>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle in 2000 >>nine. Hal Varian, Google's chief economist, said that statisticians would be the sexiest job in the coming decade. The modern big data movement >>really >>took off later in the following year. After the Second Hadoop World, which was hosted by Claudette Cloudera in New York City. Jeff Ham Abakar famously declared to me and John further in the Cube that the best minds of his generation, we're trying to figure out how to get people to click on ads. And he said that sucks. The industry was abuzz with the realization that data was the new competitive weapon. Hadoop was heralded as the new data management paradigm. Now, what actually transpired Over the next 10 years on Lee, a small handful of companies could really master the complexities of big data and attract the data science talent really necessary to realize massive returns as well. Back then, Cloud was in the early stages of its adoption. When you think about it at the beginning of the last decade and as the years passed, Maurin Mawr data got moved to the cloud and the number of data sources absolutely exploded. Experimentation accelerated, as did the pace of change. Complexity just overwhelmed big data infrastructures and data teams, leading to a continuous stream of incremental technical improvements designed to try and keep pace things like data Lakes, data hubs, new open source projects, new tools which piled on even Mawr complexity. And as we reported, we believe what's needed is a comm pleat bit flip and how we approach data architectures. Our next guest is Jean Marc de Connie, who is the director of emerging technologies That thought works. John Mark is a software engineer, architect, thought leader and adviser to some of the world's most prominent enterprises. She's, in my view, one of the foremost advocates for rethinking and changing the way we create and manage data architectures. Favoring a decentralized over monolithic structure and elevating domain knowledge is a primary criterion. And how we organize so called big data teams and platforms. Chamakh. Welcome to the Cube. It's a pleasure to have you on the program. >>Hi, David. This wonderful to be here. >>Well, okay, so >>you're >>pretty outspoken about the need for a paradigm shift in how we manage our data and our platforms that scale. Why do you feel we need such a radical change? What's your thoughts there? >>Well, I think if you just look back over the last decades you gave us, you know, a summary of what happened since 2000 and 10. But if even if we go before then what we have done over the last few decades is basically repeating and, as you mentioned, incrementally improving how we've managed data based on a certain assumptions around. As you mentioned, centralization data has to be in one place so we can get value from it. But if you look at the parallel movement off our industry in general since the birth of Internet, we are actually moving towards decentralization. If we think today, like if this move data side, if he said the only way Web would work the only way we get access to you know various applications on the Web pages is to centralize it. We would laugh at that idea, but for some reason we don't. We don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, you know, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organization there beyond the bounds of organization. And then look back and say Okay, if that's the trend off our industry in general, Um, given the fabric of computation and data that we put in, you know globally in place, then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me, that requires a paradigm shift, a full stack from how we organize our organizations, how we organize our teams, how we, you know, put a technology in place, um, to to look at it from a decentralized angle. >>Okay, so let's let's unpack that a little bit. I mean, you've spoken about and written that today's big architecture and you basically just mentioned that it's flawed, So I wanna bring up. I love your diagrams of a simple diagram, guys, if you could bring up ah, figure one. So on the left here we're adjusting data from the operational systems and other enterprise data sets and, of course, external data. We cleanse it, you know, you've gotta do the do the quality thing and then serve them up to the business. So So what's wrong with that picture that we just described and give granted? It's a simplified form. >>Yeah, quite a few things. So, yeah, I would flip the question may be back to you or the audience if we said that. You know, there are so many sources off the data on the Actually, the data comes from systems and from teams that are very diverse in terms off domains. Right? Domain. If if you just think about, I don't know retail, Uh, the the E Commerce versus Order Management versus customer This is a very diverse domains. The data comes from many different diverse domains. And then we expect to put them under the control off a centralized team, a centralized system. And I know that centralization. Probably if you zoom out, it's centralized. If you zoom in it z compartmentalized based on functions that we can talk about that and we assume that the centralized model will be served, you know, getting that data, making sense of it, cleansing and transforming it then to satisfy in need of very diverse set of consumers without really understanding the domains, because the teams responsible for it or not close to the source of the data. So there is a bit of it, um, cognitive gap and domain understanding Gap, um, you know, without really understanding of how the data is going to be used, I've talked to numerous. When we came to this, I came up with the idea. I talked to a lot of data teams globally just to see, you know, what are the pain points? How are they doing it? And one thing that was evident in all of those conversations that they actually didn't know after they built these pipelines and put the data in whether the data warehouse tables or like, they didn't know how the data was being used. But yet the responsible for making the data available for these diverse set of these cases, So s centralized system. A monolithic system often is a bottleneck. So what you find is, a lot of the teams are struggling with satisfying the needs of the consumers, the struggling with really understanding the data. The domain knowledge is lost there is a los off understanding and kind of in that in that transformation. Often, you know, we end up training machine learning models on data that is not really representative off the reality off the business. And then we put them to production and they don't work because the semantic and the same tax off the data gets lost within that translation. So we're struggling with finding people thio, you know, to manage a centralized system because there's still the technology is fairly, in my opinion, fairly low level and exposes the users of those technologies. I said, Let's say warehouse a lot off, you know, complexity. So in summary, I think it's a bottleneck is not gonna, you know, satisfy the pace of change, of pace, of innovation and the pace of, you know, availability of sources. Um, it's disconnected and fragmented, even though the centralizes disconnected and fragmented from where the data comes from and where the data gets used on is managed by, you know, a team off hyper specialized people that you know, they're struggling to understand the actual value of the data, the actual format of the data, so it's not gonna get us where our aspirations and ambitions need to be. >>Yes. So the big data platform is essentially I think you call it, uh, context agnostic. And so is data becomes, you know, more important, our lives. You've got all these new data sources, you know, injected into the system. Experimentation as we said it with the cloud becomes much, much easier. So one of the blockers that you've started, you just mentioned it is you've got these hyper specialized roles the data engineer, the quality engineer, data scientists and and the It's illusory. I mean, it's like an illusion. These guys air, they seemingly they're independent and in scale independently. But I think you've made the point that in fact, they can't that a change in the data source has an effect across the entire data lifecycle entire data pipeline. So maybe you could maybe you could add some color to why that's problematic for some of the organizations that you work with and maybe give some examples. >>Yeah, absolutely so in fact, that initially the hypothesis around that image came from a Siris of requests that we received from our both large scale and progressive clients and progressive in terms of their investment in data architectures. So this is where clients that they were there were larger scale. They had divers and reached out of domains. Some of them were big technology tech companies. Some of them were retail companies, big health care companies. So they had that diversity off the data and the number off. You know, the sources of the domains they had invested for quite a few years in, you know, generations. If they had multi generations of proprietary data warehouses on print that they were moving to cloud, they had moved to the barriers, you know, revisions of the Hadoop clusters and they were moving to the cloud. And they the challenges that they were facing were simply there were not like, if I want to just, like, you know, simplifying in one phrase, they were not getting value from the data that they were collecting. There were continuously struggling Thio shift the culture because there was so much friction between all of these three phases of both consumption of the data and transformation and making it available consumption from sources and then providing it and serving it to the consumer. So that whole process was full of friction. Everybody was unhappy. So its bottom line is that you're collecting all this data. There is delay. There is lack of trust in the data itself because the data is not representative of the reality has gone through a transformation. But people that didn't understand really what the data was got delayed on bond. So there is no trust. It's hard to get to the data. It's hard to create. Ultimately, it's hard to create value from the data, and people are working really hard and under a lot of pressure. But it's still, you know, struggling. So we often you know, our solutions like we are. You know, Technologies will often pointed to technology. So we go. Okay, This this version of you know, some some proprietary data warehouse we're using is not the right thing. We should go to the cloud, and that certainly will solve our problems. Right? Or warehouse wasn't a good one. Let's make a deal Lake version. So instead of you know, extracting and then transforming and loading into the little bits. And that transformation is that, you know, heavy process, because you fundamentally made an assumption using warehouses that if I transform this data into this multi dimensional, perfectly designed schema that then everybody can run whatever choir they want that's gonna solve. You know everybody's problem, but in reality it doesn't because you you are delayed and there is no universal model that serves everybody's need. Everybody that needs the divers data scientists necessarily don't don't like the perfectly modeled data. They're looking for both signals and the noise. So then, you know, we've We've just gone from, uh, et elles to let's say now to Lake, which is okay, let's move the transformation to the to the last mile. Let's just get load the data into, uh into the object stores into semi structured files and get the data. Scientists use it, but they're still struggling because the problems that we mentioned eso then with the solution. What is the solution? Well, next generation data platform, let's put it on the cloud, and we sell clients that actually had gone through, you know, a year or multiple years of migration to the cloud. But with it was great. 18 months I've seen, you know, nine months migrations of the warehouse versus two year migrations of the various data sources to the clubhouse. But ultimately, the result is the same on satisfy frustrated data users, data providers, um, you know, with lack of ability to innovate quickly on relevant data and have have have an experience that they deserve toe have have a delightful experience off discovering and exploring data that they trust. And all of that was still a missed so something something else more fundamentally needed to change than just the technology. >>So then the linchpin to your scenario is this notion of context and you you pointed out you made the other observation that look, we've made our operational systems context aware. But our data platforms are not on bond like CRM system sales guys very comfortable with what's in the CRM system. They own the data. So let's talk about the answer that you and your colleagues are proposing. You're essentially flipping the architecture whereby those domain knowledge workers, the builders, if you will, of data products or data services there now, first class citizens in the data flow and they're injecting by design domain knowledge into the system. So So I wanna put up another one of your charts. Guys, bring up the figure to their, um it talks about, you know, convergence. You showed data distributed domain, dream and architecture. Er this self serve platform design and this notion of product thinking. So maybe you could explain why this approach is is so desirable, in your view, >>sure. The motivation and inspiration for the approach came from studying what has happened over the last few decades in operational systems. We had a very similar problem prior to micro services with monolithic systems, monolithic systems where you know the bottleneck. Um, the changes we needed to make was always, you know, our fellow Noto, how the architecture was centralized and we found a nice nation. I'm not saying this is the perfect way of decoupling a monolith, but it's a way that currently where we are in our journey to become data driven, um is a nice place to be, um, which is distribution or decomposition off your system as well as organization. I think when we whenever we talk about systems, we've got to talk about people and teams that's responsible for managing those systems. So the decomposition off the systems and the teams on the data around domains because that's how today we are decoupling our business, right? We're decoupling our businesses around domains, and that's a that's a good thing and that What does that do really for us? What it does? Is it localizes change to the bounded context of fact business. It creates clear boundary and interfaces and contracts between the rest of the universe of the organization on that particular team, so removes the friction that often we have for both managing the change and both serving data or capability. So it's the first principle of data meshes. Let's decouple this world off analytical data the same to mirror the same way we have to couple their systems and teams and business why data is any different. And the moment you do that, So you, the moment you bring the ownership to people who understands the data best, then you get questions that well, how is that any different from silence that's connected databases that we have today and nobody can get to the data? So then the rest of the principles is really to address all of the challenges that comes with this first principle of decomposition around domain Context on the second principle is well, we have to expect a certain level off quality and accountability and responsibility for the teams that provide the data. So let's bring product thinking and treating data as a product to the data that these teams now, um share and let's put accountability around. And we need a new set of incentives and metrics for domain teams to share the data. We need to have a new set off kind of quality metrics that define what it means for the data to be a product. And we can go through that conversation perhaps later eso then the second principle is okay. The teams now that are responsible, the domain teams responsible for the analytical data need to provide that data with a certain level of quality and assurance. Let's call that a product and bring products thinking to that. And then the next question you get asked off by C. E. O s or city or the people who build the infrastructure and, you know, spend the money. They said, Well, it's actually quite complex to manage big data, and now we're We want everybody, every independent team to manage the full stack of, you know, storage and computation and pipelines and, you know, access, control and all of that. And that's well, we have solved that problem in operational world. And that requires really a new level of platform thinking toe provide infrastructure and tooling to the domain teams to now be able to manage and serve their big data. And that I think that requires reimagining the world of our tooling and technology. But for now, let's just assume that we need a new level of abstraction to hide away ton of complexity that unnecessarily people get exposed to and that that's the third principle of creating Selves of infrastructure, um, to allow autonomous teams to build their domains. But then the last pillar, the last you know, fundamental pillar is okay. Once you distributed problem into a smaller problems that you found yourself with another set of problems, which is how I'm gonna connect this data, how I'm gonna you know, that the insights happens and emerges from the interconnection of the data domains right? It does not necessarily locked into one domain. So the concerns around interoperability and standardization and getting value as a result of composition and interconnection of these domains requires a new approach to governance. And we have to think about governance very differently based on a Federated model and based on a computational model. Like once we have this powerful self serve platform, we can computational e automate a lot of governance decisions. Um, that security decisions and policy decisions that applies to you know, this fabric of mesh not just a single domain or not in a centralized. Also, really. As you mentioned that the most important component of the emissions distribution of ownership and distribution of architecture and data the rest of them is to solve all the problems that come with that. >>So very powerful guys. We actually have a picture of what Jamaat just described. Bring up, bring up figure three, if you would tell me it. Essentially, you're advocating for the pushing of the pipeline and all its various functions into the lines of business and abstracting that complexity of the underlying infrastructure, which you kind of show here in this figure, data infrastructure is a platform down below. And you know what I love about this Jama is it to me, it underscores the data is not the new oil because I could put oil in my car I can put in my house, but I can't put the same court in both places. But I think you call it polyglot data, which is really different forms, batch or whatever. But the same data data doesn't follow the laws of scarcity. I can use the same data for many, many uses, and that's what this sort of graphic shows. And then you brought in the really important, you know, sticking problem, which is that you know the governance which is now not a command and control. It's it's Federated governance. So maybe you could add some thoughts on that. >>Sure, absolutely. It's one of those I think I keep referring to data much as a paradigm shift. And it's not just to make it sound ground and, you know, like, kind of ground and exciting or in court. And it's really because I want to point out, we need to question every moment when we make a decision around how we're going to design security or governance or modeling off the data, we need to reflect and go back and say, um, I applying some of my cognitive biases around how I have worked for the last 40 years, I have seen it work. Or do I do I really need to question. And we do need to question the way we have applied governance. I think at the end of the day, the rule of the data governance and objective remains the same. I mean, we all want quality data accessible to a diverse set of users. And these users now have different personas, like David, Personal data, analyst data, scientists, data application, Um, you know, user, very diverse personal. So at the end of the day, we want quality data accessible to them, um, trustworthy in in an easy consumable way. Um, however, how we get there looks very different in as you mentioned that the governance model in the old world has been very commander control, very centralized. Um, you know, they were responsible for quality. They were responsible for certification off the data, you know, applying making sure the data complies. But also such regulations Make sure you know, data gets discovered and made available in the world of the data mesh. Really. The job of the data governance as a function becomes finding that equilibrium between what decisions need to be um, you know, made and enforced globally. And what decisions need to be made locally so that we can have an interoperable measure. If data sets that can move fast and can change fast like it's really about instead of hardest, you know, kind of putting the putting those systems in a straitjacket of being constant and don't change, embrace, change and continuous change of landscape because that's that's just the reality we can't escape. So the role of governance really the governance model called Federated and Computational. And by that I mean, um, every domain needs to have a representative in the governance team. So the role of the data or domain data product owner who really were understand the data that domain really well but also wears that hacks of a product owner. It is an important role that had has to have a representation in the governance. So it's a federation off domains coming together, plus the SMEs and people have, you know, subject matter. Experts who understands the regulations in that environmental understands the data security concerns, but instead off trying to enforce and do this as a central team. They make decisions as what need to be standardized, what need to be enforced. And let's push that into that computational E and in an automated fashion into the into the camp platform itself. For example, instead of trying to do that, you know, be part of the data quality pipeline and inject ourselves as people in that process, let's actually, as a group, define what constitutes quality, like, how do we measure quality? And then let's automate that and let Z codify that into the platform so that every native products will have a C I City pipeline on as part of that pipeline. Those quality metrics gets validated and every day to product needs to publish those SLOC or service level objectives. So you know, whatever we choose as a measure of quality, maybe it's the, you know, the integrity of the data, the delay in the data, the liveliness of it, whatever the are the decisions that you're making, let's codify that. So it's, um, it's really, um, the role of the governance. The objectives of the governance team tried to satisfies the same, but how they do it. It is very, very different. I wrote a new article recently trying to explain the logical architecture that would emerge from applying these principles. And I put a kind of light table to compare and contrast the roll off the You know how we do governance today versus how we will do it differently to just give people a flavor of what does it mean to embrace the centralization? And what does it mean to embrace change and continuous change? Eso hopefully that that that could be helpful. >>Yes, very so many questions I haven't but the point you make it to data quality. Sometimes I feel like quality is the end game. Where is the end game? Should be how fast you could go from idea to monetization with the data service. What happens again? You sort of address this, but what happens to the underlying infrastructure? I mean, spinning a PC to S and S three buckets and my pie torches and tensor flows. And where does that that lives in the business? And who's responsible for that? >>Yeah, that's I'm glad you're asking this question. Maybe because, um, I truly believe we need to re imagine that world. I think there are many pieces that we can use Aziz utilities on foundational pieces, but I but I can see for myself a 5 to 7 year roadmap of building this new tooling. I think, in terms of the ownership, the question around ownership, if that would remains with the platform team, but and perhaps the domain agnostic, technology focused team right that there are providing instead of products themselves. And but the products are the users off those products are data product developers, right? Data domain teams that now have really high expectations in terms of low friction in terms of lead time to create a new data product. Eso We need a new set off tooling, and I think with the language needs to shift from, You know, I need a storage buckets. So I need a storage account. So I need a cluster to run my, you know, spark jobs, too. Here's the declaration of my data products. This is where the data for it will come from. This is the data that I want to serve. These are the policies that I need toe apply in terms of perhaps encryption or access control. Um, go make it happen. Platform, go provision, Everything that I mean so that as a data product developer. All I can focus on is the data itself, representation of semantic and representation of the syntax. And make sure that data meets the quality that I have that I have to assure and it's available. The rest of provisioning of everything that sits underneath will have to get taken care of by the platform. And that's what I mean by requires a re imagination and in fact, Andi, there will be a data platform team, the data platform teams that we set up for our clients. In fact, themselves have a favorite of complexity. Internally, they divide into multiple teams multiple planes, eso there would be a plane, as in a group of capabilities that satisfied that data product developer experience, there would be a set of capabilities that deal with those need a greatly underlying utilities. I call it at this point, utilities, because to me that the level of abstraction of the platform is to go higher than where it is. So what we call platform today are a set of utilities will be continuing to using will be continuing to using object storage, will continue using relation of databases and so on so there will be a plane and a group of people responsible for that. There will be a group of people responsible for capabilities that you know enable the mesh level functionality, for example, be able to correlate and connects. And query data from multiple knows. That's a measure level capability to be able to discover and explore the measure data products as a measure of capability. So it would be set of teams as part of platforms with a strong again platform product thinking embedded and product ownership embedded into that. To satisfy the experience of this now business oriented domain data team teams s way have a lot of work to do. >>I could go on. Unfortunately, we're out of time. But I guess my first I want to tell people there's two pieces that you put out so far. One is, uh, how to move beyond a monolithic data lake to a distributed data mesh. You guys should read that in a data mesh principles and logical architectures kind of part two. I guess my last question in the very limited time we have is our organization is ready for this. >>E think the desire is there I've bean overwhelmed with number off large and medium and small and private and public governments and federal, you know, organizations that reached out to us globally. I mean, it's not This is this is a global movement and I'm humbled by the response of the industry. I think they're the desire is there. The pains are really people acknowledge that something needs to change. Here s so that's the first step. I think that awareness isa spreading organizations. They're more and more becoming aware. In fact, many technology providers are reach out to us asking what you know, what shall we do? Because our clients are asking us, You know, people are already asking We need the data vision. We need the tooling to support. It s oh, that awareness is there In terms of the first step of being ready, However, the ingredients of a successful transformation requires top down and bottom up support. So it requires, you know, support from Chief Data Analytics officers or above the most successful clients that we have with data. Make sure the ones that you know the CEOs have made a statement that, you know, we want to change the experience of every single customer using data and we're going to do, we're going to commit to this. So the investment and support, you know, exists from top to all layers. The engineers are excited that maybe perhaps the traditional data teams are open to change. So there are a lot of ingredients. Substance to transformation is to come together. Um, are we really ready for it? I think I think the pioneers, perhaps the innovators. If you think about that innovation, careful. My doctors, probably pioneers and innovators and leaders. Doctors are making making move towards it. And hopefully, as the technology becomes more available, organizations that are less or in, you know, engineering oriented, they don't have the capability in house today, but they can buy it. They would come next. Maybe those are not the ones who aren't quite ready for it because the technology is not readily available. Requires, you know, internal investment today. >>I think you're right on. I think the leaders are gonna lead in hard, and they're gonna show us the path over the next several years. And I think the the end of this decade is gonna be defined a lot differently than the beginning. Jammeh. Thanks so much for coming in. The Cuban. Participate in the >>program. Pleasure head. >>Alright, Keep it right. Everybody went back right after this short break.

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi everybody, welcome to this CUBE conversation. This is Dave Vellante, and as part of my CEO and CXO series I've been bringing in leaders around the industry and I'm really pleased to have Sudheesh Nair, who is the CEO of ThoughtSpot Cube alum. Great to see you against Sudheesh, thanks for coming on. >> My pleasure Dave. Thank you so much for having me. I hope everything is well with you and your family. >> Yeah ditto back at you. I know you guys were in a hot spot for a while so you know we power on together, so I got to ask you. You guys are AI specialists, maybe sometimes you can see things before they happen. At what point did you realize that this COVID-19 was really going to be something that would affect businesses globally and then specifically your business. >> Yeah it's amazing, isn't it? I mean we used to think that in Silicon Valley we are sitting at the top of the world. AI and artificial intelligence, machine learning, Cloud, IOT and all of a sudden this little virus comes in and put us all in our places basically. We are all waiting for doctors and others to figure these things out so we can actually go outside. That tells you all about what is really important in life sometimes. It's been a hard journey for most people because of what a huge health event this has been. From a Silicon Valley point of view and specifically from artificial intelligence point of view, there is not a lot of history here that we can use to predict the future, however early February we had our sales kick off and we had a lot of our sellers who came from Asia and it became sort of clear to us immediately during our sales kick off in Napa Valley that this is not like any other event. The sort of things that they were going through in Asia we sort of realized immediately that us and when it gets to the shores of the US, this is going to really hurt. So we started hunkering down as a company, but as you mentioned early when we were talking, California in general had a head start, so we've been hunkered down for almost five weeks now, as a company and as the people and the results are showing. You know it is somewhat contained. Now obviously the real question is what next? How do we go out? But that's probably the next journey. >> So a lot of the executives that I've talked to, of course they start with the number one importance is the health and well-being of our employees. We set up the work from home infrastructure, et cetera. So that's I think, been fairly well played in the media and beginning to understand that pretty well. Also, you saw I talked to Frank Slootman and he's sort of joked about the Sequoia memos, that you know eliminate unnecessary expenses and practices. I've always eliminated unnecessary expenses, keep it to the essentials, but one of the things that I haven't probed with CEOs and I'd love your thoughts on this is, did you have to rethink sort of the ideal customer profile and your value proposition in the specific context of COVID? Was that something that you deliberately did? >> Yeah so it's a really important question that you asked, and I saw the Frank interview and I a 100% agree with that. Inside the company we have this saying, and our co-founder Ajeet actually coined the phrase of living like a middle-class company, and we've always lived that, even though we have, 300 plus million dollars in the bank and we raised a big round last year. It is important to know that as a growth stage company, we are not measured on what's in the bank. It's about the value that we are delivering and how much I'll be able to collect from customers to run the business. The living like a middle-class family has always been the ethos of the company and that has been a good thing. However, I've been with ThoughtSpot for a little more than 18 months. I joined as the CEO. I was an early investor in the company and there are a couple of big changes that we made in the last 18 months, and one of is moving to Cloud which we can talk. The other one has been around narrowing our focus on who we sell to, because one of the things that, as you know very well Dave, is that the world of data is extremely complex. Every company can come in and say, "We have the best solution out there" and it can just be in the world, but the reality is no single product is going to solve every problem for a customer when it comes to a data analytics issue. All we can hope for is that we become part of a package or solution that solves a very specific problem, so in that context there's a lot of services involved, a lot of understanding of customer problems involved. We are not a bi-product in the sense of Tableau or click on Microsoft, but they do. We are about a use case based outcomes, so we knew that we can't be everywhere. So the second change we made is actually a narrower focus, exclusively sell to global. That class, the middle class mentality, really paid off now because almost all the customers we sell to are very large customers and the four work verticals that we were seeing tremendous progress, one was healthcare, second was financial sector, the third was telecom and manufacturing and the last one is repair. Out of these four, I would say manufacturing is the one where we have seen a slowdown, but the other verticals have been, I would say cautiously spending. Being very responsible and thus far, I'm not here to say that everything is fine, but the impact if you take Zoom as a spectrum, on one end of the spectrum, where everything is doing amazingly well, because they are a good product market fit to hospitality industry on the other side. I would say ThoughtSpot and our approach to data analytics is closer to this than that. >> That's very interesting Sudheesh because, of course health care, I don't think they have time to do anything right now. I mean they're just so overwhelmed so that's obviously an interesting area that's going to continue to do well I would think. And they, the Financial Services guys, there's a lot of liquidity in the system and after 2009 the FinTech guys or the financial, the banks are doing quite well. They may squeeze you a little bit because they're smart negotiators, but as you say manufacturing with the supply chains, and in retail, look, if your ecommerce I mean Amazon hit, all-time highs today up whatever, 20% in the last two weeks. I mean just amazing what's happening, so it's really specific parts of those sectors will continue to do well, won't they? >> Absolutely, I think look, I saw this joke on Twitter, what's the number one cost? What is in fact (mic cuts out). Very soon people will say it is COVID and even businesses that have been tried to, sort of relatively, reluctant to really embrace the transformation that the customers have been asking for. This has become the biggest forcing function and that's actually a good thing because consumers are going to ultimately win because once you get groceries delivered to you into your front doors, it's going to be hard to sort of go back to standing in the line in Costco, when InstaCart can actually deliver it for you and you get used to it, so there are some transformation that is going to happen because of COVID. I don't think that society will go back from, but having said that, it's also not transformation for the sake of transformation. So speaking from our point of view on data analytics, I sort of believe that the last three to four years we have been sort of living in the Renaissance of enterprise data analytics and that's primarily because of three things. The first thing, every consumer is expecting, no matter how small or the big business, is to get to know them. You know, I don't want you to treat me like an average. I don't want you treat me like a number. Treat me like a person, which means understand me but personalize the services you are delivering and make sure that everything that you send me are relevant. If there's a marketing campaign or promo or customer support call, make sure it's relevant. The relevance and personalization. The second is, in return for that. customers are willing to give you all sorts of data. The privacy, be damned, so to a certain extent they are giving you location information, medical information,-- And the last part is with Cloud, the amount of data that you can collect and free plus in data warehouse like Snowflakes, like Redshift. It's been fundamentally shifted, so when you toggle them together the customers demand for better actors from the business, then amount of data that they're willing to give and collect to IOT and variables and then cloud-based technologies that allows you to process and store this means that analyzing this data and then delivering relevant actions to the consumers is no longer a nice to have and that I think is part of the reason why ThoughtSpot is finding sort of a tailwind, even with all this global headwind that we are all in. >> Well I think too, the innovation formula really has changed in our industry. I've said many times, it's not Moore's law anymore, it's the combination of data plus AI applied to that data and Cloud for scale and you guys are at the heart of that, so I want to talk about the market space a little bit. You look at BI and analytics, you look at the market. You know the Gartner Magic Quadrant and to your point, you know the companies on there are sort of chalk and cheese, to borrow a phrase from our friends across the pond. I mean, you're not power BI, you're not SaaS. I mean you're sort of search led. You're turning natural language into complex sequel queries. You're bringing in artificial intelligence and machine intelligence to really simplify and dramatically expand and put into the hands of business people analytics. So explain a little bit. First of all, do I have that sort of roughly right? And help us frame the market space how you think about it. >> Yeah I mean first of all, it is amazing that the diverse industry and technologies that you speak to and how you are able to grasp all of them and summarize them within a matter of seconds is a term to understand in itself. You and Stew, you both have that. You are absolutely right. So the way I think of this is that BI technologies have been around and it's played out really well. It played it's part. I mean if you look at it the way I think of BI, the most biggest BI tool is still Excel. People still want to use Excel and that is the number one BI tool ever. Then 10 years ago Tableau came in and made visualizations so delightful and a pic so to speak. That became the better way to consume complex data. Then Microsoft came in Power BI and then commoditized and the visualization to a point that, you know Tableau had to fight and it ended up selling to the Salesforce. We are not trying to play there because I think if you chase the idea of visualization it is going to be a long hard journey for ThoughtSpot to catch Tableau in visualization. That's not what we are trying to do. What we are trying to do is that you have a lot of data on one hand and you have a consumer sitting here and saying data doesn't mean you treated me well. What is my action that is this quote, very customized action quote. And our question is, how does beta turn into bespoke action inside a business? The insurance company is calling. You are calling an insurance company's customer support person. How do you know that the impact that you are getting from them is customized. But turning data into insight is an algorithmic process. That's what BI does, but that's like a few people in an organization can do that. Think of them like oil. They don't mix with water, that's the business people. The merchandising specialist who figures out which one should become site and what should be the price what should be ranking. That's the merchandiser. Their customer support person, that's a business user. They don't necessarily do Python or SQL, so what happens is in businesses you have the data people like water and the business people who touch the customer and interact with them every day, they're like the water. They don't mix. The idea of ThoughtSpot is very simple. We don't want this demarcation. We don't want this chasm. We want to break it so that every single person who interact with the customer should be able to have an interactive storytelling with the data, so that every decision that they make takes data into insight to knowledge to action, and that decision-making pipeline cannot be gut driven alone. It has to be enabled by data science and human experience coming together. So in our view, a well deployed data platform, decision-making platform, will enhance and augment human experience, as opposed to human experience says, this data says that, so you've got to pick one. That's an old model and that has been the approach with natural language based interactive access with the BI being done automated through AI in the backend, parts what we are able to put very complex data science in front of a 20 year experienced merchandising specialist in a large e-commerce website without learning Python, without learning people, without understanding data warehouse >> Right so, a couple of things I want to pick up on. I mean data is plentiful, insights aren't. That's really the takeaway from one of the things that you mentioned and this notion of storytelling is very, very important. I mean, all business people, they better be storytellers in some way shape or form and what better way to tell stories than with data, and so, because as you say it's no longer gut feel, it's not the answer anymore. So it seems to me Sudheesh, that you guys are transformative. The decision to focus on the global 2000 and really not, get washed up in the Excel, well I could just do it in Excel, or I'm going to go get Power BI, it's good enough. It's really, you're trying to be transformative and you've got a really disruptive model that we talked about before, search led and you're speaking to the system, or, typing in a way that's more natural, I wonder if you could comment on that and particularly that disruption of that transformation. >> Remember we are selling to global 2000. Almost all of them will have Tableau or one of these power BI or one of these solutions already, so you're not trying to go right and change that. What we have done is very clearly focus on use cases. We're transforming data into action. We will move the needle for the bit, but for example with the COVID situation going on, one of the most popular use cases for us is around working capital management. Now a CFO who's been in the business for 20 or 30 years is an expert and have the right kind of gut feeling about how her business is running when it comes to working capital. However, imagine now she can do 20 what-if scenarios in the next five seconds or next 10 minutes without going to the SPN 18, without going to the BI team. She can say what if we reduce hiring in Japan and instead we focus them on Singapore? What if we move 20% of marketing dollars from Germany to New York? What would be the impact of AR going up by 1% versus AP going down by 1%? She needs to now do complex scenarios, but without delay. It's sort of like how do I find a restaurant through Yelp versus going to the lobby to talk to a specialist who tells me the local restaurant. This interactive database storytelling for gut enhances the decision-making is very powerful. This is why, customer have, our largest customer has spent more than $26 million with ThougthSpot and this is not small. Our average is around close to 700k. This week for example, we are having a webinar where Verizon's SVP of Analytics specifically focused on finance. He's actually going to be on a webinar with our CFO. Our CFO Sophie, one of our financial specialists and Jeff Noto from Verizon are going to be on this talking about working capital management. What parts ThoughtSpot is a portion of, but they are sharing their experience of how do we manage, so that kind of varies, like extremely rigid focus on use cases, supply chain, modeling different things so that someone who knows Asia can really interact with the data to figure out if our supply chain from Bangladesh is going to be impacted because of COVID can we go to Ecuador? What will that look like? What will be the cost? What's the transportation cost, the fuel cost, Business has become so complex you don't have time to take five, six days to look at the report, no matter how pretty that report is, you have to make it efficient. You need to be able to make a lightning fast decision and something like COVID is really exposing all of that because day by day situation on the ground is changing. You know, employees are calling in sick. The virus is breaking out in one place, other place. If it's not, curves are going up and down so you cannot have any sort of delay between human experience and data signs and all of that comes down to your point telling visual stories so that the organization can rally behind the changes that they want to make. >> So these are mission-critical use cases. They are big problems that you're solving and attacking. As you said, you're not all things to all people. One of the things you're not is a data store, right? So you've got a partner, you've got to have an ecosystem, whether it's cloud databases, the cloud itself. I wonder if you could talk about some of the key partnerships that you're forming and how you're going to market and how that's affecting your business. >> Yeah, I mean one of the things that I've always believed in Silicon Valley is that companies die out of indigestion, not out of starvation. You try to do everything. That's how you end up dying and for us in the space of data, it's an extremely humbling space because there is so much to do, data prep, data warehousing, you know a mash-up of data, hosting of data, We have clearly decided that our ability is best spent on making artificial intelligence to work, interactive storytelling for business use and that's it. With that said, we needed a high velocity agility partner in the back end and Cloud based data warehouse have become a huge tailwind for us because our entire customer deployments are on Cloud, and the number one, obviously as you know from Frank's thing, the Snowflake has actually given, customers have seen Snowflakes plus ThoughtSpot is actually a good thing and we are exclusive in global 2000 and the Snowflake is climbing up there and we are able to build a good mutual partnership, but we are also seeing a really creative partnership all the way from product design to go to market and compensation alignment with Amazon on their push on Redshift as well. Google, we have announced partnership. There is a little bit of (mic cuts out) in the beginning we are getting, and just a couple of weeks ago we started working with Microsoft on their Azure Synapse algo. Now I would say that it's lagging, we still have work to do but Amazon and Snowflake are really pushing in terms of what customers want to see, and it completely aligns with our value popular, one plus one equals three. It really works well for our customers >> And Google is what, BigQuery plus Google Cloud, or what are you doing there? >> Yep so both Amazon and Google. Well, what we are doing at three different pieces. One if obviously the hosting of their cloud platforms. Second is data warehouse and enterprise data warehouse, which is Redshift and BigQuery. Third, we are also pretty good at taking machine learning algorithms that they have built for specific verticals. We're going to take those and then ingest them and deliver better. So for example if you are one of the largest supply companies in the world and you want to know what's the shipment rate from China and it shows and then the next thing you want to know is what the failure rate on this based on last behavior when you compressed a shipment rate, and that probably could use a bit of specific algorithms and you know Google and others have actually built a library of algorithms that can be injected into ThoughtSpot. We will simply answer the question of we may have gotten that algorithm from the Google library, sort of the business use is concerned. It doesn't really matter, so we have made all that invisible and we are able to deliver democratized access to Bespoke Insights to a business user, who are too sort of been afraid to deal with the sector data. >> Since you mentioned that you've got obviously several hundred million dollars in cash. You've raised over half a billion. You've talked previously about potential acquisitions, about IPO, are you considering acquisitions? M&A at this point in time? I mean there may be some deals out there. There's certainly some talent out there, but boy the market is changing so fast. I mean, it seems to, certain sectors are actually doing quite well. Will you consider M&A at this point? >> Yes, so I think IPO and M&A are two different-- IPO definitely, it will be foolish to say that this hasn't pushed our clients back a little bit because this is a huge event. I think there will be a correction across valuation and all of that. However, it is also important for us we use this opportunity to look at how we are investing our resources and investment for long-term versus the short-term and make sure that we are more focused and more tightening at the belt. We are doing that internally. Having said that, being a private company our valuation is, you know at least in theory, frozen, and then we have a pretty good cash position of close to $300 million, which means that it is absolutely an opportunity for us to seriously consider M&A. The important thing going back to my adage of, companies don't die out of starvation. It is critical to make sure that whatever we do, we do it with clarity. Are we doing it for talent? Are we doing it for tech? Or are we doing it for market? When you have a massive event like this, it is a poor idea to go after new market. It is important to go to our existing customers who are very large global 2000 firms and then identify problems that we cannot solve otherwise and then add technology to solve those problems, so technology acquisitions are absolutely something to consider, but it needs some more time to settle in because, the first two weeks were all people who were blindsided by this, then the last two weeks we have now gotten the mojo back in sales and mojo back in engineering, and now I think it is time for us to digest and prepare for these next two, three quarters of event and as part of that, companies like us who are fortunate enough to be on a good cash position, we'll absolutely look for interesting and good deals in the M&S space. >> Yeah, it makes sense, is tell and tech and, post IPO you can worry about Tam expansion. You'll be under pressure to do that as the CEO, but for now that's a very pragmatic approach. My last question is, there's some things when you think about, you say five weeks now you've been essentially on lockdown. You must, as many of us start thinking about wow, a lot of this work from home which came so fast people wouldn't even think about it earlier. You know, some companies mandated the beehive approach. Now everybody's open to that. There are certain things that are likely to remain permanent post COVID. Have you thought much about that? Generally and specifically how it might affect your business, the permanence of post COVID. Your thoughts. >> Yeah I've thought a lot about it. In fact, this morning I was speaking with our CRO Brian McCarthy about this. I think the change will happen, think of like an onion's inner most layer, I think the most, my hope is, that the biggest change will be in every one of us internally, as a what sort of a person am I and what does my position in the world means. The ego of each one of us that we carry because if this global event in one shot did not make you rethink your own sort of position in this big universe I think that's a mess. So the first thing has to be about being a better person. The second thing is, I had this two, three days of fever which was negative for COVID but I isolated myself, but that gave me sort of an idea of dipping in the dark room where I'm hoping my family won't get infected and you know my parents are in India so I sort of also realized that what is really important for you in life and how much family should mean to you, so that goes to the first, yourself second, your relationship with family, but having said that, the third thing when it comes to business building is also the importance for building with quality people, because when things go wrong it is so critical to have people who believe in the purpose of what you are trying to build. People with good faith and unshakable faith, personal faith and unshakable faith in the purpose of the company and most importantly you mentioned something which is the story telling. People, leaders who can absolutely communicate with clarity and certainty. It becomes the most important thing to lead an organization. I mean, you are a small business owner. You know we are in a small company with around 500 people. There is nothing like sitting at home waiting to see how the company is doing over email if you're a friend line engineer or a seller. Communication becomes so critical, so having the trust and the respect of organization and have the ability to clearly and transparently communicate is the most important thing for the company and over communicating due to the time of crisis. These things are so useful even after this crisis is over. Obviously from a technology point of view, you know people have been speaking a lot about working remotely and technology changes, security, those things will happen but I think if these three things were to happen in that order. Be a better person, be a better family member and be a better leader, I think the world will be better off and the last thing I'll also tell you, that you know in Silicon Valley sometimes we have this disregard for arts and literature and fight over science. I hope that goes away, because I can't imagine living without books, without movies, without Netflix and everything. Art makes yourself creative and enriches our lives. You know, sports is no longer there on TV and the fact that people are able to immerse their imagination in books and fiction and watch TV. That also reminds you how important it is to have a good balance between arts and science in this world, so I have a long list of things that I hope we as a people and as a society will get better. >> Yeah, a lot more game playing in our household and it's good to reconnect in that regard. Well Sudheesh, you've always been a very clear thinker and you're in a great spot and an awesome leader. Thanks so much for coming on theCUBE. It was really great to see you again. All the best to you, your family and the broader community in your area. >> Dave, you've been very kind with this. Thank you so much, I wish you the same and hopefully we'll get to see face-to-face in the near future. Thanks a lot. >> I hope so, thank you. All right and thank you for watching everybody. This is Dave Vellante for theCUBE and we'll see you next time. (upbeat music)