 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hello everyone, this is Dave Vellante, welcome. We're going to do a little preview of ThoughtSpot Beyond and we're going to look at the intersection of cloud, data, search and analytics. For a decade we've been collecting all this information and tapping data sources from many, many different places. Now we're at the point where we can very cost effectively and quickly put data into the hands of many orders of magnitude or more users so that data can inform opinions and ultimately actions. With me is Sudeesh Nair, who's the CEO of ThoughtSpot. Sudeesh, it's always a pleasure to have you on theCUBE. Thanks for coming on. Absolutely my pleasure, Dave, thanks for having me. You know, it's ironic, I think that we start this decade with so much disruption to our lives. It's forced us to become digital businesses really overnight. I wonder if you could talk about the role of data as it relates to our digital lives. Yeah, I think the idea that data somehow directly impacts our lives sometimes can be far-fetched. That is because we don't really talk about it in the right way. Data can be this archaic mountain of things that people don't really connect with. What we should really be talking about is what data does, the byproduct, the end product of data, which is the signal that we get out of the mountain of data, the insight that we derive from it, and the action, the bespoke actions that makes our lives possible in this new world that we are living in. And if you really do a good job of talking about what data does for you or the byproduct of what the data does for you, I think people will understand that we are incredibly connected, incredibly dependent on the signals that we derive from the data that we are giving out to the world that we are operating in today. Yeah, we had to fire, ready, and aim because the speed at which we've had to adapt is we've never seen this before. And I'm wondering if you could share with us what you're seeing, I mean, what kind of challenges this creates for organizations, specifically in terms of being able to leverage their data assets. Yeah, see, I think if you think of the last eight, nine months, sometimes in our industry, it is easy to sort of look at this as an opportunity, more of an opportunistic way of looking at how can I sell more data-driven things when the world is sort of falling apart? You know, you walk on a downtown, you see all these restaurants closed, you know, parking lots empty. It is, you know, my sort of lesson in the last eight, nine months is to be more outside in as opposed to inside out. And that is why are we doing this is now more important than what we are doing. And in that context, my biggest lesson that I've learned is that the thing that stand in the way of delivering value for customers almost always is not technology, not product, and not even quality of data. A lot of data people will say it is the data quality that is holding me back from doing. It is lack of courage, lack of vision, lack of ability to sort of empathize with your customers and truly see what can we do to make their lives better where data-driven insights might be a part of it. So I really believe that organizations that are differentiating by, you know, providing better services where they use data to do that are clearly coming out their head as we are looking at the end of this global pandemic. You know, it's interesting what you're saying about data quality, because I agree with you. I actually think it's access to data because as a business user, I can look at data, ask a couple of questions and say, okay, this is, I can get pretty close to the truth. And if you think about organizations generally, but specifically business users, they've been clamoring for more facile access to data. And really the time is now for them to realize this vision. And I wonder if you could share with us like, what's happening in DotSpot's business in the past months? Because that's what you're all about, is that easy, fast access to data. Yeah, so I always talk about the decision-making pipeline. I know one end you have the data that customers are happy to give. However, it's a two-way street. They are saying, look, I'll give you my data. In return, I want you to do two things. Number one, make sure it is safe and protected. Number two, you are using that data to deliver bespoke experiences for me, bespoke services for me. That is, I'm giving you the data so you will get to know me and treat me as an individual, as a person with likes and dislikes that are different from someone else's. If you don't do that, you're breaking that contract. So when I think of this continuum of data, to insight, to knowledge, to action, action is where the users benefit. I sort of sometimes worry that the chasm that exists between the people who can speak the data, the SQL, the data warehouse people, who have usually the answers and not necessarily have the questions because questions are usually coming from the business users. So our sort of purpose in life as a company in the world has been simple. That is, let us break that barrier. Let's move that silos and then unify so that people with questions can get answers. People who know the business can get the answer from the data without any tax on the curiosity. Now it is easier said than done, but it is a journey. But I strongly believe that pushing the ability to inquire and get insights from the data all the way to the frontline where business users interact with their customers, the business's customers, the consumers, the clients. If you don't do that properly, there is no way to keep up with the velocity of change that the world is throwing at your business. So speaking of data sources, I mean, one of the data sources I sometimes look at, you look at the stock market as funny last month, Pfizer announces a very highly successful trial and the stock market goes up 800 points. So you sort of look at that and say, okay, it's a data point. I recently released a number of pieces on cloud and its impact. And you saw, after that, you saw a lot of cloud stocks, everybody panic, oh, sell tech. And even though, look, I've written, cloud's not immune to COVID, it's clear from our data that cloud migration has been very much accelerated since the pandemic hit and I don't really see that changing. I wonder if you could talk about the ways in which you see cloud changing, how organizations operate and really what's missing when it comes to getting the most out of their cloud investments, specifically around analytics. It is like any other function. Data analytics is not different in what the cloud does for the customers. I used to always talk about the world of computing, the world of technology as a race against commoditization. That is, imagine there is an ocean that is warming and there's an iceberg that is floating on it. As the ocean warms, the iceberg keep melting and if you wanna survive, you gotta keep going up the mountain, the iceberg mountain. In this example, the commoditization of technology is the ocean. Anything that you think is unique, anything that you think is proprietary is gonna get commoditized. And the reason why that's happening is because people wanna go up the value chain. That's the iceberg, that's the mountain. So if you use that metaphor, what you will see here is that people wanna go up the value that the data analytics deliver as opposed to how cool or how differentiated the process of delivering value is. And let me explain that in a, imagine that you are producing a lot of content. I'm pretty sure that you have ways to sort of collect the data on how it is making an impact. That is, how many people watched it? How many of them were young versus old versus sales versus engineering versus marketing versus execs. You can slice and dice the data. That is where today's data analytics stops. Now imagine, you can take it to the next level. That is, what impact is it having on my consumers? Are they able to get better jobs, for example, because of a technology that you talked about? Or the cubes ability to sort of democratize access, the way sometimes you take complex technology and simplify it. Is that making easier for some execs to catch up with the speed with which technology is changing? In turn, which makes their business more agile. So the point is our thesis is that when we stop data analytics at the noise level, the data level, the insight level, we are only doing half the job. We need to go all the way through that value chain, climb all the way up in that iceberg and think for the customer. What am I doing for the customer? I mean, there are recent examples of where banks have largest of large banks, where they had inherent bias when it comes to how they were giving loans to minorities and people of color, or the people who have an accent on the phone they're actually calling and customer support. These sort of things are not an AI problem or a BI problem. These are human problems. So by breaking the barrier between business users and their consumers, where data become an inherent part of decision-making, you can make tangible difference in the world. And I think that is what we are trying to do. I know it sounds somewhat naive and utopian, but I do think this is possible if you really approach it outside in. Well, and outside in thinking is critical. And I want to pick up on something you said about kind of moving up the value chain. And we've watched over the last decade sort of the sassification of many industries. You guys recently announced ThoughtSpot Cloud, which was your first sass offering. Tell us, how's it going? What's the uptake like, the adoption? What are customers telling you about what it's doing for their business? Again, this is the same outside in story. It is relatively new. It's only been a month. The interest is pretty high and we have closed a handful of customers. So I don't want to claim victory yet, but the signs have been very positive. And it is not surprising because it aligns with that story that I talked about growing up the value chain. Traditionally, when we deploy ThoughtSpot, we deploy it in the customers VPC, their own cloud or in the data center. The problem is when you're doing that, they are responsible for integrating the data, connecting the data, prepping the data, managing there's a lot of work that goes through. With ThoughtSpot, is it possible for us to do as much for the customer with TS Cloud? ThoughtSpot Cloud. That is, you just go to ThoughtSpot Cloud and connect to your SaaS data warehouse services that you may have, whether it's Snowflake or Redshift or in a GBQ, Google BigQuery or Microsoft Synapse. And then get going immediately to give you an idea. A typical ThoughtSpot deployment used to take around four to five months. Now it is taking around 35 minutes. That's what ThoughtSpot Cloud does for our customers. And if it happens in 35 minutes, their business of delivering value to their clients is happening that much faster. Yeah, everything shifts to actually getting insights as opposed to setting stuff up. Setting stuff up. You know, one of the other things, Siddish, that I've been reporting on, I've said in the last decade, we kind of moved from really a product-centric world to one that's more platform-centric, particularly with cloud and SaaS. And the latest research that we've been doing shows that ecosystems, we think, are going to power the next wave of innovation. And I wonder what your view is of that premise and how you're thinking about ecosystems as a lever of growth. Yeah, this word platform is one of the most abused word in our industry because people like to say, oh, don't say product, say solution. And then if they don't say solution, use platform. Ideally, in reality, platform is useless if people are not standing on it, right? If you're standing on a railway platform, nobody's there, what's the point? The same thing applies to business, our business as to when it comes to platform. A platform is only a real platform. If there are other players making money of what you have built. If you build a platform, all it does is a bunch of API nobody's consuming, it's not useful. In that context, we have long ways to go. We have really long ways to go. I do think one of the sort of, I wouldn't say mistake, one of the oversights that ThoughtSpot had was not delivering on the vision of platform that is easy to make. It is easy to make for others to come together and do commerce on ThoughtSpot. And most importantly, make sure that it is not just easy, but when customers come to them, that one plus one is like 10 or 11, as opposed to one plus one equal two. That is something that we have to remedy and at the beyond conference next month, December 9th, you will see us make some interesting announcements around this thing. And it is one of my favorite sort of projects because once we do that very well, you will see that it becomes a platform. So think of strike, think of square. These are platforms because it may, their customer's lives easier, but at the same time, multiple companies could come together to deliver joint solutions where the sum is much bigger than the equals of the parts. And that is a vision that ThoughtSpot needs to really deliver on and beyond will be a stack. Yeah, I mean the power of many versus the resources of one. And this is well understood over time. And now we're seeing it really applied to our industry. You know, Siddish, a lot of the analytics that we produced today are the result of humans clicking and typing and interacting with systems. And that's obviously going to continue to grow. But you think about things like IoT, the build out of 5G. It brings this whole new dimension of machine to machine communications and tons of new data. Much of the data out there is analog today. It's increasingly become digital. How are you thinking about these trends at the edge in terms of the impact on your company and your customers? Look, I think if anyone asked me like, what does ThoughtSpot do for the data analytics world? My answer is very simple. We have introduced a new interface to access structure data that can be used by anybody. Search that is driven by AI, that's it AI driven search. That core idea is about scale, but more importantly rate of change. That's where the new inventions around 5G, where the bottlenecks are being removed at IoT and mobile. I mean, we want to put mobile as well. So you have mobile devices, IoT devices, very big pipe, and then cloud on the backend where processing and storing is cheap. So now if you think of that, it is a 12 length super high way all the way to the end user, all the way to the end device to the mothership. When you have that much speed and when you remove everything, you have to think about the asset, the artifacts that you build out of that kind of a data stream. That's where the old way of looking at dashboards will die. It's not a question of if or when it is dying. What we need is now to make sure that at that speed, when the data is changing much faster than ever before, you have new way to deliver insight to the people who can act on it, which is business users. And if you think of it, like there used to be cases where companies used to make supply chain decisions for the year. Now supply chain decisions are made monthly because you don't know what next month will look like with COVID. When you have annual decision become monthly decision, monthly decision become weekly decision, weekly decision become minute by minute decision, sometimes like pricing, social media sentiment changes, things like that. There is no way that you can depend on a Monday morning report or a Monday morning meeting. And then send out the, you know, here is what you need to do, action act items to the front end. Everyone should have their pulse on where the business is, which is where the data is going to help them. However, human experience is so critical. You don't want to remove human experience. That's why as we deliver more and more on 5G and IoT, making the data as it is changing and then delivering those signals, insights directly to business users in the frontline, he's going to be like the de facto way, businesses will operate. And I think we are just beginning that journey in terms of what is possible. Well, it reminds me of when we were kids, the coaches would tell us, go to where you think the ball is going to be, find opportunities for that open space, not to where it is today. And that's the notion of whether it's soccer or basketball or of course hockey, skate to the puck is obviously a famous term. So, how do you stay ahead of that disruption curve in a space like analytics? What are the innovation opportunities that organizations should be tapping today and beyond? I think I was thinking about this a lot myself, which is the important thing is to be ready to unlearn. And I know it is a simple thing, but it's one of the most difficult things because as you grow up in the organizations, as you become an exec, as you gain more experience, we actually calcify our knowledge. And that's a problem because things are changing. There are new way to do things, new opportunities. Being open to unlearning is going to be more critical than learning new things sometimes. And that will require humility. So I won't say it's a learn, go learn AI or go learn a new language or Python or coding. Those things might be necessary, but having that mentality of willing to unlearn and then having the courage to make some difficult decisions. If you do those two things, I think this is an exciting world. And if you're not, you're going to go the wayside of a lot of industries have been going. It's great advice. I mean, we saw that a lot coming into the pandemic. There was a lot of complacency around digital and there isn't anymore. Siddish, thanks so much for joining me in this CUBE conversation. It's always great to talk to you. Thank you for taking the time. I appreciate it. My pleasure. And thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time.