 We're back at AWS re-invent 2021. You're watching theCUBE. I'm Dave Vellante with my co-host, Dave Nicholson. David Nicholson, I'm Dave. He's David. We're trying something new here at theCUBE. A little stand-up cube. You've heard of the pop-up cube, maybe? We're going to stand up. I work at a standing desk at my office. So let's try it. Four days, two sets, 100 plus guests. Why not? So Manish Sude is here. He's the founder and CTO of RELTO. Cube alum, Manish. Thank you for standing and good to see you again. David, it's great to see you again. And David, thank you for having me here. So tell us a little bit about yourself, your background. I'm always interested to ask founders why you started your company, but tell us the background. Yeah, so a little bit of my background and the company's history. Most of my background has been in data management and creating products for data management. I was at a company called Informatica, came through an acquisition to Informatica back in 2010 and started RELTO in 2011. The reason why we started RELTO was that if you look at the enterprise space and how things have been evolving, there have been an explosion of applications. There's almost an application for every little business process that you can possibly imagine. Enterprise customers who used to struggle with 12 or 24 different systems are now coming to us and saying they have 300 or 500 different applications that they use to run their business. And that's at the lower end of the spectrum. Even a business like RELTO today runs on 100 plus SaaS applications end to end. And that is creating one of the biggest opportunities as well as one of the biggest friction points in the enterprise. Because in order to create better, efficient business outcomes, you have silos of data and you don't know where the source of truth is. And that is something that we saw early on in 2011. At the same time, we also saw that digital transformation or cloud transformation type of requirements were going to drive a larger need for this kind of capability where RELTO type of products could act as that single source of truth to unify all of the multi-source siloed information. So that's what got us started down this journey. So when people hear single source of truth, they think, oh, database, right? But that's not what you guys do, right? I mean, it's, can I call it master data management, but it's really modern master data management. You're kind of recreating a new category that solves a similar problem. Maybe you could explain that. Yeah, a little bit of background. So the term master data management came about in 1920s. Can you believe that? When during the pandemic, the US government was trying to figure out how to know who is still alive versus not there anymore. And they created something called the death master. Now a very ominous name for a concept of just bringing data together and figuring out what's going on in the economy. But that need or problem hasn't gone away. It has just become a harder problem to solve because now we have so many different systems to deal with. Internal as well as third-party data sources that companies have to work with. And that's where the need has been around, but the technical capabilities to really keep solving the problem and delivering the solution in a manner where it can keep pace with the evolving needs, that capability has been missing. And that's where the aha moment for us was that we really needed to build it out as a foundation that would continue to grow and scale with the magnitude of the problem that we were going to see in the future. Okay, so this idea of single version of the truth, obviously critically important for reporting, financials, you can't tell an auditor one thing, you know, your customers are another thing, your consumers, it's got to be consistent and especially in regulated industries. Is there a difference, Manish, between sort of that type of data and the data maybe that's in the line of business that doesn't necessarily affect the rest of the business, can they have their own version of the truth, which is just their version, their single version, that doesn't necessarily have to affect anything else. Do you, are you seeing that changing data landscape where things are getting more distributed and ownership is becoming more distributed? So the change in the paradigm that we are seeing is because of the proliferation of the data, there is a need to establish what is the aggregated view of the information, aggregated and unified, which means that if there is a record for Dave Valenti or David Valenti, it's the same person. Establishing that fact as the truth across any number of systems that you have versus the multiple versions of the truth where somebody comes in and says, for compliance reasons, I want the entire collection of data versus for marketing reasons, I only want one third the slice of this information. So that's where this concept of aggregate wants, unify that information, but then make it ready and available for multiple consumers to partake from that, that's becoming the norm. And you mentioned something, Dave, that analytics, reporting, BI, data science, those have been some of the traditional playgrounds for this kind of information to be unified because if you're trying to roll up the revenue for, the business that you do with Coke or Coca-Cola, you don't know which name you used, then you have to go back to the analytics warehouse and aggregate all of that information and do the reporting. But the same problem is coming up in real time digital experiences as well. The only difference is that instead of having the luxury of a few hours, now you have to make the decision in a few milliseconds. So when you talk about those silos of data and seeking to have a unification of those silos, how has that changed in the era of cloud? Is it that Reltio is integrating those disparate sources that now exist in cloud? Or is it that you are leveraging cloud to address the problem that's been with us for a long time? And I have to say that Dave Vellanti, take him off the death master, he's definitely still with us. In case there was any. Another good day, I'm pretty sure too. But how have things changed with the dawn of cloud? With the dawn of cloud, there are two things that have become available to us. One is using the power of the cloud compute to solve the problem so that you can keep growing with the footprint of the problem itself and have a solution that scales along with it. But at the same time, you have systems of record, could be your mainframe systems, could be your SAP, ERP type of deployments that you have. Some of those functional applications, they're not going away anytime soon. They're there to stay. But at the same time, you also need the new digital experiences to be delivered on. The glue between those two words is the source of truth data that sits in the middle. And the best place for it to sit is the cloud because you have to open it up to the rest of the ecosystem that sits in the cloud, but you also have to maintain a connection to the on-the-ground type of systems. Putting it behind a firewall and trying to do that is next to impossible. But doing it in the cloud opens up all the doors that you need for your transformation to take place. You know, Dave, there was a time when I was part of an industry where coding, not writing code, but coding data to basically say, look, this field here is the person's last name. This field is the address where the mortgage is being held. How much of that is still manual as opposed to applying some form of AI to the problem? Let's say you have 200 different sources of information where Dave Vellante's name shows up in a variety of contexts. Are we still having to go in manually and sift through to make those correlations? How much of that has been automated at this point? So there are systems of capture where some of that information because your loan mortgage application was entered by somebody into a system will still be captured in those places. But we'll take in that information. That's a starting point. But if there are other sources, then we will apply AI ML type of capabilities to bring on those new emerging sources because at the same time, think about this equation where you started with five systems or a dozen systems. Now you're talking about 300 plus systems. You cannot keep doing this manually for every system possible. And this number is only going to grow as we move forward. So AI ML definitely has a role to play and further automate this landscape. I had, I saw an amazing stat the other day. The source was the Sand Hill Econometrics, Silicon Valley Company. And the stat was that 70% of the series A, B, and C companies failed to return at least one X to their investors. So you've made it through that, not whole. Congratulations, you just raised $120 million around. That's got to be super exciting for you. No pressure, by the way. Tell us about that. Well, I mean, you'd think the industry would have de-risked by now, right? But at any rate, so tell us about that raise, where you guys are at, very exciting times for you. Yeah, really, really exciting time for us. We just raised $120 million. The company was valued at $1.7 billion. Awesome, congratulations. And the round was, you know, all of our existing investors participated in it. We also had a new investor join in the process. They wanted their pro rata. Everybody wanted their pro rata. That's great. But, you know, one of the things that we have been very careful about in this whole process and journey is something that you and I were talking about, the step function of scale. We're making sure that we are efficient stewards of capital and applying it in a manner where we are at every turn looking at what's the next step function that we need to graduate to. Because we want to make use of this capital to efficiently grow our business and be a rule of 40 growth company. And that's something that you don't typically hear these days from a lot of the growth companies. But we are certainly focused on building long-term value and focusing on that rule of 40 growth efficiency. Yeah, so rule of 40 is growth plus EBITDA. Sometimes you use other metrics, but is that how you look at it? Growth plus EBITDA? Yes. Yeah, great. And that's the formula that we are driving for. And most of our investments with this round of capital are going to be not only pushing forward with the go to market strategy, because we have a lot of growth opportunity. We have been North America focused. Now we can take this global. At the same time, looking at the verticals where we need to double down and invest more, given that we have been a horizontal platform that is core to our capabilities that we have built with Reltio. But at the same time, making sure that we are investing in the key verticals that we are present in. Yeah, so you were explaining to me that you started in the pharmaceutical industry. That's where you got go to market fit and then you went to other industries. When you went to those other industries, were there similar patterns, or did you almost have to start from ground zero again to get that product market fit? No, so from the very beginning, the concept has been that this is a horizontal data problem. And at the heart of it, it's information about people, organizations, product locations, and most of the businesses run on that type of information. That's the core part of the data that they build their business on. Life Sciences was a perfect starting point for us because it had examples of all of those data. When you start with commercial operations, which is sales and marketing, you have people, organization, product type of information. When you go into clinical trials, you have site investigators and patient type of information. When you go into R&D, within that same space, you have drugs, compounds, substances, finished products type of information, all coming from multiple sources. So it was a perfect place for us to prove out all of the capabilities end to end, which we like to call multi-domain capabilities. And then we looked at what other verticals have similar patterns. And that's why we went after healthcare, financial services, insurance, retail, high tech. Those are some of the key verticals that we are in right now. That's awesome, with great vision. Last question, give us a sense of the futures. Where are you going? Well, first of all, what are you doing with the money? Go to market, throwing gas on the fire, and what can we expect in the coming year and years? Go to market expansion is a key area of investment, but also doubling down on the customer experience that we deliver, how we invest in the product. What are some of the adjacent capabilities that we need to invest in? Because data is a great starting point, and data should not hold businesses back. Data should be the accelerant to the business. And that's our philosophy that we are trying to bring to life. So making sure that we are making the data readily available, accessible, and usable for all of our customers is the key goal to aim for. And that's where all the investment is going. Well, Manish, it was a pleasure having you on at the AWS startup showcase. And then subsequently, you become a unicorn. So congratulations on that. Really excited to watch the continued progress. Thanks for coming back in the queue. Well, thank you so much, Dave and David. Thanks for having me. Thanks for validating that Mr. Vellante is still with us. Yeah. He's going to be with us for a long time. I hope so. I hope so. I got one more to put through college. Thank you for watching this edition of The Cube at AWS re-invent. I'm Dave Vellante for Dave Nicholson. We are The Cube, the leader in high tech coverage. Right back.