Good, good definition. Okay, so not... I use this definition for digital transformation. It's very simple. It is, do you make better decisions faster? McKinsey calls this corporate metabolism, right? Can you speed up the metabolism of an enterprise? And for me, and for the Sigma customer base, there's really not much you have to do once you've adopted Snowflake because for the first time, the barriers and the silos that existed in terms of accessing data are gone. So I think the biggest barrier that customers have is curiosity because once you have curiosity and you have access, you can start building artifacts and assets and asking questions. Our customers are up and running in the product in hours and I mean that literally in hours. We are a user in Snowflake. That's a direct live connection. They are able to explore tables raw. They can do joins themselves if they want to. They can obviously work with their data engineering team to create data sets if that's the preferred method. And once they're there and they've ever built a pivot table, they can be working in Sigma. So our customers are getting insights in the first one to two days. You referenced some, those of us that are old enough to remember, past.com are also old enough to remember, shelf wear that we would buy. We are very good at showing customers that within hours, they're getting value from their investment in Sigma and that just creates momentum, right? Oh, tremendous momentum and trust. And trust and expansion opportunities for Sigma because when you're in one of those departments, someone else says, why do you get access to that data? But I don't. How are you doing this? So I think that there is a big movement here. People, I often compare data to communication. If you go back a hundred years, our communication was not limited, as it turns out by our desire to communicate, it was limited by the infrastructure. We had the typewriter, a letter, and the US Postal Service and a telephone that was wired. And now we have walk around here. Everything is enabled for us and we send hundreds and thousands of messages a day and probably could do more. You will find that is true and we're seeing in our product is true of data. If you give people access, they have 10 times as many questions as they thought they had. And that's the change that we're going to see in business over the next few years. Frank Slutman's first book when he was CEO of Snowflake was Rise of the Data Cloud. And he talked about network effects. So basically what he described was Metcalfe's law. Again, go back to the dot com days, right? And Bob Metcalfe used the phone system. You know, if there's two people in the phone system, it's not that valuable. Right, exactly. You know, grow it and that's where the value is. And that's what we're seeing now applied to data. And even more than that, I think that's a great analogy. In fact, the direct comparison to what Sigma is doing actually goes one step beyond everything that I've been talking about, which is great at the individual level. But now the finance team and the marketing team can collaborate in the platform. They can see data lineage. In fact, one of our big emphasis points here is to eliminate the sweet products. You know, the ones where, you know, you think you're buying something but you really have a spreadsheet product here and a document product there and a slide product over there. You know, you can do all of that in Sigma. You can write a narrative. You can real-time live edit on numbers. You know, if you want to, you could put a picture in it. But you know, at Sigma, we present everything out of our product. Every meeting is live data. Every question is answered on the spot. And that's when you know, you know, to your point about Metcalfe's law, now everybody's involved in the decision-making. They're doing it real-time. Your meetings are more productive. You have fewer of them because they're no action items, right? We're answering our questions there and we're moving forward. Your meeting sounds good. Productivity is weird now with the pandemic. But you know, if you go back to the 90s, there I am, I'm dating myself again, but that's okay. You know, you didn't see much productivity going on when the PC boom started in the 80s, but the 90s had kicked in. And pre-pandemic, you know, productivity in the US and Europe anyway has been going down. But I feel like, Mike, listening to what you just described, how many meetings have we been in where people are arguing about the numbers, what are the assumptions on the numbers, wasting so much time, and then nothing gets done and then they bolt. Cut that away and you drive in productivity. So I feel like we're on a renaissance of productivity and a lot of that's going to be driven by data. And obviously, communication is the whole 5G thing. We'll see how that builds out, but data is really the main spring of I think a new renaissance in productivity. Well, first of all, if you could find an enterprise where you ask the question, would you rather use your data better and they say no? I'm like, you know, tell me that I'll short their stock immediately. But I do agree and I unfortunately have a career history in that meeting that you just described where someone doesn't like what you're showing them and their first reaction is to say, where'd you get that data? You know, I don't trust it, you know? So they just undermine your entire argument with an invalid way of doing so, right? When you walk into a meeting with Sigma, where'd you get that data? I was like, that's the live data right now. What question do you want answered? The lineage, right? And you know, it's like Slutman's book about, you know, got to move faster. I mean, this is an example of just cutting through, making decisions faster. Like, you're right, Mike. And the P&L manager in a meeting can kill the entire conversation. You know, throw a foot at it. Yeah. You know, protect his or her agenda. True, but now to be fair to the person who's tended to do that, part of the reason they've done that is that they haven't had access to that data before the meeting and they're getting blindsided. Right, so going back to the collaboration point, the fact we're coming into this discussion more informs in and of itself takes care of some of that problem. Yeah, for sure. And if everybody then agrees, we can move on and now talk about the really important stuff. That's good. It seems to me that Sigma is an enabler of that curiosity that you mentioned. If that's been lacking, people need to be able to hire for that. But you've got a platform that's going, here you go. Ask away. That's right. And the, we're very good. You know, we love being a SaaS platform. There's a lot of telemetry. We can watch what we call our mouse-to-dows, you know, which is our monthly average users to our daily average users. We can see what level of user they are, what type of artifacts they build. Are they, you know, someone that creates things from scratch? Are they people that tend to increment them? Which, by the way, is helpful to our customers because we can then advise them, hey, here's what's really going on. You might want to work with this team over here. They could probably be a little better at using the data. But look at this team over here. You know, they've originated five workbooks in the last, you know, six days. They're really on it. There's, there's, you know, that ability to even train for the curiosity that you're referring to is now there. Where are your customer conversations? Are they at the lines of business? Are they with the chief data officer? What does that look like these days? Great question. So stepping back a bit, what is Sigma here to do? And our first phase is really to replace spreadsheets, right? And so one of the interesting things about the company is that there isn't a department where a spreadsheet isn't used. So Sigma has an enormous TAM, but also isn't necessarily associated with any particular department or any particular vertical. So when we tend to have conversations, it really depends on, you know, either what kind of investment are you making? A lot of mid-market companies are making best technology investments. They're on a public cloud. They're buying Snowflake and they want to understand what's built to really make this work best over the next number of years. And those are very short sales for us because we prove that, you know, in minutes to hours. If you're working at a large enterprise and you have three or four other tools, you're asking a different question and often you're asking a question of what I call exploration. We have a product that has dashboards and they've been working for us and we don't want to replace the dashboard. But when we have a question about the data in the dashboard, we're stuck. How do we get to the raw data? How do we get to the example that we can actually manage? You can't manage a dashboard. You can't manage a trend line. But if you get into the data behind the trend line, you can make decisions to change business process, to change quality accuracy, to change speed of execution. That is what we're trying to enable. Those conversations happen between the IT team who runs technology and the business teams who are responsible for the decisions. So we are, you know, we have a cross departmental sale, but across every department. One of the things we're not talking about at this event, which is kind of interesting because it's all we've been talking about is the macro, supply chain challenges, Ukraine, blah, blah, blah, and the stock market. But how are you thinking about that macro, the impact you're seeing, you know, a lot of private companies being recapped, et cetera. You guys obviously very well funded. But how do you think about, I mean, I asked Frank a similar question. He's like, look, it's a marathon. We don't worry about it. You know, they made the public market. They got five billion in cash. How are you thinking about it? You know, first of all, what's the expression, right? You never waste a good, you know, in this case, recession. I don't know, we don't have one yet. But the impetus is there, right? People are worried. And when they're worried, they're thinking about their bottom lines. They're thinking about where they're going to get efficiency and their cost. They're already dealing with the supply chain and issues of inventory. We all have it in our personal lives. If you've ordered anything in the last six months, you used to getting it in, you know, days to weeks, and now you're getting in months. You know, we had customers like US Foods as a good example that they're constantly trying to align inventory they have with transportation that gets that inventory to their end customers, right? And they do that with better data accuracy at the end point, working with us on what we are launching, and I mentioned earlier, having more people be able to update that data, creates more data accuracy, creates better decisions. We align that then with them and better collaboration with the folks that then coordinate the trucks. With Prologis in the panel yesterday, they're the only commercial public company that reports their valuations on a quarterly basis. They work with Sigma to trim the amount of time it takes their finance team to produce that data. That creates investor confidence. That holds up your stock price. So I mean, the importance of data relative to all the stakeholders in enterprise cannot be overstated. Supply chain is a great example, and yes, it's a marathon because a lot of the technology that drives supply chain is old, but you don't have to rip out those systems to put your data into snowflake to get better access through Sigma to enable the people in your environment to make better decisions, and that's the good news. So for me, while I agree there's a marathon, I think that most of the, I don't know if I could continue this metaphor, but I think we could run quite far down that marathon without an awful lot of energy by just making those couple of changes. Awesome, Mike, this has been fantastic. Last question, I can tell, I know a lot of growth for Sigma, I can feel it in your energy alone. What are some of the key priorities that you're going to be focusing on for the rest of the year? Our number one priority, and number two priority, and number three priority are always build the best product on the market, right? We want customers to increase usage, we want them to be delighted, we want them to be, we have customers at our booth that walk up and it's like, you're building a great company, we love your product. If you want to show up happy at work, have customers come up proactively and tell you how your products changed their life, and that is the absolute most important thing because the real marathon here is that enablement over the long term, right? It is being a great provider to a bunch of great companies. Under that, we are growing, we've been tripling the company for the past few years every year that takes a lot of hiring, so I would have, alongside product, is building a great culture with bringing the best people to the company that I guess have my energy level. If you could get paid in energy, we would have more than tripled it, you know? But that's always going to be number two. Where we're focused on the segment side, you know, is really the large enterprise customer at this point. We are doing a great job in the mid-market. We have customers, we have hundreds of customers in our free trial on a constant basis. I think that without wanting to seem overconfident or arrogant, I think our technology speaks for itself in the product experience for those users. Making a great ROI case to a large enterprise takes effort, it's a different motion. We're very committed to building that motion. We're very committed to building out the partner ecosystem that has been doing that for years and then is now coming around to the Snowflake and all of the ecosystem changes around Snowflake because they've learned these customers for decades and now have a new opportunity to bring to them. How do we enable them? That is where you're going to see Sigma going over the next couple of years. Wow, fantastic, good stuff and a lot of momentum. Mike, thank you so much for joining Dave and me, talking about Sigma, the momentum, the flywheel, what you're doing with Snowflake and what you're enabling customers to achieve that massive business outcomes. Really cool stuff. Thank you and thank you for continuing to give us a platform to do this and glad to be back in conferences doing it face to face, it's fantastic. It's the best, awesome, Mike, thank you. For Mike Palmer and Dave Vellante, I'm Lisa Martin. You've been watching theCUBE, hopefully all day. We've been here since eight o'clock this morning, Pacific Time, giving you wall to wall coverage of Snowflake Summit 22. Signing off for today, Dave and I will see you bright and early tomorrow morning. Take care, guys.