>>Yeah, yeah. >>Welcome back to session, too. Thoughts about everywhere. Unlock new revenue streams with embedded search and I Today we're joined by our senior director of Global Oh am Rick Dimel, along with speakers from our thoughts about customer Hayes to discuss how thought spot is open for everyone by unlocking unprecedented value through data search in A I, you'll see how thoughts about compound analytics in your applications and hear how industry leaders are creating new revenue streams with embedded search and a I. You'll also learn how to increase app stickiness on how to create an autonomous this experience for your end users. I'm delighted to introduce our senior director of Global OPM from Phillips Spot, Rick DeMARE on then British Ramesh, chief technology officer, and Leon Roof, director of product management, both from Hayes over to you. Rick, >>Thank you so much. I appreciate it. Hi, everybody. We're here to talk to you about Fox Spot everywhere are branded version of our embedded analytics application. It really our analytics application is all about user experience. And in today's world, user experience could mean a lot of things in ux design methodologies. We want to talk about the things that make our product different from an embedded perspective. If you take a look at what product managers and product design people and engineers are doing in this space, they're looking at a couple of key themes when they design applications for us to consume. One of the key things in the marketplace today is about product led growth, where the product is actually the best marketing tool for the business, not even the sales portion or the marketing department. The product, by the word of mouth, is expanding and getting more people onto the system. Why is that important? It's important because within the first few days of any application, regardless of what it is being used binding users, 70% of those users will lose. Interest will stop coming back. Why do they stop coming back? Because there's no ah ha moment through them. To get engaged within the technology, today's technologies need to create a direct relationship with the user. There can't be a gatekeeper between the user and the products, such as marketing or sales or information. In our case. Week to to make this work, we have toe leverage learning models in leverage learning as it's called Thio. Get the user is engaged, and what that means is we have to give them capabilities they already know how to use and understand. There are too many applications on the marketplace today for for users to figure out. So if we can leverage the best of what other APS have, we can increase the usage of our systems. Because in today's world, what we don't want to do from a product perspective is lead the user to a dead end or from a product methodology. Our perspective. It's called an empty state, and in our world we do that all the time. In the embedded market place. If you look at at the embedded marketplace, it's all visualizations and dashboards, or what I call check engine lights in your application's Well, guess what happens when you hit a check engine life. You've got to call the dealer to get more information about what just took place. The same thing happens in the analytic space where we provide visualizations to users. They get an indicator, but they have to go through your gatekeepers to get access to the real value of that data. What am I looking at? Why is it important the best user experiences out on the marketplace today? They are autonomous. If we wanna leverage the true value of digital transformation, we have to allow our developers to develop, not have them, the gatekeepers to the rial, content to users want. And in today's world, with data growing at much larger and faster levels than we've ever seen. And with that shelf life or value of that data being much shorter and that data itself being much more fragmented, there's no developer or analysts that can create enough visualizations or dashboards in the world to keep the consumption or desire for these users to get access to information up to speed. Clients today require the ability to sift through this information on their own to customize their own content. And if we don't support this methodology, our users are gonna end up feeling powerless and frustrated and coming back to us. The gatekeepers of that information for more information. Loyalty, conversely, can be created when we give the users the ability toe access this information on their own. That is what product like growth is all about in thought spot, as you know we're all about search. It's simple. It's guided as we type. It gives a super fast responses, but it's also smart on the back end handling complexities, and it's really safe from a governance and as well as who gets access to what perspective it's unknown learned environment. Equally important in that learned environment is this expectation that it's not just search on music. It's actually gonna recommend content to me on the fly instantly as I try content I might not even thought of before. Just the way Spotify recommends music to us or Netflix recommends a movie. This is a expected learned behavior, and we don't want to support that so that they can get benefit and get to the ah ha moments much quicker. In the end, which consumption layer do you want to use, the one that leads you to the Dead End Street or the one that gets you to the ah ha moment quickly and easily and does it in an autonomous fashion. Needless to say, the benefits of autonomous user access are well documented today. Natural language search is the wave of the future. It is today. By 2004 75% of organizations are going to be using it. The dashboard is dead. It's no longer going to be utilized through search today, I if we can improve customer satisfaction and customer productivity, we're going to increase pretensions of our retention of our applications. And if we do that just a little bit, it's gonna have a tremendous impact to our bottom line. The way we deploy hotspots. As you know, from today's conversations in the cloud, it could be a manage class, not offering or could be software that runs in your own VPC. We've talked about that at length at this conference. We've also talked about the transformation of application delivery from a Cloud Analytics perspective at length here it beyond. But we apply those same principles to your product development. The benefits are astronomical because not only do you get architectural flexibility to scale up and scale down and right size, but your engineers will increase their productivity because their offerings, because their time and effort is not going to be spent on delivering analytics but delivering their offerings. The speed of innovation isn't gonna be released twice a year or four times a year. It's gonna It can happen on a weekly basis, so your time to market in your margins should increase significantly. At this point, I want a hand. The microphone over to Revert. Tesche was going to tell you a little bit about what they're doing. It hes for cash. >>Thanks, Rick. I just want to introduce myself to the audience. My name is Rotational. Mention the CTO Europe ace. I'm joined my today by my colleague Gillian Ruffles or doctor of product management will be demoing what we have built with thoughts about, >>um but >>just to my introduction, I'm going to talk about five key things. Talk about what we do. What hes, uh we have Really, um what we went through the select that spot with other competitors What we have built with that spot very quickly and last but not least, some lessons learned during the implementation. So just to start with what we do, uh, we're age. We are health care compliance and revenue integrity platform were a saas platform voter on AWS were very short of l A. That's it. Use it on these around 1 50 customers across the U. S. On these include large academic Medical Insight on. We have been in the compliant space for the last 30 plus years, and we were traditionally consulting company. But very recently we have people did more towards software platform model, uh, in terms off why we chose that spot. There were three business problems that I faced when I took this job last year. At age number one is, uh, should be really rapidly deliver new functionality, nor platform, and he agile because some of our product development cycles are in weeks and not months. Hey had a lot of data, which we collected traditionally from the SAS platform, and all should be really create inside stretch experience for our customers. And then the third Big one is what we saw Waas large for customers but really demanding self service capabilities. But they were really not going for the static dash boats and and curated content, but instead they wanted to really use the cell service capabilities. Thio mind the data and get some interesting answers during their questions. So they elevated around three products around these problems statements, and there were 14 reasons why we just start spot number one wars off course. The performance and speed to insights. Uh, we had around 800 to a billion robot of data and we wanted to really kind of mind the data and set up the data in seconds on not minutes and hours. We had a lot of out of the box capabilities with that spot, be it natural language search, predictive algorithms. And also the interactive visualization, which, which was which, Which gave us the agility Thio deliver these products very quickly. And then, uh, the end user experience. We just wanted to make sure that I would users can use this interface s so that they can very quickly, um, do some discovery of data and get some insights very quickly. On last but not least, talksport add a lot of robust AP ice around the platform which helped us embed tot spot into are offering. But those are the four key reasons which we went for thoughts part which we thought was, uh, missing in in the other products we evaluated performance and search, uh, the interactive visualization, the end user experience, and last but not least flexible AP ice, which we could customize into our platform in terms of what we built. We were trying to solve to $50 billion problem in health care, which is around denials. Um so every year, around 2, 50 to $300 billion are denied by players thes air claims which are submitted by providers. And we built offering, which we called it US revenue optimizer. But in plain English, what revenue optimizer does is it gives the capability tow our customers to mind that denials data s so that they can really understand why the claims were being denied. And under what category? Recent reasons. We're all the providers and quarters who are responsible for these claims, Um, that were dryland denials, how they could really do some, uh, prediction off. It is trending based on their historical denial reasons. And then last but not least, we also build some functionality in the platform where we could close the loop between insights, action and outcome that Leon will be showing where we could detect some compliance and revenue risks in the platform. On more importantly, we could, uh, take those risks, put it in a I would say, shopping card and and push it to the stakeholders to take corrective action so the revenue optimizer is something which we built in three months from concept to lunch and and that that pretty much prove the value proposition of thoughts. But while we could kind of take it the market within a short period of time Next leopard >>in terms >>off lessons learned during the implementation thes air, some of the things that came to my mind asses, we're going through this journey. The first one is, uh, focus on the use case formulation, outcomes and wishful story boarding. And that is something that hot spot that's really balance. Now you can you can focus on your business problem formulation and not really focus on your custom dash boarding and technology track, etcetera. So I think it really helped our team to focus on the versus problem, to focus on the outcomes from the problem and more importantly, really spend some time on visualizing What story are we say? Are we trying to say to our customers through revenue optimizer The second lesson learned first When we started this implementation, we did not dualistic data volume and capacity planning exercise and we learned it our way. When we are we loaded a lot of our data sets into that spot. And then Aziz were doing performance optimization. XYZ. We figured out that we had to go back and shot the infrastructure because the data volumes are growing exponentially and we did not account for it. So the biggest lesson learned This is part of your architectural er planning, exercise, always future proof your infrastructure and make sure that you work very closely with the transport engineering team. Um, to make sure that the platform can scale. Uh, the last two points are passport as a robust set of AP Ice and we were able to plug into those AP ice to seamlessly ended the top spot software into a platform. And last but not least, one thing I would like to closest as we start these projects, it's very common that the solution design we run into a lot of surprises. The one thing I should say is, along those 12 weeks, we very closely work with the thoughts, part architecture and accounting, and they were a great partner to work with us to really understand our business problem, and they were along the way to kind of government suggested, recommends and workarounds and more importantly, also, helpers put some other features and functionality which you requested in their engineering roadmap. So it's been a very successful partnership. Um, So I think the biggest take of it is please make sure that you set up your project and operating model value ember thoughts what resources and your team to make sure that they can help you as you. It's some obstacles in the projects so that you can meet your time ones. Uh, those are the key lessons learned from the implementation. And with that, I would pass this to my colleague Leon Rough was going to show you a demo off what we go. >>Thanks for Tesh. So when we were looking Thio provide this to our customer base, we knew that not everyone needed do you access or have available to them the same types of information or at the same particular level of information. And we do have different roles within RMD auto Enterprise platform. So we did, uh, minimize some roles to certain information. We drew upon a persona centric approach because we knew that those different personas had different goals and different reasons for wanting to drive into these insights, and those different personas were on three different levels. So we're looking at the executive level, which is more on the C suite. Chief Compliance Officer. We have a denial trending analyses pin board, which is more for the upper, uh, managers and also exact relatives if they're interested. And then really, um, the targeted denial analysis is more for the day to day analysts, um, the usage so that they could go in and they can really see where the trends are going and how they need to take action and launch into the auditing workflow so within the executive or review, Um, and not to mention that we were integrating and implementing this when everyone was we were focused on co vid. So as you can imagine, just without covert in the picture, our customers are concentrated on denials, and that's why they utilize our platform so they could minimize those risks and then throw in the covert factor. Um, you know, those denial dollars increase substantially over the course of spring and the summer, and we wanted to be able to give them ah, good view of the denials in aggregate as well as's we focus some curated pin boards specific to those areas that were accounting for those high developed denials. So on the Executive Overview Board, we created some banner tiles. The banner tiles are pretty much a blast of information for executives thes air, particular areas where there concentrating and their look looking at those numbers consistently so it provides them away to take a good look at that and have that quick snapshot. Um, more importantly, we did offer as I mentioned some curated pin boards so that it would give customers this turnkey access. They wouldn't necessarily have to wonder, You know, what should I be doing now on Day one, but the day one that we're providing to them these curated insights leads the curiosity and increases that curiosity so that they can go in and start creating their own. But the base curated set is a good overview of their denial dollars and those risks, and we used, um, a subject matter expert within our organization who worked in the field. So it's important to know you know what you're targeting and why you're targeting it and what's important to these personas. Um, not everyone is necessarily interests in all the same information, and you want to really hit on those critical key point to draw them and, um, and allowed them that quick access and answer those questions they may have. So in this particular example, the curated insight that we created was a monthly denial amount by functional area. And as I was mentioning being uber focused on co vid, you know, a lot of scrutiny goes back to those organizations, especially those coding and H i M departments, um, to ensure that their coding correctly, making sure that players aren't sitting on, um, those payments or denying those payments. So if I were in executive and I came in here and this was interesting to me and I want to drill down a little bit, I might say, You know, let me focus more on the functional area than I know probably is our main concern. And that's coating and h i M. And because of it hit in about the early winter. I know that those claims came in and they weren't getting paid until springtime. So that's where I start to see a spike. And what's nice is that the executive can drill down, they may have a hunch, or they can utilize any of the data attributes we made available to them from the Remittance file. So all of these data, um, attributes are related to what's being sent on the 8 35 fear familiar with the anti 8 35 file. So in particular, if I was curious and had a suspicion that these were co vid related or just want to concentrate in that area, um, we have particular flag set up. So the confirmed and suspected cases are pulling in certain diagnosis and procedure codes. And I might say 1.27 million is pretty high. Um, toe look at for that particular month, and then they have the ability to drill down even further. Maybe they want to look at a facility level or where that where that's coming from. Furthermore, on the executive level, we did take advantage of Let me stop here where, um also provided some lagged a so leg. This is important to organizations in this area because they wanna know how long does it take before they re submit a claim that was originally denied before they get paid industry benchmark is about 10 days of 10 days is a fairly good, good, um, basis to look at. And then, obviously anything over that they're going to take a little bit more scrutiny on and want to drill in and understand why that is. And again, they have that capabilities in order to drill down and really get it. Those answers that they're looking for, we also for this particular pin board. And these users thought it would be helpful to utilize the time Siri's forecasting that's made available. So again, thes executives need thio need to keep track and forecast where they're trends were going or what those numbers may look like in the future. And we thought by providing the prediction pins and we have a few prediction pins, um would give them that capability to take a look at that and be able to drill down and use that within, um, certain reporting and such for their organization. Another person, a level that I will go to is, um, Mawr on the analyst side, where those folks are utilizing, um, are auditing workflow and being in our platform, creating audits, completing audits, we have it segregated by two different areas. And this is by claim types so professional or institutional, I'm going to jump in here. And then I am going to go to present mode. So in this particular, um, in this particular view or insight, we're providing that analysts view with something that's really key and critical in their organization is denials related Thio HCC s andi. That's a condition category that kind of forecast, the risk of treatment. And, you know, if that particular patient is probably going to be seen again and have more conditions and higher costs, higher health care spending. So in this example, we're looking at the top 15 attending providers that had those HCC denials. And this is, um, critical because at this point, it really peaks in analyst curiosity. Especially, You know, they'll see providers here and then see the top 15 on the top is generating Ah, hide denial rate. Hi, denial. The dollars for those HCC's and that's a that's a real risk to the organization, because if that behavior continues, um, then those those dollars won't go down. That number won't go down so that analysts then can go in and they can drill down um, I'm going to drill down on diagnosis and then look at the diagnosis name because I have a suspicion, but I'm not exactly sure. And what's great is that they can easily do this. Change the view. Um, you know, it's showing a lot of diagnoses, but what's important is the first one is sepsis and substance is a big one. Substances something that those organizations see a lot of. And if they hover, they can see that 49.57 million, um, is attributed to that. So they may want to look further into that. They'd probably be interested in closing that loop and creating an audit. And so what allowed us to be able to do that for them is we're launching directly into our auditing workflow. So they noticed something in the carried insight. It sparked some investigation, and then they don't have to leave that insight to be able to jump into the auditing workflow and complete that. Answer that question. Okay, so now they're at the point where we've pulled back all the cases that attributed to that dollar amount that we saw on the Insight and the users launching into their auditing workflow. They have the ability Thio select be selective about what cases they wanna pull into the audit or if they were looking, um, as we saw with sepsis, they could pull in their 1600 rose, but they could take a sampling size, which is primarily what they would do. They went audit all 1600 cases, and then from this point in they're into, they're auditing workflow and they'd continue down the path. Looking at those cases they just pulled in and being able Thio finalized the audit and determine, you know, if further, um, education with that provider is needed. So that concludes the demo of how we integrated thought spot into our platform. >>Thank you, LeAnn. And thank you. Re test for taking the time to walk us through. Not only your company, but how Thought spot is helping you Power analytics for your clients. At this point, we want to open this up for a little Q and A, but we want to leave you with the fact that thought spot everywhere. Specifically, it cannot only do this for Hayes, but could do it for any company anywhere they need. Analytical applications providing these applications for their customers, their partners, providers or anybody within their network for more about this, you can see that the website attached below >>Thanks, Rick and thanks for tests and Leon that I find it just fascinating hearing what our customers are doing with our technology. And I certainly have learned 100% more about sepsis than I ever knew before this session. So thank you so much for sharing that it's really is great to see how you're taking our software and putting it into your application. So that's it for this session. But do stay tuned for the next session, which is all about getting the most out of your data and amplifying your insights. With the help of A, I will be joined by two thought spot leaders who will share their first hand experiences. So take a quick breather and come right back

(bright music) >> Hello everyone, welcome today's session for the "AWS Startup Showcase" presented by theCUBE, featuring ThoughtSpot for this track and data and analytics. I'm John Furrier, your host. Today, we're joined by Victor Chang, VP of ThoughtSpot Everywhere and Corporate Development for ThoughtSpot. Victor, thanks for coming on and thanks for presenting. Talking about this building interactive data apps through ThoughtSpot Everywhere. Thanks for coming on. >> Thank you, it's my pleasure to be here. >> So digital transformation is reality. We're seeing it large-scale. More and more reports are being told fast. People are moving with modern application development and if you don't have AI, you don't have automation, you don't have the analytics, you're going to get slowed down by other forces and even inside companies. So data is driving everything, data is everywhere. What's the pitch to customers that you guys are doing as everyone realizes, "I got to go faster, I got to be more secure," (laughs) "And I don't want to get slowed down." What's the- >> Yeah, thank you John. No, it's true. I think with digital transformation, what we're seeing basically is everything is done in the cloud, everything gets done in applications, and everything has a lot of data. So basically what we're seeing is if you look at companies today, whether you are a SaaS emerging growth startup, or if you're a traditional company, the way you engage with your customers, first impression is usually through some kind of an application, right? And the application collects a lot of data from the users and the users have to engage with that. So for most of the companies out there, one of the key things that really have to do is find a way to make sense and get value for the users out of their data and create a delightful and engaging experience. And usually, that's pretty difficult these days. You know, if you are an application company, whether it doesn't really matter what you do, if you're hotel management, you're productivity application, analytics is not typically your strong suit, and where ThoughtSpot Everywhere comes in is instead of you having to build your own analytics and interactivity experience with a data, ThoughtSpot Everywhere helps deliver a really self-service interactive experience and transform your application into a data application. And with digital transformation these days, all applications have to engage, all applications have to delight, and all applications have to be self-service. And with analytics, ThoughtSpot Everywhere brings that for you to your customers and your users. >> So a lot of the mainstream enterprises and even businesses from SMB, small businesses that are in the cloud are scaling up, they're seeing the benefits. What's the problem that you guys are targeting? What's the use case? When does a potential customer or customer know they get that ThoughtSpot is needed to be called in and to work with? Is it that they want low code, no code? Is it more democratization? What's the problem statement and how do you guys turn that problem being solved into an opportunity and benefit? >> I think the key problem we're trying to solve is that most applications today, when they try to deliver analytics, really when they're delivering, is usually a static representation of some data, some answers, and some insights that are created by someone else. So usually the company would present, you know, if you think about it, if you go to your banking application, they usually show some pretty charts for you and then it sparks your curiosity about your credit card transactions or your banking transactions over the last month. Naturally, usually for me, I would then want to click in and ask the next question, which transactions fall into this category, what time, you know, change the categories a bit, usually you're stuck. So what happens with most applications? The challenge is because someone else is asking the questions and then the user is just consuming static insights, you wet their appetite and you don't satisfy it. So application users typically get stunted, they're not satisfied, and then leave application. Where ThoughtSpot comes in, ThoughtSpots through differentiation is our ability to create an interactive curiosity journey with the user. So ThoughtSpot in general, if you buy a standalone, that's the experience that we really stand by, now you can deliberate your application where the user, any user, business user, untrained, without the help of an analyst can ask their own questions. So if you see, going back to my example, if it's in your banking app, you see some kind of visualization around expense actions, you can dig in. What about last month? What about last week? Which transactions? Which merchant? You know, all those things you can continue your curiosity journey so that the business user and the app user ask their questions instead of an analyst who's sitting in the company behind a desk kind of asking your questions for you. >> And that's the outcome that everyone wants. I totally see that and everyone kind of acknowledges that, but I got to then ask you, okay, how do you make that happen? Because you've got the developers who have essentially make that happen and so, the cloud is essentially SaaS, right? So you got a SaaS kind of marketplace here. The apps can be deployed very quickly, but in order to do that, you kind of need self-service and you got to have good analytics, right? So self-service, you guys have that. Now on the analytics side, most people have to build their own or use an existing tool and tools become specialists, you know what I'm saying? So you're in this kind of like weird cycle of, "Okay, I got to deploy and spend resource to build my own, which could be long and tiresome." >> Yeah. >> "And or rely on other tools that could be good, but then I have too many tools but that creates specialism kind of silos." These seems to be trends. Do you agree with that? And if customers have this situation, you guys come in, can you help there? >> Absolutely, absolutely. So, you know, if you think about the two options that you just laid out, that you could either roll your own, kind of build your own, and that's really hard. If you think about analyst industry, where 20, $30 billion industry with a lot of companies that specialize in building analytics so it's a really tough thing to do. So it doesn't really matter how big of a company you are, even if you're a Microsoft or an Amazon, it's really hard for them to actually build analytics internally. So for a company to try to do it on their own, hire the talent and also to come up with that interactive experience, most companies fail. So what ends up happening is you deliver the budget and the time to market ends up taking much longer, and then the experience is engaging for the users and they still end up leaving your app, having a bad impression. Now you can also buy something. They are our competitors who offer embedded analytics options as well, but the mainstream paradigm today with analytics is delivering. We talked about earlier static visualizations of insights that are created by someone else. So that certainly is an option. You know, where ThoughtSpot Everywhere really stands out above everything else is our technology is fundamentally built for search and interactive and cloud-scale data kind of an experience that the static visualizations today can't really deliver. So you could deliver a static dashboard purchase from one of our competitors, or if you really want to engage your users again, today is all about self-service, it's all about interactivity, and only ThoughtSpot's architecture can deliver that embedded in a data app for you. >> You know, one of the things I'm really impressed with you guys at ThoughtSpot is that you see data as I see strategic advantage for companies and people say that it's kind of a cliche but, or a punchline, and some sort of like business statement. But when you start getting into new kinds of workflows, that's the intellectual property. If you can enable people to essentially with very little low-code, no-code, or just roll their own analysis and insights from a platform, you're then creating intellectual property for the company. So this is kind of a new paradigm. And so a lot of CIO's that I talked to, or even CSOs on the security side of like, they kind of want this but maybe can't get there overnight. So if I'm a CIO, Victor, who do I, how do I point to on my team to engage with you guys? Like, okay, you sold me on it, I love the vision. This is definitely where we want to go. Who do I bring into the meeting? >> I think that in any application, in any company actually, there's usually product leaders and developers that create applications. So, you know, if you are a SaaS company, obviously your core product, your core product team would be the right team we want to talk to. If you're a traditional enterprise, you'd be surprised actually, how many traditional enterprises that been around for 50, 100 years, you might think of them selling a different product but actually, they have a lot of visual applications and product teams within their company as well. For example, you know, we have customers like a big tractor company. You can probably imagine who they might be. They actually have visual applications that they use ThoughtSpot to offer to the dealers so that they can look at their businesses with the tractors. We also have a big telecom company, for example, that you would think about telecom as a whole service but they have a building application that they offer to their merchants to track their billing. So what I'm saying is really, whether you're a software company where that's your core product, or you're a traditional enterprise that has visual applications underneath to support your core product, there's usually product teams, product leaders, and developers. Those are the ones that we want to talk to and we can help them realize a better vision for the product that they're responsible for. >> I mean, the reality is all applications need analytics, right, at some level. >> Yes. >> Full instrumentation at a minimum log everything and then the ability to roll that up, that's where I see people always telling me like that's where the challenge seems to be. Okay, I can log everything, but now how do I have a... And then after the fact that they say, "Give me a report, what's happening?" >> That's right. >> They get stuck. >> They get stuck 'cause you get that report and you know, someone else asked that question for you and you're probably a curious person. I'm a curious person. You always have that next question, and then usually if you're in a company, let's just say, you're a CIO. You're probably used to having a team of analysts at your fingertip so at least if you have a question, you don't like the report, you can find two people, five people they'll respond to your request. But if you're a business application user, you're sitting there, I don't know about you, but I don't remember the last time I actually went through and really found a support ticket in my application, or I really read a detailed documentation describing features in application. Users like to be self-taught, self-service and they like to explore it on their own. And there's no analyst there, there's no IT guy that they can lean on so if they get a static report of the data, they'll naturally always want to ask more questions, then they're stuck. So it's that kind of unsatisfying where, "I have some curiosity, you sparked by questions, I can't answer them." That's where I think a lot of companies struggle with. That's why a lot of applications, they're data intensive but they don't deliver any insights. >> It's interesting and I like this anywhere idea because you think about like what you guys do, applications can be, they always start small, right? I mean, applications got to be built. So you guys, your solution really fits for small startups and business all the way up to large enterprises which in a large enterprise, they could have hundreds and thousands of applications which look like small startups. >> Absolutely, absolutely. You know, that's a great thing about the sort of ThoughtSpot Everywhere which takes the engine around ThoughtSpot that we built over the last eight or nine years and could deliver in any kind of a context. 'Cause nowadays, as opposed to 10, 15, 20 years ago, everything does run in applications these days. We talk about visual transformation at the beginning of the call. That's really what it means is today, the workflows of business are conducted in applications no matter who you're interacting with. And so we have all these applications. A lot of times, yes, if you have big analytical problems, you can take the data and put into a different context like ThoughtSpot's own UI and do a lot of analytics, but we also understand that a lot of times customers and users, they like to analyze in the context the workflow of the application they're actually working in. And so with that situation, actually having the analytics embedded within right next to their workflow is something that I think a lot of, especially business users that are less trained, they'd like to do that right in the context of their business productivity workflow. And so that's where ThoughtSpot Everywhere, I know the terminology is a little self-serving, but ThoughtSpot Everywhere, we think ThoughtSpot could actually be everywhere in your business workflow. >> That's great value proposition. I'm going to put my skeptic hat on challenge you and say, Okay, I don't want to... Prove it to me, what's in it for me? And how much is it going to cost me, how do I engage? So, you know- >> Yeah. >> What's in it for me as the buyer? If people want to buy this, I want to use it, I'm going to get engaged with ThoughtSpot and how much does it cost and what's the engagements look like? >> So, what's in it for you is easy. So if you have data in the cloud and you have an application, you should use ThoughtSpot Everywhere to deliver a much more valuable, interactive experience for your user's data. So that's clear. How do you engage? So we have a very flexible pricing models. If your data's in the cloud, we can either, you can purchase with us, we'll land small and then grow with your consumption. You know, that's always the kind of thing, "Hey, allow us to prove it to you, right?" We start, and then if a user starts to consume, you don't really have to pay a big bill until we see the consumption increase. So we have consumption and data capacity-based types of pricing models. And you know, one of the real advantages that we have for cloud applications is if you're a developer, often, even in the past for ThoughtSpot, we haven't always made that development experience very easy. You have to embed a relatively heavy product but the beauty for ThoughtSpot is from the beginning, we were designed with a modern API-based kind of architecture. Now, a lot of our BI competitors were designed and developed in the desktop server kind of era where everything you embed is very monolithic. But because we have an API driven architecture, we invest a lot of time now to wrap a seamless developer SDK, plus very easy to use REST APIs, plus an interactive kind of a portal to make that development experience also really simple. So if you're a developer, now you really can get from zero to an easy app for ThoughtSpot embedded in your data app in just often in less than 60 minutes. >> John: Yeah. >> So that's also a very great proposition where modern leaders is your data's in the cloud, you've got developers with an SDK, it can get you into an app very quickly. >> All right so bottom line, if you're in the cloud, you got to get the data embed in the apps, data everywhere with ThoughtSpot. >> Yes. >> All right, so let's unpack it a little bit because I think you just highlighted I think what I think is the critical factor for companies as they evaluate their plethora of tools that they have and figuring out how to streamline and be cloud native in scale. You mentioned static and old BI competitors to the cloud. They also have a team of analysts as well that just can make the executives feel like the all of the reports are dynamic but they're not, they're just static. But look at, I know you guys have a relation with Snowflake, and not to kind of bring them into this but to highlight this, Snowflake disrupted the data warehouse. >> Yes. >> Because they're in the cloud and then they refactored leveraging cloud scale to provide a really easy, fast type of value for their product and then the rest is history. They're public, they're worth a lot of money. That's kind of an example of what's coming for every category of companies. There's going to be that. In fact, Jerry Chen, who was just given the keynote here at the event, had just had a big talk called "Castles In The Cloud", you can build a moat in the cloud with your application if you have the right architecture. >> Absolutely. >> So this is kind of a new, this is a new thing and it's almost like beachfront property, whoever gets there first wins the category. >> Exactly, exactly. And we think the timing is right now. You know, Snowflake, and even earlier, obviously we had the best conference with Redshift, which really started the whole cloud data warehouse wave, and now you're seeing Databricks even with their Delta Lake and trying to get into that kind of swim lane as well. Right now, all of a sudden, all these things that have been brewing in the background in the data architecture has to becoming mainstream. We're now seeing even large financial institutions starting to always have to test and think about moving their data into cloud data warehouse. But once you're in the cloud data warehouse, all the benefits of its elasticity, performance, that can really get realized at the analytics layer. And what ThoughtSpot really can bring to the table is we've always, because we're a search-based paradigm and when you think about search. Search is all about, doesn't really matter what kind of search you're doing, it's about digging really deep into a lot of data and delivering interactive performance. Those things have always... Doesn't really matter what data architecture we sit on, I've always been really fundamental to how we build our product. And that translates extremely well when you have your data in a Snowflake or Redshift have billions of rows in the cloud. We're the only company, we think, that can deliver interactive performance on all the data you have in a cloud data warehouse. >> Well, I want to congratulate you, guys. I'm really a big fan of the company. I think a lot of companies are misunderstood until they become big and there was, "Why didn't everyone else do that search? Well, I thought they were a search engine?" Being search centric is an architectural philosophy. I know as a North Star for your company but that creates value, right? So if you look at like say, Snowflake, Redshift and Databricks, you mentioned a few of those, you have kind of a couple of things going on. You have multiple personas kind of living well together and the developers like the data people. Normally, they hated each other, right? (giggles) Or maybe they didn't hate each other but there's conflict, there's always cultural tension between the data people and the developers. Now, you have developers who are becoming data native, if you will, just by embedding that in. So what Snowflake, these guys, are doing is interesting. You can be a developer and program and get great results and have great performance. The developers love Snowflake, they love Databricks, they love Redshift. >> Absolutely. >> And it's not that hard and the results are powerful. This is a new dynamic. What's your reaction to that? >> Yeah, no, I absolutely believe that. I think, part of the beauty of the cloud is I like your kind of analogy of bringing people together. So being in the cloud, first of all, the data is accessible by everyone, everywhere. You just need a browser and the right permissions, you can get your data, and also different kind of roles. They all kind of come together. Things best of breed tools get blended together through APIs. Everything just becomes a lot more accessible and collaborative and I know that sounds kind of little kumbaya, but the great thing about the cloud is it does blur the lines between goals. Everyone can do a little bit of everything and everyone can access a little bit more of their data and get more value out of it. >> Yeah. >> So all of that, I think that's the... If you talk about digital transformation, you know, that's really at the crux of it. >> Yeah, and I think at the end of the day, speed and high quality applications is a result and I think, the speed game if automation being built in on data plays a big role in that, it's super valuable and people will get slowed down. People get kind of angry. Like I don't want to get, I want to go faster, because automations and AI is going to make things go faster on the dev side, certainly with DevOps, clouds proven that. But if you're like an old school IT department (giggles) or data department, you're talking to weeks not minutes for results. >> Yes. >> I mean, that's the powerful scale we're talking about here. >> Absolutely. And you know, if you think about it, you know, if it's days to minutes, it sounds like a lot but if you think about like also each question, 'cause usually when you're thinking about questions, they come in minutes. Every minute you have a new question and if each one then adds days to your journey, that over time is just amplified, it's just not sustainable. >> Okay- >> So now in the cloud world, you need to have things delivered on demand as you think about it. >> Yeah, and of course you need the data from a security standpoint as well and build that in. Chances is people shift left. I got to ask you if I'm a customer, I want to just run this by you. You mentioned you have an SDK and obviously talking to developers. So I'm working with ThoughtSpot, I'm the leader of the organization. I'm like, "Okay, what's the headroom? What's going to happen as a bridge, the future gets built so I'm going to ride with ThoughtSpot." You mentioned SDK, how much more can I do to build and wrap around ThoughtSpot? Because obviously, this kind of value proposition is enabling value. >> Yes. >> So I want to build around it. How do I get started and where does it go? >> Yeah, well, you can get started as easy as starting with our free trial and just play around with it. And you know, the beauty of SDK and when I talk about how ThoughtSpot is built with API-driven architecture is, hey, there's a lot of magic and features built into ThoughtSpot core pod. You could embed all of that into an application if you would like or you could also use our SDK and our APIs to say, "I just want to embed a couple of visualizations," start with that and allow the users to take into that. You could also embed the whole search feature and allow users to ask repetitive questions, or you can have different role-based kind of experiences. So all of that is very flexible and very dynamic and with SDK, it's low-code in the sense where it creates a JavaScript portal for you and even for me who's haven't coded in a long time. I can just copy and paste some JavaScript code and I can see my applications reflecting in real time. So it's really kind of a modern experience that developers in today's world appreciate, and because all the data's in the cloud and in the cloud, applications are built as services connected through APIs, we really think that this is the modern way that developers would get started. And analysts, even analysts who don't have strong developer training can get started with our developer portal. So really, it's a very easy experience and you can customize it in whichever way you want that suits your application's needs. >> Yeah, I think it's, you don't have to be a developer to really understand the basic value of reuse and discovery of services. I think that's one of these we hear from developers all the time, "I had no idea that Victor did that code. Why do I have to rewrite that?" So you see, reuse come up a lot around automation where code is building with code, right? So you have this new vibe and you need data to discover that search paradigm mindset. How prevalent is that on the minds of customers? Are they just trying to like hold on and survive through the pandemic? (giggles) >> Well, customers are definitely thinking about it. You know, the challenge is change is always hard, you know? So it takes time for people to see the possibilities and then have to go through especially in larger organizations, but even in smaller organizations, people think about, "Well, how do I change my workflow?" and then, "How do I change my data pipeline?" You know, those are the kinds of things where, you know, it takes time, and that's why Redshift has been around since 2012 or I believe, but it took years before enterprises really are now saying, "The benefits are so profound that we really have to change the workflows, change the data pipelines to make it work because we can't hold on to the old ways." So it takes time but when the benefits are so clear, it's really kind of a snowball effect, you know? Once you change a data warehouse, you got to think about, "Do I need to change my application architecture?" Then, "Do I need to change the analytics layer?" And then, "Do I need to change the workflow?" And then you start seeing new possibilities because it's all more flexible that you can add more features to your application and it's just kind of a virtuous cycle, but it starts with taking that first step to your point of considering migrating your data into the cloud and we're seeing that across all kinds of industries now. I think nobody's holding back anymore. It just takes time, sometimes some are slower and some are faster. >> Well, all apps or data apps and it's interesting, I wrote a blog post in 2017 called, "Data Is The New Developer Kit" meaning it was just like a vision statement around data will be part of how apps, like software, it'll be data as code. And you guys are doing that. You're allowing data to be a key ingredient for interactivity with analytics. This is really important. Can you just give us a use case example of how someone builds an interactive data app with ThoughtSpot Everywhere? >> Yeah, absolutely. So I think there are certain applications that when naturally things relates to data, you know, I talk about bending or those kinds of things. Like when you use it, you just kind of inherently know, "Hey, there's tons of data and then can I get some?" But a lot of times we're seeing, you know, for example, one of our customers is a very small company that provides software for personal trainers and small fitness studios. You know, you would think like, "Oh well, these are small businesses. They don't have a ton of data. A lot of them would probably just run on QuickBooks or Excel and all of that." But they could see the value is kind of, once a personal trainer conducts his business on a cloud software, then he'll realize, "Oh, I don't need to download any more data. I don't need to run Excel anymore, the data is already there in a software." And hey, on top of that, wouldn't it be great if you have an analytics layer that can analyze how your clients paid you, where your appointments are, and so forth? And that's even just for, again like I said, no disrespect to personal trainers, but even for one or two personal trainers, hey, they can be an analytics and they could be an analyst on their business data. >> Yeah, why not? Everyone's got a Fitbits and watches and they could have that built into their studio APIs for the trainers. They can get collaboration. >> That's right. So there's no application you can think that's too simple or you might think too traditional or whatnot for analytics. Every application now can become a very engaging data application. >> Well Victor, it's great to have you on. Obviously, great conversation around ThoughtSpot anywhere. And as someone who runs corp dev for ThoughtSpot, for the folks watching that aren't customers yet for ThoughtSpot, what should they know about you guys as a company that they might not know about or they should know about? And what are people talking about ThoughtsSpot, what are they saying about it? So what should they know that know that's not being talked about or they may not understand? And what are other people saying about ThoughtSpot? >> So a couple of things. One is there's a lot of fun out there. I think about search in general, search is generally a very broad term but I think it, you know, I go back to what I was saying earlier is really what differentiates ThoughtSpot is not just that we have a search bar that's put on some kind of analytics UI. Really, it's the fundamental technical architecture underlying that is from the ground up built for search large data, granular, and detailed exploration of your data. That makes us truly unique and nobody else can really do search if you're not built with a technical foundation. The second thing is, we're very much a cloud first company now, and a ton of our over the past few years because of the growth of these highly performing data warehouses like Snowflake and Redshift, we're able to really focus on what we do best which is the search and the query processing performance on the front end and we're fully engaged with cloud platforms now. So if you have data in the cloud, we are the best analytics front end for that. >> Awesome, well, thanks for coming on. Great the feature you guys here in the "Startup Showcase", great conversation, ThoughtSpot leading company, hot startup. We did their event with them with theCUBE a couple of months ago. Congratulations on all your success. Victor Chang, VP of ThoughtSpot Everywhere and Corporate Development here on theCUBE and "AWS Startup Showcase". Go to awsstartups.com and be part of the community, we're doing these quarterly featuring the hottest startups in the cloud. I'm John Furrier, thanks for watching. >> Victor: Thank you so much. (bright music)