(gentle music) >> Welcome to today's session of the AWS Startup Showcase The Next Big Thing in AI, Security, & Life Sciences. Today we're featuring Protegrity for the life sciences track. I'm your host for theCUBE, Natalie Erlich, and now we're joined by our guest, Rick Farnell, the CEO of Protegrity. Thank you so much for being with us. >> Great to be here. Thanks so much Natalie, great to be on theCUBE. >> Yeah, great, and so we're going to talk today about the ransomware game, and how it has changed with kinetic data protection. So, the title of today's video segment makes a bold claim, how are kinetic data and ransomware connected? >> So first off kinetic data, data is in use, it's moving, it's not static, it's no longer sitting still, and your data protection has to adhere to those same standards. And I think if you kind of look at what's happening in the ransomware kind of attacks, there's a couple of different things going on, which is number one, bad actors are getting access to data in the clear, and they're holding that data ransom, and threatening to release that data. So kind of from a Protegrity standpoint, with our protection capabilities, that data would be rendered useless to them in that scenario. So there's lots of ways in which kind of backup data protection, really wonderful opportunities to do both data protection and kind of that backup mixed together really is a wonderful solution to the threat of ransomware. And it's a serious issue and it's not just targeting the most highly regulated industries and customers, we're seeing kind of attacks on pipeline and ferry companies, and really there is no end to where some of these bad actors are really focusing on and the damages can be in the hundreds of millions of dollars and last for years after from a brand reputation. So I think if you look at how data is used today, there's that kind of opposing forces where the business wants to use data at the speed of light to produce more machine learning, and more artificial intelligence, and predict where customers are going to be, and have wonderful services at their fingertips. But at the same time, they really want to protect their data, and sometimes those architectures can be at odds, and at Protegrity, we're really focusing on solving that problem. So free up your data to be used in artificial intelligence and machine learning, while making sure that it is absolutely bulletproof from some of these ransomware attacks. >> Yeah, I mean, you bring a really fascinating point that's really central to your business. Could you tell us more about how you're actually making that data worthless? I mean, that sounds really revolutionary. >> So, it sounds novel, right? To kind of make your data worthless in the wrong hands. And I think from a Protegrity perspective, our kind of policy and protection capability follows the individual piece of data no matter where it lives in the architecture. And we do a ton of work as the world does with Amazon Web Services, so kind of helping customers really blend their hybrid cloud strategies with their on-premise and their use of AWS, is something that we thrive at. So protecting that data, not just at rest or while it's in motion, but it's a continuous protection policy that we can basically preserve the privacy of the data but still keep it unique for use in downstream analytics and machine learning. >> Right, well, traditional security is rather stifling, so how can we fix this, and what are you doing to amend that? >> Well, I think if you look at cybersecurity, and we certainly play a big role in the cybersecurity world but like any industry, there are many layers. And traditional cybersecurity investment has been at the perimeter level, at the network level keeping bad actors out, and once people do get through some of those fences, if your data is not protected at a fine grain level, they have access to it. And I think from our standpoint, yes, we're last line of defense but at the same time, we partner with folks in the cybersecurity industry and with AWS and with others in the backup and recovery to give customers that level of protection, but still allow their kinetic data to be utilized in downstream analytics. >> Right, well, I'd love to hear more about the types of industries that you're helping, and specifically healthcare obviously, a really big subject for the year and probably now for years to come, how is this industry using kinetic protection at the moment? >> So certainly, as you mentioned, some of the most highly regulated industries are our sweet spot. So financial services, insurance, online retail, and healthcare, or any industry that has sensitive data and sensitive customer data, so think first name last name, credit card information, national ID number, social security number blood type, cancer type. That's all sensitive information that you as an organization want to protect. So in the healthcare space, specifically, some of the largest healthcare organizations in the world rely on Protegrity to provide that level of protection, but at the same time, give them the business flexibility to utilize that data. So one of our customers, one of the leaders in online prescriptions, and that is an AWS customer, to allow a wonderful service to be delivered to all of their customers while maintaining protection. If you think about sharing data on your watch with your insurance provider, we have lots of customers that bridge that gap and have that personal data coming in to the insurance companies. All the way to, if in a use case in the future, looking at the pandemic, if you have to prove that you've been vaccinated, we're talking about some sensitive information, so you want to be able to show that information but still have the confidence that it's not going to be used for nefarious purposes. >> Right, and what is next for Protegrity? >> Well, I think continuing on our journey, we've been around for 17 years now, and I think the last couple, there's been an absolute renaissance in fine-grained data protection or that connected data protection, and organizations are recognizing that continuing to protect your perimeter, continuing to protect your firewalls, that's not going to go away anytime soon. Your access points, your points of vulnerability to keep bad actors out, but at the same time, recognizing that the data itself needs to be protected but with that balance of utilizing it downstream for analytic purposes, for machine learning, for artificial intelligence. Keeping the data of hundreds of millions if not billions of people saved, that's what we do. If you were to add up the customers of all of our customers, the largest banks, the largest insurance companies, largest healthcare companies in the world, globally, we're protecting the private data of billions of human beings. And it doesn't just stop there, I think you asked a great question about kind of the industry and yes, insurance, healthcare, retail, where there's a lot of sensitive data that certainly can be a focus point. But in the IOT space, kind of if you think about GPS location or geolocation, if you think about a device, and what it does, and the intelligence that it has, and the decisions that it makes on the fly, protecting data and keeping that safe is not just a personal thing, we're stepping into intellectual property and some of the most valuable assets that companies have, which is their decision-making on how they use data and how they deliver an experience, and I think that's why there's been such a renaissance, if you will, in kind of that fine grain data protection that we provide. >> Yeah, well, what is Protegrity's role now in future proofing businesses against cyber attacks? I mean, you mentioned really the ramifications of that and the impact it can have on businesses, but also on governments. I mean, obviously this is really critical. >> So there's kind of a three-step approach, and this is something that we have certainly kind of felt for a long, long time, and we work on with our customers. One is having that fine-grain data protection. So tokenizing your data so that if someone were to get your data, it's worthless, unless they have the ability to unlock every single individual piece of data. So that's number one, and then that's kind of what Protegrity provides. Number two, having a wonderful backup capability to roll kind of an active-active, AWS being one of the major clouds in the world where we deploy our software regularly and work with our customers, having multi-regions, multi-capabilities for an active-active scenario where if there's something that goes down or happens you can bring that down and bring in a new environment up. And then third is kind of malware detection in the rest of the cyber world to make sure that you rinse kind of your architecture from some of those agents. And I think when you kind of look at it, ransomware, they take data, they encrypt your data, so they force you to give them Bitcoin, or whatnot, or they'll release some of your data. And if that data is rendered useless, that's one huge step in kind of your discussions with these nefarious actors and be like you could release it, but there's nothing there, you're not going to see anything. And then second, if you have a wonderful backup capability where you wind down that environment that has been infiltrated, prove that this new environment is safe, have your production data have rolling and then wind that back up, you're back in business. You don't have to notify your customers, you don't have to deal with the ransomware players. So it's really a three-step process but ultimately it starts with protecting your data and tokenizing your data, and that's something that Protegrity does really, really well. >> So you're basically able to eliminate the financial impact of a breach? >> Honestly, we dramatically reduce the risk of customers being at risk for ransomware attacks 100%. Now, tokenizing data and moving that direction is something that it's not trivial, we are literally replacing production data with a token and then making sure that all downstream applications have the ability to utilize that, and make sure that the analytic systems and machine learning systems, and artificial intelligence applications that are built downstream on that data have the ability to execute, but that is something that from our patent portfolio and what we provide to our customers, again, some of the largest organizations in retail, in financial services, in banking, and in healthcare, we've been doing that for a long time. We're not just saying that we can do this and we're in version one of our product, we've been doing this for years, supporting the largest organizations with a 24 by seven capability. >> Right, and tell us a bit about the competitive landscape, where do you see your offering compared to your competitors? >> So, kind of historically back, let's call it an era ago maybe even before cloud even became a thing, and hybrid cloud, there were a handful of players that could acquire into much larger organizations, those organizations have been dusting off those acquired assets, and we're seeing them come back in. There's some new entrants into our space that have some protection mechanisms, whether it be encryption, or whether it be anonymization, but unless you're doing fine grain tokenization, you're not going to be able to allow that data to participate in the artificial intelligence world. So, we see kind of a range of competition there. And then I'd say probably the biggest competitor, Natalie, is customers not doing tokenization. They're saying, "No, we're okay, we'll continue protecting our firewall, we'll continue protecting our access points, we'll invest a little bit more in maybe some governance, but that fine grain data protection, maybe it's not for us." And that is the big shift that's happening. You look at kind of the beginning of this year with the solar winds attack, and the vulnerability that caused the very large and important organizations found themselves the last few weeks with all the ransomware attacks that are happening on meat processing plants and facilities, shutting down meat production, pipeline, stopping oil and gas and kind of that. So we're seeing a complete shift in the types of organizations and the industries that need to protect their data. It's not just the healthcare organizations, or the banks, or the credit card companies, it is every single industry, every single size company. >> Right, and I got to ask you this questioning, what is your defining contribution to the future of cloud scale? >> Well, ultimately we kind of have a charge here at Protegrity where we feel like we protect the world's most sensitive data. And when we come into work every day, that's what every single employee thinks at Protegrity. We are standing behind billions of individuals who are customers of our customers, and that's a cultural thing for us, and we take that very serious. We have maniacal customer support supporting our biggest customers with a fall of the sun 24 by seven global capability. So that's number one. So, I think our part in this is really helping to educate the world that there is a solution for this ransomware and for some of these things that don't have to happen. Now, naturally with any solution, there's going to be some investment, there's going to be some architecture changes, but with partnerships like AWS, and our partnership with pretty much every data provider, data storage provider, data solution provider in the world, we want to provide fine-grain data protection, any data in any system on any platform. And that's our mission. >> Well, Rick Farnell, this has been really fascinating conversation with you, thank you so much. The CEO of Protegrity, really great to have you on this program for the AWS Startup Showcase, talking about how ransomware game has changed with the kinetic data protection. Really appreciate it. Again, I'm your host Natalie Erlich, thank you again very much for watching. (light music)

(upbeat music) >> Hello and welcome today's session for the AWS Startup Showcase, the next big thing in AI, Security and Life Sciences featuring Coralogix for the AI track. I'm your host, John Furrier with theCUBE. We're here we're joined by Ariel Assaraf, CEO of Coralogix. Ariel, great to see you calling in from remotely, videoing in from Tel Aviv. Thanks for coming on theCUBE. >> Thank you very much, John. Great to be here. >> So you guys are features a hot next thing, start next big thing startup. And one of the things that you guys do we've been covering for many years is, you're into the log analytics, from a data perspective, you guys decouple the analytics from the storage. This is a unique thing. Tell us about it. What's the story? >> Yeah. So what we've seen in the market is that probably because of the great job that a lot of the earlier generation products have done, more and more companies see the value in log data, what used to be like a couple rows, that you add, whenever you have something very important to say, became a standard to document all communication between different components, infrastructure, network, monitoring, and the application layer, of course. And what happens is that data grows extremely fast, all data grows fast, but log data grows even faster. What we always say is that for sure data grows faster than revenue. So as fast as a company grows, its data is going to outpace that. And so we found ourselves thinking, how can we help companies be able to still get the full coverage they want without cherry picking data or deciding exactly what they want to monitor and what they're taking risk with. But still give them the real time analysis that they need to make sure that they get the full insight suite for the entire data, wherever it comes from. And that's why we decided to decouple the analytics layer from storage. So instead of ingesting the data, then indexing and storing it, and then analyzing the stored data, we analyze everything, and then we only store it matters. So we go from the insights backwards. That allowed us to reduce the amount of data, reduce the digital exhaust that it creates, and also provide better insights. So the idea is that as this world of data scales, the need for real time streaming analytics is going to increase. >> So what's interesting is we've seen this decoupling with storage and compute be a great success formula and cloud scale, for instance, that's a known best practice. You're taking a little bit different. I love how you're coming backwards from it, you're working backwards from the insights, almost doing some intelligence on the front end of the data, probably sees a lot of storage costs. But I want to get specifically back to this real time. How do you do that? And how did you come up with this? What's the vision? How did you guys come up with the idea? What was the magic light bulb that went off for Coralogix? >> Yes, the Coralogix story is very interesting. Actually, it was no light bulb, it was a road of pain for years and years, we started by just you know, doing the same, maybe faster, a couple more features. And it didn't work out too well. The first few years, the company were not very successful. And we've grown tremendously in the past three years, almost 100X, since we've launched this, and it came from a pain. So once we started scaling, we saw that the side effects of accessing the storage for analytics, the latency it creates, the the dependency on schema, the price that it poses on our customers became unbearable. And then we started thinking, so okay, how do we get the same level of insights, because there's this perception in the world of storage. And now it started to happen in analytics, also, that talks about tiers. So you want to get a great experience, you pay a lot, you want to get a less than great experience, you pay less, it's a lower tier. And we decided that we're looking for a way to give the same level of real time analytics and the same level of insights. Only without the issue of dependencies, decoupling all the storage schema issues and latency. And we built our real time pipeline, we call it Streama. Streama is a Coralogix real time analysis platform that analyzes everything in real time, also the stateful thing. So stateless analytics in real time is something that's been done in the past and it always worked well. The issue is, how do you give a stateful insight on data that you analyze in real time without storing and I'll explain how can you tell that a certain issue happened that did not happen in the past three months if you did not store the past three months? Or how can you tell that behavior is abnormal if you did not store what's normal, you did not store to state. So we created what we call the state store that holds the state of the system, the state of data, were a snapshot on that state for the entire history. And then instead of our state being the storage, so you know, you asked me, how is this compared to last week? Instead of me going to the storage and compare last week, I go to the state store, and you know, like a record bag, I just scroll fast, I find out one piece of state. And I say, okay, this is how it looked like last week, compared to this week, it changed in ABC. And once we started doing that we on boarded more and more services to that model. And our customers came in and say, hey, you're doing everything in real time. We don't need more than that. Yeah, like a very small portion of data, we actually need to store and frequently search, how about you guys fit into our use cases, and not just sell on quota? And we decided to basically allow our customers to choose what is the use case that they have, and route the data through different use cases. And then each log records, each log record stops at the relevant stops in our data pipeline based on the use case. So just like you wouldn't walk into the supermarket, you fill in a bag, you go out, they weigh it and they say, you know, it's two kilograms, you pay this amount, because different products have different costs and different meaning to you. That same way, exactly, We analyze the data in real time. So we know the importance of data, and we allow you to route it based on your use case and pay a different amount per use case. >> So this is really interesting. So essentially, you guys, essentially capture insights and store those, you call them states, and then not have to go through the data. So it's like you're eliminating the old problem of, you know, going back to the index and recovering the data to get the insights, did we have that? So anyway, it's a round trip query, if you will, you guys are start saving all that data mining cost and time. >> We call it node zero side effects, that round trip that you that you described is exactly it, no side effects to an analysis that is done in real time. I don't need to get the latency from the storage, a bit of latency from the database that holds the model, a bit of latency from the cache, everything stays in memory, everything stays in stream. >> And so basically, it's like the definition of insanity, doing the same thing over and over again and expecting a different result. Here, that's kind of what that is, the old model of insight is go query the database and get something back, you're actually doing the real time filtering on the front end, capturing the insights, if you will, storing those and replicating that as use case. Is that right? >> Exactly. But then, you know, there's still the issue of customer saying, yeah, but I need that data. Someday, I need to really frequently search, I don't know, you know, the unknown unknowns, or some of the day I need for compliance, and I need an immutable record that stays in my compliance bucket forever. So we allowed customers, we have this some that screen, we call the TCO optimizer, that allows them to define those use cases. And they can always access the data by creating their remote storage from Coralogix, or carrying the hot data that is stored with Coralogix. So it's all about use cases. And it's all about how you consume the data because it doesn't make sense for me to pay the same amount or give the same amount of attention to a record that is completely useless. It's just there for the record or for a compliance audit, that may or may not happen in the future. And, you know, do the same with the most critical exception in my application log that has immediate business impact. >> What's really good too, is you can actually set some policy up if you want a certain use cases, okay, store that data. So it's not to say you don't want to store it, but you might want to store it on certain use cases. So I can see that. So I got to ask the question. So how does this differ from the competition? How do you guys compete? Take us through a use case of a customer? How do you guys go to the customer and you just say, hey, we got so much scar tissue from this, we learned the hard way, take it from us? How does it go? Take us through an example. >> So an interesting example of actually a company that is not the your typical early adopter, let's call it this way. A very advanced in technology and smart company, but a huge one, one of the largest telecommunications company in India. And they were actually cherry picking about 100 gigs of data per day, and sending it to one of the legacy providers which has a great solution that does give value. But they weren't even thinking about sending their entire data set because of cost because of scale, because of, you know, just a clutter. Whenever you search, you have to sift through millions of records that many of them are not that important. And we help them actually ask analyze their data and work with them to understand these guys had over a terabyte of data that had incredible insights, it was like a goldmine of insights. But now you just needed to prioritize it by their use case, and they went from 100 gig with the other legacy solution to a terabyte, at almost the same cost, with more advanced insights within one week, which isn't in that scale of an organization is something that is is out of the ordinary, took them four months to implement the other product. But now, when you go from the insights backwards, you understand your data before you have to store it, you understand the data before you have to analyze it, or before you have to manually sift through it. So if you ask about the difference, it's all about the architecture. We analyze and only then index instead of indexing and then analyzing. It sounds simple. But of course, when you look at this stateful analytics, it's a lot more, a lot more complex. >> Take me through your growth story, because first of all, I'll get back to the secret sauce in the same way. I want to get back to how you guys got here. (indistinct) you had this problem? You kind of broke through, you hit the magic formula, talking about the growth? Where's the growth coming from? And what's the real impact? What's the situation relative to the company's growth? >> Yeah, so we had a first rough three years that I kind of mentioned, and then I was not the CEO at the beginning, I'm one of the co founders. I'm more of the technical guy, was the product manager. And I became CEO after the company was kind of on the verge of closing at the end of 2017. And the CTO left the CEO left, the VP of R&D became the CTO, I became the CEO, we were five people with $200,000 in the bank that you know, you know that that's not a long runway. And we kind of changed attitudes. So we kind of, so we first we launched this product, and then we understood that we need to go bottoms up, you can go to enterprises and try to sell something that is out of the ordinary, or that changes how they're used to working or just, you know, sell something, (indistinct) five people will do under $1,000 in the bank. So we started going from bottoms up, and the earlier adopters. And it's still until today, you know, the the more advanced companies, the more advanced teams. This is our Gartner friend Coralogix, the preferred solution for Advanced, DevOps and Platform Teams. So they started adopting Coralogix, and then it grew to the larger organization, and they were actually pushing, there are champions within their organizations. And ever since. So until the beginning of 2018, we raised about $2 million and had sales or marginal. Today, we have over 1500, pink accounts, and we raised almost $100 million more. >> Wow, what a great pivot. That was great example of kind of getting the right wave here, cloud wave. You said in terms of customers, you had the DevOps kind of (indistinct) initially. And now you said expanded out to a lot more traditional enterprise, you can take me through the customer profile. >> Yeah, so I'd say it's still the core would be cloud native and (indistinct) companies. These are typical ones, we have very tight integration with AWS, all the services, all the integrations required, we know how to read and write back to the different services and analysis platforms in AWS. Also for Asia and GCP, but mostly AWS. And then we do have quite a few big enterprise accounts, actually, five of the largest 50 companies in the world use Coralogix today. And it grew from those DevOps and platform evangelists into the level of IT, execs and even (indistinct). So today, we have our security product that already sells to some of the biggest companies in the world, it's a different profile. And the idea for us is that, you know, once you solve that issue of too much data, too expensive, not proactive enough, too couple with the storage, you can actually expand that from observability logging metrics, now into tracing and then into security and maybe even to other fields, where the cost and the productivity are an issue for many companies. >> So let me ask you this question, then Ariel, if you don't mind. So if a customer has a need for Coralogix, is it because the data fall? Or they just got data kind of sprawled all over the place? Or is it that storage costs are going up on S3 or what's some of the signaling that you would see, that would be like, telling you, okay, okay, what's the opportunity to come in and either clean house or fix the mess or whatnot, Take us through what you see. What do you see is the trend? >> Yeah. So like the tip customer (indistinct) Coralogix will be someone using one of the legacy solution and growing very fast. That's the easiest way for us to know. >> What grows fast? The storage, the storage is growing fast? >> The company is growing fast. >> Okay. And you remember, the data grows faster than revenue. And we know that. So if I see a company that grew from, you know, 50 people to 500, in three years, specifically, if it's cloud native or internet company, I know that their data grew not 10X, but 100X. So I know that that company that might started with a legacy solution at like, you know, $1,000 a month, and they're happy with it. And you know, for $1,000 a month, if you don't have a lot of data, those legacy solutions, you know, they'll do the trick. But now I know that they're going to get asked to pay 50, 60, $70,000 a month. And this is exactly where we kick in. Because now, when it doesn't fit the economic model, when it doesn't fit the unit economics, and he started damaging the margins of those companies. Because remember, those internet and cloud companies, it's not costs are not the classic costs that you'll see in an enterprise, they're actually damaging your unit economics and the valuation of the business, the bigger deal. So now, when I see that type of organization, we come in and say, hey, better coverage, more advanced analytics, easier integration within your organization, we support all the common open source syntaxes, and dashboards, you can plug it into your entire environment, and the costs are going to be a quarter of whatever you're paying today. So once they see that they see, you know, the Dev friendliness of the product, the ease of scale, the stability of the product, it makes a lot more sense for them to engage in a PLC, because at the end of the day, if you don't prove value, you know, you can come with 90% discount, it doesn't do anything, not to prove the value to them. So it's a great door opener. But from then on, you know, it's a PLC like any other. >> Cloud is all about the PLC or pilot, as they say. So take me through the product, today, and what's next for the product, take us through the vision of the product and the product strategy. >> Yeah, so today, the product allows you to send any log data, metric data or security information, analyze it a million ways, we have one of the most extensive alerting mechanism to market, automatic anomaly detection, data flustering. And all the real law, you know, the real time pipeline, things that help companies make their data smarter, and more readable, parsing, enriching, getting external sources to enrich the data, and so on, so forth. Where we're stepping in now is actually to make the final step of decoupling the analytics from storage, what we call the datalist data platform in which no data will sit or reside within the Coralogix cloud, everything will be analyzed in real time, stored in a storage of choice of our customers, then we'll allow our customers to remotely query that incredible performance. So that'll bring our customers away, to have the first ever true SaaS experience for observability. Think about no quota plans, no retention, you send whatever you want, you pay only for what you send, you retain it, how long you want to retain it, and you get all the real time insights much, much faster than any other product that keeps it on a hot storage. So that'll be our next step to really make sure that, you know, we're kind of not reselling cloud storage, because a lot of the times when you are dependent on storage, and you know, we're a cloud company, like I mentioned, you got to keep your unit economics. So what do you do? You sell storage to the customer, you add your markup, and then you you charge for it. And this is exactly where we don't want to be. We want to sell the intelligence and the insights and the real time analysis that we know how to do and let the customers enjoy the, you know, the wealth of opportunities and choices their cloud providers offer for storage. >> That's great vision in a way, the hyper scalars early days showed that decoupling compute from storage, which I mentioned earlier, was a huge category creation. Here, you're doing it for data. We call hyper data scale, or like, maybe there's got to be a name for this. What do you see, about five years from now? Take us through the trajectory of the next five years, because certainly observability is not going away. I mean, it's data management, monitoring, real time, asynchronous, synchronous, linear, all the stuffs happening, what's the what's the five year vision? >> Now add security and observability, which is something we started preaching for, because no one can say I have observability to my environment when people you know, come in and out and steal data. That's no observability. But the thing is that because data grows exponentially, because it grows faster than revenue what we believe is that in five years, there's not going to be a choice, everyone are going to have to analyze the data in real time. Extract the insights and then decide whether to store it on a you know long term archive or not, or not store it at all. You still want to get the full coverage and insights. But you know, when you think about observability, unlike many other things, the more data you have many times, the less observability you get. So you think of log data unlike statistics, if my system was only in recording everything was only generating 10 records a day, I have full, incredible observability I know everything that I've done. what happens is that you pay more, you get less observability, and more uncertainty. So I think that you know, with time, we'll start seeing more and more real time streaming analytics, and a lot less storage based and index based solutions. >> You know, Ariel, I've always been saying to Dave Vellante on theCUBE, many times that there needs to be insights as to be the norm, not the exception, where, and then ultimately, it would be a database of insights. I mean, at the end of the day, the insights become more plentiful. You have the ability to actually store those insights, and refresh them and challenge them and model update them, verify them, either sunset them or add to them or you know, saying that's like, when you start getting more data into your organization, AI and machine learning prove that pattern recognition works. So why not grab those insights? >> And use them as your baseline to know what's important, and not have to start by putting everything in a bucket. >> So we're going to have new categories like insight, first, software (indistinct) >> Go from insights backwards, that'll be my tagline, if I have to, but I'm a terrible marketing (indistinct). >> Yeah, well, I mean, everyone's like cloud, first data, data is data driven, insight driven, what you're basically doing is you're moving into the world of insights driven analytics, really, as a way to kind of bring that forward. So congratulations. Great story. I love the pivot love how you guys entrepreneurially put it all together and had the problem your own problem and brought it out and to the to the rest of the world. And certainly DevOps in the cloud scale wave is just getting bigger and bigger and taking over the enterprise. So great stuff. Real quick while you're here. Give a quick plug for the company. What you guys are up to, stats, vitals, hiring, what's new, give the commercial. >> Yeah, so like mentioned over 1500 being customers growing incredibly in the past 24 months, hiring, almost doubling the company in the next few months. offices in Israel, East Center, West US, and UK and Mumbai. Looking for talented engineers to join the journey and build the next generation of data lists data platforms. >> Ariel Assaraf, CEO of Coralogix. Great to have you on theCUBE and thank you for participating in the AI track for our next big thing in the Startup Showcase. Thanks for coming on. >> Thank you very much John, really enjoyed it. >> Okay, I'm John Furrier with theCUBE. Thank you for watching the AWS Startup Showcase presented by theCUBE. (calm music)

(up-tempo music) >> Welcome to today's session of the cube's presentation of the AWS startup showcase. The next big thing in AI security and life sciences. Today, we'll be speaking with Aizon, as part of our life sciences track and I'm pleased to welcome the co-founder as well as the chief science officer of Aizon: Toni Monzano, will be discussing how artificial intelligence is driving key processes in pharma manufacturing. Welcome to the show. Thanks so much for being with us today. >> Thank you Natalie to you and to your introduction. >> Yeah. Well, as you know industry 4.0 is revolutionizing manufacturing across many industries. Let's talk about how it's impacting biotech and pharma and as well as Aizon's contributions to this revolution. >> Well, actually pharmacogenetics is totally introducing a new concept of how to manage processes. So, nowadays the industry is considering that everything is particularly static, nothing changes and this is because they don't have the ability to manage the complexity and the variability around the biotech and the driving factor in processes. Nowadays, with pharma - technologies cloud, our computing, IOT, AI, we can get all those data. We can understand the data and we can interact in real time, with processes. This is how things are going on nowadays. >> Fascinating. Well, as you know COVID-19 really threw a wrench in a lot of activity in the world, our economies, and also people's way of life. How did it impact manufacturing in terms of scale up and scale out? And what are your observations from this year? >> You know, the main problem when you want to do a scale-up process is not only the equipment, it is also the knowledge that you have around your process. When you're doing a vaccine on a smaller scale in your lab, the only parameters you're controlling in your lab, they have to be escalated when you work from five liters to 2,500 liters. How to manage this different of a scale? Well, AI is helping nowadays in order to detect and to identify the most relevant factors involved in the process. The critical relationship between the variables and the final control of all the full process following a continued process verification. This is how we can help nowadays in using AI and cloud technologies in order to accelerate and to scale up vaccines like the COVID-19. >> And how do you anticipate pharma manufacturing to change in a post COVID world? >> This is a very good question. Nowadays, we have some assumptions that we are trying to overpass yet with human efforts. Nowadays, with the new situation, with the pandemic that we are living in, the next evolution that we are doing humans will take care about the good practices of the new knowledge that we have to generate. So AI will manage the repetitive tasks, all the human condition activity that we are doing, So that will be done by AI, and humans will never again do repetitive tasks in this way. They will manage complex problems and supervise AI output. >> So you're driving more efficiencies in the manufacturing process with AI. You recently presented at the United nations industrial development organization about the challenges brought by COVID-19 and how AI is helping with the equitable distribution of vaccines and therapies. What are some of the ways that companies like Aizon can now help with that kind of response? >> Very good point. Could you imagine you're a big company, a top pharma company, that you have an intellectual property of COVID-19 vaccine based on emergency and principle, and you are going to, or you would like to, expand this vaccination in order not to get vaccination, also to manufacture the vaccine. What if you try to manufacture these vaccines in South Africa or in Asia in India? So the secret is to transport, not only the raw material not only the equipment, also the knowledge. How to appreciate how to control the full process from the initial phase 'till their packaging and the vials filling. So, this is how we are contributing. AI is packaging all this knowledge in just AI models. This is the secret. >> Interesting. Well, what are the benefits for pharma manufacturers when considering the implementation of AI and cloud technologies. And how can they progress in their digital transformation by utilizing them? >> One of the benefits is that you are able to manage the variability the real complexity in the world. So, you can not create processes, in order to manufacture drugs, just considering that the raw material that you're using is never changing. You cannot consider that all the equipment works in the same way. You cannot consider that your recipe will work in the same way in Brazil than in Singapore. So the complexity and the variability is must be understood as part of the process. This is one of the benefits. The second benefit is that when you use cloud technologies, you have not a big care about computing's licenses, software updates, antivirals, scale up of cloud ware computing. Everything is done in the cloud. So well, this is two main benefits. There are more, but this is maybe the two main ones. >> Yeah. Well, that's really interesting how you highlight how this is really. There's a big shift how you handle this in different parts of the world. So, what role does compliance and regulation play here? And of course we see differences the way that's handled around the world as well. >> Well, I think that is the first time the human race in the pharma - let me say experience - that we have a very strong commitment from the 30 bodies, you know, to push forward using this kind of technologies actually, for example, the FDA, they are using cloud, to manage their own system. So why not use them in pharma? >> Yeah. Well, how does AWS and Aizon help manufacturers address these kinds of considerations? >> Well, we have a very great partner. AWS, for us, is simplifying a lot our life. So, we are a very, let me say different startup company, Aizon, because we have a lot of PhDs in the company. So we are not in the classical geeky company with guys all day parameter developing. So we have a lot of science inside the company. So this is our value. So everything that is provided by Amazon, why we have to aim to recreate again so we can rely on Sage Maker. we can rely on Cogito, we can rely on Landon we can rely on Esri to have encryption data with automatic backup. So, AWS is simplifying a lot of our life. And we can dedicate all our knowledge and all our efforts to the things that we know: pharma compliance. >> And how do you anticipate that pharma manufacturing will change further in the 2021 year? Well, we are participating not only with business cases. We also participate with the community because we are leading an international project in order to anticipate this kind of new breakthroughs. So, we are working with, let me say, initiatives in the - association we are collaborating in two different projects in order to apply AI in computer certification in order to create more robust process for the MRA vaccine. We are collaborating with the - university creating the standards for AI application in GXP. We collaborating with different initiatives with the pharma community in order to create the foundation to move forward during this year. >> And how do you see the competitive landscape? What do you think Aizon provides compared to its competitors? >> Well, good question. Probably, you can find a lot of AI services, platforms, programs softwares that can run in the industrial environment. But I think that it will be very difficult to find a GXP - a full GXP-compliant platform working on cloud with AI when AI is already qualified. I think that no one is doing that nowadays. And one of the demonstration for that is that we are also writing some scientific papers describing how to do that. So you will see that Aizon is the only company that is doing that nowadays. >> Yeah. And how do you anticipate that pharma manufacturing will change or excuse me how do you see that it is providing a defining contribution to the future of cloud-scale? >> Well, there is no limits in cloud. So as far as you accept that everything is varied and complex, you will need power computing. So the only way to manage this complexity is running a lot of power computation. So cloud is the only system, let me say, that allows that. Well, the thing is that, you know pharma will also have to be compliant with the cloud providers. And for that, we created a new layer around the platform that we say qualification as a service. We are creating this layer in order to qualify continuously any kind of cloud platform that wants to work on environment. This is how we are doing that. >> And in what areas are you looking to improve? How are you constantly trying to develop the product and bring it to the next level? >> Always we have, you know, in mind the patient. So Aizon is a patient-centric company. Everything that we do is to improve processes in order to improve at the end, to deliver the right medicine at the right time to the right patient. So this is how we are focusing all our efforts in order to bring this opportunity to everyone around the world. For this reason, for example, we want to work with this project where we are delivering value to create vaccines for COVID-19, for example, everywhere. Just packaging the knowledge using AI. This is how we envision and how we are acting. >> Yeah. Well, you mentioned the importance of science and compliance. What do you think are the key themes that are the foundation of your company? >> The first thing is that we enjoy the task that we are doing. This is the first thing. The other thing is that we are learning every day with our customers and for real topics. So we are serving to the patients. And everything that we do is enjoying science enjoying how to achieve new breakthroughs in order to improve life in the factory. We know that at the end will be delivered to the final patient. So enjoying making science and creating breakthroughs; being innovative. >> Right, and do you think that in the sense that we were lucky, in light of COVID, that we've already had these kinds of technologies moving in this direction for some time that we were somehow able to mitigate the tragedy and the disaster of this situation because of these technologies? >> Sure. So we are lucky because of this technology because we are breaking the distance, the physical distance, and we are putting together people that was so difficult to do that in all the different aspects. So, nowadays we are able to be closer to the patients to the people, to the customer, thanks to these technologies. Yes. >> So now that also we're moving out of, I mean, hopefully out of this kind of COVID reality, what's next for Aizon? Do you see more collaboration? You know, what's next for the company? >> The next for the company is to deliver AI models that are able to be encapsulated in the drug manufacturing for vaccines, for example. And that will be delivered with the full process not only materials, equipment, personnel, recipes also the AI models will go together as part of the recipe. >> Right, well, we'd love to hear more about your partnership with AWS. How did you get involved with them? And why them, and not another partner? >> Well, let me explain to you a secret. Seven years ago, we started with another top cloud provider, but we saw very soon, that this other cloud provider were not well aligned with the GXP requirements. For this reason, we met with AWS. We went together to some seminars, conferences with top pharma communities and pharma organizations. We went there to make speeches and talks. We felt that we fit very well together because AWS has a GXP white paper describing very well how to rely on AWS components. One by one. So this is for us, this is a very good credential, when we go to our customers. Do you know that when customers are acquiring and are establishing the Aizon platform in their systems, they are outbidding us. They are outbidding Aizon. Well we have to also outbid AWS because this is the normal chain in pharma supplier. Well, that means that we need this documentation. We need all this transparency between AWS and our partners. This is the main reason. >> Well, this has been a really fascinating conversation to hear how AI and cloud are revolutionizing pharma manufacturing at such a critical time for society all over the world. Really appreciate your insights, Toni Monzano: the chief science officer and co-founder of Aizon. I'm your host, Natalie Erlich, for the Cube's presentation of the AWS startup showcase. Thanks very much for watching. (soft upbeat music)

(upbeat electronic music) >> Hello, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase. The Next Big Thing in AI, Security, and Life Sciences. In this segment, we feature Orca Security as a notable trend setter within, of course, the security track. I'm your host, Dave Vellante. And today we're joined by Gil Geron. Who's the co-founder and Chief Product Officer at Orca Security. And we're going to discuss how to eliminate cloud security blind spots. Orca has a really novel approach to cybersecurity problems, without using agents. So welcome Gil to today's sessions. Thanks for coming on. >> Thank you for having me. >> You're very welcome. So Gil, you're a disruptor in security and cloud security specifically and you've created an agentless way of securing cloud assets. You call this side scanning. We're going to get into that and probe that a little bit into the how and the why agentless is the future of cloud security. But I want to start at the beginning. What were the main gaps that you saw in cloud security that spawned Orca Security? >> I think that the main gaps that we saw when we started Orca were pretty similar in nature to gaps that we saw in legacy, infrastructures, in more traditional data centers. But when you look at the cloud when you look at the nature of the cloud the ephemeral nature, the technical possibilities and disruptive way of working with a data center, we saw that the usage of traditional approaches like agents in these environments is lacking, it actually not only working as well as it was in the legacy world, it's also, it's providing less value. And in addition, we saw that the friction between the security team and the IT, the engineering, the DevOps in the cloud is much worse or how does that it was, and we wanted to find a way, we want for them to work together to bridge that gap and to actually allow them to leverage the cloud technology as it was intended to gain superior security than what was possible in the on-prem world. >> Excellent, let's talk a little bit more about agentless. I mean, maybe we could talk a little bit about why agentless is so compelling. I mean, it's kind of obvious it's less intrusive. You've got fewer processes to manage, but how did you create your agentless approach to cloud security? >> Yes, so I think the basis of it all is around our mission and what we try to provide. We want to provide seamless security because we believe it will allow the business to grow faster. It will allow the business to adopt technology faster and to be more dynamic and achieve goals faster. And so we've looked on what are the problems or what are the issues that slow you down? And one of them, of course, is the fact that you need to install agents that they cause performance impact, that they are technically segregated from one another, meaning you need to install multiple agents and they need to somehow not interfere with one another. And we saw this friction causes organization to slow down their move to the cloud or slow down the adoption of technology. In the cloud, it's not only having servers, right? You have containers, you have manage services, you have so many different options and opportunities. And so you need a different approach on how to secure that. And so when we understood that this is the challenge, we decided to attack it in three, using three periods; one, trying to provide complete security and complete coverage with no friction, trying to provide comprehensive security, which is taking an holistic approach, a platform approach and combining the data in order to provide you visibility into all of your security assets, and last but not least of course, is context awareness, meaning being able to understand and find these the 1% that matter in the environment. So you can actually improve your security posture and improve your security overall. And to do so, you had to have a technique that does not involve agents. And so what we've done, we've find a way that utilizes the cloud architecture in order to scan the cloud itself, basically when you integrate Orca, you are able within minutes to understand, to read, and to view all of the risks. We are leveraging a technique that we are calling side scanning that uses the API. So it uses the infrastructure of the cloud itself to read the block storage device of every compute instance and every instance, in the environment, and then we can deduce the actual risk of every asset. >> So that's a clever name, side scanning. Tell us a little bit more about that. Maybe you could double click on, on how it works. You've mentioned it's looking into block storage and leveraging the API is a very, very clever actually quite innovative. But help us understand in more detail how it works and why it's better than traditional tools that we might find in this space. >> Yes, so the way that it works is that by reading the block storage device, we are able to actually deduce what is running on your computer, meaning what kind of waste packages applications are running. And then by con combining the context, meaning understanding that what kind of services you have connected to the internet, what is the attack surface for these services? What will be the business impact? Will there be any access to PII or any access to the crown jewels of the organization? You can not only understand the risks. You can also understand the impact and then understand what should be our focus in terms of security of the environment. Different factories, the fact that we are doing it using the infrastructure itself, we are not installing any agents, we are not running any packet. You do not need to change anything in your architecture or design of how you use the cloud in order to utilize Orca Orca is working in a pure SaaS way. And so it means that there is no impact, not on cost and not on performance of your environment while using Orca. And so it reduces any friction that might happen with other parties of the organization when you enjoy the security or improve your security in the cloud. >> Yeah, and no process management intrusion. Now, I presume Gil that you eat your own cooking, meaning you're using your own product. First of all, is that true? And if so, how has your use of Orca as a chief product officer help you scale Orca as a company? >> So it's a great question. I think that something that we understood early on is that there is a, quite a significant difference between the way you architect your security in cloud and also the way that things reach production, meaning there's a difference, that there's a gap between how you imagined, like in everything in life how you imagine things will be and how they are in real life in production. And so, even though we have amazing customers that are extremely proficient in security and have thought of a lot of ways of how to secure the environment. Ans so, we of course, we are trying to secure environment as much as possible. We are using Orca because we understand that no one is perfect. We are not perfect. We might, the engineers might, my engineers might make mistakes like every organization. And so we are using Orca because we want to have complete coverage. We want to understand if we are doing any mistake. And sometimes the gap between the architecture and the hole in the security or the gap that you have in your security could take years to happen. And you need a tool that will constantly monitor your environment. And so that's why we are using Orca all around from day one not to find bugs or to do QA, we're doing it because we need security to our cloud environment that will provide these values. And so we've also passed the compliance auditing like SOC 2 and ISO using Orca and it expedited and allowed us to do these processes extremely fast because of having all of these guardrails and metrics has. >> Yeah, so, okay. So you recognized that you potentially had and did have that same problem as your customer has been. Has it helped you scale as a company obviously but how has it helped you scale as a company? >> So it helped us scale as a company by increasing the trust, the level of trust customer having Orca. It allowed us to adopt technology faster, meaning we need much less diligence or exploration of how to use technology because we have these guardrails. So we can use the richness of the technology that we have in the cloud without the need to stop, to install agents, to try to re architecture the way that we are using the technology. And we simply use it. We simply use the technology that the cloud offer as it is. And so it allows you a rapid scalability. >> Allows you allows you to move at the speed of cloud. Now, so I'm going to ask you as a co-founder, you got to wear many hats first of a co-founder and the leadership component there. And also the chief product officer, you got to go out, you got to get early customers, but but even more importantly you have to keep those customers retention. So maybe you can describe how customers have been using Orca. Did they, what was their aha moment that you've seen customers react to when you showcase the new product? And then how have you been able to keep them as loyal partners? >> So I think that we are very fortunate, we have a lot of, we are blessed with our customers. Many of our customers are vocal customers about what they like about Orca. And I think that something that comes along a lot of times is that this is a solution they have been waiting for. I can't express how many times I hear that I could go on a call and a customer says, "I must say, I must share. "This is a solution I've been looking for." And I think that in that respect, Orca is creating a new standard of what is expected from a security solution because we are transforming the security all in the company from an inhibitor to an enabler. You can use the technology. You can use new tools. You can use the cloud as it was intended. And so (coughs) we have customers like one of these cases is a customer that they have a lot of data and they're all super scared about using S3 buckets. We call over all of these incidents of these three buckets being breached or people connecting to an s3 bucket and downloading the data. So they had a policy saying, "S3 bucket should not be used. "We do not allow any use of S3 bucket." And obviously you do need to use S3 bucket. It's a powerful technology. And so the engineering team in that customer environment, simply installed a VM, installed an FTP server, and very easy to use password to that FTP server. And obviously two years later, someone also put all of the customer databases on that FTP server, open to the internet, open to everyone. And so I think it was for him and for us as well. It was a hard moment. First of all, he planned that no data will be leaked but actually what happened is way worse. The data was open to the to do to the world in a technology that exists for a very long time. And it's probably being scanned by attackers all the time. But after that, he not only allowed them to use S3 bucket because he knew that now he can monitor. Now, you can understand that they are using the technology as intended, now that they are using it securely. It's not open to everyone it's open in the right way. And there was no PII on that S3 bucket. And so I think the way he described it is that, now when he's coming to a meeting about things that needs to be improved, people are waiting for this meeting because he actually knows more than what they know, what they know about the environment. And I see it really so many times where a simple mistake or something that looks benign when you look at the environment in a holistic way, when you are looking on the context, you understand that there is a huge gap. That should be the breech. And another cool example was a case where a customer allowed an access from a third party service that everyone trusts to the crown jewels of the environment. And he did it in a very traditional way. He allowed a certain IP to be open to that environment. So overall it sounds like the correct way to go. You allow only a specific IP to access the environment but what he failed to to notice is that everyone in the world can register for free for this third-party service and access the environment from this IP. And so, even though it looks like you have access from a trusted service, a trusted third party service, when it's a Saas service, it's actually, it can mean that everyone can use it in order to access the environment and using Orca, you saw immediately the access, you saw immediately the risk. And I see it time after time that people are simply using Orca to monitor, to guardrail, to make sure that the environment stays safe throughout time and to communicate better in the organization to explain the risk in a very easy way. And the, I would say the statistics show that within few weeks, more than 85% of the different alerts and risks are being fixed, and think it comes to show how effective it is and how effective it is in improving your posture, because people are taking action. >> Those are two great examples, and of course they have often said that the shared responsibility model is often misunderstood. And those two examples underscore thinking that, "oh I hear all this, see all this press about S3, but it's up to the customer to secure the endpoint components et cetera. Configure it properly is what I'm saying. So what an unintended consequence, but but Orca plays a role in helping the customer with their portion of that shared responsibility. Obviously AWS is taking care of this. Now, as part of this program we ask a little bit of a challenging question to everybody because look it as a startup, you want to do well you want to grow a company. You want to have your employees, you know grow and help your customers. And that's great and grow revenues, et cetera but we feel like there's more. And so we're going to ask you because the theme here is all about cloud scale. What is your defining contribution to the future of cloud at scale, Gil? >> So I think that cloud is allowed the revolution to the data centers, okay? The way that you are building services, the way that you are allowing technology to be more adaptive, dynamic, ephemeral, accurate, and you see that it is being adopted across all vendors all type of industries across the world. I think that Orca is the first company that allows you to use this technology to secure your infrastructure in a way that was not possible in the on-prem world, meaning that when you're using the cloud technology and you're using technologies like Orca, you're actually gaining superior security that what was possible in the pre cloud world. And I think that, to that respect, Orca is going hand in hand with the evolution and actually revolutionizes the way that you expect to consume security, the way that you expect to get value, from security solutions across the world. >> Thank You for that Gil. And so we're at the end of our time, but we'll give you a chance for final wrap up. Bring us home with your summary, please. >> So I think that Orca is building the cloud security solution that actually works with its innovative aid agentless approach to cyber security to gain complete coverage, comprehensive solution and to gain, to understand the complete context of the 1% that matters in your security challenges across your data centers in the cloud. We are bridging the gap between the security teams, the business needs to grow and to do so in the paste of the cloud, I think the approach of being able to install within minutes, a security solution in getting complete understanding of your risk which is goes hand in hand in the way you expect and adopt cloud technology. >> That's great Gil. Thanks so much for coming on. You guys doing awesome work. Really appreciate you participating in the program. >> Thank you very much. >> And thank you for watching this AWS Startup Showcase. We're covering the next big thing in AI, Security, and Life Science on theCUBE. Keep it right there for more great content. (upbeat music)

(upbeat music) (music fades) >> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, I'm your host Natalie Erlich. Today, we're going to feature Olive, in the life sciences track. And of course, this is part of the future of AI, security, and life sciences. Here we're joined by our very special guest Rohan D'Souza, the Chief Product Officer of Olive. Thank you very much for being with us. Of course, we're going to talk today about building the internet of healthcare. I do you appreciate you joining the show. >> Thanks, Natalie. My pleasure to be here, I'm excited. >> Yeah, likewise. Well tell us about AI and how it's revolutionizing health systems across America. >> Yeah, I mean, we're clearly living around, living at this time of a lot of hype with AI, and there's a tremendous amount of excitement. Unfortunately for us, or, you know, depending on if you're an optimist or a pessimist, we had to wait for a global pandemic for people to realize that technology is here to really come into the aid of assisting everybody in healthcare, not just on the consumer side, but on the industry side, and on the enterprise side of delivering better care. And it's a truly an exciting time, but there's a lot of buzz and we play an important role in trying to define that a little bit better because you can't go too far today and hear about the term AI being used/misused in healthcare. >> Definitely. And also I'd love to hear about how Olive is fitting into this, and its contributions to AI in health systems. >> Yeah, so at its core, we, the industry thinks of us very much as an automation player. We are, we've historically been in the trenches of healthcare, mostly on the provider side of the house, in leveraging technology to automate a lot of the high velocity, low variability items. Our founding and our DNA is in this idea of, we think it's unfair that healthcare relies on humans as being routers. And we have looked to solve the problem of technology not talking to each other, by using humans. And so we set out to really go in into the trenches of healthcare and bring about core automation technology. And you might be sitting there wondering, well why are we talking about automation under the umbrella of AI? And that's because we are challenging the very status quo of siloed-based automation, and we're building, what we say, is the internet of healthcare. And more importantly what we've done is, we've brought in a human, very empathetic approach to automation, and we're leveraging technology by saying when one Olive learns, all Olives learn, so that we take advantage of the network effect of a single Olive worker in the trenches of healthcare, sharing that knowledge and wisdom, both with her human counterparts, but also with her AI worker counterparts that are showing up to work every single day in some of the most complex health systems in this country. >> Right. Well, when you think about AI and, you know, computer technology, you don't exactly think of, you know, humanizing kind of potential. So how are you seeking to make AI really humanistic, and empathetic, potentially? >> Well, most importantly the way we're starting with that is where we are treating Olive just like we would any single human counterpart. We don't want to think of this as just purely a technology player. Most importantly, healthcare is deeply rooted in this idea of investing in outcomes, and not necessarily investing in core technology, right? So we have learned that from the early days of us doing some really robust integrated AI-based solutions, but we've humanized it, right? Take, for example, we treat Olive just like any other human worker would, she shows up to work, she's onboarded, she has an obligation to her customers and to her human worker counterparts. And we care very deeply about the cost of the false positive that exists in healthcare, right? So, and we do this through various different ways. Most importantly, we do it in an extremely transparent and interpretable way. By transparent I mean, Olive provides deep insights back to her human counterparts in the form of reporting and status reports, and we even, we even have a term internally, that we call is a sick day. So when Olive calls in sick, we don't just tell our customers Olive's not working today, we tell our customers that Olive is taking a sick day, because a human worker that might require, that might need to stay home and recover. In our case, we just happened to have to rewire a certain portal integration because a portal just went through a massive change, and Olive has to take a sick day in order to make that fix, right? So. And this is, you know, just helping our customers understand, or feel like they can achieve success with AI-based deployments, and not sort of this like robot hanging over them, where we're waiting for Skynet to come into place, and truly humanizing the aspects of AI in healthcare. >> Right. Well that's really interesting. How would you describe Olive's personality? I mean, could you attribute a personality? >> Yeah, she's unbiased, data-driven, extremely transparent in her approach, she's empathetic. There are certain days where she's direct, and there are certain ways where she could be quirky in the way she shares stuff. Most importantly, she's incredibly knowledgeable, and we really want to bring that knowledge that she has gained over the years of working in the trenches of healthcare to her customers. >> That sounds really fascinating, and I love hearing about the human side of Olive. Can you tell us about how this AI, though, is actually improving efficiencies in healthcare systems right now? >> Yeah, not too many people know that about a third of every single US dollar is spent in the administrative burden of delivering care. It's really, really unfortunate. In the capitalistic world, of, just us as a system of healthcare in the United States, there is a lot of tail wagging the dog that ends up happening. Most importantly, I don't know that the last time, if you've been through a process where you have to go and get an MRI or a CT scan, and your provider tells you that we first have to wait for the insurance company in order to give us permission to perform this particular task. And when you think about that, one, there's, you know the tail wagging the dog scenario, but two, the administrative burden to actually seek the approval for that test, that your provider is telling you that you need to perform. Right? And what we've done is, as humans, or as sort of systems, we have just put humans in the supply chain of connecting the left side to the right side. So what we're doing is we're taking advantage of massive distributing cloud computing platforms, I mean, we're fully built on the AWS stack, we take advantage of things that we can very quickly stand up, and spin up. And we're leveraging core capabilities in our computer vision, our natural language processing, to do a lot of the tasks that, unfortunately, we have relegated humans to do, and our goal is can we allow humans to function at the top of their license? Irrespective of what the license is, right? It could be a provider, it could be somebody working in the trenches of revenue cycle management, or it could be somebody in a call center talking to a very anxious patient that just learned that he or she might need to take a test in order to rule out something catastrophic, like a very adverse diagnosis. >> Yeah, really fascinating. I mean, do you think that this is just like the tip of the iceberg? I mean, how much more potential does AI have for healthcare? >> Yeah, I think we're very much in the early, early, early days of AI being applied in a production in practical sense. You know, AI has been talked about for many, many many years, in the trenches of healthcare. It has found its place very much in challenging status quos in research, it has struggled to find its way in the trenches of just the practicality on the application of AI. And that's partly because we, you know, going back to the point that I raised earlier, the cost of the false positive in healthcare is really high. You know, it can't just be a, you know, I bought a pair of shoes online, and it recommended that I buy a pair of socks, and I happen to get the socks and I returned them back because I realized that they're really ugly and hideous and I don't want them. In healthcare, you can't do that. Right? In healthcare you can't tell a patient or somebody else oops, I really screwed up, I should not have told you that. So, what that's meant for us, in the trenches of delivery of AI-based applications, is we've been through a cycle of continuous pilots and proof of concepts. Now, though, with AI starting to take center stage, where a lot of what has been hardened in the research world can be applied towards the practicality to avoid the burnout, and the sheer cost that the system is under, we're starting to see this real upwards tick of people implementing AI-based solutions, whether it's for decision-making, whether it's for administrative tasks, drug discovery, it's just, is an amazing, amazing time to be at the intersection of practical application of AI and really, really good healthcare delivery for all of us. >> Yeah, I mean, that's really, really fascinating, especially your point on practicality. Now how do you foresee AI, you know, being able to be more commercial in its appeal? >> I think you have to have a couple of key wins under your belt, is number one, number two, the standard, sort of outcomes-based publications that is required. Two, I think we need, we need real champions on the inside of systems to support the narrative that us as vendors are pushing heavily on the AI-driven world or the AI-approachable world, and we're starting to see that right now. You know, it took a really, really long time for providers, first here in the United States, but now internationally, on this adoption and move away from paper-based records to electronic medical records. You know, you still hear a lot of pain from people saying oh my God, I used an EMR, but try to take the EMR away from them for a day or two, and you'll very quickly realize that life without an EMR is extremely hard right now. AI is starting to get to that point where, for us, we, you know, we treat, we always say that Olive needs to pass the Turing test. Right? So when you clearly get this, this sort of feeling that I can trust my AI counterpart, my AI worker to go and perform these tasks, because I realized that, you know, as long as it's unbiased, as long as it's data-driven, as long as it's interpretable, and something that I can understand, I'm willing to try this out in a routine basis, but we really, really need those champions on the internal side to promote the use of this safe application. >> Yeah. Well, just another thought here is, you know, looking at your website, you really focus on some of the broken systems in healthcare, and how Olive is uniquely prepared to shine the light on that, where others aren't. Can you just give us an insight onto that? >> Yeah. You know, the shine the light is a play on the fact that there's a tremendous amount of excitement in technology and AI in healthcare applied to the clinical side of the house. And it's the obvious place that most people would want to invest in, right? It's like, can I bring an AI-based technology to the clinical side of the house? Like decision support tools, drug discovery, clinical NLP, et cetera, et cetera. But going back to what I said, 30% of what happens today in healthcare is on the administrative side. And so what we call as the really, sort of the dark side of healthcare where it's not the most exciting place to do true innovation, because you're controlled very much by some big players in the house, and that's why we we provide sort of this insight on saying we can shine a light on a place that has typically been very dark in healthcare. It's around this mundane aspects of traditional, operational, and financial performance, that doesn't get a lot of love from the tech community. >> Well, thank you Rohan for this fascinating conversation on how AI is revolutionizing health systems across the country, and also the unique role that Olive is now playing in driving those efficiencies that we really need. Really looking forward to our next conversation with you. And that was Rohan D'Souza, the Chief Product Officer of Olive, and I'm Natalie Erlich, your host for the AWS Startup Showcase, on theCUBE. Thank you very much for joining us, and look forward for you to join us on the next session. (gentle music)

(gentle upbeat music) >> Everyone welcome to the AWS Startup Showcase presented by theCUBE. I'm John Furrier, host of theCUBE. We are here talking about the next big thing in cloud featuring Explorium. For the AI track, we've got AI cybersecurity and life sciences. Obviously AI is hot, machine learning powering that. Today we're joined by Zach Booth, director of global partnerships and channels like Explorium. Zach, thank you for joining me today remotely. Soon we'll be in person, but thanks for coming on. We're going to talk about rethinking external data. Thanks for coming on theCUBE. >> Absolutely, thanks so much for having us, John. >> So you guys are a hot startup. Congratulations, we just wrote about on SiliconANGLE, you're a new $75 million of fresh funding. So you're part of the Amazon partner network and growing like crazy. You guys have a unique value proposition looking at external data and that having a platform for advanced analytics and machine learning. Can you take a minute to explain what you guys do? What is this platform? What's the value proposition and why do you exist? >> Bottom line, we're bringing context to decision-making. The premise of Explorium and kind of this is consistent with the framework of advanced analytics is we're helping customers to reach better, more relevant, external data to feed into their predictive and analytical models. It's quite a challenge to actually integrate and effectively leverage data that's coming from beyond your organization's walls. It's manual, it's tedious, it's extremely time consuming and that's a problem. It's really a problem that Explorium was built to solve. And our philosophy is it shouldn't take so long. It shouldn't be such an arduous process, but it is. So we built a company, a technology that's capable for any given analytical process of connecting a customer to relevant sources that are kind of beyond their organization's walls. And this really impacts decision-making by bringing variety and context into their analytical processes. >> You know, one of the things I see a lot in my interviews with theCUBE and talking to people in the industry is that everyone talks a big game about having some machine learning and AI, they're like, "Okay, I got all this cool stuff". But at the end of the day, people are still using spreadsheets. They're wrangling data. And a lot of it's dominated by these still fenced-off data warehousing and you start to see the emergence of really companies built on the cloud. I saw the snowflake IPO, you're seeing a whole new shift of new brands emerging that are doing things differently, right? And because there's such a need for just move out of the archaic spreadsheet and data presentation layers, it's a slower antiquated, outdated. How do you guys solve that problem? You guys are on the other side of that equation, you're on the new wave of analytics. What are you guys solving? How do you make that work? How do you get on that way? >> So basically the way Explorium sees the world, and I think that most analytical practitioners these days see it in a similar way, but the key to any analytical problem is having the right data. And the challenge that we've talked about and that we're really focused on is helping companies reach that right data. Our focus is on the data part of data science. The science part is the algorithmic side. It's interesting. It was kind of the first frontier of machine learning as practitioners and experts were focused on it and cloud and compute really enabled that. The challenge today isn't so much "What's the right model for my problem?" But it's "What's the right data?" And that's the premise of what we do. Your model's only as strong as the data that it trains on. And going back to that concept of just bringing context to decision-making. Within that framework that we talked about, the key is bringing comprehensive, accurate and highly varied data into my model. But if my model is only being informed with internal data which is wonderful data, but only internal, then it's missing context. And we're helping companies to reach that external variety through a pretty elegant platform that can connect the right data for my analytical process. And this really has implications across several different industries and a multitude of use cases. We're working with companies across consumer packaged goods, insurance, financial services, retail, e-commerce, even software as a service. And the use cases can range between fraud and risk to marketing and lifetime value. Now, why is this such a challenge today with maybe some antiquated or analog means? With a spreadsheet or with a rule-based approach where we're pretty limited, it was an effective means of decision-making to generate and create actions, but it's highly limited in its ability to change, to be dynamic, to be flexible. And with modeling and using data, it's really a huge arsenal that we have at our fingertips. The trick is extracting value from within it. There's obviously latent value from within our org but every day there's more and more data that's being created outside of our org. And that is a challenge to go out and get to effectively filter and navigate and connect to. So we've basically built that tech to help us navigate and query for any given analytical question. Find me the right data rather than starting with what's the problem I'm looking for, now let me think about the right data. Which is kind of akin to going into a library and searching for a specific book. You know which book you're looking for. Instead of saying, there's a world, a universe of data outside there. I want to access it. I want to tap into what's right. Can I use a tool that can effectively query all that data, find what's relevant for me, connect it and match it with my own and distill signals or features from that data to provide more variety into my modeling efforts yielding a robust decision as an output. >> I love that paradigm of just having that searchable kind of paradigm. I got to ask you one of the big things that I've heard people talk about. I want to get your thoughts on this, is that how do I know if I even have the right data? Is the data addressable? Can I find it? Is it even, can I even be queried? How do you solve that problem for customers when they say, "I really want the best analytics but do I even have the data or is it the right data?" How do you guys look at that? >> So the way our technology was built is that it's quite relevant for a few different profile types of customers. Some of these customers, really the genesis of the company started with those cloud-based, model-driven since day one organizations, and they're working with machine learning and they have models in production. They're quite mature in fact. And the problem that they've been facing is, again, our models are only as strong as the data that they're training on. The only data that they're training on is internal data. And we're seeing diminishing returns from those decisions. So now suddenly we're looking for outside data and we're finding that to effectively use outside data, we have to spend a lot of time. 60% of our time spent thinking of data, going out and getting it, cleaning it, validating it, and only then can we actually train a model and assess if there's an ROI. That takes months. And if it doesn't push the needle from an ROI standpoint, then it's an enormous opportunity cost, which is very, very painful, which goes back to their decision-making. Is it even worth it if it doesn't push the needle? That's why there had to be a better way. And what we built is relevant for that audience as well as companies that are in the midst of their digital transformation. We're data rich, but data science poor. We have lots of data. A latent value to extract from within our own data and at the same time tons of valuable data outside of our org. Instead of waiting 18, 36 months to transform ourselves, get our infrastructure in place, our data collection in place, and really start having models in production based on our own data. You can now do this in tandem. And that's what we're seeing with a lot of our enterprise customers. By using their analysts, their data engineers, some of them in their innovation or kind of center of excellences have a data science group as well. And they're using the platform to inform a lot of their different models across lines of businesses. >> I love that expression, "data-rich". A lot of people becoming full of data too. They have a data problem. They have a lot of it. I think I want to get your thoughts but I think that connects to my next question which is as people look at the cloud, for instance, and again, all these old methods were internal, internal to the company, but now that you have this idea of cloud, more integration's happening. More people are connecting with APIs. There's more access to potentially more signals, more data. How does a company go to that next level to connect in and acquire the data and make it faster? Because I can almost imagine that the signals that come from that context of merging external data and that's the topic of this theme, re-imagining external data is extremely valuable signaling capability. And so it sounds like you guys make it go faster. So how does it work? Is it the cloud? Take us through that value proposition. >> Well, it's a real, it's amazing how fast the rate of change organizations have been moving onto the cloud over the past year during COVID and the fact that alternative or external data, depending on how you refer to it, has really, really blown up. And it's really exciting. This is coming in the form of data providers and data marketplaces, and everybody is kind of, more and more organizations are moving from rule-based decision-making to predictive decision making, and that's exciting. Now what's interesting about this company, Explorium, we're working with a lot of different types of customers but our long game has a real high upside. There's more and more companies that are starting to use data and are transformed or already are in the midst of their transformation. So they need outside data. And that challenge that I described is exists for all of them. So how does it really work? Today, if I don't have data outside, I have to think. It's based on hypothesis and it all starts with that hypothesis which is already prone to error from the get-go. You and I might be domain experts for a given use case. Let's say we're focusing on fraud. We might think about a dozen different types of data sources, but going out and getting it like I said, it takes a lot of time harmonizing it, cleaning it, and being able to use it takes even more time. And that's just for each one. So if we have to do that across dozens of data sources it's going to take far too much time and the juice isn't worth the squeeze. And so I'm going to forego using that. And a metaphor that I like to use when I try to describe what Explorium does to my mom. I basically use this connection to buying your first home. It's a very, very important financial decision. You would, when you're buying this home, you're thinking about all the different inputs in your decision-making. It's not just about the blueprint of the house and how many rooms and the criteria you're looking for. You're also thinking external variables. You're thinking about the school zone, the construction, the property value, alternative or similar neighborhoods. That's probably your most important financial decision or one of the largest at least. A machine learning model in production is an extremely important and expensive investment for an organization. Now, the problem is as a consumer buying a home, we have all this data at our fingertips to find out all of those external-based inputs. Organizations don't, which is kind of crazy when I first kind of got into this world. And so, they're making decisions with their first party data only. First party data's wonderful data. It's the best, it's representative, it's high quality, it's high value for their specific decision-making and use cases but it lacks context. And there's so much context in the form of location-based data and business information that can inform decision-making that isn't being used. It translates to sub-optimal decision-making, let's say. >> Yeah, and I think one of the insights around looking at signal data in context is if by merging it with the first party, it creates a huge value window, it gives you observational data, maybe potentially insights into customer behavior. So totally agree, I think that's a huge observation. You guys are definitely on the right side of history here. I want to get into how it plays out for the customer. You mentioned the different industries, obviously data's in every vertical. And vertical specialization with the data it has to be, is very metadata driven. I mean, metadata and oil and gas is different than fintech. I mean, some overlap, but for the most part you got to have that context, acute context, each one. How are you guys working? Take us through an example of someone getting it right, getting that right set up, taking us through the use case of how someone on boards Explorium, how they put it to use, and what are some of the benefits? >> So let's break it down into kind of a three-step phase. And let's use that example of fraud earlier. An organization would have basically past historical data of how many customers were actually fraudulent in the end of the day. So this use case, and it's a core business problem, is with an intention to reduce that fraud. So they would basically provide, going with your description earlier, something similar to an Excel file. This can be pulled from any database out there, we're working with loads of them, and they would provide this what's called training data. This training data is their historical data and would have as an output, the outcome, the conclusion, was this business fraudulent or not? Yes or no. Binary. The platform would understand that data itself to train a model with external context in the form of enrichments. These data enrichments at the end of the day are important, they're relevant, but their purpose is to generate signals. So to your point, signals is the bottom line what everyone's trying to achieve and identify and discover, and even engineer by using data that they have and data that they yet to integrate with. So the platform would connect to your data, infer and understand the meaning of that data. And based on this matching of internal plus external context, the platform automates the process of distilling signals. Or in machine learning this is called, referred to as features. And these features are really the bread and butter of your modeling efforts. If you can leverage features that are coming from data that's outside of your org, and they're quantifiably valuable which the platform measures, then you're putting yourself in a position to generate an edge in your modeling efforts. Meaning now, you might reduce your fraud rate. So your customers get a much better, more compelling offer or service or price point. It impacts your business in a lot of ways. What Explorium is bringing to the table in terms of value is a single access point to a huge universe of external data. It expedites your time to value. So rather than data analysts, data engineers, data scientists, spending a significant amount of time on data preparation, they can now spend most of their time on feature or signal engineering. That's the more fun and interesting part, less so the boring part. But they can scale their modeling efforts. So time to value, access to a huge universe of external context, and scale. >> So I see two things here. Just make sure I get this right 'cause it sounds awesome. So one, the core assets of the engineering side of it, whether it's the platform engineer or data engineering, they're more optimized for getting more signaling which is more impactful for the context acquisition, looking at contexts that might have a business outcome, versus wrangling and doing mundane, heavy lifting. >> Yeah so with it, sorry, go ahead. >> And the second one is you create a democratization for analysts or business people who just are used to dealing with spreadsheets who just want to kind of play and play with data and get a feel for it, or experiment, do querying, try to match planning with policy - >> Yeah, so the way I like to kind of communicate this is Explorium's this one, two punch. It's got this technology layer that provides entity resolution, so matching with external data, which otherwise is a manual endeavor. Explorium's automated that piece. The second is a huge universe of outside data. So this circumvents procurement. You don't have to go out and spend all of these one-off efforts on time finding data, organizing it, cleaning it, etc. You can use Explorium as your single access point to and gateway to external data and match it, so this will accelerate your time to value and ultimately the amount of valuable signals that you can discover and leverage through the platform and feed this into your own pipelines or whatever system or analytical need you have. >> Zach, great stuff. I love talking with you and I love the hot startup action here. Cause you're again, you're on the net new wave here. Like anything new, I was just talking to a colleague here. (indistinct) When you have something new, it's like driving a car for the first time. You need someone to give you some driving lessons or figure out how to operationalize it or take advantage of the one, two, punch as you pointed out. How do you guys get someone up and running? 'Cause let's just say, I'm like, okay, I'm bought into this. So no brainer, you got my attention. I still don't understand. Do you provide a marketplace of data? Do I need to get my own data? Do I bring my own data to the party? Do you guys provide relationships with other data providers? How do I get going? How do I drive this car? How do you answer that? >> So first, explorium.ai is a free trial and we're a product-focused company. So a practitioner, maybe a data analyst, a data engineer, or data scientist would use this platform to enrich their analytical, so BI decision-making or any models that they're working on either in production or being trained. Now oftentimes models that are being trained don't actually make it to production because they don't meet a minimum threshold. Meaning they're not going to have a positive business outcome if they're deployed. With Explorium you can now bring variety into that and increase your chances that your model that's being trained will actually be deployed because it's being fed with the right data. The data that you need that's not just the data that you have. So how a business would start working with us would typically be with a use case that has a high business value. Maybe this could be a fraud use case or a risk use case and B2B, or even B2SMB context. This might be a marketing use case. We're talking about LTV modeling, lookalike modeling, lead acquisition and generation for our CPGs and field sales optimization. Explore and understand your data. It would enrich that data automatically, it would generate and discover new signals from external data plus from your own and feed this into either a model that you have in-house or end to end in the platform itself. We provide customer success to generate, kind of help you build out your first model perhaps, and hold your hands through that process. But typically most of our customers are after a few months time having run in building models, multiple models in production on their own. And that's really exciting because we're helping organizations move from a more kind of rule-based decision making and being their bridge to data science. >> Awesome. I noticed that in your title you handle global partnerships and channels which I'm assuming is you guys have a network and ecosystem you're working with. What are some of the partnerships and channel relationships that you have that you bring to bear in the marketplace? >> So data and analytics, this space is very much an ecosystem. Our customers are working across different clouds, working with all sorts of vendors, technologies. Basically they have a pretty big stack. We're a part of that stack and we want to symbiotically play within our customer stack so that we can contribute value whether they sit here, there, or in another place. Our partners range from consulting and system integration firms, those that perhaps are building out the blueprint for a digital transformation or actually implementing that digital transformation. And we contribute value in both of these cases as a technology innovation layer in our product. And a customer would then consume Explorium afterwards, after that transformation is complete as a part of their stack. We're also working with a lot of the different cloud vendors. Our customers are all cloud-based and data enrichment is becoming more and more relevant with some wonderful machine-learning tools. Be they AutoML, or even some data marketplaces are popping up and very exciting. What we're bringing to the table as an edge is accelerating the connection between the data that I think I want as a company and how to actually extract value from that data. Being part of this ecosystem means that we can be working with and should be working with a lot of different partners to contribute incremental value to our end customers. >> Final question I want to ask you is if I'm in a conference room with my team and someone says, "Hey, we should be rethinking our external data." What would I say? How would I pound my fist on the table or raise my hand in saying, "Hey, I have an idea, we should be thinking this way." What would be my argument to the team, to re-imagine how we deal with external data? >> So it might be a scenario that rather than banging your hands on the table, you might be banging your heads on the table because it's such a challenging endeavor today. Companies have to think about, What's the right data for my specific use cases? I need to validate that data. Is it relevant? Is it real? Is it representative? Does it have good coverage, good depth and good quality? Then I need to procure that data. And this is about getting a license from it. I need to integrate that data with my own. That means I need to have some in-house expertise to do so. And then of course, I need to monitor and maintain that data on an ongoing basis. All of this is a pretty big thing to undertake and undergo and having a partner to facilitate that external data integration and ongoing refresh and monitoring, and being able to trust that this is all harmonized, high quality, and I can find the valuable ones without having to manually pick and choose and try to discover it myself is a huge value add, particularly the larger the organization or partner. Because there's so much data out there. And there's a lot of noise out there too. And so if I can through a single partner or access point, tap into that data and quantify what's relevant for my specific problem, then I'm putting myself in a really good position and optimizing the allocation of my very expensive and valuable data analysts and engineering resources. >> Yeah, I think one of the things you mentioned earlier I thought was a huge point was good call out was it goes beyond the first party data because and even just first party if you just in an internal view, some of the best, most successful innovators that we've been covering with cloud scale is they're extending their first party data to external providers. So they're in the value chains of solutions that share their first party data with other suppliers. And so that's just, again, more of an extension of the first party data. You're kind of taking it to a whole 'nother level of there's another external, external set of data beyond it that's even more important. I think this is a fascinating growth area and I think you guys are onto it. Great stuff. >> Thank you so much, John. >> Well, I really appreciate you coming on Zach. Final word, give a quick plug for the company. What are you up to, and what's going on? >> What's going on with Explorium? We are growing very fast. We're a very exciting company. I've been here since the very early days and I can tell you that we have a stellar working environment, a very, very, strong down to earth, high work ethic culture. We're growing in the sense of our office in San Mateo, New York, and Tel Aviv are growing rapidly. As you mentioned earlier, we raised our series C so that totals Explorium to raising I think 127 million over the past two years and some change. And whether you want to partner with Explorium, work with us as a customer, or join us as an employee, we welcome that. And I encourage everybody to go to explorium.ai. Check us out, read some of the interesting content there around data science, around the processes, around the business outcomes that a lot of our customers are seeing, as well as joining a free trial. So you can check out the platform and everything that has to offer from machine learning engine to a signal studio, as well as what type of information might be relevant for your specific use case. >> All right Zach, thanks for coming on. Zach Booth, director of global partnerships and channels that explorium.ai. The next big thing in cloud featuring Explorium and a part of our AI track, I'm John Furrier, host of theCUBE. Thanks for watching.

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hello everyone and welcome to this week's episode of wiki bots cube insights powered by EGR in this breaking analysis we take a deeper dive into the world of robotic process automation otherwise known as RPA it's one of the hottest sectors in software today in fact Gartner says it's the fastest growing software sector that they follow in this session I want to break down three questions one is the RP a market overvalued - how large is the total available market for RP a and three who were the winners and losers in this space now before we address the first question here's what you need to know about RP a the market today is small but it's growing fast the software only revenue for the space was about 1 billion dollars in 2019 and it's growing it between 80 to a hundred percent annually RP a has been very popular in larger organizations especially in back-office functions really in regulated industries like financial services and healthcare RP a has been successful at automating the mundane repeatable deterministic tasks and most automations today are unattended the industry is very well funded with the top two firms raising nearly 1 billion dollars in the past couple of years they have a combined market value of nearly 14 billion now some people in the art community have said that RP a is hyped and looks like a classic pump and dump situation we're gonna look into that and really try to explore the valuation and customer data and really try to come to some conclusions there we see big software companies like Microsoft and sa P entering the scene and we want to comment on that a little later in this segment now RBA players have really cleverly succeeded in selling to the business lines and often a bypassed IT now sometimes that creates tension in or as I said customers are typically very large organizations who can shell out the hundred thousand dollar plus entry point to get into the RP a game the Tam is expanding beyond back office into broader on a broader automation agenda hyper automation is the buzzword of the day and there are varying definitions Gartner looks at hyper automation as the incorporation of RPA along with intelligent business process management I BPM and I pass or intelligent platform-as-a-service Gardner's definition takes a holistic view of the enterprise incorporating legacy on-prem app apps as well as emerging systems now this is good but I question whether the hyper term applies here as we see hyper automation as the extension of our PA to include process mining to discover new automations or new automation opportunities and the use of machine intelligence ml and a I applied to process data data where that combination drives intelligence analytics that further drives digital business process transformation across the enterprise so the point is that we envision a more agile framework and definition for hyper automation we see legacy BPM systems informing the transformation but not necessarily adjudicating the path forward we liken this to the early days of big data where legacy data warehouses and ETL processes provided useful context but organizations had to develop a new tech stack that broke the stranglehold of technical debt we're seeing this emerge in the form of new workloads powered by emerging analytic databases like redshift and snowflake with ml tools applied and cloud driving agile insights in that so-called Big Data space so we think a similar renaissance is happening here with with automation really driven by the money the mandate for digital business transformation along with machine intelligence and that tooling applied for a really driving automation across the enterprise in a form of augmentation with attended BOTS at scale becoming much much more important over time ok now let's shift gears a little bit question is the RP a market overhyped and overvalued now to answer this let's go through a bit of a thought exercise that we've put together and look at some data what this chart shows is some critical data points that will begin to help answer the question that we've posed in the top part of the chart we show the company the VC funding projected valuations and revenue estimates for 2019 and 2020 and as you can see uipath an automation any where are the hot companies right now they're private so much of this data is estimated but we know how much money they've raised and we know the valuations that have been reported so the RP a software market is around a billion dollars today and we have it almost doubling in 2020 now the bottom part of this chart shows the projected market revenue growth and the implied valuations for the market as a whole so you can see today we show a mark that is trading at about 15 to 17 times revenue which seems like a very high multiple but over time we show that multiple shrinking and settling in mid decade at just over 5x which for software is pretty conservative especially for high-growth software now what we've done on this next chart is we brought down that market growth and the implied valuation data and highlighted twenty twenty-five at seventy-five billion dollars the market growth will have slowed by then to twenty percent in this model and this thought exercise with a revenue multiple of five point four x for the overall market now eventually as growth slows RBA software will start to throw off profits at least it better so what we show here is a sensitivity analysis assuming a 20% 25% 30% and 35% for the market as a whole we're using that as a proxy and we show a 20/20 X even multiple which for a market growing the software market growing this fast you know we think is pretty reasonable consider the tech overall typically is gonna have a an even multiple of ten to fifteen you know X it really should be easy your enterprise value over a bit it's really a more accurate measure but but this is back in the Afghan on the balance sheet date and I'm a forecast all-out but we're trying to just sort of get to the question is is this market overvalued and as you can see in the Far column given these assumptions we're in the range of that seventy five billion dollar market valuation with that Delta now reality you're going to have some companies growing faster than the market overall and we'll see a lot of consolidation in this space but at the macro level it would seem that the company which can lead and when the Spoils is gonna really benefit okay so these figures actually suggest in my view that the market could be undervalued that sounds crazy right but look at companies like ServiceNow and work day and look at snowflakes recent valuation at twelve billion dollars so are the valuations for uipath and automation anywhere justified well in part it depends on the size of the market the TAM total available market in their ability to break out of back-office niches and deliver these types of revenue figures and growth you know maybe my forecasts are a little too aggressive in the early days but in my experience the traditional forecast that we see in the marketplace tend to underestimate transformative technologies you tend to have these sort of o guides where you know it takes off and really steep ins and it has a sharp curve and then tapers off so we'll see but let's take a closer look at the Tam but you know first I want to introduce a customer view point here's Eric's Lac Eric Lex who's an RPA pro at GE talking about his company's RPA journey play the clip I would say in terms of our journey 2017 was kind of our year to prove the technology we wanted to see if this stuff could really work long term and operate at scale given that I'm still here obviously we proved that was correct and then 2018 was kind of the year of scaling and operationalizing kind of a a sustainable model to support our business units across the board from an RPA standpoint so really building out a proper structure building out the governance that goes along with building robots and building a kind of a resource team to continue to support the bots that that you know we were at scale at that point so maintaining those bots is critically important that's the direction we're moving in 2019 we've kind of perfected the concept of the back office robot and the development of those and running those at scale and now we're moving towards you know a whole new market share when it comes to attended automation and citizen Development so this is a story we've heard from many customers and we've tried to reflect it in this graphic that we're showing here start small get some wins prove out the tech really in the back office and then drive customer facing activities we see this as the starting point for more SME driven digital transformations where business line pros are rethinking processes and developing new automations you know either in low code scenarios or with Centers of Excellence now this vision of hyper automation we think comes from the ability to do process mining and identify automation opportunities and then bring our PA to the table using machine learning and AI to understand text voice visual context and ultimately use that process data to transform the business this is an outcome driven model where organizations are optimizing on business KPIs and incentives are aligned accordingly so we see this vision as potentially unlocking a very large Tam that perhaps exceeds 30 billion dollars go now let's bring in some of these spending data and take a look at what the ETR data set tells us about the RPA market now the first thing that jumps out at you is our PA is one of the fastest growing segments in the data set you can see that green box and that blue dot at around 20% that's the change in spending velocity in the 2020 survey versus last year now the one caveat is I'm isolating on global 2000 companies in this data set and as you can see in in that red bar up on the left and remember our PA today is really hot in large companies but not nearly as fast growing when you analyze the overall respondent base and which includes smaller organizations nonetheless this chart shows net scores and market shares for our PA across all respondents remember net score is a measure of spending velocity and market share is a measure of pervasiveness in the survey and what you see here is that our PA net scores are holding steadily the nice rate and market shares are creeping up relative to other segments in the data set now remember this is across all companies but we want to use the ETR data understand who is winning in this space now what this chart shows is net score or spending velocity on the vertical axis and market share or pervasiveness on the horizontal axis for each individual player and as we run through this sequence from January 18 survey through today across the nine surveys look at uipath an automation anywhere but look at uipath in particular they really appear to be breaking away from the pack now here's another look at the data it shows net scores or spending velocity for uipath automation anywhere blue prism pegye systems and work fusion now these are all very strong net scores which are essentially calculated by subtracting the percent of customers spending less from those spending more the two leaders here uipath and automation anywhere August but the rest rest are actually quite good there in the green but look at this look what happens when you isolate on the 349 global 2,000 respondents in the survey uipath jumps into the 80 percent net score territory again spending velocity automation anywhere dips a little bit pegye systems interestingly jumps up nicely but look at blue prism they fall back in the larger global 2000 accounts which is a bit of a concern now the other key point on this chart is that 85% of UI customers and 70% of automation anywhere customers plan to spend more this year than they spent last year that is pretty impressive now as you can see here in this chart the global 2000 have been pretty consistent spenders on our PA for the past three survey snapshots uipath again showing net scores or spending intensity solidly in the 80% plus range and even though it's a smaller end you can see pay go with a nice uptick in the last two surveys within these larger accounts now finally let's look at what ETR calls market share which is a measure of pervasiveness in the survey this chart shows data from all 1000 plus respondents and as you can see UI path appears to be breaking out from the pack automation anywhere in pega are showing an uptick in the january survey and blue prism is trending down a little bit which is something to watch but you can see in the upper right all four companies are in the green with regard to net score or against pending velocity so let's summarize it and wrap up is this market overhyped well it probably is overhyped but is it overvalued I don't think so the customer feedback that we have in the community and the proof points are really starting to stack up so with continued revenue growth and eventually profits you can make the case that whoever comes out on top will really do well and see huge returns in this market space let's come back to that in a moment how large is this market I think this market can be very large at am of 30 billion pluses not out of the question in my view now that realization will be a function of RPAs ability to break into more use cases with deeper business integration RBA has an opportunity in our view to cross the chasm and deliver lower code solutions to subject matter experts in business lines that are in a stronger position to drive change now a lot of people poopoo this notion and this concept but I think it's something that is a real possibility this idea of hyper automation is buzzword e but it has meaning companies that bring RPA together with process mining and machine intelligence that tries process analytics has great potential if organizational stovepipes can be broken down in other words put process data and analytics at the core to drive decision-making and change now who wins let me say this the company that breaks out and hits escape velocity is going to make a lot of money here now unlike what I said in last week's braking analysis on cloud computing this is more of a winner-take-all market it's not a trillion dollar team like cloud it's tens of billions and maybe north to 30 billion but it's somewhat of a zero-sum game in my opinion the number one player is going to make a lot of dough number two will do okay and in my view everyone else is going to struggle for profits now the big wildcard is the degree to which the big software players like Microsoft and sa P poison the RPA well now here's what I think I think these big software players are taking an incremental view of the market and are bundling in RPA is a check off item they will not be the ones to drive radical process transformation rather they will siphon off some demand but organizations that really want to benefit from so-called hyper automation will be leaning heavily on software from specialists who have the vision the resources the culture in the focus to drive digital process transformation alright that's a wrap as always I really appreciate the comments that I get on my LinkedIn posts and on Twitter I'm at at D Volante so thanks for that and thanks for watching everyone this is Dave Volante for the cube insights powered by ETR and we'll see you next time

(bright music) >> Welcome back to the CUBE presents the AWS Startup Showcase The Next Big Thing in cloud startups with AI security and life science tracks, 15 hottest growing startups are presented. And we had a great opening keynote with luminaries in the industry. And now our closing keynote is to get a deeper dive on cracking the code in the enterprise, how startups are changing the game and helping companies change. And they're also changing the game of open source. We have a great guest, Katie Drucker, Head of Business Development, Madrona Venture Group. Katie, thank you for coming on the CUBE for this special closing keynote. >> Thank you for having me, I appreciate it. >> So one of the topics we talked about with Soma from Madrona on the opening keynote, as well as Ali from Databricks is how startups are seeing success faster. So that's the theme of the Cloud speed, agility, but the game has changed in the enterprise. And I want to really discuss with you how growth changes and growth strategy specifically. They talk, go to market. We hear things like good sales to enterprise sales, organic, freemium, there's all kinds of different approaches, but at the end of the day, the most successful companies, the ones that might not be known that just come out of nowhere. So the economics are changing and the buyers are thinking differently. So let's explore that topic. So take us through your view 'cause you have a lot of experience. But first talk about your role at Madrona, what you do. >> Absolutely all great points. So my role at Madrona, I think I have personally one of the more enviable jobs and that my job is to... I get the privilege of working with all of these fantastic entrepreneurs in our portfolio and doing whatever we can as a firm to harness resources, knowledge, expertise, connections, to accelerate their growth. So my role in setting up business development is taking a look at all of those tools in the tool chest and partnering with the portfolio to make it so. And in our portfolio, we have a wide range of companies, some rely on enterprise sales, some have other go to markets. Some are direct to consumer, a wide range. >> Talk about the growth strategies that you see evolving because what's clear with the pandemic. And as we come out of it is that there are growth plays happening that don't look a little bit differently, more obvious now because of the Cloud scale, we're seeing companies like Databricks, like Snowflake, like other companies that have been built on the cloud or standalone. What are some of the new growth techniques, or I don't want to say growth hacking, that is a pejorative term, but like just a way for companies to quickly describe their value to an enterprise buyer who's moving away from the old RFP days of vendor selection. The game has changed. So take us through how you see secret key and unlocking that new equation of how to present value to an enterprise and how you see enterprises evaluating startups. >> Yes, absolutely. Well, and that's got a question, that's got a few components nestled in what I think are some bigger trends going on. AWS of course brought us the Cloud first. I think now the Cloud is more and more a utility. And so it's incumbent upon thinking about how an enterprise 'cause using the Cloud is going to go up the value stack and partner with its cloud provider and other service providers. I think also with that agility of operations, you have thinning, if you will, the systems of record and a lot of new entrance into this space that are saying things like, how can we harness AIML and other emerging trends to provide more value directly around work streams that were historically locked into those systems of record? And then I think you also have some price plans that are far more flexible around usage based as opposed to just flat subscription or even these big clunky annual or multi-year RFP type stuff. So all of those trends are really designed in ways that favor the emerging startup. And I think if done well, and in partnership with those underlying cloud providers, there can be some amazing benefits that the enterprise realizes an opportunity for those startups to grow. And I think that's what you're seeing. I think there's also this emergence of a buyer that's different than the CIO or the site the CISO. You have things with low code, no code. You've got other buyers in the organization, other line of business executives that are coming to the table, making software purchase decisions. And then you also have empowered developers that are these citizen builders and developer buyers and personas that really matter. So lots of inroads in places for a startup to reach in the enterprise to make a connection and to bring value. That's a great insight. I want to ask that just if you don't mind follow up on that, you mentioned personas. And what we're seeing is the shift happens. There's new roles that are emerging and new things that are being reconfigured or refactored if you will, whether it's human resources or AI, and you mentioned ML playing a role in automation. These are big parts of the new value proposition. How should companies posture to the customer? Because I don't want to say pivot 'cause that means it's not working but mostly extending our iterating around their positioning because as new things have not yet been realized, it might not be operationalized in a company or maybe new things need to be operationalized, it's a new solution for that. Positioning the value is super important and a lot of companies often struggle with that, but also if they get it right, that's the key. What's your feeling on startups in their positioning? So people will dismiss it like, "Oh, that's marketing." But maybe that's important. What's your thoughts on the great positioning question? >> I've been in this industry a long time. And I think there are some things that are just tried and true, and it is not unique to tech, which is, look, you have to tell a story and you have to reach the customer and you have to speak to the customer's need. And what that means is, AWS is a great example. They're famous for the whole concept of working back from the customer and thinking about what that customer's need is. I think any startup that is looking to partner or work alongside of AWS really has to embody that very, very customer centric way of thinking about things, even though, as we just talked about those personas are changing who that customer really is in the enterprise. And then speaking to that value proposition and meeting that customer and creating a dialogue with them that really helps to understand not only what their pain points are, but how you were offering solves those pain points. And sometimes the customer doesn't realize that that is their pain point and that's part of the education and part of the way in which you engage that dialogue. That doesn't change a lot, just generation to generation. I think the modality of how we have that dialogue, the methods in which we choose to convey that change, but that basic discussion is what makes us human. >> What's your... Great, great, great insight. I want to ask you on the value proposition question again, the question I often get, and it's hard to answer is am I competing on value or am I competing on commodity? And depending on where you're in the stack, there could be different things like, for example, land is getting faster, smaller, cheaper, as an example on Amazon. That's driving down to low cost high value, but it shifts up the stack. You start to see in companies this changing the criteria for how to evaluate. So an enterprise might be struggling. And I often hear enterprises say, "I don't know how to pick who I need. I buy tools, I don't buy many platforms." So they're constantly trying to look for that answer key, if you will, what's your thoughts on the changing requirements of an enterprise? And how to do vendor selection. >> Yeah, so obviously I don't think there's a single magic bullet. I always liked just philosophically to think about, I think it's always easier and frankly more exciting as a buyer to want to buy stuff that's going to help me make more revenue and build and grow as opposed to do things that save me money. And just in a binary way, I like to think which side of the fence are you sitting on as a product offering? And the best ways that you can articulate that, what opportunities are you unlocking for your customer? The problems that you're solving, what kind of growth and what impact is that going to lead to, even if you're one or two removed from that? And again, that's not a new concept. And I think that the companies that have that squarely in mind when they think about their go-to market strategy, when they think about the dialogue they're having, when they think about the problems that they're solving, find a much faster path. And I think that also speaks to why we're seeing so many explosion in the line of business, SAS apps that are out there. Again, that thinning of the systems of record, really thinking about what are the scenarios and work streams that we can have happened that are going to help with that revenue growth and unlocking those opportunities. >> What's the common startup challenge that you see when they're trying to do business development? Usually they build the product first, product led value, you hear that a lot. And then they go, "Okay, we're ready to sell, hire a sales guy." That seems to be shifting away because of the go to markets are changing. What are some of the challenges that startups have? What are some that you're seeing? >> Well, and I think the point that you're making about the changes are really almost a result of the trends that we're talking about. The sales organization itself is becoming... These work streams are becoming instrumented. Data is being collected, insights are being derived off of those things. So you see companies like Clary or Highspot or two examples or tutorial that are in our portfolio that are looking at that action and making the art of sales and marketing far more sophisticated overall, which then leads to the different growth hacking and the different insights that are driven. I think the common mistakes that I see across the board, especially with earlier stage startups, look you got to find product market fit. I think that's always... You start with a thesis or a belief and a passion that you're building something that you think the market needs. And it's a lot of dialogue you have to have to make sure that you do find that. I think once you find that another common problem that I see is leading with an explanation of technology. And again, not focusing on the buyer or the... Sorry, the buyer about solving a problem and focusing on that problem as opposed to focusing on how cool your technology is. Those are basic and really, really simple. And then I think setting a set of expectations, especially as it comes to business development and partnering with companies like AWS. The researching that you need to adequately meet the demand that can be turned on. And then I'm sure you heard about from Databricks, from an organization like AWS, you have to be pragmatic. >> Yeah, Databricks gone from zero a software sales a few years ago to over a billion. Now it looks like a Snowflake which came out of nowhere and they had a great product, but built on Amazon, they became the data cloud on top of Amazon. And now they're growing just whole new business models and new business development techniques. Katie, thank you for sharing your insight here. The CUBE's closing keynote. Thanks for coming on. >> Appreciate it, thank you. >> Okay, Katie Drucker, Head of Business Development at Madrona Venture Group. Premier VC in the Seattle area and beyond they're doing a lot of cloud action. And of course they know AWS very well and investing in the ecosystem. So great, great stuff there. Next up is Peter Wagner partner at Wing.VX. Love this URL first of all 'cause of the VC domain extension. But Peter is a long time venture capitalist. I've been following his career. He goes back to the old networking days, back when the internet was being connected during the OSI days, when the TCP IP open systems interconnect was really happening and created so much. Well, Peter, great to see you on the CUBE here and congratulations with success at Wing VC. >> Yeah, thanks, John. It's great to be here. I really appreciate you having me. >> Reason why I wanted to have you come on. First of all, you had a great track record in investing over many decades. You've seen many waves of innovation, startups. You've seen all the stories. You've seen the movie a few times, as I say. But now more than ever, enterprise wise it's probably the hottest I've ever seen. And you've got a confluence of many things on the stack. You were also an early seed investor in Snowflake, well-regarded as a huge success. So you've got your eye on some of these awesome deals. Got a great partner over there has got a network experience as well. What is the big aha moment here for the industry? Because it's not your classic enterprise startups anymore. They have multiple things going on and some of the winners are not even known. They come out of nowhere and they connect to enterprise and get the lucrative positions and can create a moat and value. Like out of nowhere, it's not the old way of like going to the airport and doing an RFP and going through the stringent requirements, and then you're in, you get to win the lucrative contract and you're in. Not anymore, that seems to have changed. What's your take on this 'cause people are trying to crack the code here and sometimes you don't have to be well-known. >> Yeah, well, thank goodness the game has changed 'cause that old thing was (indistinct) So I for one don't miss it. There was some modernization movement in the enterprise and the modern enterprise is built on data powered by AI infrastructure. That's an agile workplace. All three of those things are really transformational. There's big investments being made by enterprises, a lot of receptivity and openness to technology to enable all those agendas, and that translates to good prospects for startups. So I think as far as my career goes, I've never seen a more positive or fertile ground for startups in terms of penetrating enterprise, it doesn't mean it's easy to do, but you have a receptive audience on the other side and that hasn't necessarily always been the case. >> Yeah, I got to ask you, I know that you're a big sailor and your family and Franks Lubens also has a boat and sailing metaphor is always good to have 'cause you got to have a race that's being run and they have tactics. And this game that we're in now, you see the successes, there's investment thesises, and then there's also actually bets. And I want to get your thoughts on this because a lot of enterprises are trying to figure out how to evaluate startups and starts also can make the wrong bet. They could sail to the wrong continent and be in the wrong spot. So how do you pick the winners and how should enterprises understand how to pick winners too? >> Yeah, well, one of the real important things right now that enterprise is facing startups are learning how to do and so learning how to leverage product led growth dynamics in selling to the enterprise. And so product led growth has certainly always been important consumer facing companies. And then there's a few enterprise facing companies, early ones that cracked the code, as you said. And some of these examples are so old, if you think about, like the ones that people will want to talk about them and talk about Classy and want to talk about Twilio and these were of course are iconic companies that showed the way for others. But even before that, folks like Solar Winds, they'd go to market model, clearly product red, bottom stuff. Back then we didn't even have those words to talk about it. And then some of the examples are so enormous if think about them like the one right in front of your face, like AWS. (laughing) Pretty good PLG, (indistinct) but it targeted builders, it targeted developers and flipped over the way you think about enterprise infrastructure, as a result some how every company, even if they're harnessing relatively conventional sales and marketing motion, and you think about product led growth as a way to kick that motion off. And so it's not really an either word even more We might think OPLJ, that means there's no sales keep one company not true, but here's a way to set the table so that you can very efficiently use your sales and marketing resources, only have the most attractive targets and ones that are really (indistinct) >> I love the product led growth. I got to ask you because in the networking days, I remember the term inevitability was used being nested in a solution that they're just going to Cisco off router and a firewall is one you can unplug and replace with another vendor. Cisco you'd have to go through no switching costs were huge. So when you get it to the Cloud, how do you see the competitiveness? Because we were riffing on this with Ali, from Databricks where the lock-in might be value. The more value provider is the lock-in. Is their nestedness? Is their intimate ability as a competitive advantage for some of these starts? How do you look at that? Because startups, they're using open source. They want to have a land position in an enterprise, but how do they create that sustainable competitive advantage going forward? Because again, this is what you do. You bet on ones that you can see that could establish a model whatever we want to call it, but a competitive advantage and ongoing nested position. >> Sometimes it has to do with data, John, and so you mentioned Snowflake a couple of times here, a big part of Snowflake's strategy is what they now call the data cloud. And one of the reasons you go there is not to just be able to process data, to actually get access to it, exchange with the partners. And then that of course is a great reason for the customers to come to the Snowflake platform. And so the more data it gets more customers, it gets more data, the whole thing start spinning in the right direction. That's a really big example, but all of these startups that are using ML in a fundamental way, applying it in a novel way, the data modes are really important. So getting to the right data sources and training on it, and then putting it to work so that you can see that in this process better and doing this earlier on that scale. That's a big part of success. Another company that I work with is a good example that I call (indistinct) which works in sales technology space, really crushing it in terms of building better sales organizations both at performance level, in terms of the intelligence level, and just overall revenue attainment using ML, and using novel data sources, like the previously lost data or phone calls or Zoom calls as you already know. So I think the data advantages are really big. And smart startups are thinking through it early. >> It's interest-- >> And they're planning by the way, not to ramble on too much, but they're betting that PLG strategy. So their land option is designed not just to be an interesting way to gain usage, but it's also a way to gain access to data that then enables the expand in a component. >> That is a huge call-out point there, I was going to ask another question, but I think that is the key I see. It's a new go to market in a way. product led with that kind of approach gets you a beachhead and you get a little position, you get some data that is a cloud model, it means variable, whatever you want to call it variable value proposition, value proof, or whatever, getting that data and reiterating it. So it brings up the whole philosophical question of okay, product led growth, I love that with product led growth of data, I get that. Remember the old platform versus a tool? That's the way buyers used to think. How has that changed? 'Cause now almost, this conversation throws out the whole platform thing, but isn't like a platform. >> It looks like it's all. (laughs) you can if it is a platform, though to do that you can reveal that later, but you're looking for adoption, so if it's down stock product, you're looking for adoption by like developers or DevOps people or SOEs, and they're trying to solve a problem, and they want rapid gratification. So they don't want to have an architectural boomimg, placed in front of them. And if it's up stock product and application, then it's a user or the business or whatever that is, is adopting the application. And again, they're trying to solve a very specific problem. You need instant and immediate obvious time and value. And now you have a ticket to the dance and build on that and maybe a platform strategy can gradually take shape. But you know who's not in this conversation is the CIO, it's like, "I'm always the last to know." >> That's the CISO though. And they got him there on the firing lines. CISOs are buying tools like it's nobody's business. They need everything. They'll buy anything or you go meet with sand, they'll buy it. >> And you make it sound so easy. (laughing) We do a lot of security investment if only (indistinct) (laughing) >> I'm a little bit over the top, but CISOs are under a lot of pressure. I would talk to the CISO at Capital One and he was saying that he's on Amazon, now he's going to another cloud, not as a hedge, but he doesn't want to focus development teams. So he's making human resource decisions as well. Again, back to what IT used to be back in the old days where you made a vendor decision, you built around it. So again, clouds play that way. I see that happening. But the question is that I think you nailed this whole idea of cross hairs on the target persona, because you got to know who you are and then go to the market. So if you know you're a problem solving and the lower in the stack, do it and get a beachhead. That's a strategy, you can do that. You can't try to be the platform and then solve a problem at the same time. So you got to be careful. Is that what you were getting at? >> Well, I think you just understand what you're trying to achieve in that line of notion. And how those dynamics work and you just can't drag it out. And they could make it too difficult. Another company I work with is a very strategic cloud data platform. It's a (indistinct) on systems. We're not trying to foist that vision though (laughs) or not adopters today. We're solving some thorny problems with them in the short term, rapid time to value operational needs in scale. And then yeah, once they found success with (indistinct) there's would be an opportunity to be increasing the platform, and an obstacle for those customers. But we're not talking about that. >> Well, Peter, I appreciate you taking the time and coming out of a board meeting, I know that you're super busy and I really appreciate you making time for us. I know you've got an impressive partner in (indistinct) who's a former Sequoia, but Redback Networks part of that company over the years, you guys are doing extremely well, even a unique investment thesis. I'd like you to put the plug in for the firm. I think you guys have a good approach. I like what you guys are doing. You're humble, you don't brag a lot, but you make a lot of great investments. So could you take them in to explain what your investment thesis is and then how that relates to how an enterprise is making their investment thesis? >> Yeah, yeah, for sure. Well, the concept that I described earlier that the modern enterprise movement as a workplace built on data powered by AI. That's what we're trying to work with founders to enable. And also we're investing in companies that build the products and services that enable that modern enterprise to exist. And we do it from very early stages, but with a longterm outlook. So we'll be leading series and series, rounds of investment but staying deeply involved, both operationally financially throughout the whole life cycle of the company. And then we've done that a bunch of times, our goal is always the big independent public company and they don't always make it but enough for them to have it all be worthwhile. An interesting special case of this, and by the way, I think it intersects with some of startup showcase here is in the life sciences. And I know you were highlighting a lot of healthcare websites and deals, and that's a vertical where to disrupt tremendous impact of data both new data availability and new ways to put it to use. I know several of my partners are very focused on that. They call it bio-X data. It's a transformation all on its own. >> That's awesome. And I think that the reason why we're focusing on these verticals is if you have a cloud horizontal scale view and vertically specialized with machine learning, every vertical is impacted by data. It's so interesting that I think, first start, I was probably best time to be a cloud startup right now. I really am bullish on it. So I appreciate you taking the time Peter to come in again from your board meeting, popping out. Thanks for-- (indistinct) Go back in and approve those stock options for all the employees. Yeah, thanks for coming on. Appreciate it. >> All right, thank you John, it's a pleasure. >> Okay, Peter Wagner, Premier VC, very humble Wing.VC is a great firm. Really respect them. They do a lot of great investing investments, Snowflake, and we have Dave Vellante back who knows a lot about Snowflake's been covering like a blanket and Sarbjeet Johal. Cloud Influencer friend of the CUBE. Cloud commentator and cloud experience built clouds, runs clouds now invests. So V. Dave, thanks for coming back on. You heard Peter Wagner at Wing VC. These guys have their roots in networking, which networking back in the day was, V. Dave. You remember the internet Cisco days, remember Cisco, Wellfleet routers. I think Peter invested in Arrow Point, remember Arrow Point, that was about in the 495 belt where you were. >> Lynch's company. >> That was Chris Lynch's company. I think, was he a sales guy there? (indistinct) >> That was his first big hit I think. >> All right, well guys, let's wrap this up. We've got a great program here. Sarbjeet, thank you for coming on. >> No worries. Glad to be here todays. >> Hey, Sarbjeet. >> First of all, really appreciate the Twitter activity lately on the commentary, the observability piece on Jeremy Burton's launch, Dave was phenomenal, but Peter was talking about this dynamic and I think ties this cracking the code thing together, which is there's a product led strategy that feels like a platform, but it's also a tool. In other words, it's not mutually exclusive, the old methods thrown out the window. Land in an account, know what problem you're solving. If you're below the stack, nail it, get data and go from there. If you're a process improvement up the stack, you have to much more of a platform longer-term sale, more business oriented, different motions, different mechanics. What do you think about that? What's your reaction? >> Yeah, I was thinking about this when I was listening to some of the startups pitching, if you will, or talking about what they bring to the table in this cloud scale or cloud era, if you will. And there are tools, there are applications and then they're big monolithic platforms, if you will. And then they're part of the ecosystem. So I think the companies need to know where they play. A startup cannot be platform from the get-go I believe. Now many aspire to be, but they have to start with tooling. I believe in, especially in B2B side of things, and then go into the applications, one way is to go into the application area, if you will, like a very precise use cases for certain verticals and stuff like that. And other parties that are going into the platform, which is like horizontal play, if you will, in technology. So I think they have to understand their age, like how old they are, how new they are, how small they are, because when their size matter when you are procuring as a big business, procuring your technology vendors size matters and the economic viability matters and their proximity to other windows matter as well. So I think we'll jump into that in other discussions later, but I think that's key, as you said. >> I would agree with that. I would phrase it in my mind, somewhat differently from Sarbjeet which is you have product led growth, and that's your early phase and you get product market fit, you get product led growth, and then you expand and there are many, many examples of this, and that's when you... As part of your team expansion strategy, you're going to get into the platform discussion. There's so many examples of that. You take a look at Ali Ghodsi today with what's happening at Databricks, Snowflake is another good example. They've started with product led growth. And then now they're like, "Okay, we've got to expand the team." Okta is another example that just acquired zero. That's about building out the platform, versus more of a point product. And there's just many, many examples of that, but you cannot to your point, very hard to start with a platform. Arm did it, but that was like a one in a million chance. >> It's just harder, especially if it's new and it's not operationalized yet. So one of the things Dave that we've observed the Cloud is some of the best known successes where nobody's not known at all, database we've been covering from the beginning 'cause we were close to that movement when they came out of Berkeley. But they still were misunderstood and they just started generating revenue in only last year. So again, only a few years ago, zero software revenue, now they're approaching a billion dollars. So it's not easy to make these vendor selections anymore. And if you're new and you don't have someone to operate it or your there's no department and the departments changing, that's another problem. These are all like enterprisey problems. What's your thoughts on that, Dave? >> Well, I think there's a big discussion right now when you've been talking all day about how should enterprise think about startups and think about most of these startups they're software companies and software is very capital efficient business. At the same time, these companies are raising hundreds of millions, sometimes over a billion dollars before they go to IPO. Why is that? A lot of it's going to promotion. I look at it as... And there's a big discussion going on but well, maybe sales can be more efficient and more direct and so forth. I really think it comes down to the golden rule. Two things really mattered in the early days in the startup it's sales and engineering. And writers should probably say engineering and sales and start with engineering. And then you got to figure out your go to market. Everything else is peripheral to those two and you don't get those two things right, you struggle. And I think that's what some of these successful startups are proving. >> Sarbjeet, what's your take on that point? >> Could you repeat the point again? Sorry, I lost-- >> As cloud scale comes in this whole idea of competing, the roles are changing. So look at IOT, look at the Edge, for instance, you got all kinds of new use cases that no one actually knows is a problem to solve. It's just pure opportunity. So there's no one's operational I could have a product, but it don't know we can buy it yet. It's a problem. >> Yeah, I think the solutions have to be point solutions and the startups need to focus on the practitioners, number one, not the big buyers, not the IT, if you will, but the line of business, even within that sphere, like just focus on the practitioners who are going to use that technology. I talked to, I think it wasn't Fiddler, no, it was CoreLogics. I think that story was great today earlier in how they kind of struggle in the beginning, they were trying to do a big bang approach as a startup, but then they almost stumbled. And then they found their mojo, if you will. They went to Don the market, actually, that's a very classic theory of disruption, like what we study from Harvard School of Business that you go down the market, go to the non-consumers, because if you're trying to compete head to head with big guys. Because most of the big guys have lot of feature and functionality, especially at the platform level. And if you're trying to innovate in that space, you have to go to the practitioners and solve their core problems and then learn and expand kind of thing. So I think you have to focus on practitioners a lot more than the traditional oracle buyers. >> Sarbjeet, we had a great thread last night in Twitter, on observability that you started. And there's a couple of examples there. Chaos searches and relatively small company right now, they just raised them though. And they're part of this star showcase. And they could've said, "Hey, we're going to go after Splunk." But they chose not to. They said, "Okay, let's kind of disrupt the elk stack and simplify that." Another example is a company observed, you've mentioned Jeremy Burton's company, John. They're focused really on SAS companies. They're not going after initially these complicated enterprise deals because they got to get it right or else they'll get churn, and churn is that silent killer of software companies. >> The interesting other company that was on the showcase was Tetra Science. I don't know if you noticed that one in the life science track, and again, Peter Wagner pointed out the life science. That's an under recognized in the press vertical that's exploding. Certainly during the pandemic you saw it, Tetra science is an R&D cloud, Dave, R&D data cloud. So pharmaceuticals, they need to do their research. So the pandemic has brought to life, this now notion of tapping into data resources, not just data lakes, but like real deal. >> Yeah, you and Natalie and I were talking about that this morning and that's one of the opportunities for R&D and you have all these different data sources and yeah, it's not just about the data lake. It's about the ecosystem that you're building around them. And I see, it's really interesting to juxtapose what Databricks is doing and what Snowflake is doing. They've got different strategies, but they play a part there. You can see how ecosystems can build that system. It's not one company is going to solve all these problems. It's going to really have to be connections across these various companies. And that's what the Cloud enables and ecosystems have all this data flowing that can really drive new insights. >> And I want to call your attention to a tweet Sarbjeet you wrote about Splunk's earnings and they're data companies as well. They got Teresa Carlson there now AWS as the president, working with Doug, that should change the game a little bit more. But there was a thread of the neath there. Andy Thry says to replies to Dave you or Sarbjeet, you, if you're on AWS, they're a fine solution. The world doesn't just revolve around AWS, smiley face. Well, a lot of it does actually. So (laughing) nice point, Andy. But he brings up this thing and Ali brought it up too, Hybrid now is a new operating system for what now Edge does. So we got Mobile World Congress happening this month in person. This whole Telco 5G brings up a whole nother piece of the Cloud puzzle. Jeff Barr pointed out in his keynote, Dave. Guys, I want to get your reaction. The Edge now is... I'm calling it the super Edge because it's not just Edge as we know it before. You're going to have these pops, these points of presence that are going to have wavelength as your spectrum or whatever they have. I think that's the solution for Azure. So you're going to have all this new cloud power for low latency applications. Self-driving delivery VR, AR, gaming, Telemetry data from Teslas, you name it, it's happening. This is huge, what's your thoughts? Sarbjeet, we'll start with you. >> Yeah, I think Edge is like bound to happen. And for many reasons, the volume of data is increasing. Our use cases are also expanding if you will, with the democratization of computer analysis. Specialization of computer, actually Dave wrote extensively about how Intel and other chip players are gearing up for that future if you will. Most of the inference in the AI world will happen in the field close to the workloads if you will, that can be mobility, the self-driving car that can be AR, VR. It can be healthcare. It can be gaming, you name it. Those are the few use cases, which are in the forefront and what alarm or use cases will come into the play I believe. I've said this many times, Edge, I think it will be dominated by the hyperscalers, mainly because they're building their Metro data centers now. And with a very low latency in the Metro areas where the population is, we're serving the people still, not the machines yet, or the empty areas where there is no population. So wherever the population is, all these big players are putting their data centers there. And I think they will dominate the Edge. And I know some Edge lovers. (indistinct) >> Edge huggers. >> Edge huggers, yeah. They don't like the hyperscalers story, but I think that's the way were' going. Why would we go backwards? >> I think you're right, first of all, I agree with the hyperscale dying you look at the top three clouds right now. They're all in the Edge, Hardcore it's a huge competitive battleground, Dave. And I think the missing piece, that's going to be uncovered at Mobile Congress. Maybe they'll miss it this year, but it's the developer traction, whoever wins the developer market or wins the loyalty, winning over the market or having adoption. The applications will drive the Edge. >> And I would add the fourth cloud is Alibaba. Alibaba is actually bigger than Google and they're crushing it as well. But I would say this, first of all, it's popular to say, "Oh not everything's going to move into the Cloud, John, Dave, Sarbjeet." But the fact is that AWS they're trend setter. They are crushing it in terms of features. And you'd look at what they're doing in the plumbing with Annapurna. Everybody's following suit. So you can't just ignore that, number one. Second thing is what is the Edge? Well, the edge is... Where's the logical place to process the data? That's what the Edge is. And I think to your point, both Sarbjeet and John, the Edge is going to be won by developers. It's going to be one by programmability and it's going to be low cost and really super efficient. And most of the data is going to stay at the Edge. And so who is in the best position to actually create that? Is it going to be somebody who was taking an x86 box and throw it over the fence and give it a fancy name with the Edge in it and saying, "Here's our Edge box." No, that's not what's going to win the Edge. And so I think first of all it's huge, it's wide open. And I think where's the innovation coming from? I agree with you it's the hyperscalers. >> I think the developers as John said, developers are the kingmakers. They build the solutions. And in that context, I always talk about the skills gravity, a lot of people are educated in certain technologies and they will keep using those technologies. Their proximity to that technology is huge and they don't want to learn something new. So as humans we just tend to go what we know how to use it. So from that front, I usually talk with consumption economics of cloud and Edge. It has to focus on the practitioners. And in this case, practitioners are developers because you're just cooking up those solutions right now. We're not serving that in huge quantity right now, but-- >> Well, let's unpack that Sarbjeet, let's unpack that 'cause I think you're right on the money on that. The consumption of the tech and also the consumption of the application, the end use and end user. And I think the reason why hyperscalers will continue to dominate besides the fact that they have all the resource and they're going to bring that to the Edge, is that the developers are going to be driving the applications at the Edge. So if you're low latency Edge, that's going to open up new applications, not just the obvious ones I did mention, gaming, VR, AR, metaverse and other things that are obvious. There's going to be non-obvious things that are going to be huge that are going to come out from the developers. But the Cloud native aspect of the hyperscalers, to me is where the scales are tipping, let me explain. IT was built to build a supply resource to the businesses who were writing business applications. Mostly driven by IBM in the mainframe in the old days, Dave, and then IT became IT. Telcos have been OT closed, "This is our thing, that's it." Now they have to open up. And the Cloud native technologies is the fastest way to value. And I think that paths, Sarbjeet is going to be defined by this new developer and this new super Edge concept. So I think it's going to be wide open. I don't know what to say. I can't guess, but it's going to be creative. >> Let me ask you a question. You said years ago, data's new development kit, does low code and no code to Sarbjeet's point, change the equation? In other words, putting data in the hands of those OT professionals, those practitioners who have the context. Does low-code and no-code enable, more of those protocols? I know it's a bromide, but the citizen developer, and what impact does that have? And who's in the best position? >> Well, I think that anything that reduces friction to getting stuff out there that can be automated, will increase the value. And then the question is, that's not even a debate. That's just fact that's going to be like rent, massive rise. Then the issue comes down to who has the best asset? The software asset that's eating the world or the tower and the physical infrastructure. So if the physical infrastructure aka the Telcos, can't generate value fast enough, in my opinion, the private equity will come in and take it over, and then refactor that business model to take advantage of the over the top software model. That to me is the big stare down competition between the Telco world and this new cloud native, whichever one yields in valley is going to blink first, if you say. And I think the Cloud native wins this one hands down because the assets are valuable, but only if they enable the new model. If the old model tries to hang on to the old hog, the old model as the Edge hugger, as Sarbjeet says, they'll just going to slowly milk that cow dry. So it's like, it's over. So to me, they have to move. And I think this Mobile World Congress day, we will see, we will be looking for that. >> Yeah, I think that in the Mobile World Congress context, I think Telcos should partner with the hyperscalers very closely like everybody else has. And they have to cave in. (laughs) I usually say that to them, like the people came in IBM tried to fight and they cave in. Other second tier vendors tried to fight the big cloud vendors like top three or four. And then they cave in. okay, we will serve our stuff through your cloud. And that's where all the buyers are congregating. They're going to buy stuff along with the skills gravity, the feature proximity. I've got another term I'll turn a coin. It matters a lot when you're doing one thing and you want to do another thing when you're doing all this transactional stuff and regular stuff, and now you want to do data science, where do you go? You go next to it, wherever you have been. Your skills are in that same bucket. And then also you don't have to write a new contract with a new vendor, you just go there. So in order to serve, this is a lesson for startups as well. You need to prepare yourself for being in the Cloud marketplaces. You cannot go alone independently to fight. >> Cloud marketplace is going to replace procurement, for sure, we know that. And this brings up the point, Dave, we talked about years ago, remember on the CUBE. We said, there's going to be Tier two clouds. I used that word in quotes cause nothing... What does it even mean Tier two. And we were talking about like Amazon, versus Microsoft and Google. We set at the time and Alibaba but they're in China, put that aside for a second, but the big three. They're going to win it all. And they're all going to be successful to a relative terms, but whoever can enable that second tier. And it ended up happening, Snowflake is that example. As is Databricks as is others. So Google and Microsoft as fast as they can replicate the success of AWS by enabling someone to build their business on their cloud in a way that allows the customer to refactor their business will win. They will win most of the lion's share my opinion. So I think that applies to the Edge as well. So whoever can come in and say... Whichever cloud says, "I'm going to enable the next Snowflake, the next enterprise solution." I think takes it. >> Well, I think that it comes back... Every conversation coming back to the data. And if you think about the prevailing way in which we treated data with the exceptions of the two data driven companies in their quotes is as we've shoved all the data into some single repository and tried to come up with a single version of the truth and it's adjudicated by a centralized team, with hyper specialized roles. And then guess what? The line of business, there's no context for the business in that data architecture or data Corpus, if you will. And then the time it takes to go from idea for a data product or data service commoditization is way too long. And that's changing. And the winners are going to be the ones who are able to exploit this notion of leaving data where it is, the point about data gravity or courting a new term. I liked that, I think you said skills gravity. And then enabling the business lines to have access to their own data teams. That's exactly what Ali Ghodsi, he was saying this morning. And really having the ability to create their own data products without having to go bow down to an ivory tower. That is an emerging model. All right, well guys, I really appreciate the wrap up here, Dave and Sarbjeet. I'd love to get your final thoughts. I'll just start by saying that one of the highlights for me was the luminary guests size of 15 great companies, the luminary guests we had from our community on our keynotes today, but Ali Ghodsi said, "Don't listen to what everyone's saying in the press." That was his position. He says, "You got to figure out where the puck's going." He didn't say that, but I'm saying, I'm paraphrasing what he said. And I love how he brought up Sky Cloud. I call it Sky net. That's an interesting philosophy. And then he also brought up that machine learning auto ML has got to be table stakes. So I think to me, that's the highlight walk away. And the second one is this idea that the enterprises have to have a new way to procure and not just the consumption, but some vendor selection. I think it's going to be very interesting as value can be proved with data. So maybe the procurement process becomes, here's a beachhead, here's a little bit of data. Let me see what it can do. >> I would say... Again, I said it was this morning, that the big four have given... Last year they spent a hundred billion dollars more on CapEx. To me, that's a gift. In so many companies, especially focusing on trying to hang onto the legacy business. They're saying, "Well not everything's going to move to the Cloud." Whatever, the narrative should change to, "Hey, thank you for that gift. We're now going to build value on top of the Cloud." Ali Ghodsi laid that out, how Databricks is doing it. And it's clearly what Snowflake's new with the data cloud. It basically a layer that abstracts all that underlying complexity and add value on top. Eventually going out to the Edge. That's a value added model that's enabled by the hyperscalers. And that to me, if I have to evaluate where I'm going to place my bets as a CIO or IT practitioner, I'm going to look at who are the ones that are actually embracing that investment that's been made and adding value on top in a way that can drive my data-driven, my digital business or whatever buzzword you want to throw on. >> Yeah, I think we were talking about the startups in today's sessions. I think for startups, my advice is to be as close as you can be to hyperscalers and anybody who awards them, they will cave in at the end of the day, because that's where the whole span of gravity is. That's what the innovation gravity is, everybody's gravitating towards that. And I would say quite a few times in the last couple of years that the rate of innovation happening in a non-cloud companies, when I talk about non-cloud means are not public companies. I think it's like diminishing, if you will, as compared to in cloud, there's a lot of innovation. The Cloud companies are not paying by power people anymore. They have all sophisticated platforms and leverage those, and also leverage the marketplaces and leverage their buyers. And the key will be how you highlight yourself in that cloud market place if you will. It's like in a grocery store where your product is placed and you have to market around it, and you have to have a good story telling team in place as well after you do the product market fit. I think that's a key. I think just being close to the Cloud providers, that's the way to go for startups. >> Real, real quick. Each of you talk about what it takes to crack the code for the enterprise in the modern era now. Dave, we'll start with you. What's it take? (indistinct) >> You got to have it be solving a problem that is 10X better at one 10th a cost of anybody else, if you're a small company, that rule number one. Number two is you obviously got to get product market fit. You got to then figure out. And I think, and again, you're in your early phases, you have to be almost processed builders, figure out... Your KPIs should all be built around retention. How do I define customer success? How do I keep customers and how do I make them loyal so that I know that my cost of acquisition is going to be at least one-third or lower than my lifetime value of that customer? So you've got to nail that. And then once you nail that, you've got to codify that process in the next phase, which really probably gets into your platform discussion. And that's really where you can start to standardize and scale and figure out your go to market and the relationship between marketing spend and sales productivity. And then when you get that, then you got to move on to figure out your Mot. Your Mot might just be a brand. It might be some secret sauce, but more often than not though, it's going to be the relationship that you build. And I think you've got to think about those phases and in today's world, you got to move really fast. Sarbjeet, real quick. What's the secret to crack the code? >> I think the secret to crack the code is partnership and alliances. As a small company selling to the bigger enterprises, the vendors size will be one of the big objections. Even if they don't say it, it's on the back of their mind, "What if these guys disappear tomorrow what would we do if we pick this technology?" And another thing is like, if you're building on the left side, which is the developer side, not on the right side, which is the operations or production side, if you will, you have to understand the sales cycles are longer on the right side and left side is easier to get to, but that's why we see a lot more startups. And on the left side of your DevOps space, if you will, because it's easier to sell to practitioners and market to them and then show the value correctly. And also understand that on the left side, the developers are very know how hungry, on the right side people are very cost-conscious. So understanding the traits of these different personas, if you will buyers, it will, I think set you apart. And as Dave said, you have to solve a problem, focus on practitioners first, because you're small. You have to solve political problems very well. And then you can expand. >> Well, guys, I really appreciate the time. Dave, we're going to do more of these, Sarbjeet we're going to do more of these. We're going to add more community to it. We're going to add our community rooms next time. We're going to do these quarterly and try to do them as more frequently, we learned a lot and we still got a lot more to learn. There's a lot more contribution out in the community that we're going to tap into. Certainly the CUBE Club as we call it, Dave. We're going to build this actively around Cloud. This is another 20 years. The Edge brings us more life with Cloud, it's really exciting. And again, enterprise is no longer an enterprise, it's just the world now. So great companies here, the next Databricks, the next IPO. The next big thing is in this list, Dave. >> Hey, John, we'll see you in Barcelona. Looking forward to that. Sarbjeet, I know in a second half, we're going to run into each other. So (indistinct) thank you John. >> Trouble has started. Great talking to you guys today and have fun in Barcelona and keep us informed. >> Thanks for coming. I want to thank Natalie Erlich who's in Rome right now. She's probably well past her bedtime, but she kicked it off and emceeing and hosting with Dave and I for this AW startup showcase. This is batch two episode two day. What do we call this? It's like a release so that the next 15 startups are coming. So we'll figure it out. (laughs) Thanks for watching everyone. Thanks. (bright music)

>> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, The Next Big Thing in AI, Security and Life Sciences. Today featuring Lacework for the security track. I'm your host Natalie Erlich. Thank you for joining us. And we will discuss today how LendingTree automates AWS security for DevOps teams and stays compliant with Lacework. Now we're joined by Adam Leftik the VP of Product at Lacework as well as a Arun Sankaran, CISO of LendingTree. Thank you both very much for joining us today. >> Thank you for having us. >> Well, wonderful. Adam, let's start with you. Lacework positions itself as, "cloud security at the speed of cloud innovation." What does that mean to you and how are you helping your customers? >> Great question, Natalie. I think one of the things that's really important to understand about Lacework really comes back to essentially what's happening at cloud speed, which is customers are aggressively moving more and more of their applications to the cloud, but they're doing so with the same number of resources to secure that environment. And as the cloud continues to grow, both in terms of complexity, as well as overall ability to unlock new styles of applications that were never before even possible without this new technology landscape. Fundamentally, Lacework is designed to enable those builders to go faster without worrying about all the different intricacies and threats that they face out there on the internet. And so the core mission of Lacework is really about enabling builders to build those applications and leverage those cloud resources and new cloud technologies to move quicker and quicker. >> Natalie: Fascinating. >> Yeah, thanks. If you go back to the sort of foundation of the company there we took a very different approach to how we think about security. Often, you know, security approaches in the past have been a rules driven model where you try and think of all the different vectors that attacks can come at. And fundamentally, you end up writing a series of these rules that are impossible to maintain, they atrophy over time, and that you can't possibly think ahead of all these nefarious actors. So one of the things that Lacework did from the very beginning was take a very different approach which is leveraging security as a data problem. And the way we do this is through what we refer to as our polygraph. And the polygraph essentially looks at all the exhaust telemetry that we're able to ingest both from your cloud accounts as well as the underlying infrastructure. And we take that and we build a baseline and a behavioral model for how the application should behave when it's normal. And this baseline represents the state of normalcy. And so then we leverage modern data science techniques to essentially build a model that can identify potential threats without requiring our users to build rules and ultimately play catch up to all the different threats that they face. And this is a really, really powerful capability because it allows our customers both to identify misconfigurations and remediate them, monitor all the activity to reduce the overall overhead on their security organization, and of course help them build faster and identify threats as they come into the system. And we differentiate in lots of different ways as well. So one of the things we're looking to do as part of the overall cloud transformation is really meet the DevOps teams and the security teams where they are. And so all of the information that Lacework captures, synthesizes, and produce through our automation ultimately feed into the different channels that our users are really leveraging that skill today. Whether that's through their ChatOps windows or ultimately into their CICD pipeline so that we give broad coverage both at build time as well as run time and give them full visibility and insights and the ability to remediate those quickly. You know, one of the other things that we're really proud of and this is core to our product philosophy is building more and more partnerships with our customers and LendingTree is really at the forefront of that partnership and we're super excited to be partnering with them. And that's certainly something that we've done to differentiate our product offering and I'd love to hear from Arun, how have you been working with Lacework and how has that been going so far? >> Yeah, thank you, Adam. You know, frankly I think that's a huge differentiator for us. There's a lot of players that can solve technology problems but what we've really appreciated is that as a smaller shop and a smaller organization, the level of connectedness that we feel with the development teams at Lacework. We raise a opportunity. You know, this can make things more efficient for us or this can reduce our time to triage, or this visualization or this UI could be modified to support certain security operations center use cases, maybe that's not what it's designed for. And we've enjoyed just a lot of success in kind of shaping the product in order to meet all the different use cases. And as Adam mentioned, you know, as a CISO, my primary responsibility is security, but frankly there's a lot of DevOps and tech use cases within the polygraph visualization tool, and understanding our environment and troubleshooting has frankly it saved us quite a bit of time and we're looking forward to the partnership to continue to grow out the tool. As we, as a company, scale in today's world, it's very important that we're able to scale our capability 2-3X without a corresponding 2-3X in staff and resources. I think this is the kind of tool that's going to help us get there. >> Well, speaking to you Arun, Lacework has recently grown tremendously and gotten a lot of industry attention but you saw something before everyone else. Can you tell us what really caught your attention? What stood out to you and why you decided to become an early adopter? >> Yeah, great question. Honestly, I wish it was a super tricky kind of answer but the real honest answer is it was a very easy decision because we had a need. We knew that we needed robust monitoring capability and detection of threats within containerized environments. And, you know, there are other players in the space but we have a very diverse environment. We're a combination of multiple container technologies and multiple cloud platforms. And we needed something that had the greatest diversity of coverage across our environments. And this was really the only solution that would work for us. I'd love to be able to say that it was like an aggressive bake-off and there's all these different options. But really, from a capability, and scope, and coverage, it was a fairly easy decision for us. >> And how has your threat detection and investigation process changed since you brought on Lacework? >> Yeah, it certainly has. Our environment within 24 hour period, it might generate 300, 400 million events and that's process level data from hosts and network data access. It's just a very noisy amount of alerts. With the Lacework's platform, those 300, 400 million get reduced to about a hundred alerts a day that we see and of those, five are critical and those tend to all be very actionable. So from an alert fatigue perspective, we really rely on this to give us actionable data, actionable alerts that teams can really focus on and reduces that noise. So I would say that's probably the number one way that our detection process has changed and frankly, a lot of it is what Adam mentioned as far as the underlying self-learning, self-tuning engine. There's not a whole lot of active rules that we had to create or configuration that we had to do. It's kind of a learning system and I think it's really, probably, I would estimate maybe 50-60% reduction in triage and response time for alerts as well. >> And Adam, now going to you, while 2020 was a really rough year for a lot of people, a lot of businesses, Lacework realized 300% revenue growth. So now that the economy is bouncing back and seemingly so in full force, what are your expectations for Lacework in the next year? >> Great question. I think one of the things we're seeing broadly across the industry is an acceleration, a realization that companies that are going through digital transformations have accelerated their pace and so we anticipate even faster growth. Additionally, you know, the companies that may have not been on that trajectory are now realizing that they need to move to the cloud. There's not a lot of folks right now thinking that they're going to be racking and stacking in physical data centers going forward. So we fully expect a continuation of massive growth. And increasingly as customers are moving into the cloud, they're looking for tools to help them build a secure footprint but also enable them to go faster. So, we have a point of view that we're going to continue to see this massive growth and if not, how to accelerate from here. >> Well, you're also the man behind the product. So could you go behind some of the key features that it offers? >> Sure. So, if you think about our overall product portfolio, we really have both breadth and depth. So, first and foremost, most customers who are moving to the cloud or have a large cloud footprint, the first concern they have is, do I have a series of misconfigurations? We really help our customers both identify best practices with those configurations in the cloud, and then also help them move quickly towards potential compliance standards that they need to adhere to. Everyone's operating in a regulated environment these days. And then of course, once you've got that footprint to a place where it's healthy, you really, really want to be able to monitor and track the changes to the configurations over time to ensure you're continuing to maintain that footprint. And so we provide a polygraph based model that essentially identifies potential behavioral risks that we're observing through our data clustering algorithms to help you identify potential holes that you may have created over time and help you remediate those things. And then of course, you know, every customer faces a significant challenge when it comes to just keeping up with the overall landscape changes in terms of overall vulnerability footprint in their environments. And so we have a great capability with what we call vulnerability discovery, which enables our customers to understand where they're vulnerable and not simply tell them how many vulnerabilities they have, but help them isolate, leveraging all the run time and bill time contexts we have so that they can really prioritize what's important to them and what represents the highest risk. And then of course, lastly, you know, where the company really got started is in helping customers protect their cloud workloads. And we do this by identifying threats that we're able to leverage our machine learning and data clustering algorithms so that once we have those baseline behaviors identified and modeled, we can leverage all of our threat intelligence to identify anomalies in that system and help customers really identify those risks as they're coming into the system and deal with those in a really timely manner. So those are kind of the overall key capabilities that they really help teams scale and drive their overall cloud security programs. >> And Arun, really quickly from your perspective, what is a key feature that is really beneficial to LendingTree? >> It's kind of what Adam mentioned with the kind of the self-tuning capability, the reduction of alerts and data based on behavioral-based detection versus rule-based. A lot of people have, you have fancy words, they call AI and machine learning, this and that, but I've rarely seen it work effectively. I think this is a situation where it does work really effectively and does free up time and resources on our side that we can apply to other problems we're trying to solve so I think that's the number one. >> Okay, terrific. Well, I'm really curious Adam. Got to ask you this question. I mean, we saw a really big software IPO last year. What do you think is in store for Lacework? >> Yeah, well, you know, the IPO is just a point in time as opposed to it's part of the journey. Lacework's continuing to invest and really focus on fundamentally changing the security landscape. One of the reasons why I joined Lacework and continue to be really excited about the opportunity comes back to the fundamental challenge that all security tools have. We do not want to create a platform that drives wet blanket behavior, but really fundamentally enables teams like Arun's to move faster and enable the builders to build the applications that fundamentally drive great business outcomes for our customers. And so that's what gets me out of bed. And I think everyone at Lacework is really focused on helping drive great outcomes for our customers. >> Fascinating to hear how Lacework is securing cloud around the world. Lovely to have you on the show. Adam Leftik, the VP of Lacework, as well Arun Sankaran, the CISO of LendingTree. I'm your host for the AWS Startup Network here on theCUBE. Thank you very much for watching.

(upbeat music) >> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, The Next Big Thing in AI, Security, & Life Sciences, and in this segment, we feature Sonrai security, of course for the security track I'm your host, Dave Vellante, and today we're joined by Sandy Bird, who's the co-founder and chief technology officer of Sonrai, and Avi Boru, who's the director of cloud engineering at World Fuel Services, and in this discussion, we're going to talk about 22 to two data centers, how World Fuel Services and Sonrai Security actually made it happen securely. Folks, welcome to theCUBE, come on in. >> Thank you. >> So we hear consistent themes from chief information security officers, that many if not most enterprises they struggle today with cloud security, there's confusion with various tools and depressing lack of available talent to attack this problem. So Sandy, I want to start with you, we always love to ask co-founders, why did you start your company? Take us back to that decision. >> Yeah, I think looking at Sonrai Security was interesting in that, it was a time to start over, it was a time to build a native in the cloud, as opposed to having a data center, and be able to use, you know, a vendor of infrastructure, be able to use the latest and greatest technology and really change the way people secure their workloads, what was interesting, you know, when we started the company, I believe that the world was in a more mature space probably in cloud than they were at the time when we were starting it, in that we were really focused around, if we could understand all of the rights and entitlements to data, we could understand data movement, we'd had hope in protecting the data and arriving in cloud, we realized that the maturity of the companies building in cloud, we're not quite there yet, they were really struggling with, you know, the identities models in the cloud, how to actually secure, you know, workloads, server less functions that are ephemeral these types of things, and even just sometimes basic governance problems, and the technology we had built was great at understanding all of the ways that data could be accessed, and we were able to expand that into all the resources of the cloud and it's an exciting space to be in, and it's also, I truly believe we'll be able to actually make cloud environments more secure than what we were doing in enterprise, because again for the first time ever you have full inventory, you have the ability to make controls that apply to the entire infrastructure, it's really an exciting time. >> I mean, I've said many times I feel like security is a do over and the fact that you're coming at it as a data problem and bringing in the cloud that intersection, I think is actually quite exciting. So Avi let's bring you into the conversation, you know, obviously we've seen cloud exploding it's continuing to be a staple of digital business transformations and acceleration especially around identity, so what's your point of view on cloud security, what's different and how does your company approach it? >> Sure, thank you for having me Dave, and just to give you a bit of World Fuel Services, World Fuel Services is a public company, and it's based out of Miami, and we are ranked 91 in the fortune 500 list, so we are spread all across the globe, and as part of our transformation to distress our business, we took over a big challenge to migrate all our global infrastructure from 22 data centers to AWS, that was a massive challenge for us, and we are downright now to 20 data centers, we only have two more to go, and we did this in the last two years, and that was really good for us, but as we've been doing this migration, there was also a strong need for us to build a strong security foundation, because going into the cloud as much as capabilities it gives us to innovate, it also gives us a lot of challenges to deal with from security standpoint, and as part of building the security foundation, we had to tackle some key challenges, one was how do we build our cloud security operating model and how do we up skill our people, the talent that you've been binding it out, and how do we make security a way of working in this new world, and more than choosing a solution we needed a really strong security partner who can help us guide in this journey, help us build the foundations and take us further and mature us in this, and that's where it was really interesting for us to partner with Sonrai, who helped us along the way, develop a foundation and now helping us mature our security platform. >> Avi, what were the technology underpinnings, that enticed you to work with Sonrai? >> Sonrai has lot of unique capabilities but I'll take it out on two key points, right? One, Sonrai has a cloud security posture management which is different from other platforms that are out there because they give you capability for a lot of out of the box frameworks and controls, but in addition to that, every organization has need to build unique specific frameworks, specific controls, they give you that capability, which is massive for enterprises, and the second key thing is, if you look at AWS, it has more than 200 services and every service has its unique capability but one key component they use across all the services, is Identity and Access Management, IAM and Sonrai has a unique perspective of using IAM to track risks and identify the interactions between user and machine identities which was really exciting and new for us, and we felt that was a really good foundation and stepping point to use Sonrai. >> All right, Sandy, we definitely saw the need for a better identity explode, in conjunction with the cloud migrations during the pandemic, it was sort of building and building and then it was accelerated, maybe talk a little bit about how you approach this, and specifically talk about your identity analytics and the graph solution that you guys talk about. >> Yeah, I've been a fan of graph solutions for many years, one of the great benefits in this particular space with identity is that, the cloud models for identity are fairly complex and quite different between AWS, Azure and GCP, however, the way that entitlements work, some identity is granted in entitlement, and that entitlement gives them access to do something, sometimes that's something is to assume another identity, and then do something on that identities behalf, and when you're actually trying to secure these clouds this jumping of identities, which happens a lot in the AWS model, or inheritance which happens a lot in the Azure model where you're given access at one level of the tree and you automatically gain access to things below that if you have that entitlement, those models inside of graph allow us to understand exactly how any given identity when we talk about identity we always think of people, but it's not, of course as you said, sometimes it's a machine, sometimes it's a cloud service, it could be many different things, how does every single one of those identities get access to that given resource? And it's not always as clear as, okay, well, here are the direct identities that can access this resource, it may only be able to be accessed with a single key, but who has access to the key, and what has access to the key, and what's the policy on that key, and if that's set too widely can other maybe nefarious actors get access to that key, and by using the graph, we can tie that whole model together to understand the entire list, of what gets access, I think that's actually what surprises a lot of the identity governance and data governance teams that are not in cloud, you know, when enterprise was very intentional, you configured the database to use the identity provider and the rules that you wanted it to use, and that's all that ever got access to that database. In cloud, there are a lot of configuration knobs and things and depending on how you turn them, you could open up a lot of identities to get access to whatever that resource is, often it's data, but it could be a network, it could be many things. So, the graph allows us to tie all that together, the second part of it is, it really allows us to see, we call them effective permissions, what the effective permission of that identity is, the clouds have done this phenomenal thing in using identities as a control mechanism just like in firewall, like an identity firewall, where they can take permissions away from things based on sets of conditions, so one of the great ways, let's say you didn't want to have any data stored deployed without encryption, you could write a policy at the top of your cloud, that says, anytime a data stores is deployed, if encryption is not there, deny that function. And so what happens is, is you can create this very protective environment using identity controls, but the problem is when you actually go to evaluate your cloud for risk, you may find a scenario where an identity has access as an example, to do something like create an internet gateway, or create a public endpoint, but there's this policy somewhere else, that's taking that away, and you don't want thousands of alerts because of that, you want to actually understand the model and say, look if we understand that this policy is mitigating your risk, then don't show the alert in the first place. And it really helps by putting it in a graph, because we can actually see all of these interconnections, we can see how they're interrelated, and determine the exact effective permissions of any identity and what risks that may have. >> So Avi, I mean, Sandy is really getting to the heart of sort of operationalizing you security in the cloud, and we looked at the compelling aspect of the cloud, and one of them anyway is scale, but people tell us to really take advantage of the cloud, they have to evolve that operating model maybe completely change the operating model, to really take advantage of scale, so my question is how do you operationalize your security practices, what should people think about, in terms of the time it takes to build in automations and bots for things like continuous compliance what can you share in terms of best practice? >> So traditional ways of operating if you look at it is, you identify a security risk, and a ticket is created and teams starts mitigating them. But with so many cloud services and with many solutions, the team start building in the cloud, it becomes too much of an overhead for teams to mitigate all these security risks that keep coming into the backlog, so as we partner with Sonrai in building a foundation, the way we tried to approach it is differently, we said why don't we build this using automatic recommendations, if we know what are the security risks, that we should not be creating in our environment and be noncompliant, how can we mitigate them? And with Sonrai and AWS API capabilities, it's not that hard for us to be a lot of intimidation buds because I didn't find risks, 'cause they have been taken care by Sonrai, the only aspect we need to take care is, how do we mitigate that? So that's the part we chose in building, cloud security operating model, is modeling more than an automated imitations, but as part building that there is always, where everything cannot be remediated automatically, and for these kinds of scenarios, we built a workflow where it still gets funneled to teams, so they can prioritize in their backlog, but other key thing that we did as part of operationalizing is, teams need to use Sonrai as their way of working, teams need to know what and why they should be using Sonrai. So we conduct a lot of training and onboarding and working sessions for teams, so they understand how we use Sonrai, how to consume the data coming out of Sonrai, so they can proactively start acting on how to stay compliant, but yeah, it's been an amazing experience building our foundation though. >> Sandy, I wonder if we can come back to, talking about comparisons with the traditional prevailing security models, I mean, we entering this API economy, as I said before, cloud is a staple of digital business, but you know people have been doing on-prem security for decades, you know, data loss prevention is an entire sub-industry, so what's different about doing it in the cloud, how should we think about that, in terms of whether you know, what responsibilities we have, the technology, what's your perspective on that? >> There's at least five questions in there Dave, so we'll. >> Pick your favorite. >> Yeah, you know, to feed off of what Avi was talking about, you know, he said many times, you know, teams need to solve these issues, teams need to see the issues they're creating, and it's interesting as we move to cloud, we decentralize some of these security functions, and that's actually an important part of the Sonrai solution and how you build a cloud security operating model, there's a set of findings, we'll call them, maybe there are security findings, maybe they're informational findings, that are a fairly low risk, and should be dealt with by the individual teams themselves, but that same team, you know, maybe isn't the person that can sign off on the risk if it's high enough, and if it's not then it needs to be escalated to the next level up to have that risk signed off on. A lot of times in large enterprise for workloads, that was done using unfortunately, you know tickets and systems and, you know, humans actually, you know, filling out some form of a checklist, saying, yes I met this, no I didn't, and we can automate huge numbers of those tests, including distributing them to the teams for the teams to solve themselves, and if they do their job right, there's not even the need for the central security body necessarily to know about the issues because they got solved, but when they don't get solved, that's when rather, you know, escalation to Boston automation or escalating to a centralized team starts to make sense, you kind of said a lot about DLP there as you were doing in cloud and just data security in general, and I do think, you know, cloud has given us this interesting opportunity, that's really upset data security in the old way on its head, you know, we used to do data security by putting agents on systems, or sometimes it was a proxy in front of it but either way that doesn't work well in cloud, when you're consuming platform as a service, you know, Amazon is not going to let you put an agent on their database that they're provisioning for you, and, you know, if you put in your own proxy in front of it you probably just messed up the elastic scalability that was built into the whole thing to begin with. So we needed a different way to look at this, however, we also took away a couple of things, in cloud the application teams themselves generally use fit for purpose data stores, they use the data store that's the best for the workload they're doing, our own workload has many data stores under the covers, it's not one data store, and so because of that, this kind of, you know, the old world of there being a data security team or you know, database optimization team, that you know optimize the database workloads, actually gets distributed as well all back to those teams, and so, we've gained kind of this, you know, fit for purpose smaller sets of data stores that are being used all over, and on top of that, the cloud vendors in many cases have done great things to enable monitoring, you know, part of the reason we were putting agents on database servers, is because the Oracle admin said I can't turn logging on, I don't have a big enough system to do it, it's going to crash the system, well in cloud parts of that go away, you can scale the systems up, you can enable loggings, now you can get that rich data that you wanted when you were an enterprise, and so, you know Sonrai is really kind of taken that model and said, look we can give you the visibility around data movement, we can give you the visibility around all of the entitlements to that data, we can understand, is your data at risk? And then we can profile all that for anomalies, and say, you know, it's kind of odd that the workload that normally connects into this through this automated fashion is now using its access key from a different location, that doesn't make any sense, why is that happening? And so you get kind of strong anomaly detection as well as the governance. So, you know, data security and cloud, if we kind of fast forward a few years, will look very different than it does today, I still believe some of the teams are not quite there yet in cloud, you know, they're still struggling with some of these identity problems we talked about, they still struggle some of them with CSBM problems, and so we have to solve those first obviously before we get to the true data security. But it's interesting that cloud has enabled us with such rich tooling and APIs to actually do it better than what we've done on enterprise. >> A lot of really powerful concepts in there, thank you Sandy. I mean, this notion of decentralizing security functions reminds me when Vogels describes this hyper decentralized distributed system that Amazon is building, and it is clearly a theme, you know, maybe it's bromide, but people talk about shifting left, designing security in, and it's important, not just bolting it on as an afterthought, and so, maybe this next question sort of really relates to the theme of this event, which is all about scale, here's the question Sandy, thinking about your contribution to the future of cloud, obviously you start a company, you want to grow that company, you want to serve customers and grow your revenues et cetera. But what's your defining contribution to the future of cloud scale? >> Look, we want to enable companies to scale faster, we want them to be able to put more workloads in cloud using, you know, the right set of security controls to keep those workloads safe, I know we can actually do this in a way where, you know, we talk about defense in depth for years, right? And usually in enterprise that meant many levels of networks before you got access, now we need to do defense in depth in terms of, you know, actually variety of controls, we can't throw the network control away, it still has to be there, we need an identity control, and it will be the primary control for what we do in cloud, we need a data lock, you know, rather that's through an encryption key policy or whatever it is, so we have multiple different layers of defense in depth, we can use in cloud today, and so it will be a much more secure environment than it was in the future, but we have to, again, so my contribution is hopefully I can help everybody get to that level, because right now we still see way too many breaches with very simple configuration problems that ended up exposing data unintentionally, and that's worrisome. >> You know, it's funny, a lot of people maybe can't relate to that defense in depth, I mean, obviously security people can, but we as individuals who now rely so much on our mobile phones, and things like SMS, and then you start to build in, non SMS, you know, base two factor authentication and you start to build your own personal layers, it's sort of a microcosm of the complexity that you have to think about in the enterprise, but in having tools to automate is critical, and expertise obviously, so let's wrap. Avi give us your final thoughts and key takeaways on building a world-class cloud security. >> I guess the key take of this would be, you know, to choose the right partner, it's not just the solution, another key takeaway is automate your way, because with security in the cloud is different than traditionally how do you do it, and the only fastest way to move is automate yourself away out of it and rely on talent, rely on a lot of young talent that's coming in and all the tools like Sonrai AWS are making it easier to operate in the cloud, so bring up the young talent and up skill the talent and leverage on these tools to be more secure on the cloud. >> Yeah, use automation to solve the big problem of, you know, that talent gap, there is not enough of it out there, and the adversaries they're well-equipped and quite capable. Okay Sandy, please give us your last word. >> Look again, I think a cloud is going to get us to a point where we are more secure than we were on enterprise, we have all of the right tools and controls to do it, we can decentralize the security and make it better, again, I think if anything just to encourage people to really look at a cloud security governance model, right? You can't do this ad hoc, trying to whack-a-mole small issues as they come up, you build it in as an operating model, you automate it and you deal with the exceptions. >> Yeah, I mean, you're very optimistic and I think is for good reason, I just remembered listening to Steven Schmidt a couple of years ago at reinforce, basically saying, look, we feel pretty optimistic about solving this problem, whereas, I have to say every year I look back in the enterprise and on-prem and I know it's getting worse, and so, keep up the good work gents, I really appreciate the time on theCUBE today, thank you. >> Thank you. >> Thank you. >> And thank you for watching theCUBE presentation of the AWS Startup Showcase, The Next Big Thing in AI, Security & Life Sciences. I'm Dave Vellante. (upbeat music)